This paper presents a reliability analysis framework for distributed computing in extreme edge computing (XEC) with limited information availability. XEC pushes computation to the outermost boundaries of networks by leveraging consumer-owned devices, known as Extreme Edge Devices (XEDs). Unlike traditional distributed systems with defined computational resource states, XEC operates under uncertainty due to consumer device usage patterns, varying computational capacities, and local scheduling algorithms. In this work, we address discrete task assignment particularly. The framework analyzes scenarios for computational reliability assessments with minimal knowledge of XED capabilities and service requirements. The framework adapts to different levels of available information, from operational limits to historical performance data, providing refined reliability estimates. The aim of this work is to provide generalized reliability models for distributed computing in XEC that allow decision-makers (e.g. service orchestrators) to make informed decisions about task allocation, service placement, and resource allocation under uncertainty in XEC. Simulations and experimental analysis demonstrate the framework's effectiveness in estimating reliability under various system conditions.

Low-latency computational-task execution can be achieved by leveraging device-to-device offloading and parallel processing over nearby extreme edge devices (EEDs), a paradigm known as extreme edge computing (EEC). However, EEC performance is challenged by device spatial randomness with intermittent wireless connectivity, limited device computing power, time-varying availability, and device failures. This paper introduces a novel spatiotemporal analytical framework for EEC by integrating stochastic geometry with an absorbing continuous-time Markov chain (ACTMC) to capture the interplay between communication and computation. Modeling a large-scale millimeter-wave network, we derive tractable expressions for the average task response delay and the task completion probability under both random and location-aware EED selection. Numerical results quantify the impact of location-awareness and unveil the existence of an optimal task segmentation that minimizes delay, which depends on network parameters and EED capabilities. We also demonstrate that device failures and EED scarcity exacerbate delay, which can be mitigated through a collaborative load-balancing approach between EEC and Multi-Access Edge Computing (MEC) schemes. Simulations and sensitivity analyses validate the proposed framework and offer design insights for optimizing system performance.

The deployment of ultra-dense networks (UDNs), particularly cell-free massive MIMO (CF-mMIMO), is mainly hindered by costly and capacity-limited fronthaul links. This work proposes a two-tiered optimization framework for cost-effective hybrid fronthaul planning, comprising a Near-Optimal Fronthaul Association and Configuration (NOFAC) algorithm in the first tier and an Integer Linear Program (ILP) in the second, integrating fiber optics, millimeter-wave (mmWave), and free-space optics (FSO) technologies. The proposed framework accommodates various functional split (FS) options (7.2x and 8), decentralized processing levels, and network configurations. We introduce the hierarchical scheme (HS) as a resilient, cost-effective fronthaul solution for CF-mMIMO and compare its performance with radio-stripes (RS)-enabled CF-mMIMO, validating both across diverse dense topologies within the open radio access network (O-RAN) architecture. Results show that the proposed framework achieves better cost-efficiency and higher capacity compared to traditional benchmark schemes such as all-fiber fronthaul network. Our key findings reveal fiber dominance in highly decentralized deployments, mmWave suitability in moderately centralized scenarios, and FSO complements both by bridging deployment gaps. Additionally, FS7.2x consistently outperforms FS8, offering greater capacity at lower cost, affirming its role as the preferred O-RAN functional split. Most importantly, our study underscores the importance of hybrid fronthaul effective planning for UDNs in minimizing infrastructural redundancy, and ensuring scalability to meet current and future traffic demands.

This paper characterizes integration and coordination gains in dense millimeter-wave ISAC networks through a dual-mode framework that combines monostatic and multistatic sensing. A comprehensive system-level analysis is conducted, accounting for base station (BS) density, power allocation, antenna misalignment, radar cross-section (RCS) fluctuations, clutter, bistatic geometry, channel fading, and self-interference cancellation (SIC) efficiency. Using stochastic geometry, coverage probabilities and ergodic rates for sensing and communication are derived, revealing trade-offs among BS density, beamwidth, and power allocation. It is shown that the communication performance sustained reliable operation despite the overlaid sensing functionality. In addition, the results reveal the foundational role of spatial sensing diversity, driven by the dual-mode operation, to compensate for the weak sensing reflections and vulnerability to imperfect SIC along with interference and clutter. To this end, we identify a system transition from monostatic to multistatic-dominant sensing operation as a function of the SIC efficiency. In the latter case, using six multistatic BSs instead of a single bistatic receiver improved sensing coverage probability by over 100%, highlighting the coordination gain. Moreover, comparisons with pure communication networks confirm substantial integration gain. Specifically, dual-mode networked sensing with four cooperative BSs can double throughput, while multistatic sensing alone improves throughput by over 50%.

The growing demand for foundation model (FM)-powered services has sparked debates about the scalability of their deployment, due to the intensive resource requirements, latency sensitivity, and privacy-related concerns. To overcome these limitations, recent efforts advocate for the democratization of AI through eXtreme Edge Computing (XEC). XEC leverages the idle computational capacities of user-owned devices, referred to as eXtreme Edge Devices (XEDs), to enable distributed inference. While recent advances have brought this vision closer to technical feasibility, achieving low-latency inference at the eXtreme Edge (XE) remains challenging due to the dynamic and heterogeneous nature of XEDs and the full-task dependency among subtasks required for successful inference. In this work, we introduce the Proactive Minimum Response Delay (P-MRD) scheme. P-MRD incorporates predictions of dynamic resource availability to proactively form collaborative compute clusters that ensure full inference task completion while minimizing response delays and maintaining Quality of Service (QoS) constraints. We formulate the task allocation problem as a Binary Integer Linear Program (BILP) and derive an analytical solution using Lagrangian analysis and the Karush-Kuhn-Tucker (KKT) conditions. Additionally, we propose a greedy heuristic algorithm that efficiently approximates the optimal solution. Performance evaluations using data from a realistic testbed demonstrate that P-MRD achieves a 31% reduction in the response delay and a 62% increase in the inference success rate compared to prominent baselines.

Stress detection has been widely studied using physiological signals. However, most research considers stress as a categorical level, limiting the ability to uncover stress’s latent continuous psychological structure. A quantitative representation of stress accommodates inter-individual differences, enhances measurement reliability and sensitivity to changes, and facilitates adaptive real-time applications. This study proposes a framework for estimating a Quantitative Stress Index (QSI), a continuous stress score derived from self-report questionnaires and estimated using physiological features. These features are extracted from Electrodermal Activity (EDA) and Heart Rate Variability (HRV) obtained from Blood Volume Pulse (BVP) signals collected via the Empatica E4 wrist-worn device. The results indicate that the physiological QSI model effectively differentiates between stress and relaxation states, showing improved performance compared to established approaches and demonstrating its potential for quantitative, real-time stress monitoring using wearable sensors.

Task offloading in Vehicular Edge Computing (VEC) can significantly enhance Cooperative Perception (CP) for Autonomous Vehicles (AVs), improving situational awareness and traffic safety. However, the widespread adoption of VEC is often constrained by the high deployment costs of Roadside Units (RSUs). In this paper, we propose the Truthful and Quality-Aware Task Offloading (TQTO) scheme. TQTO leverages the prolific yet underutilized computational resources of parked vehicles for CP tasks in VEC to alleviate RSU scarcity. Using vehicle-to-vehicle (V2V) communication, parked vehicles can be strategically involved in CP processing and are incentivized to contribute their resources. TQTO introduces a distributed, truthful, double-auction-based multi-agent deep reinforcement learning framework that enables user vehicles to offload CP tasks to parked vehicles in a utility-maximizing manner, while respecting their individual budget constraints. Concurrently, TQTO considers the provider-side (i.e., parked vehicles) costs and ensures a truthful, incentive-compatible, and budget-balanced marketplace for VEC. A critical value-based payment mechanism is used to ensure fair compensation for parked vehicles and to align task requesters’ payments with their utility. TQTO formulates the task offloading problem as a Double Auction Quadratic Multiple Knapsack Problem (DA-QMKP) and solves it using a QMIX-based heuristic for scalable decision-making under partial observability. Extensive evaluations show that TQTO outperforms a prominent non-auction-based scheme by up to 39% in terms of social welfare.

Crowdsourced traffic monitoring provides real-time insights for urban planning and congestion management, but directly reporting users’ GPS coordinates poses serious privacy risks. We propose HSR-PRR, an accuracy-enhanced frequency estimation scheme under Local Differential Privacy (LDP) for traffic monitoring. By combining grid-based discretization with hash-based binning, the server partitions the domain and filters irrelevant reports, reducing variance and improving estimation accuracy. The scheme is highly communication-efficient, requiring only a 1-bit response per reporting user. We prove that reporting users satisfy ε-LDP, while non-reporting users incur minimal leakage, and the query condition achieves k-anonymity. Analysis and experiments show that HSR-PRR achieves higher accuracy and lower total communication overhead than PRR, making it a practical and scalable solution for large-scale, privacy-preserving traffic monitoring.

The disparity in surgical care between urban and rural areas remains a significant challenge due to geographical and connectivity constraints. Telesurgery, enabled by digital twins, has emerged as a potential solution to bridge this gap. However, the reliability of telesurgical procedures is heavily influenced by network variability, including latency fluctuation, jitter, packet loss, and varying throughput. This paper investigates the impact of network instability on telesurgery key performance indicators (KPIs) and evaluates the correlation between network parameters and surgical precision, system responsiveness, and operational safety. To achieve this, we developed a Robotic Telesurgery Digital Twin (RTDT) framework that models network conditions and performs real-time risk analysis. Using Pearson, Spearman, and ANOVA correlation methods, we quantify the influence of network variability on telesurgical performance. Additionally, decision trees is used to predict high-risk scenarios before modeling stochastic network disruption with Monte Carlo. Simulation results indicate that a latency exceeding 150 ms results in a 98% failure rate in telesurgery. Additionally, packet loss greater than 30% can reduce surgical precision by 55%. Furthermore, a throughput below 150 Mbps drastically impacts the operation, leading to a 98% probability of failure. These findings highlight the importance of controlling latency, packet loss, and throughput variations to maintain reliable surgical performance.

Task offloading in Vehicular Edge Computing (VEC) enhances cooperative perception (CP) for Autonomous Vehicles (AVs), improving traffic awareness. However, the high cost of Roadside Unit (RSU) and the underutilization of parked vehicles pose challenges. Leveraging Vehicle-to-Vehicle (V2V) communication, parked vehicles can form collaborative worker groups for efficient perception aggregation. We propose CoGroup, a two-tier framework integrating task offloading and dynamic worker grouping. Modeled as a double quadratic multiple knapsack problem, it employs Hierarchical Reinforcement Learning (HRL): QMIX for decentralized task allocation and DQN for optimized worker grouping. Experiments show that CoGroup improves traffic awareness by 21% over non-cooperative methods, reducing RSU dependence and offering a scalable, cost-effective solution for next-generation VEC systems.

Democratizing the edge by capitalizing the underutilized computational resources of end devices, referred to as Extreme Edge Devices (EEDs), can foster various IoT applications. In this paper, we propose the Community Edge Platform (CEP). CEP is the first platform that exploits business, institutional, and social relationships to build communities of requesters and EEDs to eliminate recruitment costs and preserve privacy in EED-enabled environments. CEP promotes service-for-service exchange and utilizes a hierarchical control paradigm to prioritize the enrollment of nearby devices as workers. CEP also considers the fact that community-imposed constraints can lead to unbalanced work distribution. To alleviate this issue, we propose the Community-Oriented Resource Allocation (CORA) scheme. CORA accounts for community restrictions and strives to minimize the execution time and makespan while retaining a reasonable scheduler runtime. Towards that end, we formulate the resource allocation problem as a Bipartite Graph Matching problem. Comprehensive qualitative evaluations demonstrate the superiority of CEP compared to 12 prominent edge computing platforms in terms of various system architecture and performance features. Additionally, extensive simulations show that CORA outperforms six prominent resource allocation schemes by up to 44% and 7% in terms of makespan and execution time, respectively, while achieving a much faster runtime, outperforming the best of the six baseline resource allocation schemes by a factor of six.

Extreme Edge Computing (EEC) exploits the untapped computational power of end devices, referred to as Extreme Edge Devices (EEDs), and thus holds the potential to revolutionize the Digital Twin (DT) technology. However, traditional reactive task allocation approaches fail to address the complexities of DT processing tasks, where the execution of all the underlying subtasks is crucial. Additionally, these approaches suffer due to the intermittent availability of EEDs, which compromises the Quality of Service (QoS). In this paper, we propose the Proactive Maximum Weighted Service Capacity (P-MWSC) scheme. P-MWSC is the first scheme to employ a proactive approach, utilizing predictions of the dynamic resource usage and resource characterization of EEDs to tackle the intricacies of DT tasks while taming the effects of EEDs’ dynamicity and intermittent availability. We formulate the task allocation problem as a Binary Integer Linear Program (BILP) that aims to maximize the service capacity, weighted by the achieved gain from each fully assigned task. We derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian relaxation, and use a top-down decomposition approach to provide a solution that achieves up to 80% runtime reduction. Additionally, we propose a heuristic scheme with a bottom-up decomposition approach that is suitable for certain practical scenarios, yielding up to 90% runtime reduction. Extensive performance evaluations using data from a realistic testbed demonstrate that P-MWSC outperforms representatives of prominent reactive and proactive schemes, achieving up to 70% increase in the task success rate and a 39% reduction in the average response delay.

Mobile edge computing (MEC) is a promising paradigm for Internet of Things applications requiring synchronized user experiences. However, sustaining scalable and reliable MEC services is challenging when computational resources are overloaded, especially as MEC service providers (SPs) must minimize operational costs to maximize profits while offering competitively priced services. This article proposes the cooperative multiprovider market (CMPM) scheme, the first to cooperatively enhance service scalability and reliability while addressing the profit-pricing dilemma in a multiprovider market. CMPM enables overloaded home SPs (HSPs) to leverage underutilized computational resources from reliable foreign SPs (FSPs) via reputation-based service replication, meeting the stringent Quality of Service (QoS) requirements for real-time applications involving user groups. CMPM resolves the pricing dilemma by applying a game-theoretic approach, allowing FSPs to dynamically optimize revenue and adjust prices when HSPs cannot meet user demand. We formulate the resource allocation and pricing problem as a Stackelberg game, establish the existence of the equilibrium, and develop a distributed algorithm to reach it. Extensive evaluations show that CMPM significantly reduces unit prices, attracts more HSPs, and better manages high-density user loads compared to state-of-the-art schemes that overlook SP reputation and social welfare. CMPM also achieves up to 84% higher FSP revenue, a 67% improvement in scalability, and a 70% higher task success rate compared to baseline schemes.

Task offloading in Vehicular Edge Computing (VEC) is crucial for enhancing cooperative perception (CP) in Autonomous Vehicles (AVs), thereby improving traffic situational awareness. However, existing approaches often neglect the balance between high-quality execution of interdependent tasks and conserving AVs limited budget, including communication and financial resources. To address this, we propose the Quality and Budget-Aware Task Offloading (QBATO) framework. QBATO is the first framework to balance the quality of cooperative perception with budget conservation. QBATO models the budget as a queue to ensure stability, balancing resource use while prioritizing situational awareness in CP. Additionally, QBATO enhances CP quality by predicting vehicles movements and estimating their regions of interest, thereby improving the Value of Information (VOI). The task offloading problem is modeled as a Quadratic Multiple Knapsack Problem (QMKP), an NPhard problem that optimizes vehicle allocation by evaluating the quality of assigning multiple vehicles to the same worker through a quadratic objective function.To manage resources effectively, we apply the queue stability Lyapunov drift-minus-bonus approach. We also introduce the QBATO-Heuristic (QBATO-H), which solves the problem in a decentralized, time-efficient manner using a multi-agent deep reinforcement learning technique that leverages the Q-Mixing Network (QMIX) method, which employs monotonic value decomposition to coordinate the actions of multiple agents. Extensive evaluations show that QBATO outperforms prominent quality and budget-oblivious schemes by up to 49%, 15%, and 35% in terms of budget conservation, situational awareness, and efficiency, respectively. QBATO-H also yields a small gap of up to 7% and 11% with QBATO in terms of budget conservation and efficiency, respectively.

Extreme Edge Computing (XEC) is a serverless edge computing paradigm where computational tasks are offloaded to and from extreme edge devices (XEDs). XEDs, a subset of IoT devices that consists of consumer-owned devices capable of offering computational resources. Being data-rich, XEDs can facilitate the training of more accurate Machine Learning (ML) models. However, their unpredictable computational behavior, which follows the consumers’ usage, and transient availability pose challenges that traditional Federated Learning (FL) approaches may struggle to address. To this end, we propose a new framework for decentralized FL in XEC systems designed to address the computational reliability of XEDs and optimize the computational resource allocation. Moreover, to encourage XEDs’ participation in the FL training process, we introduce an Auction-based incentive mechanism. This mechanism models the interactions between XEDs, considering both the computational characteristics and the data quality of XEDs. Furthermore, we present two solution approaches: an optimization approach and a heuristic approach, each introducing a complexity-performance trade-off. Finally, we evaluate and demonstrate the effectiveness of our proposed framework in improving the performance and reliability of XEDs in decentralized learning environments.

In the face of growing demands for high-quality digital media, enhancing video streaming quality remains a significant challenge, particularly in environments with diverse internet connectivity and limited bandwidth. This paper proposes the Edge-enhanced Streaming (EES) scheme, which leverages edge computing and machine learning to upscale low-bitrate video frames to higher resolutions. Utilizing the distributed computational power of edge devices such as smartphones and laptops, our methodology involves segmenting each video frame into smaller sub-frames. These sub-frames are then processed using a Super-Resolution (SR) machine learning model across available edge devices within the network. This approach optimizes underutilized computational resources, improves processing times, and reduces energy consumption, making it a highly suitable approach for real-time video streaming applications. Furthermore, to address the challenge of device reliability, we incorporate a task replication strategy, ensuring consistent quality improvements even with potential fluctuations in device availability. We evaluate our proposed scheme using the PRIM dataset from the PIRM-SR Challenge. Extensive simulations demonstrate significant enhancements in video quality, confirming the effectiveness of our distributed SR technique in overcoming bandwidth constraints and improving user experience.

Edge computing seeks to optimize service provision over enterprise-owned infrastructure near the end-user at the network’s edge. However, it misses out on the opportunity to utilize user-owned hardware at the extreme edge of the network as workers in a sharing economy. In this work, we build upon the existing Incentive Vacation Queueing (IVQ) model and develop the Virtual Kiosk Model (VKM) to analyze service availability and the dynamics of multiple workers’ participation in the provision of a service on the extreme edge. We formulate an optimization problem to minimize total cost of incentive payments while maintaining service availability under temporal constraints. We propose the Model-based Incentive Strategy at the Edge (MISE) algorithm to iteratively adjust incentives in real-time. MISE is compared against traditional numerical optimizers and a baseline naive approach that greedily focuses on minimizing incentives. Our findings demonstrate that MISE ensures sustained service availability without overburdening the workers at a cost acceptable to the service provider, striking a crucial balance in the management of extreme edge computing resources.

Reconfigurable intelligent surface (RIS) technology has been widely used to enhance the performance of wireless communication networks. Specifically, to overcome the blockage issue, the RIS is equipped with passive elements that can steer the wireless signals around the obstacle and construct a virtual line-of-site (LoS) link between the transmitter and receiver. This paper presents a comprehensive analysis of packet delay in RIS-assisted communications, an area that has received limited attention in the literature. Our analysis carefully examines the cascaded wireless channel via RIS to calculate the signal-to-noise ratio (SNR) distribution within Nakagami-m fading channels. Furthermore, we utilize the SNR distribution to develop a novel expression for average packet delay. Our method was evaluated thoroughly through numerical and simulation-based performance testing. We explored the impact of various parameters on average delay performance, including the number of reflecting elements, average SNR, and distance between the user and RIS. Our research indicates that a larger number of passive reflecting elements in an RIS can significantly improve signal reception at the user, leading to better average packet delays. The findings of this paper can be used in multi-user scenarios, where the packet delay is one of the metrics used to address the user-RIS association problem based on the user's delay requirements.

This paper studies the impact of intra- and inter-cell mobility on mmWave networks with a specific focus on beam switching. The paper utilises a geometric model to partition the coverage area of a mmWave gNB cell into radial and angular sectors, thus accounting for the coverage footprints of planar antenna arrays with azimuth-tilt beam orientations (i.e., horizontal and vertical orientations). Using this model, intra-cell beam switching rate is derived analytically. We extrapolate the analysis using stochastic geometry to address inter-cell mobility and system-level beam switching. Our study establishes a relationship between the shape of the antenna array pattern and the beam switching rate. We validate our analysis via extensive Monte Carlo simulations and the results reveal the significant impact of the antenna configuration on beam switching rate. Even when the number of beams remains the same, the beam switching rate can almost double depending on how the antenna array elements are arranged.

Wireless Federated Learning (FL) is an emerging distributed machine learning paradigm, particularly gaining momentum in domains with confidential and private data on mobile clients. However, the location-dependent performance, in terms of transmission rates and susceptibility to transmission errors, poses major challenges for wireless FL's convergence speed and accuracy. The challenge is more acute for hostile environments without a metric that authenticates the data quality and security profile of the clients. In this context, this paper proposes a novel risk-aware accelerated FL framework that accounts for the client's heterogeneity in the amount of possessed data, transmission rates, transmission errors, and trustworthiness. Classifying clients according to their location-dependent performance and trustworthiness profiles, we propose a dynamic risk-aware global model aggregation scheme that allows clients to participate in descending order of their transmission rates and an ascending trustworthiness constraint. In particular, the transmission rate is the dominant participation criterion for initial rounds to accelerate the convergence speed. Our model then progressively relaxes the transmission rate restriction to explore more training data at cell-edge clients. The aggregation rounds incorporate a debiasing factor that accounts for transmission errors. Risk-awareness is enabled by a validation set, where the base station eliminates non-trustworthy clients at the fine-tuning stage. The proposed scheme is benchmarked against a conservative scheme (i.e., only allowing trustworthy devices) and an aggressive scheme (i.e., oblivious to the trust metric). The numerical results highlight the superiority of the proposed scheme in terms of accuracy and convergence speed when compared to both benchmarks.

Extreme Edge Sensing (EES) offers an enhanced approach to efficient remote sensing by utilizing the computational capabilities of user devices for immediate data processing. In contrast to traditional Mobile Crowd Sensing (MCS), EES provides both data collection and local data processing to accelerate decision-making. However, due to the variability in participant capabilities and task requirements, the complexity of task assignments becomes challenging. This complexity necessitates a mechanism that balances incentives and response time, ensuring tasks are completed within predefined budget and time limits. This paper presents a new task assignment strategy that categorizes participants based on their capabilities and task needs. Using the Hungarian algorithm, our methodology optimizes task assignments with an objective function aiming to minimize both monetary and time costs. We then evaluate the minimum budget needed for successful task completion and its dependency on objective function parameters. A comparison of our method's performance against a standard greedy approach demonstrates its effectiveness. The results suggest that our method enhances the efficiency and reliability of task assignment in EES systems, with potential applications in smart cities, environmental monitoring, and other areas requiring efficient remote sensing.

In 6G and beyond network infrastructure, Low Earth Orbit (LEO)-based Non-terrestrial Networks (NTN) are poised to play a significant role in delivering global connectivity. To serve users effectively, multiple satellites must collaborate. Each satellite has only a brief window of time to communicate with the user. Therefore, an optimal handover strategy is needed to ensure seamless user transitions between satellites while minimizing unnecessary and frequent handovers, which leads to user satisfaction. However, existing handover strategies, primarily based on system geometry, may lack efficiency in dynamically changing user demand, particularly in deep urban canyon environments. This paper introduces a Spatio-temporal Entropy-based Scoring (STEBS) handover strategy. STEBS is a multi-objective dynamic handover strategy designed to minimize the number of handovers while consistently meeting user demand. Simulation results demonstrate that STEBS reduces the number of handovers and increases throughput, achieving an exceptionally low blocking rate of one-eighth compared to benchmark schemes, resulting in a $99 \%$ user satisfaction rate.

Edge Computing aims to push services closer to end-users, greatly enhancing latency and scale. Yet, there’s untapped potential beyond the network’s last mile, on the extreme edge. Extreme Edge Computing (XEC) is a computing paradigm that exploits computational resources in the end-user’s immediate vicinity. Edge Crowd Computing (ECC) is an orchestrated sharing economy model within XEC that uses idle resources on user-owned devices for service provision, compensating owners. We analyze an orchestrated ECC where devices rent resources in exchange for incentives. Our Incentive-Vacation Queueing (IVQ) model associates performance with incentive payments using vacation queueing, considering the multi-tenancy of devices through a server vacation dependent on incentives received. In this paper, we offer a framework for analyzing any sharing economy system that can be modeled using IVQ. We discuss the relationship between incentives and vacations on performance, namely the incentive-vacation or IVQ function. We examine two families of IVQ functions that can be adjusted to benefit either the orchestrator or the worker and introduce a performance metric for such preference. We derive analytical expressions for system performance that consider the random nature of worker devices’ availability due to fluctuating incentives. The IVQ model explores commodifying user-owned resources in an ECC system, presenting a general approach for performance analysis in such environments.

Democratizing edge computing (EC) by harnessing the underused computational resources of extreme edge devices (EEDs) can revolutionize a broad spectrum of Internet of Things applications. However, studying the impact of EED/worker attrition on the Quality of Service (QoS) at the extreme edge has been overlooked. In this article, we propose the multitiered worker-oriented resource allocation (MWORA) scheme. MWORA is the first scheme that studies the impact of worker attrition on the QoS and mitigates its risk by optimizing resource allocation to ensure that workers receive a satisfactory profit to maintain their participation in the service. Furthermore, MWORA accounts for the fact that EEDs are user-owned devices, and are thus subject to a dynamic user access behavior, which can affect the level of computational resources they are willing to endow. MWORA accounts for such dynamicity by pioneering the notion of enabling multitiered computational capabilities to be solicited from each worker based on the profit gained from the assigned tasks. We formulate the problem as an integer linear program (ILP) to maximize the QoS while abiding by certain worker satisfaction, deadline, and budget constraints. We also propose the MWORA-weighted sum (MWORA-WS) scheme to derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian analysis. Extensive simulations show that MWORA outperforms prominent resource allocation schemes by up to 31%, 52%, and 85% in terms of response delay, service capacity, and worker satisfaction, respectively. Additionally, MWORA-WS yields a small gap of 7% and 8% with MWORA in terms of response delay and worker satisfaction, respectively.

Mobile edge computing (MEC) has emanated as a propitious computing paradigm that can foster delay-sensitive and/or data-intensive applications. However, it can be challenging to maintain a scalable MEC service when computational resources are overloaded. In this article, we propose the service replication between multiple service providers (SRMSPs) scheme. SRMSP is the first scheme that fosters service scalability in a cost-efficient manner, while considering the stringent QoS requirements of real-time applications involving groups of users. SRMSP enables SRMSPs to minimize the average response delay and the operational cost incurred by service providers, while satisfying the delay requirements of all user groups. We formulate the resource allocation problem as an integer linear program (ILP) and derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian analysis. In addition, we propose the SRMSP-distributed allocation (SRMSP-DA) scheme to provide a time-efficient solution in distributed scenarios. In SRMSP-DA, we use a game-theoretic strategy that formulates the resource allocation problem as a potential game. Extensive simulations show that SRMSP renders a 50% operational cost reduction compared to a baseline scheme that does not consider the operational cost. In addition, SRMSP-DA exhibits a relatively marginal difference of up to 20% and 4% in terms of the total operational cost and average response delay, respectively, compared to the optimal solution provided by SRMSP.

The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algorithms to optimize radio access network (RAN) functions via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are among the main approaches adopted in the O-RAN literature to solve dynamic radio resource management problems. However, despite the benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms in real network deployments falls behind. This is primarily due to the slow convergence and unstable performance exhibited by DRL agents upon deployment and when encountering previously unseen network conditions. In this paper, we address these challenges by proposing transfer learning (TL) as a core component of the training and deployment workflows for the DRL-based closed-loop control of O-RAN functionalities. To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates multiple services, including real VR gaming traffic to reflect practical scenarios of O-RAN slicing. We also propose and implement policy reuse and distillation-aided DRL and non-TL-aided DRL as three separate baselines. The proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the average initial reward value and the percentage of converged scenarios, and a 64.6% decrease in reward variance while maintaining fast convergence and enhancing the generalizability compared with the baselines.

The demand for ultra-low latency requirements is fueled by the growing popularity of real-time and time-critical applications such as virtual, augmented, and mixed reality, and industrial IoT. Time-critical applications and services are real-time software whose failure could result in catastrophic consequences such as fatalities, damage to property, and even financial losses. Edge computing is the main enabler of 5G ultra-low latency use cases. Edge resources are limited compared to abundant cloud computing resources. As such, provisioning time-critical applications at the edge is more challenging and demanding. Even though virtual network function (VNF) sharing improves the utilization of the service providers’ resources, service requests, including time-critical ones, can still be rejected due to insufficient resources. This paper proposes a Preemptive Prediction-based Placement scheme (PPPS) for time-critical services with VNF sharing. In addition to prioritizing time-critical premium (Pr) services over best-effort (BE) services, PPPS utilizes the predicted required resources in a defined lookahead window. In cases when no resources are available for Pr services, a preemption mechanism preempts resources for the Pr service, by deporting one or more running BE services. The experimental results show that PPPS can reduce the Pr services rejection rate to ~0% while minimizing the disturbance that BE services witness such as prolonged waiting times.

Edge computing can satisfy the requirements of latency-critical and data-intensive applications by exploiting com-putational resources of end devices. However, such devices inherently suffer from dynamic user behavior, cyclic task-switching, varying link qualities which often impact their reliability. In addition, in incentivized systems, they may often over-estimate their advertised capabilities and consequently fail on delivering. In this paper, we propose the Reputation-based Task Assignment and Replication (RTAR) scheme. RTAR is the first scheme that uses a black box approach to perform cost-efficient task replication that accounts for workers' reliability and preserves workers' privacy by not requiring or soliciting any information about their devices. RTAR incorporates a reputation model using beta distribution to estimate the worker's reputation based on past performance. We formulate the problem as an Integer Linear Program (ILP) that strives to maximize the overall reputation of recruited workers, while abiding by a certain budget limit for each task. We also propose the RTAR-Heuristic (RTAR-H) scheme. RTAR-H uses matching theory to solve the optimization problem in a time-efficient manner. Extensive evaluations show that RTAR yields 63% and 68% reduction in recruitment cost and number of replicas, respectively, compared to a baseline scheme that blindly maximizes the number of replicas. Moreover, RTAR-H closely approaches the optimal solution, rendering a small gap of up to 1% and 1.2% in terms of task drop rate and recruitment cost, respectively.

In Edge Computing, computation is pushed towards the end-user to reduce backhaul load, address nascent privacy issues, and enable a range of low latency applications. Extreme Edge Service systems (EES) are a subset of Edge Computing in which services are deployed on user-owned devices in the proximity of the end-user. In this work, we model and analyze an orchestrator-based EES in which users' devices are recruited in exchange for an incentive. We propose to model the incentives' impact on performance using Incentive-Vacation Queueing (IVQ), a vacation queueing model in which server vacations are a proxy for incentives. Moreover, we derive closed-form expressions to evaluate the performance and directly link the performance to incentives, showing the impact of each one of the system parameters.

Extreme Edge Computing (EEC) can drastically curtail the delay, reduce network bandwidth consumption, and enhance system performance by providing computing resources closer to the data-generating Internet of Things (IoT) devices. However, the use of Extreme Edge Devices (EEDs) in EEC presents unique challenges imposed by the inherent dynamic user-access behavior, which introduces highly dynamic resource usage. To tackle such challenges, it is crucial to enable accurate resource usage predictions, which in turn requires having reliable datasets. In this paper, we cultivate the Dynamic Resource Usage Data Generation for EEDs (DRUDGE) methodology. DRUDGE generates datasets that capture the resource usage dynamics of EEDs running diverse user-end applications in fine-grained intervals over extended periods. We present an in-depth characterization of resource utilization in EEDs and make the datasets publicly available to the research community. We examine the temporal variation of critical system metrics, such as CPU usage, memory usage, temperature, and network traffic. Furthermore, we apply various statistical tests to gain valuable insights into the data characteristics, including skewness, kurtosis, stationarity, volatility, cointegration, multi-collinearity, Granger causality, and Pearson correlation analysis. These insights inform model selection, feature engineering, and preprocessing techniques, leading to more accurate and reliable forecasts and analyses for EEC systems.

Leveraging the copious yet underutilized computational resources of end devices, also known as Extreme Edge Devices (EEDs), can significantly enhance the performance of various Internet of Things (IoT) applications. However, EEDs are heterogeneous and user-owned devices, which causes their availability to be highly unreliable. In this paper, we propose the Dynamic Worker Availability Prediction (DWAP) scheme. DWAP is the first scheme that predicts the availability of EEDs (i.e., workers) and adapts to the highly dynamic computing environment at the extreme edge. DWAP employs the Continuous-Time Markov Model (CTMC) to forecast the availability of workers in the upcoming time step. It does so while continuously fine-tuning the model parameters to incorporate newly available data. We use a dataset that consists of real-world Google cluster workload data traces. Extensive evaluations show that DWAP significantly outperforms a representative of state-of-the-art prediction schemes by up to 74% and 59% in terms of the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), respectively. In addition, DWAP yields 97% and 48% reduction in task drop rate compared to prominent availability-unaware and availability-based resource allocation schemes, respectively.

Harvesting the profuse yet underutilized computational resources of IoT devices, also referred to as Extreme Edge Devices (EEDs), can significantly curtail the delay in parallelized Mobile Edge Learning (MEL). However, EEDs are user-owned devices, which causes them to experience a highly dynamic user access behavior. Such dynamicity can lead to uncertainty in the available computation and communication capabilities of learners. In this paper, we propose the Minimum Expected Delay (MED) scheme. MED is the first data allocation scheme in MEL that accounts for uncertainty in learners' capabilities and enables multi task allocation. Given the state probabilities of learners, MED strives to minimize the sum of the maximum expected delay of all tasks, while abiding by certain training time and budget constraints. Towards that end, MED formulates the data allocation problem as an Integer Linear Program (ILP) and makes uncertainty-aware decisions. We conduct rigorous experiments on a real testbed of Jetson Nano devices. Extensive performance evaluations show that MED outperforms a representative of state-of-the-art uncertainty-naive schemes by up to 11 %, 11 %, 42 %, and 5 % in terms of training time, satisfaction ratio, data drop rate, and occupancy time, respectively. In addition, MED approaches a baseline scheme that assumes a perfect knowledge of the learners' states, yielding a gap of up to 10%, 5%, and 14% in terms of satisfaction ratio, data drop rate, and occupancy time, respectively.

The success of immersive applications such as virtual reality (VR) gaming and metaverse services depends on low latency and reliable connectivity. To provide seamless user experiences, the open radio access network (O-RAN) architecture and 6G networks are expected to play a crucial role. RAN slicing, a critical component of the O-RAN paradigm, enables network resources to be allocated based on the needs of immersive services, creating multiple virtual networks on a single physical infrastructure. In the O-RAN literature, deep reinforcement learning (DRL) algorithms are commonly used to optimize resource allocation. However, the practical adoption of DRL in live deployments has been sluggish. This is primarily due to the slow convergence and performance instabilities suffered by the DRL agents both upon initial deployment and when there are significant changes in network conditions. In this paper, we investigate the impact of time series forecasting of traffic demands on the convergence of the DRL-based slicing agents. For that, we conduct an exhaustive experiment that supports multiple services including real VR gaming traffic. We then propose a novel forecasting-aided DRL approach and its respective O-RAN practical deployment workflow to enhance DRL convergence. Our approach shows up to 22.8%, 86.3%, and 300% improvements in the average initial reward value, convergence rate, and number of converged scenarios respectively, enhancing the generalizability of the DRL agents compared with the implemented baselines. The results also indicate that our approach is robust against forecasting errors and that forecasting models do not have to be ideal.

In the fast dynamic environment of Vehicle Ad Hoc Networks (VANETs), proactive security measures are necessary. Reactive security has been VAVNETs' guardian angel for some time, but now it is insufficient against current security attacks. Attack prediction is a promising solution capable of keeping up with the recent cyber security challenges. First, we need to understand where prediction fits in the attack process. To accomplish this, we introduce an attack life cycle in a VANET and exploit the proactive and retroactive phases. One of the proactive phases is the after-effect of the attack or what we call attack endgame. We use the Framework for Misbehavior Detection (F2MD) to simulate an attack effect with adverse side effects on road traffic. We implement traffic warning messages in F2MD. Then, we create attacks on these messages, namely “fake accident”, and simulate the effect of these attacks on the vehicles while capturing the results using F2MD. We simulate the impact of acting on these messages or the attack endgame, which manifested in creating hazards. We use Recurrent Neural Network (RNN) models to predict the endgame of the fake accident attack on the road. We experiment with vanilla artificial neural network solutions to create a baseline. Afterward, we use Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to build a stacked RNN model to predict the attack endgame at different time windows. They effectively predict the occurrence of a hazard up to 3.5 minutes ahead with over 80% accuracy.

It is well-established that mobility is a prominent challenge for beam-based communication. Despite the beam management functions specified by 3GPP to facilitate beam-based communication, its reliability under beam-level mobility remains questionable. Hence, this paper highlights the challenges impeding the reliability of beam-based communication under user mobility and poor propagation conditions. Specifically, this paper investigates beam-switching in mmWave networks and assesses the merits of beam-switching optimization through parametrization. Several parameters, including a Hysteresis margin and a Time-To-Trigger, are investigated with regards to enhancing beam switching. To carry-out the analysis, real beamformed mmWave data is used. The results report key beam switching performance measures and show a critical beam switching optimization trade-off.

The demand for cloud services is expected to exceed the capacity of the centralized cloud. This rise compelled service providers to decentralize the cloud by physically pushing service provision to the proximity of the end-users, which led to the synthesis of solutions such as Fog and Edge computing. Edge Computing seeks to deploy services in the last mile to the end-user, however there is still opportunity on the edge beyond the last mile: the user's own devices. Extreme Edge Computing (EEC) is an edge sub-paradigm that seeks to tap into the idle computational power on non-enterprise user-owned devices. In this work, we navigate some of the challenges posed by EEC that constrain the usage of resources on user-owned devices. We evaluate an orchestrator-based extreme edge system, that oversees user-owned worker devices, and it provides resources in exchange for an incentive payment. We propose the Incentive-Vacation Queueing (IVQ) model to investigate the performance of user-owned worker devices under a vacation policy that is influenced by incentives. We derive closed-form expressions for the system performance that capture the epistemic uncertainty stemming from unexpected user behavior, to show the impact of each parameter in the system performance, and to optimize it. The IVQ model provides insight into the impact of introducing incentives on the workers' performance.

Dynamic cache allocation and pricing in Information-Centric Networks (ICNs) is a challenging problem, especially when multiple content producers and competing ICN cache service providers are involved. Many existing ICN caching schemes generalize their frameworks as producer-agnostic architectures while considering a single ICN cache service provider. Realistically, as ICNs grow, multiple cache providers will compete for valuable content that would generate higher cache hits, and the ecosystem will inevitably become market-driven. In this paper, we investigate the dynamic cache allocation and price determination problem considering a caching system consisting of multiple content producers who act as the buyers and multiple competing ICN cache providers who act as the sellers of the caching resources. We propose a novel reverse auction-based caching and pricing scheme named SEMRA that aims to maximize the caching benefits of content producers. Simulation results demonstrate how the proposed scheme improved ICN caching over several caching metrics across varying cache sizes and popularity skewness values. Future work in this domain is highlighted in the conclusion.

Autonomous vehicles heavily rely on sensor data to make pivotal driving and traffic management decisions. However, the reliability of such data can be profoundly impacted by many impairments, such as the adverse environmental and weather conditions, the presence of obstacles, and the vehicle's limited view of road and traffic conditions of larger areas. Collaboration between vehicles can help improve the perception of vehicles beyond their line-of-sight, and increase accurate detection of objects. Vehicular Edge Computing (VEC) has emerged as a propitious computing paradigm that can foster the realization of autonomous vehicles. However, maximizing the cooperative perception of vehicles has been mostly overlooked. In this paper, we propose the Cooperative Perception-based Task Offloading (CPTO) scheme. CPTO enables task offloading in VEC with the goal of maximizing the cooperative perception of vehicles and minimizing the latency of perception aggregation, while abiding by a certain deadline. Towards that end, we formulate the task offloading problem as a multi-objective 0–1 integer linear program (0–1 ILP). We also propose a greedy heuristic, called the CPTO-Heuristic (CPTO-H) scheme, to solve the optimization problem. Extensive simulations show that CPTO significantly outperforms the baseline task offloading scheme in terms of perception intensity, service capacity, and satisfaction ratio. Furthermore, CPTO-H closely approaches the optimal solution, with a small gap of up to 3.7% and 2.4% in terms of perception intensity and satisfaction ratio, respectively.

Leveraging task offloading in Vehicular Edge Computing (VEC) via V2X can present unique and robust solutions to the challenges associated with cooperative perception in Autonomous Vehicles (AVs). However, making task offloading decisions that account for the risk of communication failure due to path loss, while adhering to the stringent QoS requirements of cooperative perception has been mostly overlooked. In this paper, we propose PLTO, a Path Loss-Aware Task Offloading scheme that accounts for path loss for Line-of-Sight (LOS), Obstructed LoS (OLoS), and Non-LoS (NLoS) propagation in vehicular communications. We formulate the task offloading problem as a 0–1 Integer Linear Program (0–1 ILP) that aims to minimize the path loss and response delay, while sustaining a certain satisfactory level of improved perception and situational awareness demanded by users. We also propose PLTO-Heuristic (PLTO-H), a scheme to solve the task offloading problem using the MTHG heuristic. Extensive simulations show that PLTO yields significant improvements of up to 17%, 10%, and 23% in terms of packet delivery ratio, Received Signal Strength Indicator (RSSI), and average response delay, respectively, compared to a baseline task offloading scheme that does not consider communication efficiency. In addition, PLTO-H achieves a near optimal solution, with a small gap of up to 6%, 5% and 1.2% in terms of packet delivery ratio, RSSI, and satisfaction ratio, respectively.

IoT systems are advancing to enable higher levels of engagement and omnipresence. A critical yet uncharted domain lies in communicating affect across participants, especially in medical settings where emotions and expressions are pivotal, and eXtended Reality (XR) systems that rely on immersive virtualization of the participating parties. However, IoT systems seldom have the bandwidth or reliability to enable such services. In this demo, we present an experiment that leverages Edge Intelligence and Artificial Intelligence to extract and encode emotions at one edge, and communicate a low-footprint encapsulation of such emotions at the other edge. The proposed architecture is designed to reduce overall traffic and build on low-power video and display equipment, to realize Affective Semantic Communication (AffSeC). This demonstration shall represent AffSeC in a medical setting, where a patient interacts with a physician over a low-BW E2E route. The proposed scheme will be contrasted to standard video compression to demonstrate the efficacy and promise of this model.

Affective computing is an emerging field that aims to develop technologies capable of recognizing and responding to human emotions. However, during communication sessions, the exchange of a high volume of data can cause high latency. One approach to mitigating this issue is semantic communication, which may reduce the amount of data exchanged. Hereby, we propose a novel idea that utilizes semantic communication in affective computing by minimizing the amount of information exchanged between endpoints. Specifically, we examine a use case of a remote doctor application, where a patient’s emotions are captured, and their vital signs are obtained using wearable devices, with this information reported to a remote doctor. To reduce data exchange, we utilize semantic communication to extract the meaning of the conveyed information, rather than transmitting the raw information itself. This approach can enhance the efficiency of communication in URLLC applications and has the potential to improve patient outcomes.

Vehicular Ad Hoc Networks (VANETs) have received considerable attention because of their potential to improve road safety. However, reactive security approaches in VANETs are of concern; thus, proactive security is needed to prevent cyberattacks. The current VANET datasets are limited in their ability to evaluate proactive security approaches, limiting research in this area. This paper presents a VeReMi-based dataset named VeReMi for Attack Prediction (VeReMiAP). Developed from the Framework For Misbehavior Detection (F2MD). VeReMiAP incorporates three key elements: Cooperative Awareness Messages (CAMs), a new class of attacks known as Fake Reporting Attacks, and an evaluation of the impact of this attack, which in this case manifests as a road hazard. The ripple effect of this attack goes beyond the targeted vehicle, making it a threat to the overall security and reliability of VANETs. The VeReMiAP dataset evaluates cyberattack prediction techniques and generates countermeasure solutions for VANET attacks. To test the dataset, we conducted a temporal analysis to observe the effect of the attack on velocity and a geospatial analysis to enhance our understanding of the spatial distribution of hazards within the road network. Results show that VeReMiAP is a potential tool to advance security research in VANETs.

Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic radio resource management (RRM) problems in beyond 5G networks. However, RL-based solutions are still not widely adopted in commercial cellular networks. One of the primary reasons for this is the slow convergence of RL agents when they are deployed in a live network and when the network’s context changes significantly. Concurrently, the open radio access network (O-RAN) paradigm promises to give mobile network operators (MNOs) more control over their networks, furthering the need for intelligent and RL-based network management. O-RAN’s standardized interfaces will allow MNOs to make real-time custom changes to intelligently control various RRM functionalities. We consider a RAN slicing scenario in which MNOs can modify the weights of the RL reward function. This enables MNOs to change the priorities of fulfilling the service level agreements of the slices. However, this results in a practical challenge since the RL agent needs to adapt promptly to the changes made by the MNO. This challenge is addressed in this paper, where we first present and discuss the results from an exhaustive experiment to examine the efficiency of using transfer learning (TL) to accelerate the convergence of RL-based RAN slicing in the considered scenario. We then propose a novelpredictiveapproach to enhance the TL-based acceleration by selecting the best-saved policy for reuse. By adopting the proposed policy transfer approach, RL agents are able to converge up to 14000 learning steps faster than their non-accelerated counterparts. The proposed machine learning (ML)-basedpredictiveapproach also shows up to a 96.5% accuracy in selecting the best expert policy to reuse for acceleration.

Vehicular Ad hoc Network (VANET) is a foundation stone for connected vehicles. As vehicles’ safety depends heavily on the exchanged data’s accuracy, VANET has a low tolerance for false data. The process of intentionally exchanging inaccurate data is called misbehaving. Machine learning (ML)-based solutions were heavily invested in detecting misbehavior messages. However, they also have some limitations with respect to how much they can detect. To overcome such limitations, we introduce situation awareness (SA) as a powerful concept that can break the limits of the used ML models, leading to more accurate and reliable solutions. Situation awareness uses environmental elements and events to gain a holistic view of the system at any given time. In this paper, we propose using SA to predict the trust of the surrounding cars and consequently reevaluate the outcome of the used ML model. Based on the collected data and SA information, we may reject a message classified as benign by the ML model or vice versa. We used VeReMi dataset to evaluate the proposed approach called SAMM (Situation Awareness with Machine Learning for Misbehavior Detection in VANET) on different ML models with a wide range of features. The results show that the proposed approach improves the system’s accuracy for various misbehavior attacks by enhancing the recall rate up to 24% and 50% in some cases.

Multi-access Edge Computing (MEC) is a revolutionary computing paradigm that facilitates delay-sensitive and/or data-intensive applications associated with the Internet of Things (IoT). Harvesting copious yet underutilized computational resources of the Extreme Edge Devices (EEDs) is foreseen as a promising endeavor. Such EEDs offer a unique opportunity to bring the computing service closer to IoT devices to curtail delay. However, the efficacy of extreme-edge parallel computing paradigm is profoundly impacted by i) wireless device-to-device communication performance, that is required for task offloading; and ii) computing capabilities of the EEDs, that governs the execution time of each task. In this context, we propose a novel spatiotemporal framework that employs stochastic geometry and continuous time Markov chains to jointly analyze the interwoven communication and computation performance of extreme edge computing systems. Based on the incorporated framework, we study the influence of various system parameters on the task response delay. Our findings reveal the existence of an optimal number of EEDs that need to be recruited in order to minimize the task response delay. Moreover, we show that in some cases, our model can outperform the normal MEC offloading systems.

Edge Computing (EC) has been evolving towards harvesting latent yet underutilized computational resources of the Extreme Edge Devices (EEDs), such as autonomous vehicles, smartphones, and tablets. However, EEDs tend to be user-owned devices. This triggers a high level of uncertainty, the impact of which is mostly overlooked. Such uncertainty can stem from the potential loss of network connectivity, battery depletion, as well as the dynamic user access behavior that can affect the computational capability of EEDs and compromise the convenience of users. This uncertainty can profoundly impact the devices' reliability of executing the offloaded tasks. In this context, we propose the Replica Maximization at the Extreme Edge (RMEE) scheme. RMEE employs task replication to achieve maximum reliability and improve successful task execution while abiding by certain QoS requirements. Towards that end, RMEE aims to maximize the number of offloaded replicas for each task, while ensuring that the task execution delay is kept within a certain threshold. We formulate the task replication optimization problem as a Mixed-Integer Linear Program (MILP) and devise an analytical solution using the Karush-Kuhn-Tucker (KKT) conditions and Lagrangian analysis. Extensive simulations have shown that RMEE outperforms other baseline schemes that involve single and fixed number of replicas, in terms of drop rate, satisfaction ratio, and the number of replicas by up to 100%, 100% and 60%, and 95.1 % and 85.4%, respectively.

Fostering Edge Computing (EC) by recycling prolific yet underutilized computational resources of the Internet of Things (IoT) devices, also referred to as Extreme Edge Devices (EEDs), has gained significant momentum lately. Fair resource allocation is a primary concern in such computing paradigms. However, fairness is typically considered from the requester's perspective, whereas fairness for workers (i.e., EEDs) is mostly overlooked. In this context, we propose the Multitiered Worker-Oriented Resource Allocation (MWORA) scheme. In MWORA, the resource allocation problem is formulated as an Integer Linear Program (ILP). MWORA aims to maximize service capacity and minimize the task response delay while enabling fair resource allocation that maintains a specific satisfactory profit for workers. Such a satisfactory level is maintained to prevent the workers from leaving the system and ensure their recurrent subscription to the service. This is done while abiding by the deadline demanded by each requester and without exceeding a certain budget. MWORA also accounts for the fact that EEDs are user-owned devices and are thus subject to a dynamic user access behavior, which can affect the level of computational resources that workers are willing to offer. In particular, MWORA enables multitiered computational resources to be granted by each worker depending on the price of the allocated task. Extensive simulations have shown that MWORA outperforms other baseline resource allocation schemes regarding average response delay, service capacity, worker satisfaction ratio, and fairness.

The surging demand for Edge Computing (EC) to cope with the proliferation of latency-critical and data-intensive applications has inspired the notion of recycling ample yet underutilized computational resources of end devices, also referred to as Extreme Edge Devices (EEDs). Maintaining data privacy and cost efficiency remain core challenges for the viability of EED-enabled computing paradigms. In this context, we propose the Community-Oriented Resource Allocation (CORA) scheme. CORA exploits business, institutional, and social relationships to build clusters and communities of requesters and EEDs that can eliminate recruitment costs and preserve privacy. However, community-imposed constraints on resource allocation can lead to unbalanced work distribution. To address this issue, CORA considers community restrictions, minimizes flowtime and makespan for the allocated services, and retains a reasonable scheduler runtime for real-time resource allocation. Towards that end, CORA formulates the resource allocation problem as a Bipartite Graph Matching problem. Furthermore, CORA exposes tuneable parameters that allow prioritizing flowtime or makespan, making it suitable for different scenarios. Extensive simulations show that CORA outperforms six prominent heuristic-based resource allocation schemes by up to 24% in terms of average makespan while sustaining the same level of flowtime and runtime.

Edge sensing (ES) is rising as a potential solution for remote sensing challenges, as it exploits the proliferation of smartphones, leverages their embedded sensors to collect data from users' surrounding environments and uses their processors to perform edge computing tasks. Moreover, it is characterized by its low cost and time efficiency. Tremendous efforts have been dedicated to ES systems' quality of data (QoD) and coverage to enhance its performance. Since users incentivization plays a crucial role in enhancing the system's performance, the research community concentrated on improving incentives schemes. In this paper, we evaluate the effect of users' mobility on ES systems' quality of data and coverage, and propose a users' distribution-based dynamic-incentive scheme. In particular, we use a 2-dimensional random waypoint (RWP) model to emulate the randomness of users' mobility and velocity. The proposed incentive scheme aims to eliminate the negative impact of mobility on the QoD; by considering different factors to determine users' incentives and creating users' attraction areas in the targeted cells.

Continuous real-time electrocardiogram (ECG) monitoring can detect arrhythmia and provide early warning for heart attacks. Effective power management signals and controlling the mode of operation to reduce the need for full fidelity ECG signal. This work studies the impact of the base-delta compression technique for different cardiac conditions on power consummation. It also aims to evaluate operational strategies and their effect on the battery life when the ECG patch can switch between different operating modes (e.g., varying the number of leads according to the cardiac conditions). We use a binary classifier to inform the decision of switching between different operational strategies. Both scenarios are evaluated in terms of execution time, Bluetooth Low Energy (BLE) communication airtime, power consumption, and energy-saving ratios on a Texas Instruments CC2650 Micro-controller Unit (MCU). We compare the performance of the base-delta compression and changing the mode of operation scenarios on various cardiac abnormalities. Performance evaluation shows that operational strategies outperforms data compression in power saving for normal ECG readings by a double fold. In contrast, operational strategies incurs an additional overhead of 1011 ms during an abnormal status. However, base-delta satisfies the embedded platform constraints on execution time and airtime with 25 ms and 20 ms, respectively in the MCU environment.

Current telecommunication networks face a surge in the number of connected Machine-type Communication (MTC) devices, creating an unprecedented disproportionate demand for existing resources, especially when working with a Heterogeneous Networks (HetNets). This demand cannot be addressed adequately as the infrastructure's transition process between different generations is slow. Fourth Generation (4G) relies on Orthogonal Multiple Access (OMA), where a single user can occupy the same sub-channel, Orthogonality offers interference-free communication but for normal loaded scenarios, but under performs in overloaded scenarios. Whereas Fifth Generation (5G) is targeting more spectral efficiency by using the Non-orthogonal Multiple Access (NOMA), allowing MTC devices to share the same resources in frequency and time. However, NOMA medium access techniques in general have a complex scheduler design as group users/devices with aligned correlations. In this study, we formulate and simulate a 4G/5G Uplink scheduler that is based on dual NOMA-OMA. The objective is to achieve a tangible improvement in the spectral and scheduling efficiency of the network. We are able to optimize the system under HetNet objectives and clustering constraints in overloaded scenarios, to examine the limitations of both NOMA and OMA in overloaded scenarios.

Edge Computing (EC) has paved the way toward the realization of the Internet of Things (IoT). This can be attributed to the ability of EC to bring the computational resources within close proximity to end-users, which significantly improves the response time. However, performance gain in EC can be compromised by service interruptions triggered by various dynamic changes. Consequently, reliable service migration is crucial in EC. However, most service migration schemes either fail to consider the profound impact of the dynamic computation load on service continuity or provide impractical and time-inefficient solutions based on optimization techniques. This paper proposes the Heuristic-based Load-induced Proactive Migration (HLPM) scheme. HLPM incorporates a Finite State Machine (FSM) to model the dynamic computation load. It then makes proactive migration decisions based on the underlying transition probabilities. The proactive migration problem is solved using the MTHG heuristic algorithm. Performance evaluation shows that HLPM produces a significant decrease of up to 97% in migration decision latency compared to conventional optimization techniques. Furthermore, the performance gap of HLPM with respect to the optimal migration solution is just 1.44% latency and 3.89% number of migrations.

This paper proposes beam failure prediction to recover from inevitable link failures in beam-based mmWave communication proactively. The proposed system consists of two components. First, a prediction engine to foresee future beam failures and their severity. For this purpose, machine learning and deep learning are proposed to perform prediction. The second component is a proactive recovery mechanism, that matches the prediction failure results with a suitable recovery action, with the goal to maintain seamless connectivity and prevent service interruptions. The performance of the proposed system is compared against conventional beam failure detection and recovery. Simulations were carried out using real beamforming data. The results indicate a substantial improvement in the network performance. The improvement is measured in terms of prediction accuracy, beam failure probability and successful beam failure probability. This paper also assesses a drawback of the proposed system, particularly the increase in handover rate, and shows that the achieved gain outweighs this weakness.

Road traffic control systems can change vehicles’ speeds, density, and distribution in spatial and temporal dimensions, which may have significant impacts on the performance of pre-established cellular networks and Vehicular Ad-hoc Networks (VANETs). Identifying these impacts is crucial for satisfying service requirements, especially for the future of connected autonomous vehicles. Despite the extensive research that studied the impact of mobility on communication, the impact of traffic control on communication has not been addressed. Therefore, in this paper, we attempt to understand how traffic control strategies can affect communication network performance. We focus on vehicle navigation techniques because of their global network impacts that can significantly affect the load and handover rate on base stations. In this paper, we compare the Dynamic Shortest Path Routing (DSPR) to the state-of-the-art vehicle routing techniques, namely, the K-Shortest Path Routing (K-SPR) and Travel Time System Optimum Navigation (TTSON). We build a real network with calibrated traffic and use a microscopic traffic simulator as a testbed to measure the load and handover rates on base stations. Moreover, we developed and validated an analytical model to compute the packet drop probability based on the base station normalized load in the Fifth Generation New Radio (5G-NR) cellular networks. The developed model is integrated into the testbed to evaluate the reliability of the three traffic control systems. The analysis shows that road traffic load-balancing achieved by both TTSON and K-SPR improves communication performance in the simulated network.

Edge Computing (EC) has emerged as a key enabling paradigm for latency-critical and/or data-intensive applications. Recently, recycling abundant yet underutilized computational resources of the Extreme Edge Devices (EEDs), such as smartphones, laptops, connected vehicles, etc, has been explored. This is since EEDs can bring the computation service much closer to the edge, which can drastically reduce the delay. However, resource allocation in such environments typically follows a reactive approach, which can lead to increased delay and wasted resources. In this paper, we introduce the Optimal Proactive Resource Allocation (OPRA) benchmark to quantify the potential gains of proactive resource allocation in EC environments. OPRA exploits the predictability of request patterns to proactively perform resource allocation and create compute clusters that take future task and resource dynamics into consideration. Specifically, OPRA formulates the resource allocation problem as a Binary Integer Linear Program (BILP) problem, where it aims to minimize the total delay under full task assignment and computation capacity constraints. The optimal solution acquired under perfect knowledge acts as the upper bound on the achievable potential of predictive proactive resource allocation schemes. The effect of erroneous predictions on the performance of OPRA is also investigated. Extensive simulation results show that OPRA outperforms a reactive baseline by yielding a 50% decrease in the subtask dropping rate and 97% decrease in the service capacity.

Cooperative driving is a promising technology in the future Connected Autonomous Vehicles (CAV) because of its benefits to safety and fuel efficiency. However, since CAV will be relying heavily on wireless communication to cooperatively coordinate road maneuvering, latency and reliability of communication still pose a challenge. In this paper, we propose a novel scheme based on deep learning prediction to enhance the uplink resource allocation process in 5G C-V2X. The proposed scheme enables the base station to predict vehicle maneuvers, subsequently, assign it the required resource in advance without the need for scheduling request and granting process. This scheme improved the ability of 5G NR to support cooperative driving requirements. Moreover, we compare both traditional and proposed schemes discussing issues that arise from the introduction of prediction models and possible approaches for further enhancements in the future.

ECG monitoring systems have a significant role in detecting cardiovascular diseases and reducing the rate of sudden cardiac deaths. One of the critical factors to support real-time ECG tracking is to guarantee monitoring system availability. Hence, this work targets battery life expansion for a 12 Lead ECG patch. ECG patch operational hours are extended by reducing Bluetooth Low Energy (BLE) communication airtime, hence reducing the overall transmission power and extending the battery life. Huffman, delta, and base-delta compression techniques are implemented on a Texas Instruments CC2650 Microcontroller Unit using different sampling rates and cardiac conditions such as normal, ventricular tachycardia, and ventricular fibrillation state. The performance of each encoding algorithm is evaluated in terms of compression ratio, the execution time, and power consumption of the ECG patch. Our findings show that the base-delta encoding technique outperforms other techniques and achieves 70% data compression on normal ECG data, 41% on ventricular fibrillation, and 44% on ventricular tachycardia. The execution time of base-delta encoding takes less than 25 ms execution time and saves up to 36 % of the power consumption on the MCU environment.

Multi-Access Edge Computing (MEC) has emerged as a computing paradigm that can facilitate the use of Mobile Edge Learning (MEL), where Machine Learning (ML) models are processed at the edge. In MEL, it is important to address system heterogeneity in a way that minimizes staleness to improve learning accuracy. To do so, a centralized data allocation approach is typically used. However, this approach tends to overlook the privacy of learners, since learners' capabilities are assumed to be known beforehand by the orchestrator. In this context, we propose the Data Allocation via Benchmarking (DAB) scheme. DAB is a decentralized data allocation scheme that eliminates staleness and achieves a certain QoS while preserving the privacy of learners. DAB does not allow any information about the learners to be known to the orchestrator. Instead, each learner estimates the upper bound on the amount of data that it can train such that a certain training deadline is not exceeded. In addition, DAB proposes a novel method to enable each learner to accurately estimate its own hardware characteristics via benchmarking. Extensive performance evaluations on a real testing environment have shown that DAB can outperform the centralized data allocation scheme by up to 12% and 26% in terms of loss and prediction accuracy, respectively. Performance evaluations also show that the proposed benchmarking scheme yields an 83% reduction in benchmarking error compared to a prominent baseline scheme.

Mobile Edge Learning (MEL) is a learning paradigm that facilitates training of Machine Learning (ML) models over resource-constrained edge devices. MEL consists of an orchestrator, which represents the model owner of the learning task, and learners, which own the data locally. Enabling the learning process requires the model owner to motivate learners to train the ML model on their local data and allocate sufficient resources. The time limitations and the possible existence of multiple orchestrators open the doors for the resource allocation problem. As such, we model the incentive mechanism and resource allocation as a multi-round Stackelberg game, and propose a Payment-based Time Allocation (PBTA) algorithm to solve the game. In PBTA, orchestrators first determine the pricing, then the learners allocate each orchestrator a timeslot and determine the amount of data and resources for each orchestrator. Finally, we evaluate the PBTA performance and compare it against a recent state-of-the-art approach.

Edge networks provide ample resources for low-latency service recruitment, unlike remote resources in the Cloud. As such, smart devices and Internet of Things (IoT) nodes form a pool of Extreme Edge Devices (EED) that are within reach of Mist and Fog networks, providing significant advantages in latency, geographic cognizance, and reduced communication costs. EEDs are often recruited in Edge networks assuming they are reliable in their commitment to tasks. However, many EEDs may fail to fulfill their tasks because they operate under opportunistic approaches and are prone to intermittent connectivity. To ameliorate task failure, we aim to optimize task allocation under the assumption of failure. Additionally, we optimize CPU utilization to engage reliable EEDs, resorting to replication when needed to exceed a tunable reliability margin. We demonstrate the efficacy of our model in multiple scenarios and present future work in EED utilization.

The demand for ultra-low latency requirements is fueled by the growing popularity of time-sensitive applications including virtual, augmented and mixed reality, and industrial IoT. Edge computing is positioned to fulfill such stringent latency requirements. Addressing the increasing demand for time-sensitive applications becomes challenging due to limited resource at the edge. Even though virtual network function (VNF) sharing is known to improve the utilization of the service providers’ resources, service requests -including time-sensitive ones- can nevertheless be rejected. This paper proposes PSVS: a Prediction-based Service placement scheme with VNF Sharing at the edge. PSVS utilizes the predicted required resources in a defined lookahead window to minimize the rejection rate of premium services. A safety-margin is empirically-defined and used to add resiliency against prediction errors. Results show more than a 50% reduction in the rejection rate of premium services. Moreover, PSVS is resilient to prediction errors.

Democratizing the edge by leveraging the prolific yet underutilized computational resources of end devices, referred to as Extreme Edge Devices (EEDs), can open a new edge computing tech market that is people-owned, democratically managed, and accessible/lucrative to all. Parallel computing at EEDs can also move the computing service much closer to end-users, which can help satisfy the stringent Quality-of-Service (QoS) requirements of delay-critical and/or data-intensive IoT applications. However, EEDs are heterogeneous user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). This makes the process of determining the computational capability of EEDs increasingly challenging. Estimating the dynamic resource usage of EEDs (i.e., workers) has been mostly overlooked. The complexity of Machine Learning (ML)-based models renders them impractical for deployment at the edge for the purpose of such estimations. In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme to estimate the dynamic resource usage of workers over multiple steps ahead in a computationally efficient way while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM) to estimate the dynamic resource usage of workers in EED-based computing paradigms. Extensive evaluations on a real testbed of heterogeneous workers for multi-step sizes show an 87.5% prediction accuracy for the starting point of 2-steps and coming to as little as a 16% average difference in prediction error compared to a representative of state-of-the-art ML-based schemes.

The increasing demand on real-time and time-critical applications such as augmented reality, virtual reality, collision avoidance and industrial IoT, is fuelled by the low-latency promised by next-generation mobile networks (5G). Time-critical applications and services are real-time software whose failure could result in catastrophic consequences such as fatalities, damage to property, even financial losses. Edge computing is the main enabler of 5G ultra-low latency use cases. Edge resources are limited compared to the abundant cloud computing resources. As such, provisioning time-critical applications at the edge is more challenging and demanding. Even though virtual network function (VNF) sharing improves the utilization of the service providers’ resources, service requests -including time-critical ones- can still be rejected due to insufficient resources. This paper proposes IPTSV, an immediate placement scheme for time-critical services with VNF sharing. The proposed scheme prioritizes time-critical premium (Pr) services over best-effort (BE) services. In cases when no resources are available for Pr services, a preemption mechanism preempts resources for the Pr service, by deporting one or more deployed BE services. The experimental results show that IPTSV can reduce the Pr services rejection rate to $\sim 0\%$, while minimizing the disturbance that BE services might witness such as prolonged waiting and turnaround times.

Driver behavior profiling has been gaining increased attention due to its relevance in many applications. For instance, car insurance telematics and fleet management entities have been recently using smartphones’ embedded sensors, On-Board Diagnostics II (OBDII) units and other on-board IoT devices to collect data on vehicles’ behavior and evaluate the risk profile of drivers. In this context, this paper presents a robust data-driven framework for calculating drivers’ risk profile measured in terms of the additive inverse of the predicted risk probability. The Strategic Highway Research Program 2 (SHRP2) naturalistic driving study (NDS) dataset, which is the largest dataset of its kind to date, is utilized to build the risk prediction models. Crash and near-crash events are used to quantify riskiness whereas balanced baseline driving events (i.e., events captured during normal day to day driving episodes) are used to reflect total exposure or driving time per driver. Thirteen mutually exclusive behavioral risk predictors are identified, and the feature matrix is formulated. A sensitivity analysis is then performed to find the best number of balanced baseline events below which drivers are filtered out. Different machine learning models are selected, customized, and compared to achieve best risk prediction performance. Finally, the utilization of the proposed prediction model within an envisioned driver profiling cloud-based framework is briefly discussed.

Driver profiling is the real-time process of detecting driving behaviors and computing a driver’s expected risk based on detected behaviors. Predicting risk based solely on the inclusion of detected behaviors may not be accurate because this method of predicting ignores the environmental (e.g., weather conditions, traffic density level) context of detected behaviors. Moreover, coupling detected behaviors with their environmental context can be leveraged towards creating personalized risk profiles for drivers in each driving environment. These profiles can be utilized in various ITS applications including personalized safety-based route planning. In this paper, a novel driver profiling environment-aware framework is presented. In the proposed framework, data processing is distributed over three computational layers to enhance the overall reliability of the system. A risk prediction model is hosted on the edge/fog to determine the driving risk while considering the joint effect of the in-vehicle detected behaviors and their environmental context. Risk values along with a driver’s compliance to warnings are both utilized to compute the risk profile on the cloud. Using SHRP2 Naturalistic Driving (ND) dataset, the development of a novel risk prediction model is presented herein with the underlying sub-processes of data preprocessing, error analysis, and model selection. Then we analyze both the performance of the developed risk prediction model and the overall performance of the proposed system. Validation results for the developed model indicate a good compromise between bias and variance. Moreover, the results of the overall risk scoring model reflect its robustness and reliability in assigning accurate risk scores.

Unmanned Aerial Systems (UASs) or drones are becoming increasingly dependable tools for many civil and industrial applications. Due to the increasing usage and capabilities of drones coupled with advances in innovative technologies and algorithms for managing and conducting tasks, drones are expected to crowd low-altitude airspace in urban areas. This brings many opportunities for service providers to provide drone-related services. Hence, efficient use of drones is required. In this paper, we investigate the benefits of reconfigurable softwarized drones operated by an entity or a service provider to perform tasks for its operations or for interested customers. We model a system of reconfigurable drones that can conduct multiple tasks per flight using Virtual Network Functions (VNFs) running on on-board capable computing systems. We compare our proposed model with alternatives with limited and no softwarization capabilities. Our evaluation demonstrates the performance gains due to reconfigurability in softwarized drone networks. Results show that softwarization allows drones to perform a variety of tasks using a limited number of reconfigurable drones and in a shorter time.

Connected Autonomous Vehicles (CAV) utilize vehicular communication to collect information about the surrounding environment to make informed decisions about speed and maneuvering. This enables safe driving and decreases the number of accidents and thereby the associated fatalities. However, vehicular communication may suffer from high latency and low reliability, especially in dense vehicle environments, which may negatively affect the safety of CAVs. Therefore, it is crucial to study the impact of these metrics on the safety application performance while taking into account realistic CAV kinematics and dynamics. In this paper, we address this problem by comparing the performance of the Short Range Communication (DSRC) to that of the Fifth-Generation New Radio (5G-NR) and their impacts on the safety applications in the CAV environment under different settings. We develop a full-fledged simulation framework that can realistically model both vehicular mobility and communication and can capture the impact of communication on safety applications. Within this framework, we implement an important CAV's safety application, namely, the forward collision avoidance system, in which following vehicles use vehicular communications to gather information from leading vehicles to compute the safe speed and avoid collisions. We then use this framework to study and compare the performance safety of the forward collision avoidance system using both DSRC and 5G-NR communications. The results show that the packet delays and drops in communication networks can adversely affect CAV safety. The results also demonstrate that 5G is more capable of supporting the safety requirements under higher packet traffic loads and vehicle densities.

Driver distraction identification is crucial to improve road safety. Through vehicular communications, vehicles can exchange driver behavior information, based on which distracted drivers can be identified, and all drivers can be notified, which can mitigate the impact of driver distraction on road safety. To build such systems, it is essential to understand the different types of distraction, and how they affect driver behavior and their relationship to crashes or near-crashes. This understating should be based on real datasets, which are very limited. Therefore, in this paper, we cover this gap by building a data-driven simulation model to quantify the impact of realistic driver distraction on traffic safety. In particular, we use the 2nd Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) dataset to develop a simulation framework for driver distraction. First, we pre-process and analyze the dataset for different types of distractions. The analysis shows that the data can not be fitted to any of the known distributions. Therefore, we use the Gaussian Mixture Model (GMM) to represent the distraction intervals for the different distraction types. We then use these GMM models and the statistics collected from the data to realistically simulate the driver distraction using the Simulation for Urban MObility (SUMO) software. Finally, we use this framework to simulate the driver distraction in a real network. The data analysis and simulation results revealed important and interesting conclusions, such as decreasing the crash ratio when roads become congested.

In Information-Centric Networks (ICNs), caching decisions are mostly driven by request-centric mechanisms. Fluctuations in request rates and cache capacities typically have the most impact on where content would be cached. However, as ICNs expand in scale, producers may prefer certain nodes to cache their contents based on favorable properties, such as topological centrality, closeness of cache locations with respect to consumers, security measures, and service up-time. These factors would impact the valuation of caching nodes. Nevertheless, maximizing cache utilization via dynamic cache valuation is a challenging task in ICNs, largely due to inter-dependencies in model parameters. In this paper, we propose a novel caching model where content producers aim to dynamically valuate cache nodes to optimize caching. The model is built on a value-based utility function that considers dynamic and topological attributes of cache nodes, enabling a dynamic novel caching scheme named Max-Node Utility that aims to maximize caching utility. Simulation results demonstrate that Max-Node Utility outperforms current state-of-the art caching schemes by providing better caching utility, reducing access delay and increasing cache hit ratios across varying cache sizes and popularity skewness values. An outlook on the premise of producer-driven caching schemes is presented in the conclusion, to emphasize future directions in similar caching models.

Stability of the ideal plant environment in a greenhouse can be maintained by using wireless sensor networks, which are used for monitoring and controlling temperature, light, and humidity. Tracking plant growth is the best method for early detection of disease thus preventing significant crop losses. Wireless Visual Sensor Network (WVSN) are used for monitoring plant growth with the added feature of a camera. This paper presents a mathematical formulation and an optimal solution for the placement of the WVSN cameras to guarantee coverage of a large area while maintaining high quality images and minimizing overlap between cameras. Simulation results show the effectiveness of the proposed model in finding the minimum number of cameras with the exact position to cover the entire monitored area of the greenhouse, with the desired image quality resolution.

Federated Learning (FL) is a novel distributed learning paradigm in which local learning models are simultaneously trained using the stored data on multiple devices, then ultimately aggregated into a global model. A promising use case of FL is the training of a global model using the data collected by unmanned aerial vehicles (UAVs) during their flight, which is invaluable in scenarios in which an infrastructure cannot be accessed (e.g., disaster). However, this is challenging as limited resources are to be distributed between flight time, sensing, processing, and communication. In this paper, we address the resource problem for a set of heterogeneous UAVs with different computation and communication capabilities from distributed point of view. We propose the usage of Device-to-Device (D2D) communication to fairly distribute the data so-far collected by UAVs with different capabilities by posing it as an optimal transport problem. Our contribution is two-fold: (1) We obtain the fairest distribution of data given the UAVs’ computational capabilities such that global learning time is minimal; (2) We devise a scheme using Optimal Transport (OT) to achieve such a fair distribution between UAVs. The performance of the proposed techniques is demonstrated in an FL setting with different UAV topologies with the FL training done using the MNIST dataset.

Unmanned Aerial Vehicles (UAV) are expected to play a crucial role in the future of 5G and beyond. However, designing efficient routing protocols for UAV is challenging due to the mobility and energy constraints. This problem becomes harder in UAV-assisted 5G networks because of its impact on the time slot assignment for the uplink and downlink in Time Division Duplex (TDD) frame structure. Thus, in this paper, we propose a new optimum routing technique for UAV-assisted TTD 5G networks, the Optimized Load-Balancing Routing (OLBR). The optimum routing problem is formulated in such a way that the decision variables are used to compute the time slot assignment in the 5G connection between UAV nodes. The objective of the optimization model is to minimize the network-wide delay. By distributing traffic across different alternative routes, OLBR minimizes network congestion, resulting in shorter queuing delays. Such a load-balancing also decreases the possibility of node failure due to energy depletion. The proposed OLBR is compared to the shortest path routing using Monte Carlo simulation on two different network topologies at different network traffic loads. The simulation results show that the OLBR produces significant savings in network-wide packet delay compared to the shortest path.

Recently, there had been several proposals to use deep-learning based prediction models in estimating channel state information (CSI). However, all of these proposals were investigated under a fixed indoor-outdoor environment. In this paper, we propose two models to perform uplink CSI prediction in dynamic vehicular environments. One of these models is a tailoring of an existing state-of-the-art deep learning model, based on a combination of convolutional and recurrent neural networks (CNN-RNN), so as to suit the mobility factor in vehicular environments. The other model is a proposed simpler artificial neural network (ANN) model, again tailored to cope with the vehicular settings. We perform a comparative sensitivity analysis of the two models, in which we investigate the effect of changing vehicle speed, prediction horizon, and history horizons on the performance of both models. Interestingly, we have show that the simpler ANN model performs much better than the more sophisticated CNN-RNN model at broad range of vehicular driving speeds as long as the prediction horizon is smaller than the history horizon in the prediction process. The CNN-RNN becomes naturally more efficient in the opposite scenarios.

The ability to accurately forecast network resource utilization is vital in next-generation wireless networks. Based on the predicted load, telecom operators can proactively allocate network resources in an efficient way. In this paper, we perform a thorough analysis of a cellular network downlink load dataset collected at millisecond resolution. We first evaluate various statistical metrics of the physical resource block (PRB) utilization data to investigate its predictability. Then, we develop deep learning-based models to forecast PRB utilization in radio access networks (RANs). In particular, we propose univariate and multivariate long short-term memory (LSTM) network-based architectures for the forecasting task and investigate the impact of various prediction horizons and history lengths. When predicting PRB utilization, our approach showed up to 49% improvement in the Coefficient of Determination (r2score) and 19.5% decrease in the Root Mean Square Error (RMSE) compared with the baseline methods used.

Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow convergence experienced by traditional DRL agents puts many doubts on their practical adoption in cellular networks. In this paper, we first discuss the need to have accelerated DRL algorithms. We then analyze the exploration behavior of various state-of-the-art DRL algorithms for slice resource allocation, and compare it with the traditional 5G Radio Access Network (RAN) slicing baselines. Finally, we propose a transfer learning-accelerated DRL-based solution for slice resource allocation. In particular, we tackle the challenge of slow convergence by transferring the policy learned by a DRL agent at an expert base station (BS) to newly deployed agents at target learner BSs. Our approach shows a remarkable reduction in convergence time and a significant performance improvement compared with its non-accelerated counterparts when tested against multiple traffic load variations.

We present a novel energy-efficient approach to wireless real-time sensing. For a sensor node (SN) transmitting samples of a discrete time series in real-time, its lifetime depends largely on its battery capacity. With most of the energy consumed in wireless transmission, we present an energy efficient scheme that can significantly reduce the number of transmitted samples, while maintaining a low mean absolute error between the original and the recovered signals. We introduce the concept of instantaneous entropy and we derive a computationally efficient iterative formula for computing Shannon’s entropy. The SN evaluates the information content in each sample and decide whether to transmit or omit the sample. At the sink, we use incremental machine learning to recover the omitted samples in real-time. Our approach showed an average of 60% reduction in energy consumption by the SN with less than 2% mean absolute error in the recovered signal.

Recent studies into streaming media delivery suggest that performance gains from ubiquitous caching in Information-Centric Networks (ICN) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. Bitrate adaptation mechanisms, that drive video streaming, clash with caching mechanisms in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this article we use this evidence to draw a novel insight: in adaptive streaming over ICN, bitrates should be prioritized alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for ICNs shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Our results demonstrate that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, all while achieving the highest levels of video quality; thus indicating overall improvements to QoE.

The exploding increase in the number of connected devices and growing sizes of their generated data gave more opportunities for distributed learning to dominate fast data analytics in mobile edge environments. In this work, we aim to jointly optimize the allocation of learning tasks and wireless resources in such environments with the aim of maximizing the number of local training cycles each device executes within a given time constraint, which was shown to achieve a faster convergence to the desired learning accuracy. This joint problem is formulated as a non-linear constrained integer-linear problem, which is proven to be NP-hard. The problem is then simplified into a simpler form by deducing the optimal solution for some parameters. We then employ numerical solvers to efficiently solve this simplified problem. Simulation results show gains up to 166% and 250% compared to the task allocation only and the resource allocation only techniques, respectively.

The emerging use of drones to support communication infrastructure and to deploy temporary networks in emergency scenarios is under active research. In this paper, we consider virtualizing drones' computing resources when deploying drone networks by utilizing Virtual Network Functions (VNFs) to process and deliver mission-related data traffic. This is aimed at cases where off loading and processing traffic to the cloud is not an option due to the unavailability of terrestrial and satellite communications. After discussing possible applications, we propose and evaluate a scheme for the deployment and placement of a drone network as well as the VNFs needed to deliver a set of traffics flowing from different locations in the task area.

Monitoring the climate is one of the most important and challenging practices by which to obtain optimum crop production in a greenhouse. In a smart greenhouse, a wireless sensor network (WSN) can be used to monitor the microclimate. Constant monitoring and sensing can result in excessive energy consumption. Prediction of the microclimate can be used to control the operation of sensors and hence lower the energy consumed by sensor nodes. We develop a Long Short-Term Memory (LSTM) based on time series for the prediction of the maximum, minimum, and mean values of the air temperature, relative humidity, pressure, wind, and dew point. Microclimate data inside and Macroclimate data outside the greenhouse are collected daily and used for the analysis of the best-fitting LSTM model. After determining the network structure and parameters, the network is then trained. The statistical criteria for measuring the network performance are the Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). A comparison is made between the measured and predicted values of temperature, relative humidity, pressure, dew point and wind. Results indicate the effectiveness of the predictive model performance LSTM in predicting the microclimate. Statistical analysis of the RMSE and MAE results demonstrate the prediction accuracy of our proposed LSTM model.

The past decade has witnessed a staggering evolution in cellular networks. Mobile wireless technologies have undergone four distinct generations; from uncomplicated voice calls in the first generation to high-speed, low latency and video streaming in the fourth generation. The numerous services brought to the users by 4G network have caused an increasing load demand. This increasing demand in network usage has proven the necessity of further service enhancements, such as predictive resource allocation techniques and handover analysis. For these techniques to be deployed, network quality and performance analysis must be performed on real-world network data. Since throughput is a major indicator of the network's performance, throughput modelling and prediction can be utilized for analyzing network quality. In this paper, two approaches for throughput analysis are examined: classical machine learning and time series forecasting. For the first approach, various machine learning models were deployed for throughput prediction and our analysis showed that the random forest model achieved the highest prediction performance. For time series forecasting, statistical methods as well as deep learning architectures were used. The evaluation shows that the machine learning models had a higher throughput prediction performance than the time series forecasting techniques.

With the advancements in wireless network technologies over the past few decades and the deployment of 4G LTE networks, the capabilities and services provided to end-users have become seemingly endless. Users of smartphones utilize high-speed network services while commuting on public transit and hope to have a consistent, high-quality connection for the duration of their trip. Due to the massive load demand on cellular networks and frequent changes in the underlying radio channel, users often experience sudden unexpected variations in the connection quality. To overcome such variations and maintain a consistent connection, these variations need to be predicted before they occur. This can be accomplished by the spatio-temporal analysis of the different network quality parameters and the investigation of the main factors that affect the network's performance and QoS. To this end, we conducted a network survey via Kingston Transit in Kingston, Ontario, Canada. We used the Android network monitoring application G-NetTrack Pro to build a dataset of various client-side wireless network quality parameters. The dataset consists of 30 repeated public transit bus trips at three different times of the day, each lasting around one hour. In this paper, we describe the data collection process, present an analysis of the collected data, and investigate the effects of time and location on the network's measured throughput and signal strength. We made the collected data, including more than 190 thousand unique records, publicly available to researchers in a domain where open data is rare.

In this work, we discuss the problem of accidentally detached Ground Engaging Tools (GETs). We overview potential solutions and demonstrate the design and implementation of a hardware sensing platform and a Wireless Sensor Network (WSN) for monitoring the GETs of an electric-rope shovel digging in an oil-sand mine. The designed system utilize a rugged hardware sensing platform equipped with a myriad of sensors monitoring mechanical integrity and shovel utilization. Field testing on an operational shovel allowed for collecting and analyzing real sensory data to evaluate the system's performance. All emulated detachments events were successfully detected and reported by the system.

A rapid increase has been lately noticed in the number of individual and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers, thus overloading them and increasing risks of service interruptions. In response to this issue, reactive service replication has been suggested to enable individual and groups of users to access services on remote edge servers, thus guaranteeing system scalability. In this paper, we propose a task offloading and service replication scheme on local and remote MEC servers, which minimizes the response time of all users while satisfying the delay requirements of user groups involved in same traffic-heavy and/or multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate the problem as an integer non-linear problem, and solve it using numerical solvers. We then compare the performance of our optimized solution with distance-based and resource-based greedy approaches. Simulation results show that our optimized solution can achieve up to 14% and 13% performance gains in comparison to these two greedy approaches, respectively.

Large sport and entertainment events such as soccer games or concerts attract an immense number of fans, most of whom use personal vehicles to get to the event. Such a large number of cars presents a “vehicular crowd” that needs to leave in an organized, timely, and safe manner after the event. Combining vehicular crowds with a constrained road networks raises the need for efficient techniques for vehicular crowd management which is a fundamental building block in smart cities. We introduce a novel Vehicle Departure Control (VDC) and navigation system to clear the network in a shorter time and reduce network congestion and system-wide travel time. The proposed system collects network information from a variety of sensory devices: connected vehicles, smartphones, and traffic cameras. Then, it fuses this data to compute the current state conditions of each road link. Based on these parameters, the VDC module determines the allowable vehicle departure rates, and the navigation module computes the system-optimum routes for drivers to take. The proposed system is implemented in a microscopic simulator. The FIFA World Cup 2022 is used as a case study. We compare the proposed system to the Sup-population Dynamic Time-dependent Incremental Traffic Assignment (SFDTIA) which is a typical real-time navigation system that is currently in use by commercial systems. The results show that our optimum navigation and departure control reduced the network clearance time on average by 16%, and by 37% in certain extreme conditions.

This paper presents a ferrous-selective magnetic force proximity sensor (FMPS) for ferrous targets. The proposed simple design utilizes a neodymium magnet and a force sensing resistor. Compared to currently available ferrous-selective inductive proximity sensors (FIPS), FMPS is more power efficient (8mW) and cost effective. The design can be tweaked to satisfy various application requirements. Experimental results demonstrate the advantages of FMPS-like proximity sensors as it is more suited for energy-constrained wireless sensors (WSs) in Industrial Internet of Things (IIoT) applications.

Traffic forecasting is imperative to Intelligent Transportation Systems (ITS), and it has always been considered as a challenging research topic, due to the complex topological structure of the urban road network and the temporal stochastic nature of dynamic change. Popular sports events attract vast numbers of spectators travelling to the event, which will have a substantial effect on ITS, showing peaks on the network that can collapse a smart city's ITS. In this paper, we tackle traffic forecasting and use the Doha network in Qatar and the FIFA World Cup 2022 (FWC 2022) event as a case study. We propose a novel technique for embedding road network graphs into a Temporal-Graph Convolutional Network. The embedding process includes a modification to the graph weights based on graph theory and the properties of the line graph. Extensive simulations are carried out on a real-world calibrated dataset from Doha's road network. Our Temporal Line Graph Convolutional Network (TLGCN) proposal shows outstanding performance when compared to state-of-the-art techniques, not only for huge special events but also for the regular daily traffic.

In the era of the Internet of Things (IoT), it has become possible for a set of smart devices to collaborate autonomously and communicate seamlessly to achieve complex tasks that require a high degree of intelligence. Unlike traditional internet devices, a compromised IoT device can cause real-world damages. The severity of these damages increases dangerously in sensitive contexts especially when these devices are controlled by system insiders. Detecting abnormal access behaviors in such environments is quite challenging, due to frequent changes in the access contexts under which the IoT device can be accessed. In this paper, we propose an adaptive access control policy framework that dynamically refines the system access policies in response to changes in the device-to-device access behavior. We apply supervised machine learning to model and classify the device access behavior based on a real-life data set. We provide a use case scenario of a door locking system to validate our work. Results show that our framework provides improved security, dynamic adaptability and sufficient scalability to the target application domain.

This article presents a dynamic access control framework for the Internet of Things (DACIoT). The main objective of DACIoT is to prevent unauthorized access to IoT devices and tightens the authorized access while an IoT device is in use. The rigidness of existing access control (AC) techniques in terms of manual policy specification, discontinuity of access decision making, and immutability to changing access behaviors makes these solutions fall short in highly dynamic IoT environments. DACIoT supports three functionalities that are lacking in existing AC solutions: 1) automatic policy generation; 2) continuous policy enforcement; and 3) adaptive policy adjustment. The DACIoT extends the standard reference model of the extensible AC markup language (XACML) with the added three functionalities to improve the adaptability of attribute-based AC policies to highly dynamic IoT environments. Results show that DACIoT provides improved security, dynamic adaptability, and can scale efficiently to IoT environments.

Mobile crowdsensing (MCS) is a paradigm that exploits the presence of a crowd of moving human participants to acquire, or generate, data from their environment. As a part of the Internet-of-Things (IoT) paradigm, MCS serves the quest for a more efficient operation of a smart city. Big data techniques employed on this data produce inferences about the participants' environment, the smart city. However, sufficient amounts of data are not always available. Sometimes, the available data are scarce as it is obtained at different times, locations, and from different MCS participants who may not be present. As a consequence, the scale of data acquired may be small and susceptible to errors. In such scenarios, the MCS system requires techniques that acquire reliable inferences from such limited data sets. To that end, we resort to small data (SD) techniques that are relevant for scarce and erroneous scenarios. In this article, we discuss SD and propose schemes to tackle the problems associated with such limited data sets, in the context of the smart city. We propose two novel quality metrics: 1) MAD quality metric (MAD-Q) and 2) MAD bootstrap quality metric (MADBS-Q), to deal with SD, focusing on evaluating the quality of a data set within MCS. We also propose an MCS-specific coverage metric that combines the spatial dimension with MAD-Q and MADBS-Q. We show the performance of all the presented techniques through closed-form mathematical expressions, with which simulation results were found to be consistent.

In the era of autonomous cars, accurate vehicular positioning becomes very essential. The global navigation satellite systems (GNSS) suffer from signal blockage and severe multipath in urban canyons, which degrades the positioning accuracy and availability. Therefore, vehicles solely relying on positioning from GNSS receivers have limited performance. In this research, we present a novel unified cooperative positioning solution which enhances positioning accuracy and availability in urban canyons. The proposed system exploits the fact that vehicles have different positioning resources and is based on angle approximation, which artificially generates the hindered pseudorange by sharing angle information between vehicles using dedicated short-range communication. In addition, we propose a system that employs the proposed cooperative technique to assist the loose integration between the inertial navigation system (INS) and the GPS system (using extended Kalman filter) during partial GPS outages. Using raw data from inertial sensors and GPS receivers in the real road trajectories, we implement the cooperative INS/GPS loose integration and show that our cooperative integrated system outperforms the non-cooperative integrated system. The performance metrics used are the 2-D positioning root-mean-square error, the maximum 2-D positioning error, and the positioning accuracy gain (PAG). Specifically, the PAG gain is around 88%, 80%, and 60% when the number of blocked satellites is one, two, and three, respectively.

Controller placement is one of the most important aspects in Software Defined Networking (SDN) as it is critical in determining the performance of the network. While SDN is mainly applied in wired networks, we attempt to utilize it in a futuristic drone-based network architecture to capitalize on the programmability and flexibility offered by SDN. Such drone network comprises a multi-hop network of drones as the nodes of the network which also act as programmable network nodes and forwarding elements. In such a dynamic and mobile network, maintaining SDN controller connectivity becomes a challenge, especially when the drone network requires operating independently from a ground infrastructure for some deployment scenarios. In this work, we attempt to address this challenge by implementing a dynamic scheme for controller placement that deploys a minimum number of drones that operate as SDN controllers and adjust their locations dynamically as the controlled nodes adjust their locations to meet changing mission requirements.

Today deployments of massive Internet of Things (IoT) applications are expected from 5G networks. A primary challenge however is designing scalable wireless resource management schemes that can adapt to the varying temporal and spatial demand of IoT applications. As such, intelligence-based solutions that are agile to, and are able to exploit IoT traffic patterns are emerging as key enablers for 5G IoT applications. For example, Predictive Resource Allocation (PRA) has been proposed in wireless network literature as a mechanism to provide significant energy-savings and Quality of Experience (QoE) gains by leveraging predictions of the user location. While the results are very promising, further research is needed to 1) model and handle the inherent uncertainty in the predicted rates of PRA, and 2) develop low-complexity solutions for practical adoption. This is the topic of this paper, where we present a credibility-based chance-constrained fuzzy programming solution for PRA that enables the operator to control the energy efficiency-QoE tradeoff for different users and services. We demonstrate the use of a Kalman Filter (KF) to adaptively model rate prediction uncertainty by modifying the limits of the fuzzy membership functions in real-time. Our simulation results indicate that the proposed credibility-based framework provides a low-complexity solution for robust PRA.

Following the revolutionary changes the Internet of Things (IoT) has introduced to sensor networks, the Mobile Crowd Sensing (MCS) paradigm aims to utilize people and their smartphones as an extended instrument to sense. However, the benefit of MCS is limited when it comes to microeconomic entities rather than macroeconomic entities. In this paper, we propose CrowdDelegate (CD), an extension of MCS that aims to delegate employee tasks of a consumer hypermarket to customer-workers, utilizing store's loyalty programs interface to recruit participants and reduce operational and logistical costs. This is done by assigning CD tasks to customer-workers present around the store requesting their engagement in a gamified loyalty membership. Customers are rewarded points for the execution of CD activities, allowing the retail business to channel a portion of the loyalty program budget towards the reduction of labor costs. The benefits are two-fold as this approach increases cost-efficiency as well as customer retention. A restricted optimal transport is proposed over the topology of the store to recruit customer-workers based on task costs. This paper sheds light on an unexplored potential of human-centric sensing, extending it to benefit businesses and to engage participants in "doing" instead of only sensing.

Non-recurrent events (e.g. football events or evacuation in case of natural disasters) pose great challenges to vehicle routing and traffic management in Intelligent Transportation Systems (ITSs). The high traffic demand during such events, combined with the road network resource constraints, bring forward the need for efficient and smart management techniques that better utilize the network facilities while maintaining a certain performance level. Vehicular Ad-hoc Networks (VANETs) and the advancement in information technology bring new opportunities to address such problems. This paper utilizes VANETs to build a vehicular crowd management system that utilizes system-optimum stochastic routing. The objective is to clear the network in a shorter time by better utilizing the available network resources. To build this system, vehicles are used as sensors that communicate the network state information to the Traffic Management Center (TMC) in real time. A linear programming model is developed to minimize the network-wide travel time constrained by the road link capacities based on the collected information. The results show that the proposed system decreases the network-wide travel time and is successful in clearing the network earlier by up to 38% compared to deterministic user-equilibrium traffic assignment.

In this study, we propose an anomaly detection algorithm on sensor traffic data. The algorithm is composed of three distinct steps: temporal detection, spatial detection, and GPS calibration. The temporal detection is based on time series analysis and detects anomalies in real-time when measured sensor values are far offset from expected readings. The spatial detector is used to prune the output of the temporal detector, identifying those anomalies which are not consistent with measurements from neighboring sensors. Both temporal and spatial prediction use the widely adopted ARIMA model. The final step is to compare the predicted speed with the average speed gathered from vehicles equipped with GPS devices and subscribed to provide their data. Experimental results on real data demonstrate that the proposed algorithm effectively differentiates between abnormal traffic events and malicious manipulation of traffic data with an average accuracy of 94%.

Social media traffic is considered the primary source of Internet traffic. Such traffic is largely facilitated by mobile devices. Consequently, cellular networks tend to experience significantly high traffic load, and mobile users tend to incur high cellular costs. In order to alleviate such effects, we strive to allow social media users to have more reliance on vehicular rather than cellular networks for data access. However, this can be impeded by the high delay and low packet delivery ratio often associated with content access from remote data providers in vehicular networks. Thus, we introduce the Vehicular Optimal Proactive Caching (VOPC) benchmark to quantify the potential gains of predictive proactive caching in improving the quality of Internet services in vehicular networks. In VOPC, we exploit the fact that some users tend to exhibit a somewhat predictable behavior in terms of the type and time of social media access during the daily route they follow, as well as the period of encounter with road segments along that route. Such a predictable behavior is utilized to pre-cache the data at parked vehicles to be proactively procured by requesters as they pass by. The objective is to maximize cache hits by assigning replicas to caching spots that yield maximum certainty in their spatiotemporal availability for requesters. This is while sustaining a cache capacity limit. VOPC formulates the caching problem as an integer linear programming optimization problem and can thus act as an upper bound on reachable potential. Performance evaluation substantiates the ability of VOPC to act as a benchmark that can quantify the potential gains of the heuristic-based predictive caching scheme in terms of delay, packet delivery ratio, and cache hit ratio.

Information-Centric Networks (ICN) are aiming to shift the current host-oriented Internet model towards a content-centric one, by focusing on highly scalable and efficient content distribution and retrieval. Content caching is a fundamental building block in ICN, optimized to enable fast, reliable, and scalable content distribution and delivery. In this paper, we propose a novel utility value-based caching scheme, named Max- Utility for maximizing the aggregated utility- value of an ICN cache service provider. This novel approach considers attributes of both the content, and its producer, to determine the aggregated value, and builds on a dynamic caching algorithm that aims to maximize the aggregated utility value to guide cache placement and replacement decisions. Simulation results demonstrate that, Max-Utility outperforms current state-of-the art caching schemes by providing better caching utility while significantly eliminating caching redundancy and incurring less access delay to retrieve good quality contents across varying cache sizes and popularity skewness values.

The growth in the number of smart devices has mobilized the rise of CrowdSensing (CS) as an enabler of smart cities, where state-of-the-art technologies are utilized to improve citizens' quality of life. CS is a novel sensing paradigm that leverages data collected from smart devices to support a wide range of services. Particularly, smart emergency management systems are attracting increasing attention due to their potential to save lives, as they accelerate the delivery of emergency services including detection, mitigation and recovery. In this paper, we study the problem of the detection of an abnormal change in a monitored sensory variable, where the change is suggestive of an emergency situation. Specifically, we formulate our problem as a sequential change-point detection problem, where the underlying distribution of the variable changes at an unknown time. Our aim is to detect the change-point with minimal delay, subject to certain performance constraints. We utilize Shiryaev's optimal solution in two variants of the problem depending on the mobility behaviour of the participants, and conduct simulation experiments to show the performance of our schemes.

The demand for edge resources is increasing and will continue to rise especially because of delay- sensitive applications. Because of the limited resources at the network edge, efficient resource utilization will play a crucial role. In this paper, we demonstrate the gain of VNFs sharing- based service function chaining (SFC) requests placement, as a way of satisfying more requests with average less resources per request. We formulated the sharing-based SFC placement as an integer linear program (ILP) to minimize the overall deployment cost, hence optimize resource utilization and yet satisfy the QoS requirements. Our experiments show that sharing deployed underutilized VNFs will help satisfy 9-47% more SFC requests with on average 14-46% less resources per request.

Driver profiling is an emerging scheme that has a wide range of applications in the field of Intelligent Transportation Systems (ITS). Driver profiling is the real-time process of detecting driving behaviors and computing a driver's competence level based on detected behaviors. In this paper, a novel driver profiling framework is presented. A risk prediction model is hosted in the cloud to determine the risk associated with detected behaviors in specific driving environments. Risk values along with a driver's compliance to warnings are both utilized to compute a driver's risk profile. Using SHRP2 large-scale Naturalistic Driving (ND) dataset, the development of the risk prediction model is presented herein with the underlying sub-processes of data preprocessing, error analysis, and model selection. Validation results show that a developed randomized trees supervised learning model is proven to have a good tradeoff between bias and variance with evidently high performance results.

Drones or Unmanned Aerial Vehicles (UAVs) are utilized in a wide range of applications, as they are considered flexible and cost-effective. Novel applications have been recently explored, such as providing communications and Internet coverage where ground infrastructure is lacking or in temporary situations. In this paper, we propose a drone-based network architecture enabled by Software Defined Networking (SDN) to provide dynamic and flexible networking capabilities, suitable for different types of drone applications and deployments, while we discuss associated challenges related to SDN in done networks.

The following topics are dealt with: learning (artificial intelligence); resource allocation; telecommunication traffic; optimisation; MIMO communication; cellular radio; Internet of Things; probability; wireless channels; 5G mobile communication.

Video traffic is growing in dominance in today's Internet, prompting new challenges in timely delivery of video content. As Dynamic Adaptive Streaming (DAS) is becoming the de facto paradigm for video delivery, there is growing evidence on how caching improves users' Quality of Experience (QoE) in DAS. However, there is no consensus on how to maximize the utilization of in-network caching resources. Specifically, there are conflicting proposals on the impact of caching based on its distance (in hops) from the network edge. That is, contrasting ubiquitous network-wide caching to edge-caching. Supporters of the ubiquitous caching paradigm propose bitrate-aware caching schemes for optimizing video streaming, while counter-proposals suggest that edge-caching only the highest bitrate with online transcoding, may offer superior performance to ubiquitous caching. In this paper, we answer a contentious question: Can transcoding at the edge outperform bitrate-aware ubiquitous caching for DAS? We devise an extensive simulation environment using NS-3 to contrast both paradigms, experimenting with different bandwidth fluctuation patterns, under the FESTIVE user-based bitrate adaptation protocol. Caching performance was evaluated under five established QoE metrics, gauging delivered video quality, playback freezing and bitrate oscillation. We further assume zero processing delay for online transcoding at the network edge, to contrast to an upper bound performance from the edge-caching paradigm. Our experiments demonstrate that neither transcoding nor bitrate-aware caching offer a silver bullet for all cases. We present our insights on networking scenarios where each model would dominate in performance, and present our concluding remarks on their development.

Mobile computing proved to be essential in today's cyber communications. However, entities in mobile computing are known of having limited energy, physical, and logical resources. This imposes various challenges that greatly affect communication quality and performance of those mobile entities, especially when applying computationally-intensive security measures that are essential for protecting the communication sessions. Therefore, it becomes vital to seek suitable security techniques that balance between the communication performance and the resource context of those mobile entities. In this paper, we introduce the use of external aiding entities to assist in securing communications between feature-limited and resource-challenged next-generation mobile entities. We start with outlining different resource aiding approaches that help in securing communications. Then we discuss, in brief, both the design criteria and directions for a security resource aider. We, in the end, outline some of the challenges toward using security resource aiding in mobile and next generation communications.

In this tutorial we will start with a literature review on the chronological order for IoT, crowd sensing and crowd management, with the objective to set a common underlying reference models. We will tackle and present recent efforts in data collection techniques, where the quality of the reported data highly faces tangible interoperability issues because it has been collected over heterogeneous sources. Such issues include inaccuracies, data-labelling inconsistencies, time-sensitivities and different reporting granularities and finally; how to infer information about the actual situations. Along the whole tutorial, we will highlight the different technologies that enable crowd sensing and management, and their availability in the market. In the last part of the tutorial we will comment on privacy issues, and how privacy is jeopardized in emergency situations where the accuracy level highly depends on the collected data. An interesting trade-off privacy-accuracy will be described.

The unforeseen proliferation of smart devices has set in motion research efforts aimed at building Smart Cities (SCs) that improve the well-being of their citizens. One of the key technologies to achieve a SC is Mobile Crowd Sensing (MCS). In MCS, data is collected from the environment surrounding the smart device owners and utilized in the provision of a wide array of SC services. A prevalent class of services which is attracting increasing attention is smart emergency services, where MCS is leveraged to facilitate the detection and mitigation operations of crises. In this paper, we study the problem of an emergency situation detection based on MCS-provided data from heterogeneous participants. Specifically, we formulate our problem based on Detection Theory and underline its computational complexity. We present a greedy algorithm that aims to balance the trade-off between the decision time and the quality of the final decision. We perform extensive simulation experiments that show how our scheme improves the correct detection rate compared to a naive reputation-unaware baseline.

Advancements and convergence in IoT enabling technologies along with ubiquitous connectivity have led to the generation of new wave of smart services and applications based on real-time data access. The popularity of ubiquitous data access and accelerated adoption of these services pose significant challenges on user and data privacy. Thus, controlling access to such services in highly dynamic environments with continuously changing context becomes even more challenging. The wide adoption of IoT in our everyday life in many vital domains such as healthcare and military operations requires continuous and tight access control to prevent unauthorized and unintended access. A delay in making access decisions when context changes may result in consequences that cause harm and property damage. Therefore, continuity in access policy enforcement becomes a necessity in highly dynamic IoT environments for the entire access session not only at the time of request. This paper presents CAPE, a continuous access policy enforcement framework for IoT deployments. CAPE describes access control elements using predicates, and stores them as primitive facts in a K Dimensional tree data structure. Our algorithms automatically match access requests with primitive facts, generate access policies, make context-aware access decisions at run time and continuously monitor access control parameters based on which access decisions were made. Performance evaluation of CAPE demonstrates that this framework efficiently controls access in highly dynamic IoT environments.

Future smart cities are profoundly looking forward to providing services that assure daily competent functionality. Efficient traffic management and related vehicular services are crucial aspects when considering the city's decent operation. The significant presence of the vehicular and smartphone sensing and computing capabilities within and amongst the vehicles open the door towards robust vehicular and road services. The retrofitted present and future vehicles will be able to provide accurate real-time information about the road conditions and hazards, driver behaviour, and traffic. Adequate geo-referencing is remarkably demanded in order to preserve robustness while providing vehicular services. Present and widely spread global positioning systems (GPS) receivers are providing low- resolution position update at 1 Hz, which is not sufficient at high speeds. Also, alternative high data rate geo-referencing technologies may face self-contained or environmental-based performance limitations. In this paper, we propose an adaptive resolution integrated geo-referencing framework that augments GPS and inertial sensors to provide accurate localization and positioning for road information services. Also, we examine the effectiveness of the proposed system in geo- referencing for selected real-life road services.

Smart cities are becoming more complex and greater volumes of data are required for its efficient operation. Mobile Crowdsensing (MCS) is a paradigm that employs smartphones as instruments to collect data, where the recruitment of participants is based on rewards and incentives. However due to the mobile nature of people, sensing may not be available in a specific area of interest, reducing the quality of the MCS inference of that region. In this paper, we propose a method that utilizes optimal transport so that the MCS administrator could direct participants towards areas with poor quality to improve overall quality. An analysis of optimal transport is presented where the method is evaluated using computer simulations, where it is shown to be efficient for moving participants among spatiotemporal cells.

The Tactile Internet (TI) is building on the premise of remote operation in perceived real-time, and enables a plethora of applications that involve immersive interactions. As we build a future for globalizing skills, delivering haptic feedback across continents, and immersing users in remote environments, we are faced with significant challenges in understanding the context of Tactile Internet interactions, which we refer to as tactile cognizance. The challenge of understanding a remote terminals' context impacts not only the quality and depth of haptic feedback, but our ability to deliver perceived real-time operation. That is, as we develop AI techniques to compensate for the inevitable delay in remote operation, we need more information about a terminal's context and interactions to improve our prediction of movement and feedback. The Internet of Things (IoT) is promising to interconnect billions of sensors, and augment multiple tiers of cognition to expedite and fine-tune sensory acquisition from heterogeneous contexts. In this paper, we will survey recent developments in the IoT, and novel techniques for cloudlet-based cyber foraging (i.e., edge computing) to project how Tactile Internet interactions could benefit from IoT contextualization. We present a taxonomy of edge IoT systems designed for rapid data acquisition, with an emphasis on systems that prioritize stringent reliability and latency mandates. This paper builds on edge computing techniques to propose a framework for multi-tiered cognition in the Tactile Internet to feed its signaling systems, and how future TI codecs could embed contextual information in haptic feedback.

Driving behavior profiling has important relevance in many driving applications. For instance, car insurance companies have been recently applying a new insurance paradigm in which a driver's insurance premium is adapted based on realtime driving behavior. Driver profiling process is composed of two sub processes. The first is the detection of certain driving behaviors by acquiring data from onboard devices such as smartphones and OBDII units, whereas the second is the scoring process in which the detected behaviors are used to measure the actual driving risk. The scoring process has been viewed as an intricate problem due to the lack of reliable and large-scale datasets that can provide statistically trustworthy insights. This paper presents a data-driven approach for calculating a driver's risk score by utilizing the SHRP2 naturalistic driving dataset, which is the largest dataset of its kind to date. Two machine learning algorithms, which are support vector regression (SVR) and decision tree regression (DTR) are trained to reflect a driver's score. Driver's score is quantified in terms of the additive inverse of the predicted risk probability. After data filtering and preprocessing, models are trained using thirteen predictors, which represent twelve unique driving behaviors and the total driving time per driver. Validation results show that risk probability can be accurately predicted using the proposed models.

Participatory sensing allows individuals and groups to contribute to an application using their handheld sensor devices. Data collected from participants including their location, time, contacts, etc. are vital to the accuracy of the application but are considered private to the participants. The design of a successful participatory sensing application must consider the challenge of the accuracy-privacy trade-off. In more critical situations when a crisis occurs, however, the accuracy-privacy trade-off becomes more complex. When a participant is at risk, data accuracy becomes more important than participant's privacy. In this paper, we propose a Context-Aware Privacy (CAP) scheme. CAP aims to provide privacy- preserved data to authorized recipients based on the status of participants. Depending on the recipient category, their role and policies enforced, a different level of participants' private data may be received. Experimental results show that the CAP scheme achieves a high level of privacy protection in safe areas. In risk areas/situations the scheme achieves a higher level of data accuracy than existing privacy schemes.

This paper considers Mobile Crowd-Sensing (MCS) systems that suffer from scarce participant availability due to small sample sizes in each sensing cycle. With such small sample sizes, a sample in error would dramatically affect the MCS system performance. Therefore, we propose a novel quality of source metric targeted for small sample sizes through the non-parametric bootstrap, the trimmed mean, and the Median Absolute Deviation Trimming-based mean (MAD-mean). This statistic permits outlier detection, and therefore allows the estimation of quality under the stringent conditions of small sample sizes present in MCS independent sensing cycles. We introduce an algorithm that allows MCS administrators to control the accuracy of the metric, and therefore control the range of accepted values. Such control is achieved by means of introducing the MAD-mean, which deliberately widens the statistic's distribution, and therefore the perception of quality. In combination with the bootstrap, our metric allows quality estimation for samples as small as 8. We develop our robust quality of source metric algorithm, showing the impact of all the involved parameters; and we compare it to computer simulations to demonstrate its viability.

Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes. However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips. Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs. Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system. Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized. In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices. Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment. Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route. Extensive road experiments are held to build and show the potential of the proposed system.

Social media traffic constitutes the highest percentage of Internet traffic, which is mostly facilitated by mobile devices. This leads to high cellular costs incurred by mobile users. To reduce these costs, we strive to enable social media users to rely more on vehicular rather than cellular networks for content access. However, this can be hindered by the high delay and low packet delivery ratio often associated with accessing data from distant content providers in vehicular networks. Thus, to bring the data closer to the requester, we propose the Probabilistic Cooperative Caching at Moving and Parked Vehicles (PCCMPV) scheme. In PCCMPV, we exploit the static and mobile nature of parked and moving vehicles, respectively, to dynamically populate valuable road segments with diverse cached data. To do so, we dynamically assign a probability of caching to nodes along the data delivery path to assess their importance as caching nodes. For parked vehicles, such a probability relies primarily on the traffic density of the corresponding road segment, as well as its closeness centrality, and remoteness from the nearest data holder. PCCMPV provides an implicit form of off-path caching by assessing the trajectory of moving vehicles encountered along the data delivery path to calculate their probability of caching. Performance evaluation of PCCMPV demonstrates significant improvements in terms of delay, packet delivery ratio, and cache hit ratio compared to other caching schemes in vehicular networks.

Crowd Sensing (CS) is a paradigm empowered by the pervasiveness of mobile smart devices, in which crowds of device owners cooperate to provide information about their surrounding environment. In this paper, we introduce the Data and Participant Assessment and Remuneration Scheme (DPARS) for cooperative CS applications. DPARS implements a three-stage procedure to estimate a fair reputation-based payoff for CS participants. We achieve this by first applying a consensus- based outlier detection technique on the received data. The output of this technique is used to statistically evaluate participants' reputations based on the Dirichlet process. Consequently, a fair payoff for every participant is determined by treating participants as coalitions of players in a cooperative game. Performance results indicate that our proposed scheme efficiently detects misbehaving participants, and decreases the amount of incentives allocated to them.

The majority of Internet users are active users of social networks and thus social media traffic represents the highest percentage of Internet traffic. The average daily usage of social media has been rigorously growing. Mobile devices are considered to be the most common facilitators of such usage. The aforementioned facts contribute to the excessive traffic load on the Internet, poor users experience in terms of quality of service, and high cellular costs spent by mobile users. In this paper, we strive to reduce these effects by proposing a scheme called Proactive Caching at Parked Vehicles (PCPV). PCPV aims to provide a better quality of service to vehicular users who tend to have a somewhat consistent social networking behavior. In particular, users who have a predictive behavior in terms of the type and time of access of social media platforms as a part of their daily routine during transit from one place to another. This is done by having the required data pre-cached and ready for users to proactively acquire at roadside parked vehicles as they pass by, rather than sending a request to the far-away data center and waiting for the reply. Performance evaluation of PCPV shows significant improvements in terms of delay, packet delivery ratio, and cache hit ratio compared to the reactive approach typically used in infotainment applications.

In a practical Mobile Crowd Sensing (MCS) system, there usually exist multiple heterogeneous MCS tasks, each with a different set of requirements for completion. The coexistence of these MCS tasks presents a need to study the user selection and task allocation problem while considering factors like task and user heterogeneity, coverage, sensing data quality and total cost. In this paper, we study this issue by formulating a Multi-task User Selection (MTUS) problem with the aim of minimizing the total number of recruited workers subject to task requirements, user sensing capability while maintaining coverage uniformity. We show that our formulated problem is NP-hard. Consequently, we propose two variants of a greedy heuristic where the decision criteria for recruiting users is based on the sensing capability and the coverage contribution to the final workers set. A simple cost-efficient incentive scheme that reduces costs for task creators and increases profitability for task workers is also proposed. We perform simulations to test our model and we show that it achieves high coverage uniformity while reducing the number of users compared to a single-task oriented scheme.

In the last decade, naturalistic driving studies (NDSs) have given researchers an unprecedented way to study the behavior of drivers through the deployment of, and capturing the data from, on-board vehicle sensors and cameras. The ability to determine the dominant driving risk facto rs can play an essenti al role in shaping transportation policies and education programs for drivers. This paper presents a cohort study statistical analysis to determine the risks associated with traffic and road surface conditions, quanti fied in terms of crash and near crash events. Two risk quantification measures, odds ratio (OR) and relative risk (RR, are utilized to signify the associated risk. For this research we used the 100-CAR data set, with a total of 829 crash and near crash and 19616 baseline events, which are driving events captured randomly in normal driving episodes. In the 100-CAR data set, traffic density is divid ed into six levels according to the traffic flow con dition. Similarly, road su rface condition is divided into four categories. To quantify the statistical significance of the results, measures such as the p-value are employed. The results show that icy roads with level-of-service (LOS) A, wet roads with LOS D, and dry roads with LOS D have the highest risk for crashes and near crashes. These results are proven to be of statistical significance.

The Internet of Things (IoT) attract a great deal of research and industry attention recently and are envisaged to support diverse emerging domains including intelligent transportation, smart cities, and health informatics. Operating system support for IoT plays a pivotal role in developing interoperable and scalable applications that are efficient and reliable. IoT is implemented by both high-end and low-end devices that require an operating system to run. Recently, we have witnessed a diversity of OSs emerging into IoT environment to facilitate IoT deployments and developments. In this paper, we present an overview of the common and existing closed source OSs for IoT. This paper is written in a tutorial style where each OS is described in details based on a set of designing and development aspects that we established. These aspects include architecture and kernel, memory management, scheduling, power consumption, networking protocols support, security, programming model, and multimedia support. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT applications/devices based on their functional and non-functional requirements.

Data privacy becomes a primary impediment to the realization of the IoT vision. One approach to the IoT security and privacy problem is to restrict access to sensitive data via access control and authorization models. Yet access context in IoT changes frequently raising the need for flexible and dynamic access control policies. Towards developing dynamic access control policies, context-based access control techniques are being investigated due to their robustness in assigning dynamic access permissions according to changes in context. In this paper, we propose to automate the generation of access control policies to overcome the inflexibility in traditional access policy specification techniques, and improve its adaptability to dynamic IoT environments. In our framework, we use context, attributes, and predication to describe the core access control elements. In response to access requests, our algorithm automatically produces conflict-free access control policies and makes the final access decisions at runtime. Our framework prevents non-authorized data accesses, and satisfies privacy constraints for authorized access requests in highly dynamic IoT environments. Our preliminary evaluation shows that the proposed approach offers greater flexibility and improved scalability than the current state-of-the-art methods.

Recently, smart cities functionality and management have captured notable consideration. Owing to the rapid development in the information and communication technologies (ICT), various applications and services are highly engaged in the cities' operation. Specifically, intelligent road services as traffic management, driver behavior assessment and crowdsensing based road condition monitoring contribute towards better operability. To sustain decent performance of these applications, accurate and continuous positioning is an essential concern. Generally, Global Navigation Satellite System (GNSS) receivers are vulnerable to partial or complete outages due to multipath or signal blockage. Consequently, inertial navigation systems integrated with GNSS receivers are affected by inertial sensors noises and biases. In this paper, we apply wavelet packet de-nosing to eliminate noises of the Micro-Electro-Mechanical Systems (MEMS) grade inertial sensors. Afterwards, we integrate the de-noised reduced inertial sensor system (RISS) with GNSS receivers in real road experiment to assess the system performance. In addition, we show the significance ofthe proposed integration over the conventional one during multiple GNSS outages under various driving scenarios.

LoRa is a PHY layer technology that has been gaining popularity with IoT platfrom developers, due to its low-power long-range communication. As a result, different classes of LoRabased MAC layer protocols have been proposed, with the key ones being either contention-based or centralized-synchronous. Since most of the research literature focused on analyzing the efficiency of contention-based LoRa protocols, we sought to study the efficiency of centralized-synchronous protocols. We utilized a tailored simulator to analyze the energy efficiency of LoRa-based centralized-synchronous protocols. Our findings, are backed up by hardware performance measurements. After comparing the energy efficiency of the centralized-synchronous protocols against that of other LoRa-based MAC layer protocol classes, we found that the lifetime of a device using a centralized-synchronous protocol was up to four times longer than that of a contentionbased device. These findings, as well as our insights, will aid the development of future energy-efficient LoRa-based MAC protocols.

With the growing demand on the IoT market and limited wireless resources, it is essential to fully utilize the available spectrum by improving the total throughput of the network. Many MAC protocols for IoT rely on pure ALOHA-based channel access. Being that these are contention-based the packet collisions cause a massive drop in both the throughput and packet d elivery ratio. The se, proto cols a re unsuitable formission-critical applications which usually req uire long-range communication with guaranteed packet delivery and high throughput. In this paper, we propose a new hybrid scheduling-based protocol MAC on Time (MoT) that guarantees the delivery of all uplink packets in the network and addresses mos t of the importantparameters re qui red by mission-critical a pplica tio.MoT improves the utilization of the bandwidth ca pacity while providing deterministic latency and increased throughput when compared to other IoT MAC protocols. We then designed a simulator for MoT to allow us to compare its performance against that of LoRaWAN. This work provides valuable insight on the performance of both protocols and will aid future research.

In Mobile Crowd Sensing (MCS), task assignment and user selection to minimize the total incentive cost is a crucial issue that has received a lot of attention recently. Previous works studied incentive techniques for single-task assignment schemes, but multi-task assignment schemes are a more practical consideration as MCS applications are becoming increasingly popular. In this paper, we aim at minimizing the total incentive cost for all task publishers while considering related variables such as coverage, workload and sensor availability. Specifically, we motivate and propose a cost-efficient incentive mechanism that encourages multi-tasking versus a single-task assignment scenario. We present a novel metric called Combined Satisfaction Factor (CSF) that measures the total amount of satisfaction for both task publishers and task workers. We conduct simulations experiments to test our proposed scheme and show that our scheme improves the cost-efficiency for task creators by 13% and significantly increases the satisfaction levels in the system.

Recent studies suggest that performance gains for content delivery over Information-centric Networks (ICNs) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieval of multimedia content. The bitrate adaptation mechanism that drives video streaming appears to clash with generic ICN caching techniques in ways that affect users' Quality of Experience (QoE). Cache performance diminishes as video consumers dynamically select content encoded at different bitrates. Motivated by preliminary evidence suggesting the merits of bitrate-based cache partitioning, we introduce a scheme to dissect the cache capacity of routers along a forwarding path according to dedicated bitrates. To facilitate this partitioning, we propose a guiding principle RippleCache, which stabilizes bandwidth fluctuation while achieving high cache utilization by safeguarding high-bitrate content on the edge and pushing low-bitrate content into the network core. We further propose a cache placement scheme, RippleFinder, to realize this RippleCache principle and highlight its impact on users' QoE by cache partitioning. The performance gains are reinforced by evaluations in NS-3. Measurements show RippleFinder can significantly reduce bitrate oscillation, while ensuring high video quality, indicating overall improvement to QoE.

The Internet of Things (IoT) infrastructure, systems, and applications demonstrate potential in serving smart city development. Crowdsensing approaches for road surface conditions monitoring can benefit smart city road information services. Deteriorated roads induce vehicle damage, traffic congestion, and driver discomfort which influence traffic management. In this paper, we propose a framework for monitoring road surface anomalies. We analyze the common road surface types and irregularities as well as their impact on vehicle motion. In addition to the traditional use of sensors available in smart devices, we utilize the vehicle motion sensors (accelerometers and gyroscopes) presently available in most land vehicles. Various land vehicles were used in this paper, spanning different sizes, and year model for extensive road experiments. These trajectories were used to collect and build multiple labeled data sets that were used in the system structure. In order to enhance the performance of the sensor measurements, wavelet packet de-noising is used in this paper to enable efficient classification of road surface anomalies. We adopt statistical, time domain, and frequency domain features to distinguish different road anomalies. The descriptive data sets collected in this paper are used to build, train, and test a system classifier through machine learning techniques to detect and categorize multiple road anomalies with different severity levels. Furthermore, we analyze and assess the capabilities of the smart devices and the other vehicle motion sensors to accurately geo-reference the road surface anomalies. Several road test experiments examine the benefits and assess the performance of the proposed architecture.

With the abundant on-board resources in smart vehicles, they have become major candidates for providing ubiquitous services, including public sensing. One of the challenges facing such ubiquitous utilization is the recruitment of the participating vehicles. In this paper, we present the reputation-aware, trajectory-based recruitment (RTR) framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget cap. The framework consists of a reputation assessment scheme, a pricing model, and a selection scheme collaborating for a main recruitment objective; maximizing coverage with minimum cost. We propose greedy heuristic solutions targeting the selection problem in real-time. The RTR framework generalizes the basic selection problem to handle some practical scenarios, including departing vehicles and varying redundancy requirements. We also propose a reputation assessment scheme and a pricing model as parts of the framework. Extensive performance evaluation of the proposed framework is conducted and the evaluation shows that the proposed greedy heuristics are able to achieve results close to previously obtained optimal benchmarks under different scenarios, and that the framework succeeds in achieving high levels of coverage even when vehicles do not stick to their announced trajectories.

Recent research on predictive video delivery promised optimal resource utilization and quality of service (QoS) satisfaction to both dynamic adaptive streaming over HTTP (DASH) providers and mobile users. These gains were attained while presuming an idealistic environment with perfect predictions. Thus, a robust QoS-aware predictive-DASH (P-DASH) is of paramount importance to handling the practical uncertainty implied in predicted information. In this paper, we propose a stochastic QoS-aware robust predictive-DASH (RP-DASH) scheme over future wireless networks that takes into account imperfect rate predictions. The objective is to achieve long-term quality fairness among the DASH users while capping the probability of service degradation by an operator predefined level. A deterministic formulation is then obtained using the scenario approximation, which adopts the probability density function (PDF) of predicted rates. A linear conservative approximation is introduced to provide an NP-complete formulation, which can be optimized by commercial solvers. Since exact PDF might not be available, Gaussian approximation is adopted by the introduced scheme to provide a closed form less complexity formulation. To support real-time implementations, a guided heuristic algorithm is devised to obtain near-optimal resource allocations and quality selections, while satisfying the predefined QoS level. Previous non-robust P-DASH schemes are evaluated in this paper, while considering typical error models in predicted rates. Such schemes resulted in increased QoS and the quality of experience degradations with the network load, which was avoided by the introduced RP-DASH. Results further revealed the ability of RP-DASH to reach optimal and fair QoS satisfactions.

Internet of Things (IoT) systems are inherently built on data gathered from heterogeneous sources. In the quest to gather more data for better analytics, many IoT systems are instigating significant challenges. First, the sheer volume and velocity of data generated by IoT systems are burdening our networking infrastructure, especially at the edge. The mobility and intermittent connectivity of edge IoT nodes are further hampering real-time access and reporting of IoT data. As we attempt to synergize IoT systems to leverage resource discovery and remedy some of these challenges, the rising challenges of Quality of Information (QoI) and Quality of Resource (QoR) calibration, render many IoT interoperability attempts far-fetched. We survey a number of challenges in realizing IoT interoperability, and advocate for a uniform view of data management in IoT systems. We delve into three planes that encompass Big Sensed Data (BSD) research directions, presenting a building block for future research efforts in IoT data management.

Recently, several car insurance companies got interested in classifying the behavior of drivers. Usage-based insurance (UBI), such as Pay-How-you- Drive (PHYD) scheme, is an innovative idea in which the insurance premium changes based on the driving behavior. This behavior is usually evaluated in terms of vehicle-related variables such as distance, speed, and acceleration to determine the expected risk profile for drivers. In this paper, an additional level of classification in the hierarchy of profiling is proposed. Using the 100-CAR naturalistic driving study (NDS) data set, five different Hidden Markov Models (HMMs) are trained to determine the fault responsibility of a Subject Vehicle (SV) in a crash or near-crash events. Two specific driving situations, which are conflicts with leading and following vehicles, are investigated in this study. Results show that these models can achieve a reasonable classification accuracy.

When a crisis occurs, an immediate response by rescue personnel is crucial. Decisions for a rescue plan are based solely on data about the crisis from the location. It stands to reason that increasing the amount of such data will result in a faster, efficient rescue response. To make this possible, a crisis response system accepts inputs from people near the crisis via their handheld sensor devices such as smartphones and tablets through a participatory sensing system. However, receiving data from the public could potentially result in corrupted and inaccurate data that will negatively impact the rescue plans. Given that risk, assessing the accuracy of the participant's data contribution becomes essential. In this paper, we present a Participant Contribution Trust (PCT) scheme. PCT aims to provide the crisis response system only with the trusted accurate contributions. The steps involved in filtering the contributions include splitting the crisis area into sectors, comparing the contributions with other intra- and inter-sector contributions and confirming the accuracy of the sensed data. Our experimental results show that PCT has a high detection rate for eliminating inaccurate contributions resulting in the delivery of the most accurate data to the crisis response system.

The exploitation of mobility traces and rate predictions has enabled predictive delivery of video content that can achieve optimal resource utilization and long-term Quality of Service (QoS) satisfaction. The network recognizes users moving towards poor radio conditions in order to prioritize them over other users with better future conditions. In this paper, we propose a QoS-aware predictive Dynamic Adaptive Streaming over HTTP (DASH) scheme that leverages future information to select both the resource sharing and video qualities over a time horizon. The scheme minimizes the number of quality switches while achieving a minimal average quality level with no video stops. We firstly define the maximum prediction gains under idealistic conditions by a scheme referred to as Optimal QoS-Aware Predictive-DASH (OQP-DASH). Then, a robust stochastic based formulation is introduced to handle the practical uncertainty in predicted information, where the scheme is denoted by Robust QoS-Aware Predictive-DASH (RQP-DASH). A chance constraint programming model based on Scenario Approximation (SA) is adopted to cap the risk of service degradation while using the Probability Mass Function (PMF) of predicted rates. Under idealistic conditions, OQP-DASH outperforms the non-predictive opportunistic counterpart and results in fewer quality switches. Applying estimation errors, RQP-DASH avoids QoS degradation without compromising the prediction gains which supports the application of predictive DASH in future network.

Road information services (RIS) is a major component of the information and communication technologies with the main purpose of RIS-based systems is to monitor road health conditions, weather information and traffic congestion. Considering the road conditions, there are various kinds of road surface types and anomalies with lack of efficient analysis of their behavior on the vehicle sensor measurements. Consequently, there are difficulties in detecting and categorizing the different road types and anomalies. This paper demonstrates road test results for the measurements of inertial sensors mounted in land vehicles while monitoring various road surface types and anomalies. In addition, a wavelet-based feature extraction together with statistical approach for the road types and anomalies are explored in this study. Two road test experiments on two different vehicles performed in Kingston, ON, Canada together with in-depth analysis are discussed in this paper.

Named Data Networking (NDN) is a promising paradigm for the future Internet to survive the growing data demand. Supporting seamless operation during user mobility is one of the main challenges in NDN. In this paper, we investigate optimal caching for producer mobility under prediction uncertainties. Mainly, we propose a stochastic optimization framework that exploits location and data requests' predictors to cache data proactively before handover. We model the problem using Chance Constraint Programming (CCP) that probabilistically incorporates the uncertainty in data prediction and models the trade-off between network overhead and Consumer satisfaction. A deterministic formulation is derived to obtain a closed form Integer Linear Programming model based on the prediction error model. The proposed framework is then implemented in ndnSIM and Gurobi, and simulation experiments are conducted to provide benchmark solutions for robust proactive caching. The results show that such robust scheme satisfies the consumers' quality of experience under imperfect prediction of future content requested from mobile producers. Hence, sustains the prediction gains over conventional non- predictive schemes without compromising the network overhead. We believe that such results drive incentives for deploying proactive mobility management in future NDN.

Highly predictable users' location and traffic have enabled a new video delivery paradigm over wireless networks referred to as Predictive Resource Allocation (PRA). Existing research assumes perfect prediction of information in order to derive the performance bounds of PRA and define its gains over conventional Resource Allocation (RA). In this paper we sustain the application of energy-efficient PRA under prediction uncertainties. To that end, we propose a stochastic robust PRA scheme that models the uncertainty in future demands and incorporates them in the mathematical formulation. A linear Recourse Programming (RP) model is adopted in order to represent the trade-off between the energy-savings and the risk of wasting resources while considering the probability of a user terminating or skipping the video session. Thus, avoids prebuffering the video chunks that might be skipped by the user. A low complexity near optimal algorithm is then introduced to provide real-time solutions for the formulated RP model. Simulation results demonstrate the ability of the introduced robust PRA to deliver energy-efficient video streaming with lower resources than the existing PRA while promising QoS satisfaction. These results provide the impetus to implement the robust PRA in future wireless networks.

The growing demand for video streaming is straining the Internet, and mandating a fundamental change in future networking paradigms. Current advancements in Information-centric Networks (ICN) promise a novel approach to intrinsically handling content dissemination, caching and retrieval. While streaming technologies are converging towards Dynamic Adaptive Streaming (DAS), in-network caching in ICN facilitates serving users with better video qualities, potentially beyond their actual bandwidth-mandated throughput. In this paper, we propose an assessment framework for adaptive video streaming to evaluate the performance of ICN caching schemes, and measure their impact on improving users' streaming experience. We present a thorough study of core performance metrics and adopt those metrics which are designed for video caching. We conduct experiments on a NS-3 based simulator, ndnSIM, and propose insights which will aid the development of future caching schemes that cater to inevitable bitrate variations.

In the Era of the Internet of Things (IoT) the validity of sensing coverage is of utmost importance as it affects the reliability of sensing services. The presence of anchor misplacement poses a challenge on the validity of sensing coverage. This kind of challenge has generally been overlooked in sensing coverage research. In this paper, we investigate the sensing validity under several scenarios of anchor misplacement with different displacement values. We also investigate the error components of measurement and anchor misplacement, and their resultant impact on sensing validity. We model the problem using computational geometry. We provide a theoretical approach to test such validity. Then we propose an algorithm that implements the suggested approach. Our results are further validated through extensive simulation. This study shows interesting results that could be used to mitigate the negative impact anchor misplacement has on sensing coverage.

We study the task scheduling problem in vehicular clouds. Task scheduling in vehicular clouds must deal with the transient nature of the cloud resources and a relaxed definition of non-preemptive tasks. Despite a rich literature in machine scheduling and grid computing, this problem has not been examined yet. We show that even the problem of finding a minimum cost schedule for a single task over unrelated machines is NP-hard. We then provide a fully polynomial time approximation scheme and a greedy approximation for scheduling a single task. We extend these algorithms to the case of scheduling n tasks. We validate our algorithms through extensive simulations that use synthetically generated data as well as real data extracted from vehicle mobility and grid computing workload traces. Our contributions are, to the best of our knowledge, the first quantitative analysis of the computational power of vehicular clouds.

Named Data Networks (NDNs) offer a promising paradigm for the future Internet to cope with the growing demand for data and the shifts in applications. One of the main challenges in NDNs is how to support a seamless operation during mobility. In this paper, we propose a proactive caching scheme (named ProCacheMob) to support Producer mobility that exploits location predictors and data requests patterns to cache data before handover occurs. In essence, ProCacheMob adopts the predicted future Interests, that will be sent to the mobile Producers, and caches their data contents ahead of handover. Thus, avoids Interest retransmission that increases the Consumer's delay and decreases the network efficiency during Producer's mobility. ProCacheMob is simulated in ndnSIM and evaluated against mainstream NDN mobility solutions. The simulation results show how the scheme is successful to avoid packets drops and decreases the delay experienced by Consumers by 52% compared to other schemes.

Monitoring the reliability of equipment in industrial environments requires the installation of wireless sensors inside such equipment. A common challenge associated with having a wireless sensor inside a metallic enclosure is hindered wireless signal propagation and reception. We design a wireless sensor platform for such industrial applications and test its resilience to the shielding effect of metallic enclosures. A generic model of an industrial metallic enclosure is created with only 1 mm gap in its structure. We show that our sensing platform can successfully establish a low-data-rate wireless link. Signal measurements are made to quantify the received signal strength.

As competing industries delve into the Internet of Things (IoT), a growing challenge of interoperability and redundant deployments is magnified. Specifically, as we augment more “things” in the IoT fabric, how will these components interact across their heterogeneity, let alone collaborate. In this paper, we address the core issue of component interaction and operation under the IoT umbrella. We present our contribution in the framework of wireless sensor networks (WSNs), as a founding block in the IoT. More importantly, we present a novel paradigm in the design of WSNs, to build a resilient architecture that decouples operational mandates from the nodes. We abstract IoT things as wirelessly interfaced components, which introduce functionality physically decoupled from their devices; boosting resilience, dynamicity, and resource utilization. This approach dissects the study of any IoT nodal capacity to its “connected” components, and empowers dynamic associativity between things to serve varying functional requirements and levels. It also enables reintroducing only the components required to suffice for network operation, or only those needed to meet a new requirement. More importantly, critical resources in the network will be shared within their neighborhoods. Thus network lifetime will relate to functional cliques of dynamic IoT nodes, rather than individual networks. We evaluate the cost effectiveness and resilience of our paradigm via simulations.

The growing demand for video content is reshaping our view of the current Internet, and mandating a fundamental change for future Internet paradigms. A current focus on Information-Centric Networks (ICN) promises a novel approach to intrinsically handling large content dissemination, caching and retrieval. While ubiquitous in-network caching in ICNs can expedite video delivery, a pressing challenge lies in provisioning scalable video streaming over adaptive requests for different bit rates. In this paper, we propose novel video caching schemes in ICN, to address variable bit rates and content sizes for best cache utilization. Our objective is to maximize overall throughput to improve the Quality of Service (QoS). In order to achieve this goal, we model the dynamic characteristics of rate adaptation, deriving caps on average delay, and propose DaCPlace which optimizes cache placement decisions. Building on DaCPlace, we further present a heuristic scheme, StreamCache, for low-overhead adaptive video caching. We conduct comprehensive simulations on NS-3 (specifically under the ndnSIM module). Results demonstrate how DaCPlace enables users to achieve the least delay per bit and StreamCache outperforms existing schemes, achieving near-optimal performance to DaCPlace.

Predictive resource allocations (PRAs) have recently gained attention in wireless network literature due to their significant energy-savings and quality of service (QoS) gains. This enhanced performance was primarily demonstrated while assuming the perfect prediction of both mobility traces and anticipated channel rates. While the results are very promising, several technical challenges need to be overcome before PRAs can be practically adopted. Techniques that model the prediction uncertainty and provide probabilistic quality of service (QoS) guarantees are among such challenges. This differs from the traditional robust optimization of wireless resources, as PRAs use a time horizon with predicted demands and anticipated data rates. In this paper, we tackle this problem and present an energy-efficient stochastic PRAs framework that is robust to prediction uncertainty under generic error probability density functions. The framework is applied for video delivery, where the desired video demands are modeled as probabilistic chance constraints over the prediction time horizon, and a deterministic closed form is then derived based on the Bernstein approximation (BA). In addition to handling prediction uncertainty, mechanisms that track the variance of the channel in real-time are practically needed. Towards this end, we demonstrate how a particle filter (PF) can be adopted to effectively achieve this functionality. A low complexity guided heuristic algorithm is also integrated with the BA-based allocations, and particle filter (PF), to provide a real-time solution. Extensive numerical simulations using a standard compliant long term evolution system are then presented to examine the developed solutions under various operating conditions. Results indicate the ability of our framework to significantly reduce base station energy consumption while satisfying users' QoS under practical prediction uncertainty.

Participatory sensing is an approach to data collection that offers individuals and groups the opportunity to participate in an application using their sensor devices. Receiving contributions from multiple individuals may result however, in corrupted and inaccurate sensed data. Therefore, it becomes important to be able to have enough knowledge about each participant in order to evaluate their contributions and trustworthiness. In this paper, we present a Reputation System to Evaluate Participants (RSEP). The RSEP, starts with grouping participants based on their contributions, and then selects the highest group value based on its participant reputation values. The RSEP filters the sensed data to separate out the most accurate contributions that enhance the purpose of the participatory sensing applications. Experimental results show that the proposed RSEP has a high accuracy level in evaluating and selecting participant contributions.

Predictive Resource Allocation (PRA) has demonstrated its ability to provide smooth video delivery with minimal and fair interruptions. Recent work on PRA techniques exploited rate predictions to strategically allocate the limited radio resources for delivering video content. However, existing PRA techniques assume perfect prediction of future information in order to define the maximum attainable gains. In this paper, we introduce a probabilistic robust PRA framework that handles prediction errors. By adopting chance constraint programming we were able to define a probabilistic measure on the QoS degradation due to prediction uncertainties. A deterministic non-convex formulation is then obtained using the statistical parameters of predicted rates. Accordingly, we propose a convex approximation to the formulated fair PRA, which can be solved using optimal solvers to obtain a benchmark solution for future robust PRA schemes. We evaluate non-PRA and non-robust PRA schemes considering typical error models of the predicted rates. We found these schemes to result in suboptimal fairness and increased QoS degradations with the network load. Results further reveal the ability of the introduced robust fair PRA to reach the optimal and fair QoS satisfaction levels. Our approach provides a step towards applying PRA in future wireless networks to deliver video streaming content.

Mobility management in Named Data Networks (NDNs) is one of the main challenges of seamless operation in the future Internet. Techniques used in existing proposals for Consumer mobility are either reactive or semi-proactive, which try to reduce data access time, but yet retransmissions are required. We propose a fully proactive optimal scheme (OpCCMob) that adopts location and data patterns forecasts to proactively support Consumers movements in the network. In essence, the scheme will optimally cache the predicted content close to the Consumer such that it will be satisfied before handover and avoid Interests retransmissions. A mathematical formulation of the problem is provided such that it bounds the overhead on the network and minimizes the delay of fetching the data. OpCCMob is implemented in ndnSIM and used as a benchmark to evaluate mainstream NDN mobility schemes under various practical scenarios. The results of different experiments show that the delay can be maintained during Consumers movements using control messages as an overhead. Moreover, a sensitivity analysis is conducted to measure the robustness of proactive schemes during imperfect predictions.

Route guidance and navigation services have been widely attracting researchers and application developers due to the serious problems of traffic congestion and the ceaseless need to improve the driving experience. Motivated by such driving concerns, this paper proposes a real-time, dynamic route guidance system with the main focus on the driver safety and satisfaction. As a unique feature compared to other existing systems, the proposed driver-centric route guidance (DCRG) system considers the driver behavior in the route guidance process for the sake of boosting the safety levels on roads. The system also considers the driver preferences targeting a personalized satisfying driving experience. As most drivers prefer traversing the fastest and healthiest route to their destination, the DCRG system takes into account as well the real-time traffic and road conditions while guiding drivers towards their targeted destinations. Performance evaluation of DCRG shows significant improvements in the travel time, on-road safety, and preference satisfaction levels compared to the shortest and fastest route guidance schemes.

Current Internet architecture is becoming inadequate for new requirements of highly scalable and efficient distribution of contents. Information Centric Networking (ICN) is one of the alternatives for the Next Generation Internet (NGI), which focuses mainly on contents. In-network caching is one of the major attributes of ICN, which allows contents to be cached in any ICN node. Any user can access ICN contents from different distributed locations. This attribute maximizes the problem of unauthorized access to ICN contents. In this paper, we propose a Decentralized Access Control Protocol for ICN architectures (DACPI). In this protocol, fewer public messages are needed for access control enforcement between ICN subscribers and ICN nodes than the existing access control protocols. DACPI depends on ICN self-certifying naming scheme. We perform security analysis on DACPI for the following attacks: man-in-the-middle, forward security, replay attacks, integrity, and privacy violations. According to the security analysis, DACPI prevents unauthorized access to ICN contents with fewer messages passed.

Named Data Networks (NDNs) offer a promising paradigm for the future Internet to cope with the growing demand for data. One of the main challenges in NDNs is how to support a seamless operation during mobility. In this paper, we investigate optimal caching for Producer mobility support and propose a scheme (named OpCacheMob) that exploits location predictors and data requests' patterns to cache the data proactively before handover occurs. In essence, OpCacheMob adopts the predicted future Interests, that will be sent to the mobile producers, and caches their data contents ahead. Thus, avoids Interest retransmission or redirection that increase the consumer's delay and decreases the network efficiency during producer's mobility. We provide a mathematical formulation for such caching problem that bounds both the cache update cost and the consumer delay while minimizing the total network overhead due to the change of content availability. OpCacheMob is then implemented in ndnSIM and evaluated against mainstream NDN mobility solutions. We demonstrate how the scheme can be used as a benchmark to measure the performance of other mobility schemes. In addition, a sensitivity analysis is presented to measure the impact of errors on the prediction gain of such solution.

The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of Information-Centric Networks (ICN) promises a novel architecture for addressing this exponential growth in data traffic, with ubiquitous caching to facilitate video delivery. In this paper, we present a novel in-network video caching policy in ICN, named StreamCache, catering to variable video contents with different sizes and bit rates. Our objective is improving the average throughput of users which consequently enhances the Quality of Experience (QoE), under the heterogeneity of users' devices and network conditions. StreamCache is a popularity-based policy, which operates distributively at routers, designed for the online processing in order to narrow the gap between the offline theoretical optimal solution and the real-world application. StreamCache operates in rounds, making caching decisions based on video request statistics and minimal cache coordination. We show that, StreamCache achieves near-optimal performance compared with the offline benchmark scheme, DASCache and outperforms current state-of-the-art protocols, such as ProbCache by presenting an elaborate evaluation carried out on ndnSIM, over NS-3.

This paper addresses the challenge of vehicle selection in a Vehicular Ad-hoc Network (VANET) used to assist vehicles with limited satellite visibility in urban environments. In [1], we proposed a Non-Range cooperative positioning system which uses pseudoranges from only one assisting vehicle at any given time. However, many vehicles are within the communication zone of the target vehicle especially in dense urban canyons. In this paper, we elevate the performance of our cooperative system by proposing a distributed vehicle selection criterion named Absolute Sum of Single Differencing (ASOSD). To test the viability of the proposed system, we design a cooperative experiment using three NovAtel receivers and show that the Positioning Accuracy Gain (PAG) of our system has increased by 60% compared to a system that averages Artificial Candidate Pseudoranges (ACPs) from two assisting receivers. Moreover, we study the effect of; the number of assisting vehicles, multipath, receiver noise, satellite clock bias, ionospheric and tropospheric errors on the selectivity of the proposed system. We show that as the number of assisting vehicles increase, the Root-Mean-Square-Error (RMSE) of the generated ACP decreases. Moreover, the selectivity of the ASOSD selector is not affected by the common errors in the shared pseudoranges.

Mobile computing proved to be essential in today's cyber communications. However, entities in mobile computing are known of having limited energy, physical, and logical resources. This imposes various challenges that greatly affect communication quality and performance of those mobile entities, especially when applying computationally-intensive security measures that are essential for protecting the communication sessions. Therefore, it becomes vital to seek suitable security techniques that balance between the communication performance and the resource context of those mobile entities. This paper investigates some possible options toward implementing an adaptive security measures that work with various mobile and next generation Internet entities. The paper basically studies the communication performance of mobile entities when security functions are running on them with and without operating adaptations. While the focus in this paper is about the Message Authentication Code group of security functions, the work can be generalized to include any resource-intensive security measures including both other cryptographic (such as encryption) and non-cryptographic measures (such as challenges).

Robust and prompt emergency response is a crucial service that smart cities should provide to citizens, communities, and corporations. Emergency management strategies that are currently supported by cities yield pre-determined protocols that can only handle well-understood incidents. However, there are incidents whose nature, shape, scale, and timing are not as predictable. The lack of adequate data management platforms to harvest emergency-related data from the proliferation of data sources scattered around a city is a major shortfall in current emergency response and risk assessment processes. We propose an improved information infrastructure to assist emergency personnel in responding effectively and proportionally to large-scale, distributed, unstructured natural and man-made hazards such as multi-vehicle accidents, outbreaks of human or animal diseases, major weather events, large fires, and terrorist attacks. The proposed infrastructure will crowdsource the multitude of human and physical sensing resources that can generate data about incidents (e.g. smartphones, sensors, vehicles, etc.) in order to build a comprehensive understanding of emergency situations and provide situational awareness and recommendations to emergency teams on the scene. Our infrastructure consists of three components: (1) large-scale crowdsensing and data quality valuation, (2) heterogeneous data integration and analytics, and (3) decision making, alternative generation and recommendations. Leveraging crowdsensing and heterogeneous data analytics will improve the response coordination to critical incidents and real-time incident management, which will contribute to saving lives and reducing injuries, improving the quality of life, and saving resources by deploying them more effectively.

Location-Based services are gaining momentum as an important advancement in context aware services. That is, empowering users to identify potential services in their current space, and the prospect for services that are able to target local users, are pushing interest in research and industry alike. This paper explores the use of non-audible sound as a communication medium to tag and access location based services and gain access to their pertinent information. We propose and demonstrate the indoor implementation of a prototype of a location-based service-enabling system for hand-held devices. The system allows users to use their hand-held devices to search and interact with available services in their surroundings. A beacon placed in the service location broadcasts a service code mappable to the services particular to that location, and encoded via an ultrasound signal. The hand-held device can then identify that signal and prompt the user with the available services. We detail the novel system design and the ensuing architecture, and demonstrate the viability of the system which is tested over a variety of environments and scenarios. We conclude with an overview of the wide range of applications of this system, and note how it can enhance the way clients access location based services.

The execution of sensing services within the Internet of Things (IoT) mandates considering IoT characteristics which include heterogeneity, scalability, dynamicity, randomness, and multiple ownership. In such environment new types of sensing coverage holes posed by anchor misplacement arise. The first type is actual coverage holes that have been falsely hidden and unreported. The second one is perceived coverage holes that have been falsely generated by anchor misplacement. These types have generally been overlooked in sensing coverage research. We study these types of coverage holes in the locality of the affected sensing objects. Then we calculate the ratio of the area of each coverage holes to the total area of coverage. We utilize Delaunay Triangulation (DT) to partition the sensing region into triangles. Then we apply the concept of history in graph theory to characterize the DT structure before and after anchor misplacement. We locally detect the intra-triangle coverage hole, determine its type, and then provide the ratio of the area of this hole to the total triangle area.

Predictive resource allocation (PRA) techniques that exploit knowledge of the future signal strength along roads have recently been recognized as promising approaches to save base station (BS) energy and improve user quality of service (QoS). Recent studies on human mobility patterns and wireless signal strength measurements along buses and trains have indeed supported the practical potential of PRA. An unresolved challenge, however, is modeling the uncertainty in the predictions, and developing real-time robust solutions that incorporate probabilistic QoS guarantees. This is of paramount importance in PRA due to the prediction time horizon that adds considerable complexity and increases the rate uncertainty in the problem. With these developments in mind, this paper addresses energy-efficient PRA applied to stored video streaming using chance constrained programming. The proposed solution incorporates: 1) uncertainty in predicted user rates; 2) a joint level of probabilistic constraint satisfaction over a time horizon; and 3) both optimal gradient-based and real-time guided heuristic solutions. Our framework fundamentally differs from previous PRA work in the literature where nonstochastic approaches with assumptions of perfect prediction were primarily used to demonstrate the potential energy savings and QoS gains. Numerical simulations based on a standard compliant long term evolution (LTE) system are provided to examine and compare the developed solution. Unlike existing energy-efficient PRA, the proposed framework achieves the desired QoS level under imperfect channel predictions. This robustness is attained without compromising the energy-efficiency compared to opportunistic schedulers, and thus supports PRA implementation in practice.

The widespread use and increasing capabilities of mobiles devices are making them a viable platform for offering mobile services. However, the increasing resource demands of mobile services and the inherent constraints of mobile devices limit the quality and type of functionality that can be offered, preventing mobile devices from exploiting their full potential as reliable service providers. Computation offloading offers mobile devices the opportunity to transfer resource-intensive computations to more resourcefulcomputing infrastructures. We present a framework for cloud-assisted mobile service provisioning to assist mobile devices in delivering reliable services. The framework supports dynamic offloading based on the resource status of mobile systems and current network conditions, while satisfying the user-defined energy constraints. It also enables the mobile provider to delegate the cloud infrastructure to forward the service response directly to the user when no further processing is required by the provider. Performance evaluation shows up to 6x latency improvement for computation-intensive services that do not require large data transfer. Experiments show that the operation of the cloud-assisted service provisioning framework does not pose significant overhead on mobile resources, yet it offers robust and efficient computation offloading.

Abstract Deployments over large geographical areas in the Internet of Things (IoT) pose a major challenge for single‐hop localization techniques, giving rise to applications of multi‐hop localizations. And while many proposals have been made on implementations for multi‐hop localization, a close understanding of its characteristics is yet to be established. Such an understanding is necessary, and is inevitable in extending the reliability of location based services in IoT. In this paper, we study the characteristics of multi‐hop localization and propose a new solution to enhance the performance of multi‐hop localization techniques. We first examine popular assumptions made in simulating multi‐hop localization techniques, and offer rectifications facilitating more realistic simulation models. We identify the introduced errors to follow the Gaussian distribution, and the estimated distance follows the Rayleigh distribution. We next use our simulation model to characterize the effect of the number of hops on localization in both dense and sparse deployments. We find that, contrary to common belief, it is better to use long hops in sparse deployments, while short hops are better in dense deployments – despite the traffic overhead. Finally, we propose a new solution that decreases and manages the overhead generated during the localization process. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper we study the problem of designing a distributed data storage system using rateless codes for resource constrained systems such as Wireless Sensor Networks (WSNs). Rateless codes, e.g. LT-codes, can achieve reduced complexity of both encoding and decoding, which caters well to the nature of limited resources in such systems. However, data in WSNs is inherently decentralized and that poses an additional challenge when attempting to build codes with unconventional degree distributions. We propose an energy efficient distributed dissemination and coding scheme to build a decentralized LTcodes based storage over a network of resource-limited nodes to provide data survivability against possible failures. In the proposed scheme, each sensor node assigns selection probabilities to storage nodes using Robust Soliton Distribution (RSD) in a distributed fashion, and disseminates its data over the storage network randomly. The proposed scheme is compared to similar schemes in the literature by means of simulations. The results show that energy consumption can be substantially reduced while achieving the required storage requirements.

The goal to achieve accurate and ubiquitous localization is the driving force for location based services in vehicular ad hoc networks (VANETs). In urban areas, global positioning system (GPS) and in-vehicle navigation sensors (e.g. odometers) suffer from prolonged outages and unsustainable error accumulation, respectively. The need for precise vehicle localization remains paramount, and cooperative vehicle localization based on ranging techniques are being exploited to this end. This paper presents a novel cooperative localization scheme that utilizes round trip time (RTT) for inter-vehicle distance calculation, integrated with inertial sensor measurements to update the position of not only the vehicle to be localized, but its neighbors as well. We adopted the extended Kalman filter (EKF), to limit the effect of errors in both the sensors and the neighbors' positions, in computing the new location. In comparison to the existing cooperative localization techniques, our proposed cooperative scheme does not depend on GPS updates for the neighbors' positions thus making it far more suitable in urban canyons and tunnels. In addition, our scheme considers updating the neighbors' positions using their current inertial sensor measurements resulting in; better position estimation. The scheme is implemented and tested using the network simulator 3 (ns-3), vehicle traces are generated using SUMO and error models are introduced to the sensors and initial positions for different velocities and densities. Results show that our scheme outperforms the inertial navigation systems (INS) technology typically used in environments where GPS fails.

Sensing coverage in Wireless Sensor Network (WSN) research has received significant attention. The usage of WSNs within the Internet of Things (IoT) mandates taking IoT characteristics into account when considering sensing coverage. These characteristics include heterogeneity, ultra-large scale, dynamicity, randomness, and multiple ownership. This paper provides an analytical study of sensing coverage in IoT where sensing resources (sensors) are: random, mobile or static, belong to different owners, and which are heterogeneous in terms of sensing and communication capabilities. We utilize Delaunay Triangulation (DT) to partition the target sensing region into triangles. The vertices of these triangles are IoT sensors. Since intra-triangle coverage holes are not uniform, our goal is to locally detect each hole and provide its bounds. First we determine the existence of an intra-triangle coverage hole, and then we provide a computation of lower and upper bounds of each local coverage hole. Our results are promising, and can be utilized in a multiplicity of coverage applications regardless of the sensors or deployment types.

The growing demand for video streaming is straining the current Internet, and mandating a novel approach to future Internet paradigms. The advent of Information-Centric Networks (ICN) promises a novel architecture for addressing this exponential growth in data-intensive services, of which video streaming is projected to dominate (in traffic size). In this paper, we present a novel strategy in ICNs for adaptive caching of variable video contents tailored to different sizes and bit rates. Our objective is to achieve optimal video caching to reduce access time for the maximal requested bit rate for every user. At its core, our approach capitalizes on a rigorous delay analysis and potentiates maximal serviceability for each user. We incorporate predictors for requested video objects based on a popularity index (Zipf distribution). In our proposed model, named DASCache, we present delay queuing analysis for cached objects, providing a cap on expected delay in accessing video content. In DASCache, we present a Binary Integer Programming (BIP) formulation for the cache assignment problem, which operates in rounds based on changes in content requests and popularity scores. DASCache reacts to changes in network dynamics that impact bit rate choices by heterogeneous users and enables users to stream videos, maximizing Quality of Experience (QoE). To evaluate the performance of DASCache, in contrast to current benchmarks in video caching, we present an elaborate performance evaluation carried out on ndnSIM, over NS-3.

MIMO is a key technology in improving the performance of wireless communications. Recently the concept of cooperative communication has emerged as a solution to exploit the potential gains of MIMO on a distributed scale. We propose to use cooperative-MIMO links in LTE-A networks where vehicles act as relaying terminals to a designated vehicle using Decode-and-Forward relaying. To maintain orthogonality of signals, a modified Alamouti-based Space-Time Block Coding (STBC) technique is proposed. Our approach allows exploitation of the multiplexing capability and spatial diversity of typical MIMO schemes in distributed way. We further contribute by deriving error rate and diversity gain as a benchmark to assess our analysis and future research studies. Our findings indicate that significant diversity gains and reduced error rates are achievable. As well, a noticeable reduction in the required transmitting power are observed compared to traditional single antenna deployment.

Maintenance is a core challenge in all routing protocols. The utilization of IPv6 for Low Power and Lossy Networks (6LowPAN) resulted in the recent standardization of a dedicated routing protocol called RPL (Routing Protocol for Low Power and Losy Neworks). In 6LowPAN, a critical challenge exists in decreasing packet loss under the stringent energy-efficiency mandate to increase network longevity. Moreover, the challenge of failed nodes/links, and operating in a lossy environment where connections require rapid maintenance, present significant challenges. Recent attempts at routing maintenance in RPL presented advancements in handling failures, yet under reactive mechanisms that respond to failures and attempt to reduce network down-time. In this paper we design and implement a proactive RPL (Pro-RPL) maintenance scheme that enables the network to selectively predict and mitigate failures before they impact network connectivity. In Pro-RPL we capitalize on a suffering index that is associated with RPL nodes, and monitors their tendency to result in a failure. This dynamic monitoring is decentralized in nature, and presents a conforming yardstick across RPL nodes, to eliminate overhead in implementation and potential control-traffic over the network. We evaluate the efficiency of Pro-RPL in reducing packet loss, energy consumption and extending network lifetime via extensive simulations with the Cooja Simulator over the Contiki OS.

Localization errors posed by anchor misplacement have generally been overlooked in localization research. Previous studies in this field were focused on measurement errors. In this paper, we study the effects of anchor node misplacement, in terms of distance rather than orientation, on the localization error. We propose a distributed and deterministic detection algorithm to identify misplaced anchor nodes and to discard them. We evaluate the performance of our proposed algorithm, and compare it to the algorithm in [1]. Results show that our proposed algorithm is far more conducive to wireless sensor networks (WSNs), results in higher detection rates of misplaced anchors, and provides more effective mitigation.

The promising energy saving and QoS gains of Predictive Resource Allocation (PRA) techniques have recently been recognized in the wireless network research community. These gains were primarily introduced in light of perfect prediction of both mobility traces and anticipated channel rates. However, under real world considerations of prediction errors, the reported gains cannot be guaranteed and further investigation is needed. In this paper, we demonstrate the practical potential of PRA by developing a robust, probabilistic framework that guarantees QoS satisfaction for video streaming under imperfect predictions, without compromising the energy saving gains. The proposed PRA framework uses chance-constrained programming to model video streaming QoS for all users during the foreseen time horizon. Closed form solutions are developed using the Gaussian and Bernstein approximations based on the channel statistical measures. Extensive numerical simulations using a standard compliant Long Term Evolution (LTE) system are presented to examine the developed solutions, for different user mobility scenarios and target QoS levels. The results demonstrate the various design trade-offs involved toward the practical deployment of predictive video streaming in future generation networks.

Information-centric Networks (ICNs) offer a promising paradigm for the future Internet to cope with an ever increasing growth in data and shifts in access models. Different architectures of ICNs, including Named Data Networks (NDNs) are designed around content distribution, where data is the core entity in the network instead of hosts. One of the main challenges in NDNs is handling mobile content providers and maintaining seamless operation. Accordingly, attempts at handling mobility in NDNs have been proposed in the literature are mostly studied under simplistic and/or special cases. There is a lack of benchmarking tools to analyze and compare such schemes. This paper introduces a comprehensive assessment framework for mobility management schemes in NDNs, under varying topologies, heterogeneous producers and consumers, and different mobility models. We develop a generic and modular simulation environment in ns-3 that is made available for NDN researchers to evaluate their mobility management proposals. We implement and compare the performance of three mainstream Producer mobility management schemes, namely, the Mobility Anchor, Location Resolution and Hybrid approaches in NDNs. We demonstrate how mobility impacts NDN operation, specifically in terms of latency and delivery ratio. We also argue for the superior operation of the hybrid approach to handling mobility in NDNs, yet highlight its high control overhead.

Location-Based Services (LBS) and Intelligent Transportation Systems (ITS) demand positioning accuracy and availability requirements. In urban canyons, Global Navigation Satellite Systems (GNSS) suffer from signal blockage, jamming , severe multipath and low Carrier-to-Noise (C/No) ratio which degrade location accuracy and availability. Therefore, applications solely relying on GNSS have limited performance. In this paper, we present a novel unified Cooperative Positioning (CP) solution which enhances positioning accuracy and availability in urban canyons. Our proposed approach is named Angle Approximation (AA). AA requires no infrastructure or other aiding sensors, AA is distributed and addresses two core challenges (limited positioning accuracy and availability) in a unified solution. AA artificially generates the hindered pseudorange by sharing angle information between vehicles using Dedicated Short Range Communication (DSRC). To enhance the performance of the AA technique, we propose and analytically derive the Absolute Sum of Double Differencing (ASODD) method which increases the probability of selecting the most accurate generated pseudorange. We experimentally evaluate the performance of the proposed system through real measurements using NovAtel receivers. We also carry out extensive simulations to demonstrate the ability of the proposed system to increase solution availability. Our experimental and simulation results demonstrate that the solution accuracy of our approach is inversely proportional to the distance between vehicles. Specifically, the mean error of the generated pseudorange is limited to 14 percent of the distance between vehicles.

Emerging mobility-aware content delivery approaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is to forecast the user locations and associated link capacity, and then proactively counter service fluctuations in advance. For instance, a user that is heading towards low coverage can be prioritized and have video content prebuffered. While the reported gains are encouraging, the results are primarily based on assumptions of perfect prediction. Investigating the predictability of mobility and future signal variations is therefore imperative to evaluate the practical viability of such predictive content delivery paradigms. To this end, this paper presents a large-scale measurement study of 33 repeated trips along a 23.4 km bus route covering urban and sub-urban areas in Kingston, Canada. We provide a thorough analysis of the collected traces to investigate the effects of geographical area, time, forecasting window, and contextual factors such as signal lights and bus stops. The collected dataset can also be used in several other ways to further investigate and drive research in predictive vehicular content delivery.

Peer-to-peer (P2P) systems present a unique medium for resource sharing among cliques of participants (peers) in a distributed and self-organized manner. With the advent of mobile users and the increasing power of mobile devices, the spectrum of P2P capabilities should scale. Peers establish transient or persistent relationships with other peers based on mutual interest. Communicating peers may use intermediary peers to forward communication messages, if a direct link is beyond their communication range. A critical design parameter is establishing a resilient communication topology, yet reduce the overhead of control messages required to instill and maintain it. This rises as a significant hindrance in mobile environments, which pose additional challenges on P2P networks due to the heterogeneity of nodes, limited resources, dynamic contexts in addition to the inherited wireless network stringencies. Thus far, efforts in establishing P2P networks via super peers (SPs) have been capped by considering a subset of peer properties to evaluate their candidacy. This paper presents RobP2P, a robust architecture to construct mobile P2P networks and efficiently maintain network state. RobP2P introduces a SP selection protocol based on a dynamic score function that takes into account peers’ capabilities and context, such as location and quality of connectivity. The paper also presents an agile utility function through which SPs can delegate monitoring responsibilities to comparably powerful and stable peers to ensure self-healing topology maintenance. We present an elaborate performance evaluation of RobP2P implemented on Network Simulator NS-3. Our results illustrate the efficiency of RobP2P, its resilience to failures, and the improvements in lowering overhead traffic while reliably maintaining the consistency of network state.

We consider the problem of allocating clients to base stations in wireless networks. Two design decisions are the location of the base stations, and the power levels of the base stations. We model the interference, due to the increased power usage resulting in greater serving radius, as capacities that are non-increasing with respect to the covering radius. Clients have demands that are not necessarily uniform and the capacity of a facility limits the total demand that can be served by the facility. We consider three models. In the first model, the location of the base stations and the clients are fixed, and the problem is to determine the serving radius for each base station so as to serve a set of clients with maximum total profit subject to the capacity constraints of the base stations. In the second model, each client has an associated demand in addition to its profit. A fixed number of facilities have to be opened from a candidate set of locations. The goal is to serve clients so as to maximize the profit subject to the capacity constraints. In the third model, the location and the serving radius of the base stations are to be determined. There are costs associated with opening the base stations, and the goal is to open a set of base stations of minimum total cost so as to serve the entire demand subject to the capacity constraints at the base stations. We show that for the first model the problem is NP-complete even when there are only two choices for the serving radius, and the capacities are 1,2. For the second model, we give a 1/2 approximation algorithm. For the third model, we give a column generation procedure for solving the standard linear programming model, and a randomized rounding procedure. We establish the efficacy of the column generation based rounding scheme on randomly generated instances.

Abstract In current cellular networks, schedulers allocate wireless channel resources to users based on instantaneous channel gains and short‐term moving averages of user rates and queue lengths. By using only such short‐term information, schedulers ignore the users' service history in previous cells and, thus, cannot guarantee long‐term quality of service (QoS) when users traverse multiple cells with varying load and capacity. In this paper, we propose a new long‐term lookback scheduling (LLS) framework, which extends conventional short‐term scheduling with long‐term (QoS) information from previously traversed cells. We demonstrate the application of (LLS) for common channel aware, as well as channel and queue‐aware schedulers. The developed long‐term schedulers also provide a controllable trade‐off between emphasizing the immediate user (QoS) or the long‐term measures. Our simulation results show high gains in long‐term (QoS) without sacrificing short‐term user requirements. Therefore, the proposed scheduling approach improves subscriber satisfaction and increases operational efficiency. Copyright © 2014 John Wiley & Sons, Ltd.

Estimating the location of sensor nodes in wireless sensor networks is a fundamental requirement in a variety of sensing applications. In large scale dense deployments where the area covered by sensor nodes is very large, it is impossible to localize all sensor nodes using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate sensor node positions. In some deployments it is required to maintain the anchor nodes at the edge of the simulated area. In previous work, we introduced a new localization scheme that uses distance measurements to localize sensor nodes using a collinear and non-collinear mobile anchor nodes placed at the edge of the sensed area. A Kalman Filter was then used to improve the location accuracy for each node. In this scheme each SN estimated its location from two independent directions then use such information to improve localization accuracy. In this paper, we extend the work to use side localization using hop measurements and fixed anchor node. We also compare the performance of using side localization for both hop and distance measurement. Through simulation we show that side localization using distance and hop measurements outperform DV-Hop and DV-Distance, which are mainstream localization protocols. The weighted mean hop measurement gives higher localization accuracy than using using distance measurement. However, if Kalman Filter is used distance measurement gives better localization accuracy.

Developing novel video delivery mechanisms have become imperative to cope with the unprecedented growth in mobile video traffic. In this paper, we present video transmission schemes that improve the streaming experience by looking ahead at the future rates users are expected to face. Such an approach is useful for the delivery of stored videos that can be strategically buffered in advance at the users' devices. For instance, if it is known a user is entering a low coverage area, content can be prebuffered to support smooth streaming. Therefore, the Base Stations (BSs) can now plan long-term multi-user rate allocations based not only on current channel states, but also on future conditions. To provide a performance benchmark we first develop a lookahead multi-objective Linear Program (LP) that offers a trade-off between minimizing overall network video degradation, and providing fairness in individual user degradation. Then, to efficiently solve the problem, we present a polynomial-time algorithm that closely follows the pareto-optimal trade-off of the multi-objective LP. We provide an extensive performance analysis of the proposed methods by simulations, and numerical results demonstrate that significant improvements in video streaming are achievable by the lookahead rate allocation strategies.

Novel mobility-aware resource allocation schemes have recently been introduced for efficient transmission of stored videos. The essence of such mechanisms is to lookahead at the future rates users will experience, and then strategically buffer content into user devices when they are at peak radio conditions. For example, a user approaching poor coverage will be preallocated additional video segments to ensure smooth streaming. Advances in mobility prediction and real-time radio environment map updates are driving forces for such Predictive Video Streaming (PVS) mechanisms. Although previous efforts have demonstrated the large potential gains of PVS, ideal channel predictions were assumed. This paper addresses the problem of channel uncertainty in PVS, and proposes a robust resource allocation framework that 1) models channel uncertainty, 2) solves the PVS problem with a tunable level of quality of service guarantees, and 3) learns the degree of uncertainty, and adapts the channel model accordingly. Numerical results demonstrate the effectiveness of the proposed approach for PVS under channel variability.

The availability of location information is essential for context- and location-aware services, which are typically provided by a large number of applications. RFID systems are extensively utilized to provide localization service typically through a centralized and coordinated approach. In this paper, we propose a distributed location information dissemination scheme using heterogeneous uncoordinated mobile RFID readers with the support of inexpensive "memory spots". In the proposed scheme, mobile RFID readers localize passive RFID-tagged objects and leverage the available memory spots in a given smart environment to disseminate location information. Mobile RFID readers use such memory spots to store tag locations and queries enabling exchange location information without the need for direct communication among each other. We study the behavior of the proposed scheme and compare its performance with a typical pull dissemination strategy through extensive simulations using ns-3. Our results indicate that the proposed scheme outperforms the typical pull dissemination strategy in terms of localization delay and average overhead under different dynamicity settings.

Personal Health Record (PHR) systems provide patients with access to their own records, as well as control over who accesses their record. There are many PHR system providers available on the market. These PHR systems, however, have little means to integrate with healthcare facilities in the healthcare system network. This paper proposes a Personal Health Record (PHR) system solution which allows for exchange of patient data at the point-of-care using the patient's mobile device. The objective is to outline and address the issues that arise when adopting an hybrid PHR architecture that comprises a mobile component and an online remote server component. Preliminary tests are conducted in order to assess the system's usability.

Multimedia applications are fast becoming one of the dominating workloads in the Internet today. Since these applications normally have large datasets, they suffer from bandwidth requirement problems. With the limitation of network bandwidth, multimedia traffic can cause congestion; especially when UDP is used as it has neither flow control nor congestion control mechanisms. Congestion can be avoided when arrival rate to gateways is maintained close to outgoing link capacity and gateways' queue lengths are kept small. This guarantees the availability of buffer capacity for successful buffering and consequent forwarding in case of temporary traffic upsurges which could otherwise cause buffer overflows and packet loss. This paper presents a responsive UDP-based multimedia system which can help to decrease congestion occurrence and enhance network performance, especially in realtime environments. Our results show that the proposed system helps in alleviating congestion, reducing packet drops, increasing throughput, and providing improved network utilization.

Communication security measures are becoming prominent standards of next generation mobile networks. These measures usually involve resource-intensive cryptographic operations that can greatly affect communication performance, particularly when it comes to time-based guarantees such as delay and jitter. It this becomes vital to understand the computational characteristics of such operations from a communication perspective. The determination of these characteristics, however, is challenging with mobile computing as computational resources are dynamically managed for balanced performance and energy-efficiency. This paper investigates different mobile computing resource management features, and evaluates their impact on the evaluation of cryptographic functions used in message authentication, and on the design considerations for future context-aware security protocols.

Much has been deliberated lately on the adaptability of Wireless Sensor Networks (WSNs) to transition into a Cloud-based paradigm. This divergence has been mainly attributed to enabling a dynamic design, larger spread and a more distributed control scheme for WSNs that are inherently static and data-centric. Thus, transitioning into a service-centric paradigm, with the “Cloud” as an enabler, seems appealing. In this paper we argue against the seemingly straight forward transition, and emphasize the pitfalls in transitioning WSNs to an inherently distributed architecture. We articulate on four grounds, temporal and spatial limitations, resilience measures, energy efficiency and functional decomposition. Sheer connectivity, as an intrinsic property that presents hindrances in all these factors, is addressed in light of each. Finally, we present insights into future progressions of WSNs that boosts their dynamic presence without impacting intrinsic design dimensions. This paper serves both as an analytic overview of current directions and hindrances, and an overview to where we can go next in remedy to current and projected bottlenecks in Cloud-based sensing systems.

Small cell deployments have proven to be a cost-effective solution to meet the ever growing capacity and coverage requirements of mobile networks. While small cells are commonly deployed indoors, more recently outdoor roll-outs have garnered industry interest to complement existing macrocell infrastructure. However, the problem of where and when to deploy these small cells remains a challenge. In this paper, we investigate the small base station (SBS) placement problem in high demand outdoor environments. First, we propose a dynamic placement strategy (DPS) that optimizes SBS deployment for two different network objectives: minimizing data delivery cost, and minimizing macrocell utilization. We formulate each problem as a mixed integer linear program (MILP) that determines the optimal set of deployment locations among the candidate hot-spots to meet each network objective. Then we develop two greedy algorithms, one for each objective, that achieve close to optimal MILP performance. Our simulation results demonstrate that significant delivery cost and MBS utilization reductions are possible by incorporating the proposed deployment strategies.

Cellular operators have concluded that small cell deployments are a very cost-effective and quick solution to meet the ever growing demands on capacity and coverage in cellular networks for indoor and outdoor environments. While traveling in public transit vehicles, cellular subscribers usually experience poor signal reception and low bandwidth. We hence consider deploying small cells onboard (i.e., mobile small cells) such vehicles. This should enhance subscribers' quality of experience (QoE). We consider a Small Base Station (SBS) mounted in a public transit bus (i.e., mobile SBS) to serve onboard users. The mobile SBS aggregates users' traffic to and from the macroBSs. To further extract the underlying rich multipath-Doppler diversities resulting from the fast mobility and the associated selective fading channel, a pre-coded transmission is deployed in the mobile SBS.We examine the achievable gain from enabling aggregation through mobile SBS in terms of the outage probability. We derive a tight-bound closed-form expression for the outage probability in the downlink (DL). Our results indicate that significant gain in outage probability and coverage are achievable.

RFID technology as an enabler of the Internet of Things (IoT) is extensively utilized for object localization. Existing RFID-based object localization techniques follow a centralized and coordinated approach. Indeed, none is designed to leverage RFID crowdsourcing for the purpose of object localization. In this paper, we propose a cooperative scheme to localize mobile RFID tags using heterogeneous, distributed and dynamic mobile RFID readers in indoor/outdoor environments. In addition, we introduce the concept of Time-Shifted Multilateration (TSM) to enhance location estimation accuracy of mobile tags when sufficient synchronous detection information is not available. We validate the proposed scheme and the TSM technique through extensive simulations using ns-3. Results show that our approach can achieve accurate location estimation in typical IoT settings.

Smart vehicles are considered major providers of ubiquitous information services. In this paper, we propose a solution for expedited and cost-effective access to vehicular public sensing services. The proposed caching-assisted data delivery (CADD) scheme applies caching on the delivery path of the collected data. Cached information can be used for later interests without having to request similar data from vehicles. CADD relies on the deployment of a “light-weight” road caching spot (RCS) at intersections, and on vehicles for communication to/from RCSs. CADD involves a novel caching mechanism that utilizes real-time information for selecting the caching RCSs while considering popularity in cache replacement. A data chunk to be replaced may be forwarded to another less-loaded RCS. CADD considers vehicles' headings to direct communication towards the destination, which reduces access delay. Performance evaluation of CADD shows significant improvements in the access cost and delay compared to a scheme that does not deploy RCSs.

Femtocells play a critical role in LTE and LTE-Advanced networks. Their particular advantage is realized by their autonomy in management and optimization. Our interest in this work is in a special category of self-optimization use cases overseeing femtocell handovers. Specifically, we present a flexible, extendable and standards-compliant environment written in MATLAB for studying handover related self-optimization schemes. The paper describes the overall structure and design of the environment, and offers a detailed explanation of the different modules involved. Sample results that validate the environment are also given. To the best of our knowledge, no such environment has been publicly accessible so far.

Novel radio access techniques that leverage mobility predictions are receiving increasing interest in recent literature. The essence of these schemes is to lookahead at the future rates users will experience, and then devise long-term resource allocation strategies. For instance, a YouTube video user moving towards the cell edge can be prioritized to pre-buffer additional video content before poor coverage commences. While the potential of mobility-aware resource allocation has recently been demonstrated, several practical design aspects and evaluation approaches have not yet been addressed due to the complexity of the problem. Furthermore, since prior works have focused on specific applications there is also a strong need for a unified framework that can support different user and network requirements. For this purpose, we present a novel two-stage Predictive Radio Access Network (P-RAN) framework that can efficiently leverage both future data rate predictions in the order of tens of seconds, and instantaneous fast fading at the millisecond level. We also show how the framework can be implemented within the open source Network Simulator 3 (ns-3) LTE module, and apply it to optimize stored video delivery. A thorough set of performance tests are then conducted to assess the performance gains and investigate sensitivity to various prediction errors. Our results indicate that P-RANs can jointly improve both service quality and transmission efficiency. Additionally, we also observe that P-RAN performance can be further improved by modeling prediction uncertainty and developing robust allocation techniques.

We advocate for a novel paradigm in Wireless Sensor Networks (WSNs). As a technology, it has evolved to a scalable networking paradigm with minimalistic operational mandates. However, inherited design principles of static functionality, that are pre-determined at design stage, hinder WSN evolvement. More importantly, while we design WSNs to endure harsh environments and scale in both urban and remote settings, we neglect two major factors. The over-deployment of WSNs renders many sensing nodes redundant in functionality, and inflates the cost of running applications; not to mention the resulting medium contention. In this paper we present a novel approach to expanding the operational scale of WSNs by adapting to the environment in which it is deployed. That is, capitalizing on an organic approach in thriving on available resources in the region of interest to reduce deployment cost, and solicit incentivized interaction among communicating resources to deliver dynamic sensing. Not only does this span a new dimension of reliability, over garnered resources, but presents a novel approach to assigning sensing tasks to available resources in correlation to their abundance and serviceability. We present our performance evaluation of reduction in operational costs, and the uptake of sensing tasks by neighboring resources via extensive simulations. We aim to benchmark WSN operational versatility and present a rigorous basis for evaluating the ability of WSNs to resiliently scale to new applications as well as handle intermittent and permanent failures.

A Self Organizing Network (SON) scheme monitors certain Key Performance Indicators (KPIs) and responds by adjusting system control parameters. Multiple SON schemes may have related KPIs or use the same control parameters. This leads these schemes and their use cases to interact either constructively or destructively. In this paper, we study these interactions between three SON use cases all aiming at improving the overall handover procedure in LTE femtocell networks. These use cases are namely: handover self optimization, call admission control self optimization and load balancing self optimization. This work is motivated by the lack of interaction studies conducted so far between these three self optimization use cases. First, we have surveyed related individual scheme proposals in order to identify schemes which represent these three use cases in our interaction study. Then, several interaction experiments are conducted in realistic scenarios using our in-house built and LTE-compliant simulation environment. We conclude by drawing guidelines that we believe can help designers realize better coordination policies between these three handover-related SON use cases.

Novel transmission mechanisms are imperatively needed to cope with the exponential growth of mobile traffic and its associated power consumption. To address such challenges, we present lookahead video delivery schemes that jointly improve the streaming experience and reduce BS power consumption. This is accomplished by exploiting knowledge of future wireless rates users are anticipated to face. Such an approach is useful for delivering stored videos that can be strategically buffered in advance at the users' devices. For instance, a user leaving the cell center may have content prebuffered efficiently before poor channel conditions prevail. This will save energy as transmission will not be needed during poor conditions. In this paper, we first formulate Lookahead Resource Allocation (LRA) as a multi-cell optimization problem that leverages predictions of user mobility rates. Then, we present centralized and distributed algorithms that closely follow the benchmark results of the optimal solution. Numerical results demonstrate that significant improvements in video streaming and BS power consumption are achievable by the LRA strategies.

Public sensing services utilizing the abundant on-vehicle resources are gaining high interest nowadays. One of the challenges facing such ubiquitous utilization is the recruitment and selection of the participating vehicles. In this paper, we present an optimal reputation-aware, trajectory-based framework that handles recruitment of vehicles for public sensing. The framework considers the spatiotemporal availability of participants along with their reputation to select vehicles that achieve desired coverage of an area of interest within a budget limit. In addition, we present a reputation assessment scheme and a pricing model for computing a reputation score and a recruitment cost for each candidate participant. The framework is formulated as an integer linear programming optimization problem and hence provides a benchmark and upper bound on achievable potential. We present analysis for two different practical recruitment objectives and show results under various scenarios.

This study presents the symbol error probability (SEP) analysis of relay‐multiplexing scheme in relay‐assisted cellular systems such as the IEEE 802.16j and upcoming fifth generation (5G) systems. The availability of multiple relay paths in these systems motivates two relay configurations: (i) relay diversity and (ii) relay multiplexing. Available works in the literature have focused on the relay diversity methods. In this study, we explore the relay‐multiplexing alternative whereby relay stations (RSs) act as independent data pipes for transmitting multiple independent data streams from the base station (BS) to the mobile station (MS). We examine the case when the BS is equipped with massive multiple‐input multiple‐output (MIMO) antennas, transmitting independent data streams to several MS simultaneously via different relay paths. We derive analytical expressions for the SEP performance of the proposed scheme, and also complement the analysis with simulations. The results show that parallel relaying of independent data streams via different RSs has acceptable SEP performance while boosting the capacity of the system linearly with the number of parallel RSs available in the system. The results also show that when this relaying approach is combined with the emerging massive MIMO techniques, a tremendous boost in data rate can be achieved.

The next-generation mobile networks will be equipped with sophisticated communication security, ensuring the safety and authenticity of the transmitted information. However, many of today's prominent security measures are cryptography-based and present several operational challenges in mobile computing systems. Thus, enforcing security measures can greatly affect communication performance, particularly, when it comes to time-based guarantees such as delay and jitter. Moreover, mobile computing systems have limited energy sources, which can be depleted quickly by improperly enforcing such resource-intensive operations. Therefore, it becomes vital to understand the computational characteristics of security measures from a communication perspective. By observing these characteristics, it may be possible for existing and future mobile systems to be suited with security functions that provide the sufficient communication security while maintaining both the power-efficiency and the delay/jitter requirements. In this paper, we propose a benchmarking environment for evaluating cryptography-based security functions from a communication perspective. The paper investigates how mobile systems' design and operation characteristics have a significant impact on the computational characteristics of security functions. The paper explores the evaluation metrics that can be used in benchmarking security functions within various communication settings and proposes the use of a simple and effective delay-based metric for the benchmarking process. The computational characteristics of some selected security functions are evaluated under the proposed benchmarking environment and presented in this paper. While the main focus of the work is the widely utilized mobile communication settings, the proposed evaluation scheme can be applied for other communication settings and for noncryptographic security functions.

Reliable and energy-efficient reading of Radio Frequency IDentification (RFID) tags is of utmost importance, especially in mobile and dense tag settings. We identify tag collisions as a main source of inefficiency in terms of wasting both medium access control (MAC) frame slots and reader's energy. We propose modulation silencing (MS), a reader-tag interaction framework to limit the effect of tag collisions. Utilizing relatively simple circuitry at the tag, MS enhances the performance of existing anti-collision protocols by allowing readers to terminate collision slots once a decoding violation is detected. With shorter collision slots, we revisit the performance metrics and introduce a new generalized time efficiency metric and an optimal frame selection formula that takes into consideration the MS effects. Through analytical solutions and extensive simulations, we show that the use of MS results in significant performance gains under various scenarios.

ABSTRACT The demand for higher data rate has spurred the adoption of multiple‐input multiple‐output (MIMO) transmission techniques in IEEE 802.11 products. MIMO techniques provide an additional spatial dimension that can significantly increase the channel capacity. A number of multiuser MIMO system have been proposed, where the multiple antenna at the physical layer are employed for multiuser access, allowing multiple users to share the same bandwidth. As these MIMO physical layer technologies further evolve, the usable bandwidth per application increases; hence, the average service time per application decreases. However, in the IEEE 802.11 distributed coordination function‐based systems, a considerable amount of bandwidth is wasted during the medium access and coordination process. Therefore, as the usable bandwidth is enhanced using MIMO technology, the bandwidth wastage of medium access and coordination becomes a significant performance bottleneck. Hence, there is a fundamental need for bandwidth sharing schemes at the medium access control (MAC) layer where multiple connections can concurrently use the increased bandwidth provided by the physical layer MIMO technologies. In this paper, we propose the MIMO‐aware rate splitting (MRS) MAC protocol and examine its behavior under different scenarios. MRS is a distributed MAC protocol where nodes locally cooperate with one another to share bandwidth via splitting the spatial channels of MIMO systems. Simulation results of MRS protocol are obtained and compared with those of IEEE 802.11n protocol. We show that our proposed MRS scheme can significantly outperform the IEEE 802.11n in medium access delay and throughput. Copyright © 2012 John Wiley & Sons, Ltd.

Common Radio Resource Management techniques have shown great promise in both enhancing network operation and user satisfication.Such gains are achieved through the joint management of the individual access technologies in a Heterogeneous Wireless Network.The objective of this work is to expand on the existing body of work to accommodate heterogeneity not just at the traditional access-network level but to other connectivity modes such as dynamic spectrum access.Such modes affect operator profitability in both the long and short terms.Specifically, we explore the design of a cost-management model that adapts to the short-term variability in connectivity costs.We also display the operational aspects and effectiveness of this functionality through both simulation and an analytical model.

The abundance of sensed data and its correlation between wireless entities has recently increased significantly. Understanding the context of each entity in a given environment is not-trivial. Mainly due to the need of realizing an efficient scheme for context fusion over multiple sensing/polling technologies. In this paper we present a selective context fusion model that utilizes an integrated architecture which encompasses both RFID systems and information collected from Wireless Sensor Networks (WSNs). We integrate the identification capabilities of the former with the group intelligence sustained by the latter. A mediator acts as both the reader and relay (RR) node to communicate both technologies respectively. Thus, collecting context information from sensors and tags, then aggregating, filtering and carrying out analysis to selectively enhance the quality of context collected in its vicinity. As such, the network will fuse information over a multiplicity of devices. The goal of this system is to utilize contextual information about the devices generating the data to better the selection process. The filtration process eliminates irregularities in the data as well as redundancy. Moreover, a weighted function stresses the value of data generated by higher-end nodes. Weight is also attributed to log-based evaluation protocols that identify a reliability metric. To further strengthen the fusion approach, local RRs will collect and aggregate context information from neighboring RR nodes, as well as knowledge databases over the Internet. As a load balancing measure, and to avoid resource draining, participating nodes will have an inversely proportional likelihood of participation in providing context information as their contribution count increases. Our system is further elaborated upon via an extensive use case.

The popularity of smart objects in our daily life fosters a new generation of applications under the umbrella of the Internet of Things (IoT). Such applications are built on a distributed network of heterogeneous context-aware devices, where localization is a key issue. The localization problem is further magnified by IoT challenges such as scalability, mobility and the heterogeneity of objects. In existing localization systems using RFID technology, there is a lack of systems that localize mobile tags using heterogeneous mobile readers in a distributed manner. In this paper, we propose the GOSSIPY system for localizing mobile RFID tags using a group of ad hoc heterogeneous mobile RFID readers. The system depends on cooperation of mobile readers through time-constrained interleaving processes. Readers in a neighborhood share interrogation information, estimate tag locations accordingly and employ both proactive and reactive protocols to ensure timely dissemination of location information. We evaluate the proposed system and present its performance through extensive simulation experiments using ns-3.

Cooperative communication has been recently applied to vehicular networks to enable coverage extension and enhance link reliability through distributed spatial diversity. In this paper, we investigate the performance of cooperative vehicular relaying over a doubly-selective (i.e., frequency-selective and time-selective) fading channel for an LTE-Advanced downlink session. Using Amplify-and-Forward (AF) relaying with orthogonal cooperation protocol and Multiple-Input Multiple-Output (MIMO) deployment at the source and destination, we derive a pairwise error probability (PEP) expression and demonstrate the achievable diversity gains. Space-Time Block Coding (STBC) is used to ensure the orthogonality of the transmitted-received signals. Our results demonstrate that, via proper linear precoding constellation, the proposed cooperative-MIMO vehicular relaying is capable of extracting the maximum available diversity in frequency (through multipath diversity), time (through Doppler diversity) and space (through cooperative diversity as well as the MIMO deployment) dimensions. We further conduct numerical simulations to confirm the analytical derivations and present the error rate performance of the cooperative relaying vehicular scheme under consideration.

With the abundant on-board resources in intelligent vehicles, they have become major candidates for providing ubiquitous services, including urban sensing. This paper proposes an efficient recruitment scheme for vehicles in urban sensing applications. Our trajectory-based recruitment (TBR) scheme solves the problem of participant selection by considering spatiotemporal availability of participants. The aim of TBR is choosing the minimum number of vehicles that achieve a required level of coverage for the area of interest. TBR utilizes the easy-to-acquire trajectories of the candidate vehicles as indicators of the availability of participants, and applies a minimal-cover greedy algorithm for selection. The basic greedy algorithm is adapted to handle some practical scenarios, including departing vehicles and varying redundancy requirements. The paper also discusses two data acquisition models for retrieving the sensing data (on-demand and unsolicited). Assessment of TBR shows that it achieves high levels of coverage even when vehicles do not stick to their announced trajectories.

Estimating the location of sensor nodes in wireless sensor networks is a fundamental problem, as sensor node locations play a critical role in a variety of applications. In many cases the area covered is very large making it impossible to localize all sensor nodes using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate sensor node positions. Multi-hop localization techniques are classified into two main categories: range-based and range-free. Despite the numerous existing localization techniques, the fundamental behavior of multi-hop localization is yet to be fully examined. The aim of this paper is to study the effect of errors in a multi-hop localization environment and how this impacts localization accuracy. There has been a general belief that a fewer number of hops results in higher accuracy. Through different experiments on two generic localization techniques representing both categories of localization schemes, we show that such belief is not true in all cases, as in dense environments often using shorter hops gives better accuracy.

This paper discusses energy consumption reduction in Zigbee based networks. We introduce energy consumption benchmarking platform (ECBP) - a versatile data acquisition based energy consumption profiling system for WSNs. The ECBP can be utilized to find power intensive processes in a given system, discover energy consumption anomalies and wasted power, measure active duty and sleep cycles, measure voltage, current, power, and energy consumption for a given time period, and discover power consumption related faults in sensor networks. We show how to use ECBP on a Zigbee based sensor network to reduce power consumption by an order of magnitude, thus, extending the network lifetime by the same factor.

In integrated RFID and Wireless Sensor Networks (RSNs), the abundance of wirelessly enabled mobile devices facilitates forwarding data packets. This presents a beneficial alternative to offload transmission from relay nodes to access points. However, there is no incentive for such mobile devices to carry the relaying task. Hence, we introduce heuristics for Monetary-based Courier Relaying (MCR) that incorporates price negotiation for relaying from source nodes to access points via mobile couriers in RSN architectures. Our heuristics employ a threshold price for each packet prior to transmission. Whether to forward the packet to a courier or to directly transmit it to access points depends on a criticalness function, in addition to the courier's charge with respect to the packet's threshold price. We compare our MCR model with other dominant mobile Ad hoc delivery schemes; eliciting its efficiency in terms of energy, cost and delivery rate.

The global mobile application market is booming and business giants, viz. Google and Apple, have acknowledged the huge expansion in their respective application market. There is a demand though for a system that can elevate the momentum of context-aware mobile applications, where application behavior can be customized according to various context information. This paper proposes AppaaS, a context-aware system that provides mobile applications as a service. AppaaS handles various context information to provision the best relevant mobile application to such a context. AppaaS also supports state preservation, where application-specific data is stored for future references. Our prototype demonstrates an agile system performance with respect to finding relevant applications to a specific context and controlling the applications functions according the users requirements and access privileges.

We extend vehicular cooperation into downlink LTE-A networks in what we call Infrastructure-to-Relaying-Vehicles (I2RV) cooperation. In I2RV, vehicles are used as relaying terminals between eNodeB/BS and a receiving user equipment located or mounted on another traveling vehicle, for the aim of extending coverage, improving performance, and attaining distributed transmission. Initial works on cooperative vehicular communications build upon the assumption of flat and quasi-static fading channels, this can be justified only for narrowband systems in very slow traffic flows such as in rush-hours. In this paper, we consider highway traffic with high-speed mobility resulting in doubly-selective (i.e., time- and frequency-selective) channels. To overcome the performance degradation, we make use of precoded cooperative transmission accompanied with an opportunistic best-relay selection technique to extract the rich underlying multipath-Doppler-spatial diversity gains. Our performance analysis through pairwise error probability (PEP) derivation shows that, through proper precoding, the proposed system is able to extract maximum available diversity in time, frequency and space. Furthermore, we derive a closed-form expressions for the outage probability as a bench-mark for future analysis for the proposed scheme. Through numerical analysis, we demonstrate that significant coverage advantage by extending the transmission distance targeting a specific error rate and using the same transmitting power can be achieved.

In this paper, we propose a bandwidth adaptation mechanism for 3GPP LTE which partially releases bearers' resources for admission of a new bearer during congestion periods. Each active bearer may contribute into the downgrading mechanism up to its minimum resource requirement according to a bearer contribution attribute called the “downgrading index”. This index incorporates three attributes of an active bearer into the downgrading process. These attributes are the bearer priority, QoS over-provisioning and communication channel quality. The performance of the proposed mechanism is evaluated using simulation. Numerical results show that the probabilities of bearer blocking and handoff bearer dropping improve significantly when employing our proposed mechanism. Further, the results show the capability of the proposed mechanism in fine-tuning the service provider's generated revenue.

The ever growing data traffic generated by users in cellular networks is becoming more challenging and straining for cellular operators. Thus, developing efficient mechanisms that enable cellular operators to offload data traffic from their networks in a cost-effective manner is essential. To this end, we propose a generic framework (MFW) that exploits femtocells and WiFi networks. The framework allows cellular operators to offload part of the traffic load generated by mobile users in public transportation systems, viz.; buses, streetcars. Regular Femto Base Stations (FBSs) are installed in these vehicles to offer cellular coverage for mobile devices, called the mobile FBS (mobFBS). The mobFBS utilizes ubiquitous WiFi access points as a backhaul to route the traffic to the cellular operator's network through WiFi instead of the loaded macrocells. Mobile data users are categorized in our framework in different prioritized classes in order to efficiently allocate the mobFBS bandwidth to the maximum number of users. Efficiency is considered in terms of bandwidth utilization, enhancing capacity and managing grouped data traffic in vehicles. We elaborate on the performance of MFW via numerical experiments, emulating practical applications, viz. “Skype” and “YouTube”, and demonstrate the efficiency of our framework in terms of data traffic offloading.

In current cellular networks, schedulers allocate wireless channel resources to users based on short-term moving averages of the channel gain and of the queuing state. Using only such short-term information, schedulers ignore the user's service history in previous cells and, thus, cannot meet long-term Quality of Service (QoS) guarantees when users traverse cells with varying load and capacity. We propose a new scheduling framework, which extends conventional short-term scheduling with long-term QoS information from previously traversed cells. We demonstrate our scheme for relevant channel-aware as well as for channel and queue-aware schedulers. Our simulation results show high gains in long-term QoS while the average throughput of the network increases. Therefore, the proposed scheduling approach improves subscriber satisfaction while increasing operational efficiency.

Public sensing is an application in which sensory systems embedded in smart devices, vehicles, residential and public spaces form a collective cloud of data sources from which multi-owned access points realize end-users' service requests. This conception can be further extended under the umbrella of integrated RFID-Sensor Networks (RSNs) to include RFID systems. Such a configuration is heterogeneous by nature and faces many challenges in terms of data delivery and resource management. In this paper, we represent a Reciprocal Public Sensing (RPS) scheme for integrated RSN architectures. Our scheme incorporates heuristic solutions for static sensors and mobile data collectors, in addition to a reciprocal agreement for data exchange over the tiers of the proposed architecture adhering to the social welfare of the network as a whole. We provide simulation results showing how RPS outperforms other data delivery schemes in terms of minimizing delay, packet loss, and energy consumption, in addition to prolonging the overall network lifetime.

We investigate the performance gains of a transmission scheme in LTE-A networks where vehicles act as relaying cooperating terminals. The advantages of vehicular relaying networks are the abundant energy and computing power, the predictable mobility patterns of vehicles, and the availability of information from positioning systems and map-based technologies. The frequent availability of traveling vehicles, operating in an ad hoc fashion, eliminates the need for establishing a dedicated relaying infrastructure. However, the associated wireless links are characterized by a doubly-selective fading channel. This causes performance degradation in terms of error probability. Hence, we propose a precoded cooperative transmission technique to extract the underlying rich multipath-Doppler-spatial diversity. Furthermore, we implement a relay selection scheme to take advantage of the potentially large number of available relaying vehicles. We further contribute by the derivation of a closed-form error rate expression as a bench mark to assess our analysis and future research studies of such an approach. Our analytical and simulation results indicate that significant diversity gains are achievable, and that error rates can be greatly reduced. As well, there is a noticeable reduction in the required transmitting powers compared to the traditional transmission schemes.

This paper discusses the application use of our WSN platform called Sprouts to monitor the current erosion conditions and retrieval of lost shovel-teeth in the Oil Sands mining operations of Ft. McMurray, Alberta, Canada. The modular architecture design of Sprouts allows us to customize the platform to monitor the thickness of shovel teeth and localize their position upon detachment. Utilizing our Sprouts plug-and-play protocol, we implement three sensor modules to monitor the thickness of the shovel tooth in operation using ultrasound waves, detect the event of a fallen shovel-tooth, and aid the retrieval of fallen shovel teeth using trilateration localization before they cause damage to crushers.

In this paper, we present an Enhanced 2-Phase Data Delivery (E2-PDD) framework for Information-Centric Networks (ICNs), focusing on efficient content access and distribution as opposed to mere communication between data consumers and publishers. We employ an approach of growing eminence, where requests are initiated by consumers seeking particular services that are data-dependent. High-level Controllers (HCs) receive the consumers' requests and issue queries to a multitude of data publishers. The publishers in our topology include a wide variety of ubiquitous nodes that could be either stationary or mobile, operating under different protocols. In order to consider fundamental challenges in ICNs such as node mobility and data disruption, our E2-PDD framework employs Low-level Controllers (LCs) that act as moderators between the HCs and the data publishers, executing data queries for a top tier and replying back with a set of candidate rendezvous points obtained from a bottom tier. The HCs maximize selection based on the nearest rendezvous. Extensive simulation results have been used to evaluate our E2-PDD framework in terms of key performance metrics in ICNs viz., average in-network delay, and publisher load, given different mobility pause time durations and data consumers' densities.

Femtocell is a promising technology that will improve wireless resources efficiency through frequency spatial reuse however, its integration with the current cellular systems has been considered a fundamental challenge. In this study, we propose a mechanism by which the femto and macro capacity resources are jointly priced according to a dynamic pricing-based call admission mechanism. We study the performance of the proposed mechanism through a queuing theory approach. Several performance metrics such as the service provider's revenue, call blocking and call deferral probabilities are determined, and the scheme is compared to a regular call admission control serving as a benchmark. The results show how much a joint dynamic pricing scheme can satisfy service providers in terms of increased revenue and congestion control, while keeping users satisfied in terms of their perceived quality of service. The proposed solution can be considered as a new charging model for a two-tier macro-femto network.

Wireless Sensor Networks (WSNs) are approaching an operational stalemate. While sheer emphasis on energy efficiency and resilience aid network longevity, WSNs face many hindering design principals. Prominently, an application-oriented view that isolates WSNs from ubiquitous networks, and nodes with static hardware and functional goals. We present a novel paradigm in the design of WSNs. Our goal is to achieve a resilient architecture that decouples operational mandates from the nodes. We present wirelessly interfaced components, which introduce functionality physically decoupled from the sensing nodes; boosting resilience, dynamicity and resource utilization. This approach dissects the study of nodal capacity to its “connected” components. It also enables re-introducing only the components required to suffice for network operation. More importantly, critical resources in the network will be shared within their neighborhoods. Thus network lifetime will relate to functional cliques of dynamic nodes. We present our paradigms with insights into application and design novelty.

Mobile devices with their various form factors have become the most convenient and pervasive computing platform, whether to carry out everyday business or to get online. Mobile users tend to adopt the fast food trend even in consuming online mobile services and functionalities. The Web service approach promises great flexibility in offering software functionality over the network, while maintaining interoperability between heterogeneous platforms. However, the diversity that exists in mobile devices and their platforms with variations in capabilities present unique challenges in developing services that can accommodate such diversity. Recent years have witnessed the rise of user-facing service developments that can be consumed on the go with standard interface, such as RESTful Web services. However, the discovery of such services does not match their growing popularity. In addition, existing discovery approaches lack supporting mechanisms that ensure the proper functioning of discovered services within the user context and failing to match personal preferences. This paper introduces personalized Web service discovery for mobile environments. Preliminary results show that incorporating user preferences and context significantly improves the overall precision of service discovery.

This paper introduces personal mobile Web services, a new user-centric architecture that enables service-oriented interactions among mobile devices that are controlled via user-specified authorization policies. Personal mobile Web services exploit the user's contact list (ranging from phonebook to social lists) in order to publish and discover Web services while placing users in full control of their own personal data and privacy. We present a proof-of-concept implementation of an example personal mobile Web service to demonstrate the usefulness and feasibility of the concept.

SUMMARY This paper evaluates the effect of the choice of frame structure type in WiMAX networks employing non‐transparent relay stations (ntRS). The IEEE 802.16j‐2009 amendment allows two frame structure types, single frame and multiframe. Thus far, a comparison of the two choices, in terms of network performance, has not been made. To facilitate this evaluation, we expanded on light WiMAX ns2 add‐on to support ntRS, the two‐frame structure types, in addition to the relevant operational requirements (e.g. QoS support). We observed that whereas the multi‐frame structure allows for higher throughput and voice capacities, the single frame shows some general advantage in terms of delay. A serious need exists, however, for schedulers that exploit the advantages of a dynamic multi‐frame structure in order to realize its full potential. Copyright © 2011 John Wiley & Sons, Ltd.

The bit‐error‐rate (BER) performance of orthogonal frequency division multiplexing (OFDM) systems in mobile multi‐hop relaying (MMR) system is severely degraded by the effect of Doppler shift and the severity of this degradation increases with the number of hops traversed by the OFDM signal. In this study, the authors propose a method to mitigate the effect of Doppler shift in MMR system (such as the IEEE 802.16j system) using directional antennas. It is shown that the effect of the resulting inter‐carrier interference (ICI) because of the phase noise generated by the Doppler shift over multi‐hop relaying channels, can be reduced by employing directional antennas at both the mobile and relay stations. Consequently, the BER performance of MMR system is significantly enhanced. Analysis and simulation results show that the BER enhancements using the proposed approach are strongly related to the orientation and beamwidth of the directional antenna employed. As the antenna beamwidth is reduced, the BER enhancement increases for both the perpendicular and parallel antenna orientations, and comparing these two orientations, the parallel orientation case provides slightly better BER enhancements.

ABSTRACT This paper analyzes the bit error rate performance of orthogonal frequency‐division multiplexing systems in mobile multihop relaying channels. We considered the uplink scenario and quantified the effects of mobile channel impairments such as Doppler shift due to user mobility per hop, high power amplifier distortions when amplifying the transmitted/relayed orthogonal frequency‐division multiplexing symbol per hop, and the cumulative effects of these impairments on multihop relaying channels. It was shown that the resulting intercarrier interference due to the cumulative effects of the phase noise generated by these impairments per hop becomes very significant in a multihop relaying communication system and severely degrades the bit error rate performance of the system. Simulation results agree well with and validate the analysis. Copyright © 2011 John Wiley & Sons, Ltd.

SUMMARY In this paper, we present a scheduling algorithm that enhances the performance of the standard IEEE 802.11e scheduler for the Hybrid Coordination Function Controlled Channel Access mode. The main contribution in designing the proposed scheduler is the ability to accommodate multiple streams with different levels of Quality of Service requirements concurrently running on the same station. This is achieved by dynamically calculating the Transmission Opportunities of each active traffic stream (TS) and the appropriate Service Interval of each active station. The proposed algorithm optimizes the utilization of the scarce bandwidth resources by only polling active stations. The algorithm incorporates a selectivity function to assign polling priorities to the active streams only based on their diverse requirements and their link‐attainable transmission rates. The performance of the proposed Selectivity Function Scheduler (SFS) scheme is evaluated against the standard scheduler. Simulation results show that the SFS outperforms the standard scheduler in terms of enhancing streams' throughput, reducing packet dropping ratio and maintaining high fairness amongst the admitted TS. Copyright © 2011 John Wiley & Sons, Ltd.

Forward error correction (FEC) coding is the preferred error correction technique for interactive video streaming applications on the Internet. Because its performance is impaired by the burstiness of packet loss of Internet links, peer-to-peer (P2P) networks are often proposed to provide multiple paths between a sender and a receiver. However, peers may leave abruptly and the number of disjoint paths may be limited; it is unclear whether or when the use of P2P networks for path diversity can be justified. In this paper, we study the packet loss ratio after FEC correction when using P2P networks to provide multiple paths. We examine two situations: a sender can find enough disjoint paths, or uses a limited number of disjoint paths. We model Internet links using Markov chains, provide numerical analysis of the performance of systematic FEC codes, and verify the results by simulation. We find that although using P2P networks for path diversity often results in a lower post-FEC loss ratio, conditions apply. There exist guidelines but no simple formula to determine when to use P2P networks for path diversity and coding parameters. An application should carefully evaluate the performance gain before taking actions.

In this paper, we present URIA, a Ubiquitous Robust Integrated Approach for data delivery in integrated RFID-Sensor Networks (RSNs). The proposed approach deploys ubiquitous wireless nodes equipped with transceivers as couriers between integrated reader/relay nodes and access points in an IoT setting. In addition to guaranteeing a specific level of connectivity across the network, URIA maintains constraints on delay, such that a multi-path minimal-delay route is always provided between any source-destination pair. Our approach is formulated via a Semi-Definite Programming (SDP) solution and is compared against other IoT integrated schemes targeting connectivity and delay metrics. Simulation results show that our proposed approach outperforms rival schemes in terms of total latency and delivery rate. This is achieved while considering vast data generation rates, instantaneous topology changes, and high probabilities of failure over the established end-to-end paths.

In this paper, we propose a fairness-based preemption algorithm for 3GPP LTE-Advanced. It considers bearers' priorities as well as their QoS over-provisioning with respect to their minimum QoS needs into the partial preemption decision. We define a preemption contribution metric for each established bearer and we evaluate the proposed scheme both in terms of inter- and intra-priority preemption fairness. Through a simulation approach, we conclude substantial improvements in preemption fairness when compared to a traditional preemption approach. We discuss that the proposed scheme does not affect the main performance measurements of the network, i.e. the bearers' blocking and dropping probabilities due to congestion. Finally, we discuss that the preemption contribution metric can be effectively used to variate the total generated revenue.

Recent research endeavours are capitalizing on state of the art technologies to build a scalable Internet of Things (IoT). Envisioned as a technology to integrate the best of Wireless Sensor Networks and RFID systems, there is much promise for a global network of objects that are identifiable, track-able, and harmoniously informing. However, the realization of an IoT framework is hindered by many factors, the most pressing of which is attributed to the integration of these heterogeneous nodes and devices. A considerable subset of these nodes undergoes movement and dynamically enters and leaves the network backbone/topology. Routing packets and inter-nodal communication has received little attention; mainly due to the sheer reliance on the Internet as a backbone. However, spatially correlated entities in the IoT, and those which most often interact, would pose a significant overhead of communication if all intermediate packets need to be routed over distant backhauls. In remedy, we present a Pruned Adaptive IoT Routing (PAIR) protocol that selectively establishes routes of communication between IoT nodes. Since nodes in the IoT belong to different owners, we also introduce a pricing model to cater for the exchange of monetary costs by intermediate nodes to utilize their relaying resources. We also establish a cap on inter-nodal routing to dynamically utilize the Internet backbone if the source to destination distance surpasses a preset (case optimized) threshold. The PAIR routing protocol is elaborated upon, building upon the detailed system model presented in this paper. We finally present a use case to demonstrate the utility and practicality of PAIR in the heterogeneous IoT as it scales.

With the increased popularity of both Internet and mobile computing, several security mechanisms, each using various cryptography functions, have been proposed to ensure that future generation Internets will guarantee both authenticity and data integrity. These functions are usually computationally intensive resulting in large communication delays and energy consumption for the power-limited mobile systems. The functions are also implemented in variety of ways with different resource demands, and may run differently depending on platform. Since communications within the next generation Internet are to be secured, it is important for a mobile system to be suited to the function that provide sufficient communication security while maintaining both power-efficiency and delay requirements. This paper benchmarks mobile systems with cryptographic functions used in message authentication. This paper also introduces a metric, namely apparent processing, that makes benchmarking meaningful for mobile systems with multiple processing cores or utilizing hardware-based cryptography. In addition, this paper discusses some of evaluated functions' computational characteristics observed through benchmarking on selected mobile computing architectures.

We study the problem of data survivability in WSNs. We propose a coding scheme that achieves Data survivability through the use of Decentralized Erasure Codes (DEC) which provide many advantages including flexibility, scalability, and decentralized implementation. We quantify the redundancy required in terms of storage nodes and the duplicated source data to achieve data survivability under a given erasure rate. When applied to WSNs, the proposed solution is designed to take into consideration the specifics of WSNs such as energy and resource constraints. The performance of the proposed scheme is based on established theoretical results and is verified by means of extensive simulations.

In RFID based inventory systems, counting and estimating the number of the surrounding tags without reading each tag individually is a challenge. In this paper we propose an estimation function that considers the variance of collision and empty slots during the estimation frame. In addition, two schemes, Variance and Modulation Silencing based Estimation (VMSE) and Modulation silencing count (MSC), are proposed to utilize the accuracy of the estimation function and modulation silencing mechanism [1] in counting and estimating the number of RFID tags. In the proposed schemes, tags participating in collision and success slots are silenced to accelerate the counting process. Requiring only minimal modification to the reader-to-tag communication procedure, the proposed schemes achieve a significant performance gain when compared to existing counting protocols in the literature.

This paper describes the application use of Sprouts, a modular plug-and-play (PnP) wireless sensor network (WSN) platform architecture, to monitor the reliability conditions of industrial equipment. The platform architecture was specifically designed to be rugged in order to survive harsh industrial environments with features such as versatile metallic packaging and a mount-on-metal patch antenna. The Sprouts platform design incorporates four modular ports using a unique PnP protocol, described in this paper, to identify attached modules. We describe three sensing methods utilized by our modular architecture to monitor the reliability and fault detection of large vibration screens used in the petroleum production process at the Oil Sands of Alberta, Canada. We emphasize our unique nondestructive ultrasound-based solution to monitor the thickness of the two metal compounds, steel and tungsten, composing the vibration screen ligaments.

The recent introduction of femtocells enabled high data rates and better coverage indoors without the need for upgrading the network infrastructure or site establishment. However, Femtocells may suffer from major interference problems due to their dense and ad hoc deployment. In this paper, we propose a frequency assignment optimal scheme for downlink femtocell networks. The algorithm is based on optimization search rather than greedy or heuristic methods. Simulation results show that the proposed scheme outperformed two representative algorithms used for comparison.

Designing reliability solutions for WSNs poses intricate challenges due to limitations in processing power and available energy. Such networks are often deployed in harsh and inaccessible environments and are therefore required to be highly reliable. However, reliability normally translates to redundancy in hardware and other resources implying both complexity and higher costs. In this study, we consider data survivability in WSNs. We present a data-centric framework based on Decentralized Erasure Codes (DEC) to increase the likelihood of data survivability in case of sensor nodes failure. The proposed framework enables network engineers to estimate the redundancy in hardware and data to achieve a given data survivability level. We also show two approaches to reduce the energy requirements of the proposed coding scheme using Random Linear Network Coding (RLNC). In addition to being decentralized, the proposed schemes are low in complexity requiring only binary coding over F2. We evaluate the performance of the proposed schemes by simulations and compare them to schemes with no network coding.

Over-the-air reprogramming is an important aspect in the deployment and management of Wireless Sensor Networks (WSNs). However, WSNs reprogramming poses significant challenges due to scarce available energy, low computational power, and limited memory capabilities of the WSNs nodes; all are required for transmission and processing of the created patches. In existing reprogramming schemes, any change in the program layout and/or global variables, produces a significantly large patch size, hence consumes the node's limited resources. Furthermore, to apply the patch, existing schemes require rewriting internal flash, large volume of external flash, as well as rebooting the node. In this paper, we devise a novel reprogramming scheme that we call Queen's Differential (QDiff), which mitigates the effects of program layout changes and retains the maximum similarity between ”old” and ”new” codes using clone detection techniques. Moreover, QDiff organizes the global variables in a novel way to eliminate the effect of variable shifting. To assess the performance of Qdiff, we have carried out a TinyOS implementation using an IRIS mote platform. Our experiments show that QDiff requires near-zero external flash, and significantly lower internal flash rewriting and transmission overhead than leading existing differential reprogramming mechanisms.

Vehicular networks are expected to enable vehicles on the road to exchange safety information; enhancing traffic flow and minimizing accidents. With vehicle positions being the most frequently exchanged information in vehicular networks, it becomes imperative to establish a strong level of trust in the announced positions before a vehicle initiates a response. This paper proposes a position verification scheme as part of a misbehavior detection framework that encompasses analysis techniques needed for the verification of exchanged vehicular messages. The scheme involves the estimation of a vehicle's trajectory via the integration of road and map information, as opposed to only depending on the vehicle's kinematics for future trajectory prediction. The trajectory estimation procedure uses the vehicle's previously received position updates to find a bestfit area within the road topology in which the vehicle is expected to be. The vehicle's current position announcement is compared against this plausible area to decide whether the position is consistent with the expected location. Our proposed scheme is verified via extensive simulations, developed on ns2, demonstrating the importance of integrating road and map information into position verification.

Advances in sensing and wireless communication technologies have enabled a wide spectrum of Outdoor Wireless Sensor Network (OWSN) applications. Some applications require the existence of a communication backbone federating different OWSN sectors, in order to collaborate in achieving more sophisticated missions. Federating (connecting) these sectors is an intricate task due to the huge distances between them, and due to the harsh operational conditions. A natural choice in this case is to have multiple Relay Nodes (RNs) that provide vast coverage and sustain the network connectivity in harsh environments. However, these RNs are not cheap and, thus, a constraint on their count holds. That being said and considering the harsh conditions in outdoor environments, placement of the RNs becomes crucial and has to be in a way that tolerates failures in communication links and deployed nodes. In this paper, we propose a novel approach in optimizing the RNs placement, called ST-DT approach, with the objective of federating different OWSNs with the maximum connectivity under a cost constraint on the RNs count to be deployed. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in large-scale outdoor environments.

Intelligent Transportation Systems (ITS) and their applications are attracting significant attention in research and industry. ITS makes use of various sensing and communication technologies to assist transportation authorities and vehicles drivers in making informative decisions and provide leisure and safe driving experience. Data collection and dispersion are of utmost importance for the proper operation of ITS applications. Numerous standards, architectures and communication protocols have been anticipated for ITS applications. However, existing schemes are based on assumption that vehicles and roadside devices are equipped with sensing and communication capabilities. One of the major gaps of these approaches is their inability to capture events that can easily be logged by drivers using their mobile phones. In this paper, we propose to fill the gap by the use of Crowdsourcing in ITS namely, CrowdITS. In CrowdITS human inputs, along with available sensory data, are collected and communicated to a processing server using mobile phones. The basic idea is to use the Crowd with smart mobile phones to enable certain ITS applications without the need of any special sensors or communication devices, both in-vehicle and on-road. Alternatively, we integrate and aggregate human inputs with multiple information sources, and then selectively disseminate the aggregated information based on the driver's geo-location. Conceptually, the major change is to integrate human inputs, with multiple information sources, aggregate and finally it is localized according to the driver's geo-location. We describe the design of CrowdITS, report on the development of key ITS applications using Android and iPhone mobile phones, and outline the future work in the development of crowdsourced-based applications for intelligent transportation systems.

In a technology where multiple networks are often deployed in concurrency, significant resource underutilization is witnessed in Wireless Sensor Networks (WSNs). As the manufacturing and deployment costs drop, multiple networks are introduced in overlapping vicinities to satisfy new functional requirements. Mostly with dedicated goals and deterministic operation schemes, practitioners seldom investigate the usability of visible resources already deployed in the region of interest, their utilization and the accommodation for transient resources that “pass-by” with a set of functional capacities. This paper presents a framework for classifying resources that contribute to the set of functional capacities of WSNs deployed in a given region, and the mapping of functional requirements set by multiple applications on these resources. We present a utility function to cater for successful utilization of transient resources; highlighting their importance in WSN longevity as well as dynamicity. An optimal formulation is presented for this mapping, with tunable rounds that cater for the temporal behavior of the network and its constituting resources. A use case further explains this paradigm.

ABSTRACT The introduction of femtocells enabled high data rates and better indoor coverage without the need for expanding or increasing the density of cellular networks deployment or upgrading the cellular network infrastructure. Despite this direct advantage, the introduction of femtocells challenges traditional interference mechanisms because of the ad hoc and dense nature of femtocell deployment. In this paper, we propose an optimal downlink frequency assignment scheme. The proposed algorithm is modeled as a transportation problem to optimally allocate frequency subcarriers with the objective of maximizing the system capacity. The scheme can be used as a benchmark for assessing the suitability of interference mitigation schemes in femtocellular networks.Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT The number of cellular communication subscribers continues to grow, attesting to the great success of this technology. However, cellular networks have inherent limitations on cell capacity and coverage and shortcomings such as the dead spot and the hot spot problems. Multi‐hop cellular networks (MCNs) help enhance the cell capacity and coverage, while, at the same time, alleviating the dead spot and hot spot problems, increasing the utilization of radio resource, and reducing the power consumption of mobile terminals. In the past decade, more than a dozen of MCN architectures were proposed. In this paper, we study various types of MCN proposals. We identify and discuss the design decision factors and use these factors to classify most existing MCN proposals. Future research directions, including studies of capacity and energy consumption, and approaches addressing design issues such as cell size, routing, channel assignment, load balancing for MCNs are discussed. Copyright © 2010 John Wiley & Sons, Ltd.

Federating partitioned Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM), where the deployed sensor nodes are prone to significant damage and harsh operational conditions, becomes a necessity to prolong the WSN lifetime. Consequently, redundancy-based deployment strategies have been extensively studied in the literature. However, federating WSNs using node redundancy is expensive in OEM due to large-scale targeted areas, and frequent node/link failures. A natural choice in defeating these challenges is to employ multiple Data Collectors (DCs) that provide extendable and sustainable WSNs in harsh environments for long lifetime intervals. In this paper, we propose a grid-based deployment for DCs in which they are optimally repositioning on the grid vertices to connect disjointed WSN sectors. Towards this optimality, we design an Optimized DCs Repositioning (ODR) approach that maximizes the federated WSN lifetime while maintaining cost and connectivity constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.

Today's abundance of sensors and their wireless/wired networks, coupled with a growing plethora of applications, necessitate a dynamic approach to the assignment of tasks to a network. The current practice in WSN design is almost always application specific, due to functional and resource tradeoffs that have justified much of the tailored research done so far. Identifying this as a major bottleneck in WSN advancement, this paper presents a new paradigm which decouples applications from WSN architectures and protocols. This paradigm views the network as an abundance of connected resources (hence functionalities) to match requirements of applications (old and new) based on utilization and feasibility factors. We present an elaborate abstraction of network resources, with detailed description of its governing utility attributes. Then we describe the view of applications as an aggregation of functional requirements based on a given set of resources. The intermediate mapping between applications and resources is then solved by a reduced linear optimization formulation, to realize the system as a whole. The paradigm is further explained via a multiple-application scenario and its representation and operation under our paradigm.

Radio Frequency Identification (RFID) is an emerging wireless network technology that poses new fundamental challenges. One such challenge is coverage in RFID networks which is the ability to accurately read a set of RFID tags. Accurate coverage is of utmost importance in RFID networks as missing some tags may result in missing important events and, for some RFID applications, losing asset and revenue. In this paper, we address an optimization problem related to the deployment of RFID readers to cover a set of RFID tags with the objectives of minimizing the number of readers, reducing overlapping among readers coverage and balancing the load. We propose a set-cover based approximation algorithm for RFID coverage with the minimum number of readers. We extend this algorithm to consider the objective of reducing overlap and interference among readers interrogation ranges. And finally, we devise a load balancing algorithm that evenly distributes the load among different readers. Comprehensive experiments that study the performance of our algorithms are presented.

3GPP's Long Term Evolution is defined by the standardization body's Release 8 and 9, and provides more than a substrate for 3GPP's IMT-Advanced candidate, namely LTE-Advanced, which is due to be defined in Release 10. Both LTE and LTE-Advanced have SC-FDMA in their uplink, a multi-carrier access technique requiring contiguous subcarriers allocations for each UE. No scheduling algorithm, however, is dictated by the standard and several proposals have hence been presented to implemented by vendors. A definite scheduling requirement is the support of QoS attributes of different types of uplink traffic. Our intent in this study is evaluate the connection-level performance of representative scheduling proposals, with focus on QoS aspects. Specifically, we utilize a mixed type of traffic flows and evaluate the schedulers in terms of per-user throughput, delay, packet loss and fairness.

Efficient network utilization is essential towards the deployment of large scale peer-to-peer (P2P) video live streaming services on the Internet. Push-pull hybrid schemes and pull schemes provide practical solutions to P2P video live streaming, but network utilization remains unaddressed. To the best of our knowledge, a study of network utilization in the context of P2P video live streaming does not exist in the literature. In this paper, we use "typical" hybrid and pull schemes to investigate network utilization and whether the neighbour-with-nearby-peers strategy is applicable to P2P video live streaming applications, and provide a model to analyze peers' behavior. We find hybrid and pull schemes have limited capability in choosing low cost overlay edges although hybrid schemes are slightly better. The chunk tree height in hybrid schemes is significantly larger than in pull schemes. The neighbour-with-nearby-peers strategy reduces the chunk tree cost; however, it also results in a significantly larger tree height, lower reliability, and is less robust against peer churn, especially in hybrid schemes.

Push-pull hybrid schemes for peer-to-peer (P2P) video live streaming applications achieve a short playback delay and are robust in the presence of peer churn. Most hybrid schemes construct an overlay randomly, which causes unnecessary traffic on the Internet. Simply applying the neighbour-with-nearby-peers strategy can localize the traffic but results in a large tree height, more lost chunks, and increased playback delay. In this paper, we propose a new neighbouring strategy to construct a hierarchical overlay that has a low average edge cost and helps to build a short and robust tree. Using a heuristic degree-bounded shortest path tree algorithm and an efficient pull mechanism to recover late chunks, the hierarchical overlay achieves performance close to the random overlay but has only 1/3 of the network cost; 98% of peers receive all the chunk with playback delays well within the acceptable range.

Smart Spaces offer very promising means to creating context-aware environments. Unfortunately, the lack of enough information about users within Smart Spaces limits their usefulness. We propose a novel solution that integrates smart spaces with social networks through the IP multimedia subsystem to create truly context-aware and adaptive spaces. By utilizing the wealth of user information present within Social Networks, smarter and more adaptive spaces can be created. We therefore propose the design and implementation of SocioSpace, a Smart Spaces framework that utilizes the Social context. We design and implement all components of SocioSpace including the central server, the location management system, social network interfacing components, service delivery server and user agents. We then run various scenarios to test the reliability of the system. The results show the effectiveness of our framework in successfully creating smart spaces that can truly utilize social networks to deliver adaptive services that enhance the users' experiences and make the environment more beneficial to them.

Nowadays Wireless Sensor Networks (WSNs) are used to provide vast coverage areas in environmental applications, and thus relay nodes with wide transmission ranges are employed. However, these relays usually operate under harsh conditions with a very limited energy resources, making the network very prone to severe node failures and disconnectivities. In this paper, we propose a proactive Optimized Relay Repositioning (ORR) approach in which relays are regularly repositioned to maintain a specific level of fault-tolerance in addition to minimize the total network energy consumption. ORR is a grid-based approach, in which nodes are placed on grid vertices to limit the huge search space in large-scale environmental applications. This approach is formulated as a Mixed Integer Linear Program (MILP) for solid mathematical solutions. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant approach can introduce a significant lifetime extension as compared to other heuristic and MILP-based approaches.

Federating Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM) becomes a necessity as advances in sensing technologies are achieved. Where several WSN sectors pursuing identical/different tasks intend to collaborate with each other in order to achieve more sophisticated and challenging missions, or intend to recover a significant damage in the network. Connecting (federating) these sectors is an intricate task due to the huge distances between the sectors, and the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes that provide vast coverage areas and sustain the network connectivity in harsh environments. However, these relays are expensive and thus, the least number of such devices has to be populated. In this paper, we propose a grid-based deployment for relay nodes in which the relays are efficiently placed on the grid vertices to connect the disjointed WSN sectors. Towards this efficiency, we design an Optimized Relay Placement (ORP) approach that maximizes the disjointed sectors connectivity while maintaining cost constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.

This paper describes the design and implementation of a unique WSN platform specifically researched to monitor the health conditions of the vibration screens used by Oil Sand operators in Canada. Previous to WSN, wired sensing solutions have been attempted for this project, but failed to sustain integrity in the harsh conditions imposed by the environment. The researched platform allowed, for the first time, to monitor the thickness of the screen ligaments by providing real-time thickness measurements of the mesh screen. The architecture design of the platform was made modular and scalable to easily adopt the sensor platform for other industrial facilities making it flexible across other applications. A complete system was realized at Queen's University TRLab and successfully presented to the Oil Sand operator on a miniature working lab model.

Vehicular networks will enable vehicles on the road to utilize wireless communication to exchange safety information; enhancing traffic flow and minimizing accidents. With vehicle positions being the most frequently exchanged information in vehicular networks; it becomes imperative to establish a strong level of trust in the announced positions before a vehicle may take action in response. This paper proposes a position verification scheme that involves the collaborative exchange of one-hop neighbor information in order to help a vehicle make better judgments of position announcements. Vehicles can assess neighborhood connectivity and use the logical traffic flow to form a verdict on trusting a position announcement, thus enabling the detection of possible position falsifications. The scheme analyzes accumulated 2-hop neighbors' information in order to define a plausibility area within which a vehicle should exist in order for its position to be considered "correct". In very sparse traffic scenarios, a vehicle will depend on measuring the consistency of a vehicle's Received Signal Strength (RSS) with its announced position. Performance evaluation was carried out via simulation, and results show that defining this plausibility area yields accurate detection of position falsifications with low false positives.

Recent advancements in the design of mobile devices and wireless technologies have produced a successful coupling of mobile devices and Web services, where mobile devices can be a service provider or a consumer. However, finding relevant Web services that match requests remain a major hindrance to its booming. The challenges facing Web service discovery are further magnified by the stringent constraints of mobile devices, and the inherit complexity of wireless heterogeneous networks. While significant research has focused on service discovery protocols in isolation, they mostly lack a holistic capacity to address the different limitations collectively. We introduce a novel discovery framework that addresses all aspects of mobile Web service discovery, yet does not jeopardize the efficiency requirement for this discovery; especially as an application run in resource- constrained environments.

Localization will play an important role in the Internet of Things (IoT), and will employ several complementary mechanisms, each applicable to Things with different characteristics. This paper investigates the impact of scale and mobility on wireless multi-hop localization mechanisms, which specifically target Things with limited capabilities inhibiting them for self- localization, i.e., cannot locally perform trilateration or angle of arrival analysis. Building on a representative system for wireless multi-hop localization, we evaluate the impact of network size and node mobility on various operational aspects, including number of messages sent, collisions, localization accuracy, in addition to the percentage of unlocalized nodes. We also show how a basic optimization can result in substantial gains. More critically, however, we establish the need for further optimizations in realizing localizations in IoT.

Deployments of Radio Frequency Identification (RFID) networks are anticipated to be dense and ad-hoc. These deployments usually involve redundant readers having overlapping interrogation zones and, hence, causing immense reader collisions. Elimination of redundant readers, from the network, is of utmost importance as otherwise they affect the lifetime and the operational capacity of the overall RFID network. In this paper, we propose a light-weight greedy algorithm that detects and eliminates redundant readers from the network. Our algorithm uses the ratio of tag counts to the number of neighboring readers of each reader to estimate the likelihood for that reader to be redundant. The proposed algorithm is highly scalable and poses a minimal communication overhead as compared with existing schemes in the literature.

ABSTRACT Advances in sensing and wireless communication technologies have enabled a wide spectrum of Outdoor Environment Monitoring applications. In such applications, several wireless sensor network sectors tend to collaborate to achieve more sophisticated missions that require the existence of a communication backbone connecting (federating) different sectors. Federating these sectors is an intricate task because of the huge distances between them and because of the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes (RNs) that provide vast coverage and sustain the network connectivity in harsh environments. However, these RNs are expensive; thus, the least possible number of such devices should be deployed. Furthermore, because of the harsh operational conditions in Outdoor Environment Monitoring applications, fault tolerance becomes crucial, which imposes further challenges; RNs should be deployed in such a way that tolerates failures in some links or nodes. In this paper, we propose two optimized relay placement strategies with the objective of federating disjoint wireless sensor network sectors with the maximum connectivity under a cost constraint on the total number of RNs to be deployed. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments. Copyright © 2011 John Wiley & Sons, Ltd.

Tag collisions impose a significant hindrance to reading rates of Radio Frequency Identification systems. The parallel singulation approach, being a major milestone, clusters tags and autonomously interrogates each cluster in parallel. This technique reduces the number of tags being interrogated at a given time, reducing collisions, and achieves higher reading rates. However, such an approach faces two limitations as the number of clusters increase. The exponential increase in tag responses may hinder tag functionality due to energy spent on communication. Moreover, energy inefficiency is incurred at cluster-heads to process significantly more tag responses. These issues overshadow the promising benefits of employing parallel singulation. In this paper, we remedy such hindrances by proposing energy efficient enhancements to the parallel singulation technique. The essence of these enhancements lies in minimizing an important measure of communication overhead, referred to as tags traffic rate, which indicates the efficiency of interrogation cycles in communicating with all tags without incurring unnecessary overhead. Analyses carried out via simulation demonstrate significant improvements by the proposed schemes in reducing energy consumption of cluster-heads, without posing constraints on tag operations nor incurring significant degradation of reading rates.

Accurate and efficient localization of tags are of utmost importance for numerous existing and forthcoming RFID applications. In this paper, we introduce two novel methods for three dimensional localization of the passive RFID tags. In the first approach, namely Adaptive Power Multilateration (APM), using four RFID readers, distance estimations parameters are processed based on the minimal interrogation power and multilateration. Whereas in the second approach, namely Adaptive Power with Antenna Array (APAA), a single RFID reader equipped with horizontal and vertical smart antennas alongside with the reader's adaptive power levels are used for the tags distance estimations. The APM scheme localizes the tags with comparatively finer granularity whereas the APAA scheme supports reader's mobility and facilitates highly dense tag environments. Simulation results show that our proposed schemes provide more accurate localization than other indoor localization schemes.

Advances in smart technologies, wireless networking, and the increased interest in services have led to the emergence of ubiquitous and pervasive computing as one of the most promising areas of computing in recent years. Smart Spaces in particular have gained a lot of interest within the research community. Most smart spaces rely on physical components such as sensors to sense and acquire information about the real world environment. Although sensor networks can provide useful contextual information, they are known for their high degree of unreliability and limited resources. We believe that it is necessary to augment physical sensors with other kinds of data to create more reliable and truly context-aware smart spaces. In this paper we therefore utilize mobile devices and social networks to acquire more detailed useful contextual information that can help create smarter spaces. We then propose a Smart Spaces architecture that utilizes these new contexts and in particular the Social context.

P2P networks are extremely popular on the Internet, with their uses including file sharing, gaming, IP telephony, and much more. Users are increasingly moving toward wireless networks and expect to continue using P2P applications in these new environments. Meanwhile, MANET usage is expected to grow as wireless mesh and 4G networks become more prevalent. In P2P file sharing networks, peer clients must choose which servers to download from, and in a MANET must also select the paths to those servers. It is often possible to download from multiple servers simultaneously. This paper formulates and solves the problem of optimally selecting which servers and paths to choose, such that the cost or download time is minimized.

Orthogonal frequency division multiplexing (OFDM) system has been proposed as a technique for broadcasting digital signals and for wireless communication. The bit-error-rate (BER) performance of the OFDM system is severely affected by the nonlinearity of the high power amplifier and by the Doppler effect impairments. In this paper, we analyze the effects induced on the OFDM signal by the amplifier non-linearity and by the Doppler effect over multi-hop relaying channels. Moreover, simulation results are presented to validate the analysis. It is shown that the resulting inter-carrier interference (ICI) due to the cumulative effects of the phase noise generated by these impairments per hop becomes very significant in a multi-hop relaying communication system, and severely degrades the BER performance of the system. Theoretical results show perfect agreement with those obtained by simulation.

The choice of SC-FDMA for uplink access in Long Term Evolution (LTE) facilitates great flexibility in allocating medium resources to users while adapting to medium condition. A multicarrier multiple access technique, SC-FDMA gains an advantage over OFDMA in that it reduces the energy requirements in user equipment. 3GPP Releases 8 and 9, however, do not detail a specific scheduler and, accordingly, proposals have been made in the literature in designing an efficient and capable uplink scheduler for LTE. This paper presents a preliminary performance evaluation for representative proposals, and offers medium of comparison in order to highlight the individual characteristics of each proposals.

Grid-based deployments of Wireless Sensor Networks (WSNs) are widely used in a multiplicity of applications. However, practical factors such as communication irregularity and placement uncertainty have to be considered for more efficient deployments. In this paper, we examine connectivity properties of the 3D grid-based deployment when sensor placements are subject to random errors around their corresponding grid locations and hindrances to wireless communication channels exist. A generic approach is proposed to evaluate the average connectivity of the deployed network. This generic approach is independent of the grid-shape, random error distributions, and the environment wireless channel characteristics. The average connectivity is computed numerically and verified via extensive simulations. Based on the numerical results, quantified effects of positioning errors and grid edge length on the average connectivity are demonstrated. Furthermore, we discuss several ways of achieving efficient grid-based deployment planning for connectivity, and illustrate these approaches through numerical examples.

In this paper, we propose two schemes for sensor and relay node placement in environmental sensing applications. The first scheme aims at maximizing the network lifetime by reducing the total energy consumption. The second does so while maintaining fault-tolerance constraints. It guarantees a lower bound on the minimum required number of faulty nodes. Both schemes are based on a 3-D hierarchical architecture, in which nodes are placed on grid vertices to limit the search space. We divide the lifetime of the network into fixed-length rounds and find the placement which reserves more energy in each round to prolong the lifetime. These problems are formulated via Integer Linear Programs (ILPs). An ILP solver is used to find the optimal placement of nodes in addition to multi-hop routing from the sensors to the base-station in both schemes. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant scheme introduces a significant lifetime extension as compared to the first one under the same harsh operational conditions.

Social Networks have recently experienced a significant increase in popularity and are now an integral part of millions of people's daily lives. Through these Social Networks, users create profiles, build relationships, and join groups forming intermingled sets and communities. There is a wealth of information within Social Networks, which if exploited properly and combined with rules and policies, can lead to a whole new level of smart contextual services. A mechanism is therefore needed to extract data from heterogeneous Social Networks, link profiles across different networks and aggregate the data obtained. We design a Presence and Policies Server that manages the information exchange between Social Networks, services and the environment and passes along the relevant information and rules to different entities. The Presence and Policies server is capable of querying, importing and aggregating data from across multiple Social Networks and services and then converting that data into standardized semantic information that can be interpreted and translated into meaningful information by other users and services.

This study evaluates the effect of the choice of frame structure type in WiMax networks employing non-transparent relay stations (ntRS). The IEEE 802.16j–2009 amendment allows two frame structure types, single frame and multi frame. Thus far, a comparison of the two choices, in terms of network performance, has not been made. To facilitate this evaluation, we expand on Light WiMax (LWX) ns2 add-on to support ntRS, the two frame structure types, in addition to the relevant operational requirements (e.g. QoS support). We observe that while the multi-frame structure allows for higher throughput and voice capacities, the single frame shows some general advantage in terms of delay.

Several push-pull hybrid peer-to-peer (P2P) video live streaming schemes introduce data-driven push trees into the pull scheme and have a short playback delay while being robust against peer churn, but the substrate Internet utilization remains unaddressed. In this paper, we propose TreeClimber, a network-driven robust push-pull scheme for large-scale P2P video live streaming applications. The scheme first constructs a neighbourhood overlay with short edges and a small diameter, then uses a robust distributed algorithm to build a short tree on the overlay. We have implemented a discrete-event simulator to examine TreeClimber and a comparative data-driven scheme. Results show that TreeClimber achieves high network utilization while having better or similar playback delay and robustness.

In this paper, we quantify the expected symbol loss probability in mobile multi-hop relaying systems employing Orthogonal Frequency Division Multiple Access (OFDMA) Techniques. We obtain the expected number of collisions and calculate the probability of symbol loss when a conflict occurs between subcarriers of two or more non-transparent relay stations, as well as we calculate the average symbol loss probability in the system. Also, the proportion of symbol with degraded SNR is measured and the collision rate is calculated. Our analysis shows that the resulting collision due to the simultaneous use of a subcarrier in different non-transparent relay stations can be very significant in a multi-hop relaying communication system, and severely degrades the SNR, and affects the QoS support in the system.

P2P applications are enormously popular on the Internet and their uses vary from file sharing to Voice-over-IP to gaming and more. Increasingly, users are moving toward wireless networked devices and wish to continue using P2P applications in these new environments. MANETs are expected to grow in use as wireless mesh and 4G networks increase in popularity. P2P and MANETs share some similarities, such as self-organization, dynamism, and resilience to failure, but it is necessary that P2P algorithms should take advantage of the realities of MANETs. In P2P networks, the overlay peers must form a topology of connections between themselves and this topology should reflect the underlying network in order to reduce delay and energy consumption. We study the results of a game-theoretic topology control algorithm which considers energy and distance between nodes in a P2P network running atop a MANET. We find that the minimum cost topologies are very highly connected and thus resilient, but in most cases the topologies do not stabilize even without peer mobility or churn.

Multihop cellular networks (MCNs) enhance the capacity and coverage and alleviate the dead-spot and hot-spot problems of cellular networks. They also allow faster and cheaper deployment of cellular networks. A fundamental issue of these networks is packet delay because multihop relaying for signals is involved. An effective channel assignment is the key to reducing delay. In this paper, we propose an optimal and a heuristic channel assignment scheme, called OCA and minimum slot waiting first (MSWF), respectively, for a time division duplex (TDD) wideband code division multiple access (W-CDMA) MCN. OCA provides an optimal solution in minimizing packet delay and can be used as an unbiased or benchmark tool for comparison among different network conditions or networking schemes. However, OCA is computationally expensive and, thus, inefficient for large real-time channel assignment problem. In this case, MSWF is more appropriate. Simulation results show that MSWF achieves on average 95 percent of the delay performance of OCA and is effective in achieving high throughput and low packet delay in conditions of different cell sizes.

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.

Supporting the diverse QoS requirements of multimedia applications is an essential requirement for broadband wireless access (BWA) networks. Due to the projected dynamics in traffic patterns, more capable resource management functionalities are needed. We propose a game-theoretic, congestion-based pricing scheduler that incorporates two sub-schemes: a bandwidth provisioning sub-scheme to address the bandwidth scarcity to provision in fourth generation (4G) BWA technologies and an efficient packet scheduler sub-scheme. To the best of our knowledge, the proposed scheduler is the first one to simultaneously control congestion and fairness while providing differentiated QoS guarantees in BWA networks. Simulation results show that the proposed scheme realizes our objectives of controlling congestion, providing differentiated QoS guarantees, and catering to proportional fairness among the different network classes and among connections within the same class.

In this paper, we propose a passive solution which is low- cost, facilitates high adaptation rates and utilizes the non-IV vehicles to assist in ITS applications. RFID technology, a prominent identification technology, has the potential to turn everyday objects into a mobile network of nodes, which can then be used to track and trigger events. This briefly outline stand-alone and co-operative systems based on the anticipated ITS architecture.

P2P overlay networks are a natural fit for MANETs because both are decentralized and have dynamic topologies. Most nodes in P2P file sharing networks are freeloaders, nodes that do not contribute to the network. In order to have a useful P2P network, it is important that nodes not only share files but also forward data. In this paper we present Incite, an incentive scheme for P2P file sharing over MANETs in which nodes cooperate with one another based on their reputation and energy levels. Simulation results show that Incite allows more file downloads than typical P2P networks, is fairer, and uses less energy per downloaded byte.

Tag collisions can impose a major delay in radio frequency identification (RFID) systems. Such collisions are hard to overcome with passive tags due to their limited capabilities. In this paper, we look into the problem of minimizing the time required to read a set of passive tags. We propose the novel concept of parallel singulation which, aided by the multiple antennas configuration, interrogates tags in sets. Multiple instances of the anti-collision scheme are executed for each set of tags in parallel and in an autonomous manner. Simulation results show that our approach makes significant improvements in reducing collisions and interrogation delay and thus increasing the reading rate and throughput of RFID systems.

Peer-to-peer networks are a popular means of obtaining large files. Network coding has been shown as an efficient means of sharing large files in a P2P network. With network coding, all file blocks have the same relative importance. This paper presents Deluge, which uses network coding to share large files in a P2P overlay running on a MANET. Peers request file blocks from multiple server nodes and servers multicast blocks to multiple receivers, providing efficient multipoint-to-multipoint communication. Simulation results show that compared to other common download techniques, Deluge performs very well, having low download time and low energy consumption. Further, more peers participate in uploading the file and the performance of Deluge varies little with increasing overlay size, indicating good scalability.

Decentralized protocols offer high adaptability to topology changes prominent in Wireless Sensor Networks (WSN). Protocols resilient to topology changes stemming from nodes dying, being added, relocating or duty cycling, improve network performance in terms of lifetime and percent of events sensed and reported. Topology dependant protocols, such as cluster-based, face many hindrances especially in terms of scalability, dynamicity, and adapting to varying traffic rates. Accordingly, a novel approach is introduced in sensing, whereby a single node is elected to report a sensed event, in a decentralized manner, thereby avoiding redundant reports by other nodes which exhaust network resources. Election is based on the node with the highest likelihood of successfully reporting the event. This protocol is coupled with a localized multi-hop routing protocol, to route that report back to the sink, by electing the most reliable next-hop neighbor to relay the report. Simulation results demonstrate the increase in network lifetime, detection/reporting efficiency, and resilience to varying node density.

Multihop communication has been proposed in cellular networks to overcome some inherent limitations. Congestion relief is amongst the promised gains. In this paper, the concept of inter-cell relay, which uses multihop communication to divert calls from heavy loaded cells to less loaded adjacent cells, is introduced. We show that using inter-cell relay, the number of supported calls inside a congested cell can be significantly increased. We devise two approaches for congestion relief based on the conditions of the network, to maximize the number of supported calls inside a congested cell. The distribution-based approach determines the number of extra hops for inter-cell relay based on call distribution. On the other hand, the delay sensitive approach assumes that the number of extra hops for inter-cell relay is limited by calls quality of service requirements. By imposing a limit on the number of extra hops, the approach decides the number of inter-cell relayed calls and the number of calls connected to the congested BS. Our results illustrate the benefits gained from inter-cell relay in congestion relief. We demonstrate that inter-cell relay can decrease congestion of a cell by fully utilizing the available resources in surrounding cells.

In 3G-based multihop cellular networks (MCNs), the cell size affects the cell capacity and the network reachability, which in turn affect the total demand of source nodes that can be served or the system throughput. Improper cell size assignment greatly affects the performance of the networks. To address the cell size issue, we recently proposed the optimal cell size (OCS) scheme to find optimal cell sizes to maximize the system throughput for a 3G TDD W-CDMA MCN. Although OCS provides an optimal cell size solution, it is computationally expensive. In this paper, we propose a heuristic cell size scheme, called Small Cell Size First (SCSF), which is more efficient and provides good results in terms of throughput compared to the optimal solutions provided by OCS. SCSF outperforms the fixed small cell size (SCS) multi-hop case when the network is sparse and the large cell size single-hop case regardless of the network density.

Device deployment plays a key role in the performance of any large-scale wireless sensor network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, viz. coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in a large scale harsh environment. In this paper, we explore the problem of relay node (RN) placement in 3D forestry space. We formulate a generalized RN deployment optimization problem aimed at maximizing the network connectivity with constraints on RNs count. We investigate how the number of RNs can affect the connectivity of a WSN in a harsh environment. Based on quantitative analysis of such effects, the paper sets a threshold on the minimum number of required RNs.

The IEEE 802.16 standard, also known as WiMAX, defines flexible frame structures to support applications classified into different WiMAX traffic classes. The standard defines two types of scheduling to guarantee the quality of service (QoS) requirements of these applications - centralized and distributed. However, the standard does not specify how the frame can be dynamically partitioned among its centralized and distributed schedulers or uplink and downlink schedulers. Through efficient partitioning that dynamically adapts the partitioning based on demand, network can support more user applications with different QoS requirements, and hence increase the revenues of service providers. In this paper, we propose a novel and general dynamic frame partitioning scheme for IEEE 802.16 mesh and IEEE 802.16j multi-hop relay networks. The scheme uses a dynamic Markov model that studies the frame utilization over current frames to predict efficient partitions for future frames. Simulation results show that the proposed scheme improves average frame utilization and decreases packet dropping.

In this paper, we propose a distributed scheme for data gathering using a mobile data collector in wireless sensor networks (WSNs). In our scheme, a mobile data collector moves along a predefined track over the sensing field and data are forwarded to nodes whose transmission disks overlap with the trajectory of the data collector; these nodes are called relaying nodes. Data are classified into two categories: delay-sensitive data and delay-tolerant data. While delay-sensitive data are sent to the data collector directly, delay-tolerant data may be sent to a nearby relaying node, where they wait for the data collector to come and pick them up. We give a theoretical analysis to quantify the impact of data collector mobility on the lifetime of the network as compared to a WSN with a stationary data collector. Moreover, we use simulations to evaluate our scheme in practice. Simulation results show that our scheme has the potential to prolong the lifetime of the network significantly.

One of the main challenges for metro-scale WiMAX mesh network deployments is related to capacity scaling. In a full mesh mode, a WiMAX node acts as a mesh router as well as a client access node. To improve latency and speed performance, typical dual- and multi-radio mesh solutions use different radio channels to create separate links for access and mesh relaying services. The available spectrum is therefore split between mesh and client access services. Network operators operating the WiMAX system over the licensed spectrum are not keen to provide separate radio channels for access and mesh relay services, as this reduces the total number of users serviced per spectrum allocation. MIMO-multiplexing relaying approach however provides separate links for access and mesh relaying services on the same radio channel. In this paper, we discuss the multi-hop capacity of OFDM-based MIMO-multiplexing relaying in WiMAX networks. For an NxN MIMO-multiplexing relaying with amplification factor alpha at relay nodes, R-hops relaying degrade the capacity by at most -Nlog2(alpha2R/(1 +SigmaRr-1alpha2rNr)) +RN/log2(N) bits/sec/Hz. Therefore, greater capacity loss is experienced in networks employing high-order MIMO- multiplexing relaying. We also show that the capacity loss is independent of the OFDM configurations employed; thus network operators could employ higher OFDM configurations to compensate data rate loss in access services when some of the MIMO-multiplexing links are dedicated to mesh relay. This analysis provides useful guidelines for operators planning MIMO-multiplexing option for mesh support in WiMAX network.

In this paper, we propose a Call Admission Control-based dynamic pricing scheme that aims at preventing congestion and maximizing the utilization of broadband wireless access systems. The main aim of our scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally; hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically; hence, ensuring a congestion-free system. The proposed scheme is general and can be implemented with different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.

In this paper, the IEEE 802.11 Distributed Coordination Function (DCF) collision avoidance mechanism of Wireless Local Area Networks (WLANs) is enhanced based on Adaptive Spatial Channels Sharing (ASCsS) of Multiple Inputs Multiple Outputs (MIMO) transmission systems. Simply, instead of accessing the medium using all the spatial channels, to avoid collision the contending nodes use an optimal subset of the available spatial channels during the medium contention period. As the total number of concurrent used spatial channels is fewer than or equal to the spatial degree-of-freedom, receivers can still decode the transmitted singles of multiple medium access contenders. The performance of the ASCsS scheme demonstrates that the scheme further reduces medium access collisions and boosts the medium utilization.

The IEEE 802.11e standard defines a reference scheduler that allocates transmission opportunities (TXOP) to traffic streams based on mean data rate and minimum physical rate. However, the reference scheduler is only efficient for streams with strict constant bit rate (CBR) characteristics. This paper presents a scheduling scheme named as the selectivity function scheduler (SFS) for the IEEE 802.11e hybrid coordination function (HCF) controlled channel access (HCCA). The SFS enhances the procedure of computing the number of variable sized packets by not only considering the new arrivals, but also accounting for the packets remaining in the queue due to channel conditions. SFS incorporates a selectivity function that assigns polling priorities to already admitted streams based on their actual requirements. The performance of the proposed SFS is evaluated and compared with the reference scheduler defined by the standard. Simulation results show that the SFS outperforms the standard scheduler in terms of enhancing streams throughput, reducing the packet dropping ratio, and maintaining high fairness among different traffic streams.

Multihop communication has been proposed in cellular networks to overcome some inherent limitations. Congestion relief and load balancing are amongst the promised gains. In this paper, we investigate inter-cell relay - which means diverting calls from one congested (heavy loaded) cell to an adjacent less loaded cell for congestion relief (load balancing) — in multihop CDMA cellular networks. The application of inter-cell relay changes the resulting interference inside the heavy loaded cell as well as at the supporting BSs. Toward analyzing the gains from inter-cell relay, we derive formulas for interference experienced at the congested and supporting BSs. Upper bound on number of calls supported inside the area of the congested cell is derived based on interference formulas. We show that inter-cell relay can increase the number of accepted calls inside a congested cell.

This paper analyzes the bit error rate (BER) performance of orthogonal frequency division multiplexing (OFDM) system. We derive the BER performance of OFDM system, and quantify the effects of channel impairments such as Doppler shift due to user mobility and high-power amplifier (HPA) distortion when amplifying the transmitted OFDM symbol. It is shown that the resulting inter-carrier interference (ICI) generated by these impairments becomes very significant in OFDM system, and severely degrades its BER performance. Simulation results match well and verify the analysis.

Maximizing network connectivity while maintaining a useful period of lifetime is a challenging design objective for wireless sensor networks (WSNs). Satisfying such objective becomes an even more intricate task in harsh operational environments such as those found in forestry applications. While much work has been presented aimed at forestry applications, only a few have addressed the unique characteristics of forestry settings, such as 3-D deployment and operational requirements. In this paper, we introduce a novel deployment strategy for relay nodes in WSNs for forestry applications. The strategy optimizes network connectivity, while guarantying specific network lifetime. Key to our contribution is a revised definition for network lifetime that is more realistic and more fitting to forestry applications. The effectiveness of our strategy is validated through extensive simulation and comparisons.

Many shortcomings in radio frequency identification (RFID) systems stem from the underlying communication architecture; affecting performance, scalability and usability. This paper remedies such hindrance by introducing the novel paradigm of distributed receiving in RFID systems. The proposed scheme entails dispersing the routine functions of conventional readers onto spatially distributed entities, within the reader's interrogation zone, enabling formation of micro-zones. Such micro-zones facilitate the novel concept of multi-point communication in RFID systems. We also present a new breed of anti-collision algorithms - parallel singulation - exploiting the spatially isolated nature of micro-zones and the new multi-point communication architecture. Simulation analysis validates the distributed receiving system by demonstrating significant performance improvements, enhanced scalability and usability.

Intelligent transportation systems (ITS) in the form of vehicular adhoc networks (VANETs) have engaged significant interest from the academic, industry and government sectors. Data dissemination is of the utmost importance for the day-to-day operation of ITS applications. Numerous standards, architectures and communication protocols have been anticipated for data diffusion in ITS applications. However, existing schemes are based on an essential condition that the relaying vehicle has to be equipped with an active communication module - termed Intelligent Vehicle (IV). One of the major drawbacks of these schemes is that they do not exploit the potentially large number of non-intelligent vehicles (non-IVs), i.e., the vehicles without any active communication module for relaying and diffusion purposes. In this paper, we fill this gap by proposing a novel data dissemination scheme utilizing a non-IV to act as a data ferry. The non-IV is tagged with a low-cost passive RFID tag whereas the IV is equipped with an embedded RFID reader. The non-IVs ubiquitously store and carry the events in the passive tags, as recorded by the IV or by the roadside equipment, as they maneuver around the city blocks. Two system configurations, namely co-operative and stand-alone with and without vehicle-to-infrastructure (V2I) support, respectively, are also proposed. Simulation results show the proposed scheme's effectiveness and performance superiority over the existing active-based data dissemination methods.

Wireless local area networks (WLANs) provide an affordable solution for last mile network access. They also allow for extension of a network by configuring a wireless mesh network (WMN) where it may otherwise be physically infeasible or cost prohibitive to do so. With the increasing use of real-time applications such as video conferencing and Voice over IP (VoIP), networks are stressed to guarantee QoS requirements for these applications. Examples of key requirements include bounded delay and packet loss ratios. Addressing this issue in WLANs, the IEEE 802.11e amendment was proposed to provide a QoS mechanism. However, the performance of 802.11e in meshed environments is yet to be studied. In this work, we study VoIP call quality in a meshed environment with provisions for QoS. We study the call quality and throughput of background traffic in an experimental WMN testbed in order to test how well the IEEE 802.11e QoS provisions support voice calls. Call quality is tested in different configurations and scenarios. We study the effect of the number of wireless hops on VoIP call quality. In addition, we investigate the number of VoIP calls that can be supported simultaneously for different numbers of wireless hops. We also study how fairly the network treats different calls in different configurations. Then, we look at how much effective bandwidth a VoIP call uses on the network. Finally, we examine the VoIP call quality of different calls when calls have different QoS parameters and study the effect that a busy central node has on traffic passing through it. We provide suggestions to improve call quality on a WMN and hint at possible future work.

The success of emerging Broadband Wireless Access Networks (BWANs) such as 4G wireless cellular networks championed by Long Term Evolution (LTE) and IEEE 802.16 broadband wireless networks (WiMAX) will depend, among other factors, on their ability to manage their shared wireless resources in the most efficient way. This is a complex task due to the heterogeneous nature of access networks and the diverse bandwidth and Quality of Service (QoS) requirements of the applications that these networks are required support.

This special issue includes a collection of 19 outstanding research papers which cover a diversity of topics on the application of network coding in wireless communication networks.

The success of emerging Broadband Wireless Access Systems (BWASs) will depend, among other factors, on their ability to manage their shared wireless resources in the most efficient way. This is a complex task due to the heterogeneous nature, and hence, diverse Quality of Service (QoS) requirements of different applications that these systems support. Therefore, QoS provisioning is crucial for the success of such wireless access systems. In this paper, we propose a novel downlink packet scheduling scheme for QoS provisioning in BWASs. The proposed scheme employs practical economic models through the use of novel utility and opportunity cost functions to simultaneously satisfy the diverse QoS requirements of mobile users and maximize the revenues of network operators. Unlike existing schemes, the proposed scheme is general and can support multiple QoS classes with users having different QoS and traffic demands. To demonstrate its generality, we show how the utility function can be used to support three different types of traffic, namely best-effort traffic, traffic with minimum data rate requirements, and traffic with maximum packet delay requirements. Extensive performance analysis is carried out to show the effectiveness and strengths of the proposed packet scheduling scheme.

This paper derives the multi-hop capacity of OFDM-based MIMO-multiplexing relaying systems. MIMO-multiplexing relaying presents a spectrally efficient means of realizing mesh supports in wireless networks operating over licensed bands by providing separate links for access and mesh relaying services on the same broadband radio channel. We show that for an NtimesN MIMO-multiplexing relaying system with amplification factor alpha at relay nodes, R-hops relaying degrade the capacity by at most -Nlog2(alpha2R/ (1 + Sigmar=1Ralpha2rNr)) + RNlog2(N) bits/sec/Hz. Therefore, greater capacity loss is experienced in MIMO-multiplexing relaying involving high order MIMO systems. We also illustrate that the capacity loss is independent of the OFDM configurations employed; thus network operators could employ higher OFDM configurations to compensate for data rate loss in access services when some of the MIMO-multiplexing links are dedicated to mesh relay. This pioneering analysis provides useful guidelines for network operators planning to employ MIMO-multiplexing option for mesh relay supports.

Abstract The IEEE 802.16 standard is gaining broad consideration to serve the expanding demand for broadband access networks. In this standard, the best effort traffic uses the reservation multiple access control (MAC) mechanism, which is widely adopted in recent broadband network technologies. The goal of this paper is to study the performance of the MAC protocol of the best effort traffic in the IEEE 802.16 standard with emphasis on the size of the reservation period. We use a two‐stage Markov chain model to capture all possible events on the reservation and service periods. This allows the computation of the inflow and outflow of bandwidth requests (BWRs) and their associated data packets which leads to the delay and throughput formulas. By means of illustrative examples and numerical results, validated through simulation, we investigate the key importance of the size of reservation period. We highlight potential performance improvement, through opportunistic dynamic control of the size of the reservation period to enhance the performance of reservation MAC protocol. Copyright © 2008 John Wiley & Sons, Ltd.

The increasing demand for wireless heterogeneous multimedia applications presents a real challenge to mobile service providers. Even with the substantial increase in the supported bandwidth in future broadband wireless systems such as 3.5G wireless cellular networks and 802.16 broadband wireless networks (WiMAX), these systems suffer from the same inherited problem in wireless networks, which is limited spectrum. Therefore, bandwidth provisioning is crucial for the success of such broadband wireless systems. In this paper, we propose a novel dynamic bandwidth provisioning scheme for broadband wireless communication. The proposed scheme spans multiple time slots/frames and optimally allocates them to the different classes of traffic depending on their weights, the real-time bandwidth requirements of their connections and their channel quality conditions. Simulation results show that satisfactions of different classes of traffic can be improved by implementing our scheme.

In this paper, we propose an enhancement to the distributed coordination function (DCF) of the IEEE 802.11 MAC protocol, called the MIMO-based DCF (MBD). The MBD scheme exploits the multiple spatial channels (MSC) created by multiple inputs multiple outputs (MIMO) systems to enhance the IEEE 802.11 DCF collision-avoidance mechanism. The key idea is the sharing of the spatial channels during the contention period, i.e., instead of accessing the channel using the total spatial channels, the contending nodes only use a set of the available spatial channels. As the total concurrent used spatial channels are fewer than or equal to the degree of freedom (DOF), receivers can receive multiple contention attempts and hence they can coordinate their responses to avoid collisions. The spatial channel sharing produces other properties such as medium contention termination and medium contention selection. The former refers to the accessing node's capability to terminate its contention upon detecting other ongoing medium access attempts to avoid potential collisions. Contention selection happens when receivers detect multiple medium access requests initiated by different transmitters. Consequently, receivers either respond to some requests or keep silent to avoid further collisions. Simulation results obtained using OPNET modeler show that the MBD scheme substantially reduces the medium access collisions. Actuating the medium contention termination and medium contention selection mode further reduces the medium contention collisions. The results of the MBD based on an adaptive MSC sharing scheme demonstrate that with different network loads using the adaptive MSC sharing scheme further reduces medium access collisions.

Congestion based pricing algorithms are considered efficient approaches to control congestion and to distinguish services provided for users in computer networks. Game theory lends itself as a prevailing tool to design such algorithms. In this work, we propose a game theoretic congestion based bandwidth provisioning algorithm to address the scarcity for bandwidth provisioning scheme in IEEE 802.16 standard. To the best of our knowledge, the proposed algorithm is the first one to simultaneously control congestion and fairness while providing differentiated QoS guarantees. Simulation results reveal that the proposed scheme realizes our objective of controlling congestion, and provides differentiated QoS guarantees and proportional fairness among the different network classes.

Peer-to-peer networks are very popular but the problem of bootstrapping them has largely been ignored. In a fully decentralized environment such as a mobile ad hoc network (MANET) the usual bootstrapping solutions, which typically require a centralized service, are not possible. We present a method of bootstrapping P2P overlay networks running on MANETs which involves multicasting P2P overlay join queries and responses, and caching results at all nodes. Node choose which overlay members to join to based on a utility function that considers both the distance in hops and the overlay neighbors' available energy. Simulation results show that the P2P overlay can closely reflect the underlying top.ology, which reduces energy consumption, that caching the join requests reduces the number of messages required to join the overlay, and that compared to Random Address Probing, there is less overhead and significantly less delay.

Prolonging network lifetime while efficiently detecting and reporting events are arguably the most important objectives of wireless sensor networks (WSNs). Different WSN protocols aim to improve such measures, yet partially focus on certain aspects (eg. reliability and time latency) and sacrifice others (eg. power efficiency) in application specific approaches. We present DMULD (decentralized multi-level duty cycling), a cross-layer design paradigm aiming at raising performance measures of general WSNs. It integrates tailored multi-level sleep states having varying levels of performance (hence energy consumption) with novel sensing, medium access control (MAC) and routing protocols. Nodes carry on tasks in a decentralized manner with efficient load balancing. DMULD is a dynamic model which is adaptable to application specific requirements, through fine tuning its parameters. DMULD was thoroughly simulated, examining the effects of varying its parameters on network lifetime and efficiency. It achieved over double the lifetime of multi-hop CSMA/CA.

3G-based Multi-hop cellular networks (MCNs) inherit a special characteristic of 3G systems, namely the relationship between a cell's coverage and its capacity. At the planning stage, a cell may be statically set to have small coverage with high capacity, a large coverage with small capacity, or some other fixed settings in between. Such static settings, however, do not adapt to the projected dynamic nature of users in 3G-especially in an MCN environment. In this paper, we propose the Optimal Cell Size (OCS) scheme for a 3G TDD W-CDMA MCN multi-cell environment. Given the cell capacity function, users' distribution and demands, OCS yields optimal cell sizes that maximize the system throughput through balancing coverage and capacity. Not only can OCS cope with dynamic network conditions, but it can also be considered as an aid to the network planning process. To the best of our knowledge, this is the first multi-cell optimal cell size scheme in the context of MCNs.

The success of emerging wireless broadband communication systems such as 3.5 G wireless cellular systems and 802.16 broadband wireless systems (WiMAX) will depend, among other factors, on their ability to manage their shared wireless resources in the most efficient way. This is a complex task due to the heterogeneous nature and, hence, diverse bandwidth requirements of applications that these communication systems support and the reliance on high speed shared channels for data delivery instead of dedicated ones. Therefore, bandwidth provisioning is crucial for the success of such communication systems. In this paper, we propose a novel dynamic bandwidth provisioning scheme for broadband wireless communication. The proposed scheme spans multiple time slots/frames and optimally allocates them to the different classes of traffic depending on their weights, the real-time bandwidth requirements of their connections, their channel quality conditions and the expected obtained revenues. Simulation results are provided to show the potential and effectiveness of our scheme.

In order to support different types of user applications, the IEEE 802.16 standard defines different service classes together with their associated Quality of Service (QoS) parameters. However, the standard lacks a MAC scheduling architecture that guarantees these defined QoS requirements. The importance of efficient scheduling is crucial to QoS provisioning for multimedia flows. In this paper we propose an opportunistic and optimized downlink scheduler that pledges fairness among admitted connections. Our approach involves separating the scheduling problem into two sub-problems. In the first problem, the proposed scheduler calculates the number of time-slots in each time frame corresponding to the service classes with the objective minimizing the blocking probability of each class. In the second problem, time-slots for each class connection are allocated using an integrated cross-layer priority functions that guarantee proportional fairness. The simulation results reveal that the proposed scheduler realizes our objectives, and provides efficient QoS scheduling without starving the connections of the best effort class.

For the sake of maximizing the network lifetime, we propose a novel placement scheme for mobile data collectors in underwater acoustic sensor networks (UASNs). Our scheme is based on a 3D architecture, in which on-the-surface data collectors gather data from underwater sensors and relay them to an on-shore sink. We divide the lifetime of the network into fixed length rounds and move the data collectors to new locations at the beginning of each round. We formulate the problem as an integer linear program (ILP) and use an ILP solver to find the optimal placement of data collectors together with the multi-hop routing paths to deliver data from underwater sensors to data collectors. Our work is a pioneering effort in the placement of mobile data collectors in three-dimensional UASNs. Comprehensive experiments show that our scheme prolongs the lifetime of the network significantly as compared to other data collector placement schemes.

A synergy exists between MANETs and P2P networks since both are decentralized and have dynamic topologies. This paper presents a utility-based incentive scheme for P2P file sharing over MANETs, designed to encourage users to contribute to the network by sharing files. The scheme combines a virtual pricing mechanism with file transfer delay estimation and allows users to choose the best peer to download from based on their preference for price vs. delay. Users can choose to lower the delay experienced or focus on obtaining files for the lowest price. Simulation results show that the utility-based scheme does a much better job of promoting sharing of files by users in comparison to an unpriced and a fixed price scheme, the delay per downloaded file tends to be lower, and the MANET has a greater network lifetime.

Underwater sensor networks (USNs) promise innovative and exciting applications, viz. oceanographic data collection, environment monitoring, exploration, and tactical surveillance. Underwater wireless sensor networks (UWSNs) though pose significant research challenges due to the harsh underwater environment. In UWSNs, acoustic is thought to be the only viable means of communication. Underwater wireless acoustic sensor networks (UW-ASNs) present a wireless channel with key challenges, specifically in shallow oceans such as long propagation delays, signal attenuation, man-made and ambient noise, low bandwidth and high transmission energy. We propose a new paradigm for UWSNs, namely underwater wireless hybrid sensor networks (UW-HSNs), which introduce the concept of hybrid communication. UW-HSNs combine the best of both worlds, i.e., the practicality of underwater acoustics and the high-performance of radio communication. The basic idea is to use radio communication for large and/or sustained traffic and traditional acoustic methods for small data volume. Furthermore, we introduce TurtleNet, an architecture based-on UW-HSNs concept, and we propose an asynchronous and distributed routing protocol for TurtleNet. Based on the nodepsilas state, the protocol decides which communication channel to utilize. TurtleNet is simulated using the ns-2 simulator. Simulation results reveal the promising performance for TurtleNet, and hence validate the UW-HSNs concept.

To efficiently utilize the limited spectrum of interference limited Wide-band Code-Division Multiple-Access (WCDMA) cellular networks, transmission rates allocation and base station association for mobile users need to be optimal. In this paper, Power-Controlled Rate and Coverage Adaptation (PCRCA) module is proposed to balance network load, maximize number of users admitted to the system while assuring their Quality of Service (QoS) requirements. It is a cooperative scheme which allows nearby sectors of two adjacent cells to dynamically change their coverage to meet the optimal transmission rates allocation for their mobile users. A heuristic algorithm is implemented to solve an optimization model of the proposed scheme. The obtained power and capacity gains as well as outage probability of the proposed algorithm is compared to the obtained results of a system with only power-controlled rate adaptation mechanism.

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in next generation Broadband Wireless Access Systems (BWASs), it is expected that these systems will severely suffer from congestion due to the rapid increase in demand of bandwidth intensive applications. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer immense amount of revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of Call Admission Control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the arrival rates to the system are less than or equal to the optimal ones computed dynamically, hence, guaranteeing a congestion-free system. Simulation results show the effectiveness and strengths of our proposed approach.

Our previous work proposed a novel mechanism for coverage control in WCDMA systems that can be used in instances of congestion and load imbalance. In this paper, we expand our work to accommodate the heterogeneous nature of traffic in future networks. More importantly, we derive a mathematical model to involve more realistic considerations for inter-cell interference in a system with mixed coverage. Based on different load scenarios in a hotspot area and different coverage combinations of the loaded and supporting sectors, the model is used to quantify the degradation level in the QoS parameters of low priority traffic to preserve the QoS level of high priority traffic. Achievable data rates and Bit Error Rates (BER) are determined for every possible coverage combination. The effect of rate and coverage adaptation on the transmission powers of mobile users is also analyzed.

Tag collisions can impose a major delay in radio frequency identification (RFID) systems. Such collisions are hard to overcome with passive tags due to their limited capabilities. In this paper, we look into the problem of minimizing the time required to read a set of passive tags. We propose a novel approach, the distance-based clustering, in which the interrogation zone of an RFID reader is divided into equal sized clusters (discs), and tags of different clusters are read separately. The novel contributions of this paper are the following. First, we provide a mathematical analysis to the problem and derive a closed-form formula relating delay to the number of tags and clusters. Second, we devise a method to efficiently find the optimal number of clusters. The proposed scheme can be augmented with any tree-based anti-collision scheme, and substantially improve its performance. Simulation results show that our approach makes significant improvements in reducing collisions and delay.

We propose a scheme for routing and placement of mobile data collectors in Underwater Acoustic Sensor Networks (UASNs). The proposed scheme maximizes the lifetime of the network with an upper bound on the maximum delay. We assume a 3D architecture, in which on-the-surface data collectors gather data from underwater sensors and relay them to an on-shore sink. We divide the lifetime of the network into fixed length rounds and move the data collectors to new locations at the beginning of each round. This problem is formulated as an Integer Linear Program (ILP), and we use an ILP solver to find the optimal placement of data collectors together with the multi-hop routing paths to deliver data from underwater sensors to data collectors. To the best of our knowledge, this is the first attempt towards the placement of data collectors in a 3D environment with delay constraints. When compared with other schemes, our scheme has shown the capability to achieve longer lifetime and shorter delay.

Wireless mesh networks (WMNs) have the potential in supporting multimedia applications with last-mile Internet access. To achieve this objective, shortcomings such as scarcity of the wireless link capacity and the lack of robust QoS scheduling must be overcome. In this paper, we consider WMNs utilizing the new IEEE 802.11n standard. Based on the standardpsilas physical and medium access control (MAC) layer enhancements, we propose adapting the modulation and code scheme (MCS) index and aggregation frame length according to the online assessed link quality, performing frame aggregation by packing multiple small subframes. We also propose performing QoS bandwidth provisioning by optimally aggregating subframes according to their QoS constraints and fairness. Performance results show that incorporating link adaptation, frame aggregation, and QoS bandwidth provisioning can considerably improve the system performance in terms of MAC delay, achieved throughput and packet dropping ratio. The results also show that packet aggregation scheme has a crucial impact on system performance when aggregating small packet sizes.

The IEEE 802.16 standard, which includes specification for the Medium Access Control (MAC) and Physical (PHY) layers, was designed to handle the requirements of different contemporary applications with diverse QoS requirements. IEEE 802.16 standard is equipped with signaling and bandwidth allocation algorithms that can accommodate many connections with a variety of QoS requirements at one Subscriber Station (SS). The connections may be varied in their bandwidth and latency requirements, so several researchers proposed an IEEE 802.16 schedulers with different objectives to provide for wireless resource allocations over a different range of traffic models. In this paper, we conduct a comprehensive performance study of scheduling algorithms in Point to Multipoint mode of WiMAX. We first make a classification of WiMAX scheduling algorithms, then simulate a representative number of algorithms in each class taking into account the characteristics of the IEEE 802.16 standard. We evaluate the algorithms with respect to their abilities to support multiple classes of service, providing Quality of Service (QoS) guarantees, fairness amongst service classes and bandwidth utilization. To the best of our knowledge, no such comprehensive performance study has been reported in the literature. Simulation results indicate that none of the current algorithms is capable of effectively supporting all WiMAX classes of service. We demonstrate that an efficient, fair and robust scheduler for WiMAX is still an open research area. We conclude our study by making recommendations that can be used by WiMax protocol designers.

In future cellular networks, random users' mobility as well as time-varying multimedia traffic activity make cellular networks design a challenging task. To efficiently utilize the limited wireless spectrum, it is crucial to enable cellular systems to reactively and dynamically reconfigure cells' service area and capacity. This paper proposes an effective dynamic directional cell coverage adaptation scheme combined with a rate adaptation scheme that together are aimed maximizing radio resource utilization of Wideband Code Division Multiple Access (WCDMA) systems. This approach has the capability of reconfiguring the cell coverage area online by being aware of the system conditions. It takes into account the load on the uplink direction and the pilot power allocation on the downlink direction. Simulation results show the effectiveness of the proposed scheme in increasing WCDMA system efficiency.

Abstract We propose two schemes for routing and placement of mobile data collectors in underwater acoustic sensor networks (UASNs): the delay tolerant placement and routing (DTPR) and the delay constrained placement and routing (DCPR). As their names reveal, while the DTPR maximizes the lifetime of the network without any delay considerations, the DCPR maximizes the lifetime of the network with an upper bound on the maximum delay. Both schemes are based on a 3D architecture, in which on‐the‐surface data collectors gather data from underwater sensors and relay them to an on‐shore sink. We divide the lifetime of the network into fixed length rounds and move the data collectors to new locations at the beginning of each round. These problems are formulated as integer linear programs (ILPs), and we use an ILP solver to find the optimal placement of data collectors together with the multi‐hop routing paths to deliver data from underwater sensors to data collectors. Our work is a pioneering effort in the placement of mobile data collectors in three‐dimensional UASNs. Comprehensive experiments show that our schemes prolong the lifetime of the network significantly as compared to other data collector placement schemes. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this article we present an optimal Markov Decision‐based Call Admission Control (MD‐CAC) policy for the multimedia services that characterize the next generation of wireless cellular networks. A Markov decision process (MDP) is used to represent the CAC policy. The MD‐CAC is formulated as a linear programming problem with the objectives of maximizing the system utilization while ensuring class differentiation and providing quantitative fairness guarantees among different classes of users. Through simulation, we show that the MD‐CAC policy potentially achieves the optimal decisions. Hence our proposed MD‐CAC policy satisfies its design goals in terms of call‐class‐differentiation, fairness and system utilization. Copyright © 2006 John Wiley & Sons, Ltd.

The emergence of high-speed wireless cellular networks such as high speed downlink packet access (HSDPA) and 1x evolution data optimized (1xEV-DO) will enhance the support of existing applications and will enable the development of a wide range of heterogeneous "content rich" multimedia applications that have different quality of service (QoS) requirements. However, due to scare wireless resources and high traffic demands, new resource management techniques are needed in order to satisfy the QoS requirements of the different heterogeneous applications and maximize the network capacity at the same time. In this paper, we propose a novel resource management scheme through packet scheduling for high-speed wireless cellular networks. The proposed scheme utilizes utility and opportunity cost functions to satisfy the needs of mobile users and service providers. Simulation results are provided to show the effectiveness and potential of our proposed scheme.

A typical task in many applications for vehicular networks is to disseminate information to a subset of vehicles on a highway. In this work we describe a theoretical estimation for the average number of such independent tasks that can be sustained concomitantly in ad-hoc vehicular networks using contention free medium access and a fixed number of wireless channels. We call this measure the vehicle to vehicle (V2V) capacity of the network. Using our estimation, we perform an analysis of the V2V network capacity under different highway traffic conditions and scenarios. A surprising conclusion drawn is that congestion actually determines an increase in capacity. Based on these observations, we propose a simple strategy that can increase the capacity by filtering communication tasks with length greater than a certain threshold which can be determined numerically.

In this paper, we utilize rate adaptation for Quality of Service (QoS) provisioning in multi-hop CDMA networks where multiple classes are considered. We quantify the interference resulting from each class. Based on the requirements and the resulting interference from ongoing calls of each class, achievable data rate and bit error rate (BER) are determined. We quantify the level of degradation required of a given class to achieve a desirable QoS of the other class. Maintaining a certain QoS also requires power adjustment. To this effect, we propose a generic rate adaptation and power adjustment (RAPA) scheme. RAPA adapts the quality of classes within pre-determined thresholds. The operation of RAPA is illustrated through examples.

Capitalizing on the unifying capabilities of IP, future wireless network operators (WNO) will be able to deploy heterogeneous wireless networks (HWNs). The heterogeneity will enable users to continuously connect to the technology that best meets the users' application requirements. It will also allow WNOs to change the associations of users to attain certain operational objectives. Despite the possible employment of cost reduction mechanisms based on long term observations, we argue that the user-centric nature of HWNs and the possible permutations of triggers for vertical handoffs (VHs) may effectively render user assignment beyond the WNO's control. In this paper, investigate the potential of a module dedicated to reduce the cost of service delivery. In doing so, we discuss the considerations of such a module, including the factors affecting the cost of service delivery and the elements involved in identifying and selecting users to undergo a forced VH.

Wireless networks have made great gains in usability and popularity. However, inherent limitations on cell capacity and coverage still exist. There are also dead-spots and hotspots problems in these networks. Ad hoc multi-hop relaying enhances cell capacity and coverage, alleviates the dead-spots problem, and helps to ease congestion in hotspots. However, multi-hopping also increases packet delay. Effective channel assignment is key to reducing delay. Existing channel assignment schemes are heuristics and may not guarantee optimal solutions in terms of minimum delay. In this paper, we provide an optimal channel assignment (OCA) scheme for ad hoc TDD W-CDMA multi-hop cellular networks (MCN) to minimize packet delay. OCA can also be used as an un-biased tool for the comparison among different network topologies, network densities, and protocols. To the best of our knowledge, this is the first time that a minimum delay optimal channel assignment is proposed in a multi-hop cellular environment.

Future wireless cellular networks such as high speed downlink packet access (HSDPA) and 1x EVolution data optimized (1xEV-DO) promise to revolutionize the mobile user's wireless experience by offering high downlink data rates that are much more beyond what 2.5 G and 3 G cellular systems could offer. In order to support as many users as possible, these systems exploit the bursty nature of the data traffic by utilizing high speed downlink shared channels that are shared among the users according to the packet scheduling scheme being used. However, to support acceptable performance levels especially at peak loads, these systems require much more efficient downlink packet scheduling schemes than ever before. In this paper, we propose a novel generic centralized downlink (GCD) packet scheduling scheme for next generation wireless cellular networks. The proposed scheme utilizes novel utility and opportunity cost functions to satisfy the mobile users as well as the service providers. We show that our scheme converges to two well known schemes, Max CIR and PF. Simulation results are provided to show the effectiveness and strength of the GCD scheme.

In this paper we study efficient triangular grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices, where the random errors are modeled by uniform displacements inside error disks of a given finite radius. The average coverage percentage of the sensing field is derived as a function of the length of the grid tiles d, and the radius of the random error disks, R. Our expressions for the average coverage percentage are computed numerically and verified by Monte-Carlo simulations. The analytical methods can be used with other types of grid-based deployment with little modification, such as square grid-based deployment. One appealing feature of grid-based deployment that we observe is that the sensing coverage is rather resilient to random errors. Based on this observation and the quantitative results from our analysis, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.

In this paper, we introduce a reactive congestion control scheme for wideband CDMA (WCDMA) cellular networks and study its performance with respect to network throughput and call dropping rates. This scheme utilizes the idea of directional cell breathing (DCB), in which network cells are partitioned into N-sectors where each sector is served by a directional smart antenna. We propose a heuristic algorithm called directional cell breathing based-reactive congestion control (DCBB-RCC) that controls the transmission power of the common pilot channel (CPICH) such that the coverage area of a cell sector can dynamically be extended towards a nearby loaded sector or shrunk towards cell center for a loaded sector. Therefore, this mechanism activates a handoff procedure to shift some traffic of a loaded cell towards a lightly loaded cell. The effectiveness of our proposal is investigated through snap shot simulation using numerical examples.

The capacity of CDMA cellular networks is interference limited. Multi-hop communication promises to reduce interference, hence increasing capacity. However, such capacity gains depend on the actual interference. The location of a call determines its interference effect on the network. In this paper, we study the effect of call distribution on the capacity of multi-hop CDMA cellular networks. The capacity of multi-hop case is compared to that of single hop case. The effect of non-uniform call distribution is studied. It is shown that in the multi-hop case call distribution in a cell affects the capacity of this cell but hardly the capacity of its neighboring cells. The case is reversed in the single-hop case. Call distribution in a cell has no effect on the capacity of this cell, but it can have a significant effect on the capacity of surrounding cells. It is shown that if calls tend to originate near the border of one cell, this can seriously degrade the capacity of the whole network. This scenario is alleviated in the multi-hop case due to the shorter distances signals have to travel, resulting in lower interference. This paper also highlights scenarios where multi-hop communication is deeply needed.

Tag collisions can impose a major inefficiency in RFID systems, resulting in low identification rates, short reading range and ineffective resource utilization. They are more problematic in passive tags due to limitations on power and functionality. In this paper, we present a novel approach to overcome the passive tag collision problem. The novelty in our scheme is that it requires no additional memory, results in a higher identification rate and reduces power and medium consumption. The proposed scheme can be augmented to tree- based anti-collision proposals, both existing and to come, and substantially improve their performance. Performance results indicate that the augmented schemes bear significant gains that are achieved by reducing collision, overhead and delay.

Denial of service (DoS) attacks impose an increasingly growing threat to the Internet These attacks result in wastage of scarce Internet resources and service disruptions. Existing packet filtering schemes are deployable at either source, intermediate or victim networks. In this paper, we propose a hybrid of the source and the victim networks-based packet filtering approach, source router marking and hop-count (SRHC), to detect and filter high-rate traffic flows and IP-spoofing attacks. Packets are marked at the source network based on their arrival rate threshold. At a victim network, the spoofed packets are marked based on the IP source arrival rate using their respective TTL value. Both source and victim networks collaborate to filter high-rate and IP-spoofing attacks. The ns-2 simulator is used to generate attack scenarios. Our simulation results show that the SRHC scheme effectively filters out high-rate and IP-spoofing attack packets, with minimal collateral damage.

Multiple input multiple output (MIMO) offers significant advantages in terms of rate and reliability. We consider sharing the MIMO increased rate in wireless mesh networks (WMNs) links. Such sharing allows nodes in a WMN to concurrently meet more demanding communication requirements such as high data rates, increased transmission range, power savings and low dropping rates. In this paper, we propose the rate splitting MIMO-based Mac protocol (RSMP) which is a distributed Mac protocol where pairs of nodes can locally cooperate with other nodes in their vicinities to reserve the required rate through effective selection on transmit antennas. Nodes can then use any preferable MIMO coding technique that best suit their communication requirements. RSMP is evaluated using OPNET interfaced with Matlab. OPNET is used to model the details of the Mac protocol while Matlab is used to compute the MIMO channel capacity and interference. Simulation results are obtained and compared to those of 802.11n MIMO-based DCF MAC protocol. We show that our proposed RSMP scheme outperforms MIMO DCF MAC in medium access delay and throughput. We also show that nodes can always attain their requested rate and that RSMP can be applied to satisfy multiple QoS requirements.

The IP multimedia subsystem (IMS) provides a framework that accommodates current and future services in wired and wireless networks. However, IMS does not handle non-3G elements such as wireless local area networks (WLANs). In order to provide interconnection at the service layer between 3G and WLANs, interworking between IMS and WLAN is necessary. Extending IMS beyond 3G to WLANs is a crucial step towards the evolution of a seamless universal next generation wireless network, commonly known as 4G. In this paper, a novel architecture for service layer interworking between WLAN and 3G is presented. The architecture takes into consideration the interaction of WLAN Application SIP servers with the IMS call session control functions (CSCFs) and the extensibility of application servers (ASs) beyond the core IMS network. A WLAN AS is introduced into the IMS network and an SIP server into the WLAN. These act as interworking arbitrators and communicate with each other to provide service and session continuity. The main advantage of this architecture is its feasibility within the standard. It is also non-intrusive to the IMS core or the WLAN.

HSDPA is a 3.5G wireless cellular system that can offer data rates of up to 14.4 Mbps which is much beyond what 2.5G and 3G cellular systems could offer. Due to its high supportable data rate, HSDPA can support a wide range of applications with diverse QoS. Data generated by these applications consists of different traffic classes; including conversational, streaming, interactive and background; each having certain QoS requirements. HSDPA scheduling schemes need to accommodate these traffic classes as well as provide certain QoS provisions for them. This paper proposes a delay-based scheduler (DBS) that prioritizes traffic based on its delay requirements. In addition, the DBS is designed to achieve a high throughput and a fair allocation of resources among users. Simulation results indicate that the DBS achieves a lower average queuing delay while maintaining other performance metrics when compared with existing HSDPA scheduling schemes.

In this paper we study efficient grid-based sensor deployment planning for coverage when sensor placements are perturbed by random errors around their corresponding grid vertices. We propose a generic approach to evaluate the average coverage percentage of the sensing field. Our generic approach can be used with all types of grid-shapes, all kinds of random error distributions, and different sensing models. We apply the generic approach to two practical deployment scenarios, namely, the triangular grid-based deployment with bounded uniform random errors and unbounded normal random errors. For both cases, the average coverage percentage is computed numerically and verified by Monte-Carlo simulations. Based on the numerical results, we discuss several approaches to efficient grid-based deployment planning for coverage and illustrate these through numerical examples.

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.

Abstract Effective support of real‐time multimedia applications in wireless access networks,viz. cellular networks and wireless LANs, requires a dynamic bandwidth adaptation framework where the bandwidth of an ongoing call is continuously monitored and adjusted. Since bandwidth is a scarce resource in wireless networking, it needs to be carefully allocated amidst competing connections with different Quality of Service (QoS) requirements. In this paper, we propose a new framework called QoS‐adaptive multimedia wireless access (QoS‐AMWA) for supporting heterogeneous traffic with different QoS requirements in wireless cellular networks. The QoS‐AMWA framework combines the following components: (i) a threshold‐based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold‐type connection admission control algorithm, and (iii) a bandwidth adaptation algorithm that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. The framework can be modeled as a multi‐dimensional Markov chain, and therefore, a product‐form solution is provided. The QoS metrics—new call blocking probability (NCBP), handoff call dropping probability (HCDB), and degradation probability (DP)—are derived. The analytical results are supported by simulation and show that this work improves the service quality by minimizing the handoff call dropping probability and maintaining the bandwidth utilization efficiently. Copyright © 2006 John Wiley & Sons, Ltd.

Channel dependant scheduling schemes for High Speed Downlink Packet Access (HSDPA) system such as Max CIR and Proportional Fairness (PF) have been proven to provide a significant throughput gain by exploiting the channel fluctuations of the users. However, their inability to ensure a fair distribution of the radio resources among the mobile users has been a major concern. In earlier study [6], we proposed a Fair and Efficient Channel Dependent (FECD) algorithm for HSDPA to provide a priority scheduling between users based on their instantaneous channel conditions and their average throughputs. The FECD algorithm aims at increasing the data rates of the users by exploiting the variations of their channel conditions while at the same time ensure a fair distribution of the radio resources. We evaluated the performance of the FECD algorithm in Pedestrian A environment. In this paper, however, we extended our simulation model to include evaluating the proposed algorithm in Vehicle A environment. Simulation results show that the proposed algorithm in both environments outperforms the maximum CIR and the Proportional Fair schemes in terms of providing minimum throughput assurance and, therefore, it has a better degree of fairness.

The IEEE 802.16 technology is increasingly being considered for fixed and mobile voice and high speed data access. The success of IEEE 802.16 in a mobile or mesh network environment is contingent on its ability to embrace and sustain dynamic traffic conditions. To this end, we study the reservation multiple access protocol of the IEEE 802.16 standard to understand the protocol performance and potentials. Basically for its primary role in controlling the protocol performance, we emphasize the design of the contention-based reservation period. We present an analytical model for computing the contention delay, data transmission delay, and throughput resulting from different contention period allocations under dynamic traffic conditions. We illustrate the performance compromises and remedies with respect to the contention period allocation. Based on the analytical model and performance evaluation results, we institute a research base to enhance the performance of the reservation multiple access protocol in IEEE 802.16 standard.

Wireless mesh networks (WMN) have great potential to support high quality multimedia delivery. However, multimedia applications require quality of service (QoS) guarantees for end-to-end (E2E) transmission. An important element in providing such guarantees is mapping E2E transmission requirements to link QoS requirements. While different algorithms have been proposed for mapping in connection oriented wired networks, it is yet to be addressed in multihop wireless networks. Even algorithms proposed for QoS partitioning in wired networks are either near optimal or heuristic, and only yield solutions for a single E2E QoS requirement. In this paper, we propose a partitioning algorithm capable of partitioning multiple E2E QoS requirements simultaneously. We define QoS as the pair of required E2E delay and the probability to violate this delay requirement, i.e. violation probability. Our approach is motivated by experiments concluding that the delay probability distribution is accurately characterized by a gamma distribution. This conclusion is used to formulate a mathematical linear program that optimally partitions the E2E delay and the logarithm of the E2E violation probability into link delays and the logarithm of the link violation probabilities. Extensive simulation verify the effectiveness of the algorithm compared to two QoS partitioning algorithms. The proposed algorithm outperforms the other algorithms for loose and stringent QoS requirements and over different path lengths.

Channel dependant scheduling schemes for high speed downlink packet access (HSDPA) system such as Max CIR and proportional fairness (PF) have been proven to provide a significant throughput gain by exploiting the channel fluctuations of the users. However, their inability to ensure a fair distribution of the radio resources among the mobile users has been a major concern. In this paper, we propose a novel Medium Access Control Packet Scheduler (MAC-PS) scheme for HSDPA that is motivated by realistic economic models to satisfy the mobile users as well as the service providers through the use of utility and opportunity cost functions. Simulation results reveal the superiority of the MAC-PS in terms of fairness, user satisfaction and flexibility.

Multi-hop cellular communication has been proposed mainly to overcome some of the short-comings of cellular networks. Increasing network capacity and reducing energy consumption are amongst the promised advantages of this concept. In this paper, energy consumption in multi-hop CDMA cellular networks is investigated with the objective of quantifying the resulting effect. Reducing energy consumption in CDMA cellular networks, using multi-hop communication, is shown to be possible. Furthermore, comparing multi-hop cellular networks with and without power control, it is shown that power control further reduces energy consumption - especially with small number of hops. The effect of varying the hardware specifications is also studied. It is shown that multi-hopping is more effective in environments with high path loss.

Many mission-critical applications of wireless sensor networks generate traffic that have a stringent delay requirement. In this paper, we study the effects of relaying delay- constrained traffic in a wireless sensor network according to two different strategies. The first strategy allows traffic splitting, in which data flow can be split and sent on multiple paths from the source to the destination. The second strategy disallows traffic splitting, in which data flow cannot be split and must be sent on a single path from the source to the destination. We present a model based on linear and integer linear programming for finding an optimal allocation of splittable and unsplittable traffic in a wireless sensor network, in which traffic is subject to soft delay constraints. The objective is to minimize the total energy consumption spent on communication and the penalty incurred from the violation of delay constraints. Based on this model, we perform an empirical analysis to quantify the performance gains and losses of a splittable and unsplittable traffic allocation strategy for wireless sensor networks with delay-constrained traffic. The experiment results show that splitting traffic does not provide a significant advantage in energy consumption, but can afford strategies for relaying data with a lower delay penalty.

Due to cost constraints and limited sensing capability of sensor nodes, it may be impossible or unrealistic for a wireless sensor network to provide full area coverage in some circumstances. In this paper, we introduce the novel concept of information-oriented sensing coverage, which measures the actual information utility obtained by a wireless sensor network. An information-oriented sensor deployment algorithm is proposed. The algorithm makes use of the fact that in many applications the importance or the information utility at different locations is non-uniform across the sensing field. The effectiveness of the proposed algorithm is studied using simulation. Being a pioneering effort in this area, our work paves a new direction of sensor deployment.

Due to the cost constraint, it is impossible or unnecessary for a wireless sensor network to provide full area coverage in many applications. To mitigate the negative impacts of incomplete coverage, it is critical to realize that the importance of different locations of a sensing field is often non-uniform in practice. By strategically allocating more sensor nodes to the area of more importance, the differential random deployment can effectively decrease the loss for not detecting interesting events, as compared to the uniform random deployment. In this paper, we study the differential random deployment of sensor nodes in depth. We discuss a number of factors that affect the design of the differential random deployment density function. Because of the inherent complexity, we propose a heuristic structure of the differential deployment density function. The effectiveness of our proposal is verified in the performance study via simulation.

In wireless sensor networks (WSN), reliable monitoring of a phenomenon (or event detection) depends on the collective data provided by the target cluster of sensors and not on any individual node. In this paper we define a WSN reliability measure that considers the aggregate flow of sensor data into a sink node (gateway or cluster head). Given an estimation of the data generation rate and the failure probability of each sensor, we formulate the reliability measure and show that computing this measure for an arbitrary WSN is WSN. We then consider some special cases where we can either compute or approximate (bound) the reliability using an efficient algorithm. Finally, we present some numerical results that demonstrate some of the applications of our algorithms. Reliability evaluation tools are important in the context of design and analysis of sensitive information gathering sensor networks.

Major research challenges in the next generation (4G) of wireless networks include the provisioning of worldwide seamless mobility across heterogeneous wireless networks, the improvement of end-to-end Quality of Service (QoS) and enabling users to specify their personal preferences. Under this motivation, we design a novel cross-layer architecture that provides context-awareness, smart handoff and mobility control in heterogeneous wireless IP networks. We develop a Transport and Application Layer Architecture for vertical Mobility with Context-awareness (Tramcar). Tramcar is tailored for a variety of different network technologies with different characteristics and has the ability of adapting to changing environment conditions and unpredictable background traffic. Furthermore, Tramcar allows users to identify and prioritize their preferences. Simulation results demonstrate that Tramcar increases user satisfaction levels and network throughput under rough network conditions and reduces overall handoff latencies.

Vertical Handoffs occur when a user changes association from one type of wireless access technology to another while maintaining an active session. Much work has been done in ensuring seamless handoffs that also preserve QoS. However, service providers can exploit vertical handoffs as a Radio Resource Management (RRM) means to relieve congestion, load balance and uphold QoS requirements. Nevertheless, this exploitation requires rigorous study in order to realize its full potential. In this paper, we advocate the use and study of forced vertical handoffs as a powerful RRM tool. We also discuss the different factors involved in the design and operation a forced vertical handoff module (FVHM). Furthermore, we provide a RRM framework for future wireless network where an FVHM is employed and set to interact with a bandwidth adaptation algorithm. In the framework, we introduce the Willingness function, a novel representation for a user's instantaneous willingness to undergo a forced vertical handoff.

Energy is often considered the primary resource constraint in a wireless sensor network. Compared to sensing and data processing, the cost of communication is among the highest in energy consumption. In this paper, we study the effects of relaying data in a wireless sensor network according to two different strategies. The first strategy allows traffic splitting, in which data can be sent on multiple paths from the source to the destination. The second strategy disallows traffic splitting, in which data must be sent on a single path from the source to the destination. We present algorithms based on linear and integer programming for finding an optimal allocation of splittable and unsplittable traffic in a wireless sensor network that minimizes total energy consumption. The technique provides optimal solutions, and can be used by designers of communication protocols to assess the energy efficiency of a data relaying scheme for a given network configuration. We also perform an empirical analysis to quantify the comparative performance gains and losses of a splittable and unsplittable traffic allocation strategy for wireless sensor networks. Results show that although the energy savings of a splittable traffic allocation strategy is relatively small when compared to the unsplittable case (on average, ranging from 0% to 1.82%), an allocation of splittable traffic can tolerate up to an additional 14.1% increase in network traffic load until any further load increase returns no feasible solutions.

Service discovery, essential for many wireless applications, is more difficult to achieve in Mobile Ad hoc NETworks (MANETS) than in both wired and traditional wireless networks due to the lack of central control. In addition, the heterogeneity, mobility, and limited energy of the devices precludes the use of traditional service discovery protocols. This paper presents HESED, a fundamentally different service discovery protocol based on multicast query and multicast reply. Clients multicast their service query and matching servers multicast their response to all nodes. This service information is cached by all, reducing the number of queries. HESED uses only symmetric links, providing reliability for its forwarding algorithms. Simulation results show that HESED significantly outperforms a traditional on-demand service discovery algorithm.

In an ad hoc network, broadcast data communication is an unavoidable type of data transmission that provides synchronization, routing, and other messages to all the neighboring devices. Guaranteeing that all neighboring MTs receive broadcast messages is a hard task in multi-hop wireless ad hoc networks, especially when the traffic load is heavy. In this paper, we propose the sequential medium access control (SeMAC) protocol which aims at collision-free communication and reduced overhead for predictable broadcast message to sustain dynamic changes in network topology, as well as facilitate smooth operation of peer-to-peer unicast in multi-hop ad hoc networks SeMAC is intended to complement the IEEE 802.11 DCF function and is shown in this paper to greatly enhance the performance of broadcast and unicast communication of the IEEE 802.11.

The Multi-hop CDMA cellular concept has been proposed to overcome cellular drawbacks, like congestion and load imbalance. Although it is a widely accepted that multi-hopping increases cellular capacity, it has never been quantified. In this paper, the capacity increase in multi-hop CDMA cellular networks is quantified. To this end, and since CDMA networks are interference limited, we derive equations for interference in multi-hop cellular networks at base stations (BSs) and relaying mobile terminals (MTs) in the uplink. The interference formulas are used to verify that capacity can be increased, by increasing either the number of simultaneous calls or data rate. A 10% increase in the number of simultaneous calls is shown to be possible even under worst-case scenarios. This increase is achievable while keeping interference at relaying MTs below acceptable thresholds. The novelty of this paper is that it quantifies, and for the first time, interference levels at MTs and BSs as well as potential capacity enhancements.

Much of the literature have mainly investigated the seamlessness and the effects of Vertical Handoffs (VHs), in addition to the design of policy-based Radio Resource Management (RRM) models that cater for their unique characteristics. However, VHs can be viewed as a powerful RRM tool. A service provider may force users to handoff to other networks in a coverage overlay to relieve congestion or balance the load. In this paper, we present a novel multi-class, subadaptation module for forced vertical handoffs. We discuss added arguments required by the multi-class setting, and show the flexibility of our model through its capability to meet different objectives for different service providers.

In this paper, we outline the basis of a novel cooperative module for congestion control and load balancing in WCDMA networks. Through carefully exploiting the capabilities of smart and directional antennas, we propose a controllable directional breathing where a base station's (BS) sectorized coverage is varied reactively in instances of congestion, and proactively in instances of exercising load balancing. This proposal, called Directional Cell Breathing (DCB), overcomes the drawbacks of non-sectorized and non-controllable WCDMA breathing management by optimizing the coverage level within each sector under constraints minding sectoral traffic load and interference bounds. Interference analysis is carried out to determine the eligibility of a cell to be considered in DCB if one of its adjacent cells becomes overloaded. This analysis is then used in a mathematical formulation to facilitate the optimization of the proposed scheme.

The concept of multi-hop CDMA cellular networks has been around for sometime now. It is a widely accepted assumption that using multi-hopping in cellular networks will increase the cellular capacity. This capacity increase has yet to be quantified. In this paper, this quantification is done, for the first time, for multi-hop CDMA cellular networks with power control. CDMA networks are interference limited. For this reason, interference is calculated at BSs and relaying MTs during an uplink slot, assuming power control is in use in all hops. The results for interference calculations show the possible increase in capacity, by increasing either the number of simultaneous calls or data rates. An increase of 23% in the number of supported simultaneous calls is shown to be possible even with relaying MTs being different than active MTs sending their own data. This paper derives formulas to calculate interference at BSs and MTs using power control in all hops. It also quantifies the potential capacity increase using multi-hopping with power control.

Multi-hop relaying is an important concept in tackling the inherent problems of limited capacity and coverage in cellular networks. It helps to solve the dead-spots problem and to ease congestion in hotspots. However, to obtain good performance of multi-hop relaying, an effective channel assignment scheme is needed. In this paper, we study the design goals of a good channel assignment scheme. We then propose a channel assignment scheme, called Extended Delay-Sensitive Slot Assignment (E-DSSA), which achieves all these goals. The distinctive feature of E-DSSA is the use of a novel transmission zone testing technique which allows high flexibility and precision in channel assignment in TDD W-CDMA multi-hop cellular environment. Performance evaluation shows that E-DSSA outperforms its existing counterparts in terms of data throughput with low delay for both sparse and dense networks. E-DSSA is also shown to adapt to different cell sizes achieving high throughput and call acceptance ratios.

Grid-based sensor deployment is an effective and efficient practice for provisioning wireless sensor networks. Previous work has addressed grid-based deployment of sensors in order to guarantee sensing coverage under the assumption that each device can be placed exactly at the grid vertices. However, in reality, the accuracy of device placement may be subject to various errors, which are shown to impair the sensing coverage. To overcome the negative impacts of these errors, the grid resolution and the number of devices to be deployed should be re-evaluated. In this paper, two deployment errors are identified, namely, misalignment and random errors. We derive the minimum number of sensors required by a robust grid-based sensor deployment assuming that the errors are bounded. This research shows that when designing a realistic large-scale grid-based sensor deployment, errors in device placement must be taken into account.

Multi-hop relaying is an important concept in future generation wireless networks. It can address the inherent problems of limited capacity and coverage in cellular networks. However, most multi-hop relaying architectures are designed based on a small fixed-cell-size and a dense network. In a sparse network, the throughput and call acceptance ratio degrades because distant mobile nodes cannot reach the base station to use the available capacity. In addition, a fixed-cell-size cannot adapt to the dynamic changes of traffic pattern and network topology. In this paper, we propose a novel multi-hop relaying architecture called the adaptive multi-hop cellular architecture (AMC). AMC adapts the cell size to an optimal value that maximizes throughput by taking into account the dynamic changes of network density, traffic patterns, and network topology. To the best of our knowledge, this is the first time that adaptive (or optimal) cell size is accounted for in a multi-hop cellular environment. AMC also achieves the design goals of a good multi-hop relaying architecture. Simulation results show that AMC outperforms a fixed-cell-size multi-hop cellular architecture and a single-hop case in terms of data throughput, and call acceptance ratio

AmbiTalk is a ubiquitous system based on the session initiation protocol (SIP) and Bluetooth that allows a mobile device to automatically adapt the behavior of its communication services as its user moves from one location to another. The adaptation is policy-based and occurs both pre-call and mid-call. We present the AmbiTalk architecture and discuss how it allows a device to automatically adapt its communication properties to its current environment and other devices. We demonstrate the viability of the AmbiTalk approach with the implementation of a prototype

The currently most popular medium access control (MAC) protocol, namely IEEE 802.11 distributed coordination function (DCF), is not energy efficient. In this paper we present the principles of achieving energy-efficiency in MAC protocol design for WLANs. Along these principles, we propose a novel dual-channel MAC protocol, called power-control binary-countdown-carrier-sense/request-to-send (RTS)/clear-to-send (CTS)/ensure-to-send (ETS) (PBRCE). PBRCE aims not only at energy-efficiency, but also at higher network throughput. The enhanced network performance is achieved by easing the "exposed terminal" and "hidden terminal" problems. The energy efficiency is achieved by reducing the collision rate-thus saving energy through avoiding retransmission of the same packets-and by controlling the signal transmission power and going to sleep mode to avoid the unnecessary passive listening

Summary form only given. A distributed denial of service (DDoS) attack is an explicit attempt to interrupt an online service by generating a high volume of malicious traffic. These attacks consume all available network resources, thus rendering legitimate users unable to access the services. Most existing solutions propose to detect and drop attack packets at or near the destination network where the attack packets have already traversed the network and consumed considerable bandwidth. The aggregate traffic at the destination router may consist of hundreds of thousands of flows making it hard for the router to distinguish between legitimate and malicious packets. So, collateral damage is unavoidable. In this paper, we present a source router preferential dropping (SRPD) scheme to detect possible DDoS attacks and defeat them at their sources. SRPD monitors only high-rate outgoing flows at source networks and preferentially drops the packets belonging to these flows when it senses the existence of an attack. A simulation model is constructed and a number of simulation experiments have been conducted to evaluate the performance of the proposed scheme. Simulation results show that SRPD effectively controls DDoS attacks at their sources and reduces collateral damage to a minimum level.

Summary form only given. In this paper we present a call admission control (CAC) policy for multimedia services that characterize the next generation of wireless cellular networks. The well-known CAC guard-channel policy is modified to maintain a pre-specified level of quality of services for multimedia calls. A semi-Markov decision process (SMDP) is used to represent the multi-class guard channel CAC policy with constraints on the dropping probabilities of multimedia handoff calls. The SMDP is formulated as a linear programming problem with the objectives of maximizing the system utilization and guaranteeing QoS of multiple classes of handoff calls with each class having particularly different QoS requirements. We show numerically that the multi-class guard channel policy deploying SMDP outperforms the existing upper-limit CAC policy as it improves the service quality by maximizing the bandwidth utilization while stratifying the quality of service constraint to upper bound of the handoff dropping probability.

Most of today's broadband networks, including the recently ratified IEEE 802.16 standard, employ reservation based multiple media access control. A problem pertinent to reservation MAC protocols is the division of frame slots between the contention and data transmission processes. In most of the reservation MAC protocols no specific ratio is standardized, leaving proprietary solutions address the local network environment. As both processes are equally important for maintaining efficient delay and throughput performance, a solution must consider the timely varying traffic load. For example, heterogeneity and cooperation of networks promote access technologies that can sustain waves of increasing traffic load. In this paper, we start by instituting a framework for efficient allocation of frame resources to the contention and data transmission processes in light of the delay and throughout performance. We then propose a dynamic resource allocation controller based on a Markovian optimization model, where the optimization parameters are tuned according to specific preferential criteria of service providers. Our model achieves opportunistic performance improvements, on a per frame basis, over the best-case static allocation. Through simulation, we study the merits of our proposed optimized controller with respect to the framework. We show by illustrative examples and numerical results that the controller successfully fulfills the framework objectives.