Accurate traffic prediction plays a vital role in intelligent transportation systems by enabling efficient routing, congestion mitigation, and proactive traffic control. However, forecasting is challenging due to the combined effects of dynamic road conditions, varying traffic patterns across different locations, and external influences such as weather and accidents. Traffic data often consists of several interrelated measurements—such as speed, flow and occupancy—yet many deep-learning approaches either predict only one of these variables or require a separate model for each. This limits their ability to capture joint patterns across channels. To address this, we introduce the Multi-Channel Spatio-Temporal (MCST) Mamba model, a forecasting framework built on the Mamba selective state-space architecture that natively handles multivariate inputs and simultaneously models all traffic features. The proposed MCST-Mamba model integrates adaptive spatio-temporal embeddings and separates the modeling of temporal sequences and spatial sensor interactions into two dedicated Mamba blocks, improving representation learning. Unlike prior methods that evaluate on a single channel, we assess MCST-Mamba across all traffic features at once, aligning more closely with how congestion arises in practice. Our results show that MCST-Mamba achieves strong predictive performance with a lower parameter count compared to baseline models.

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.

Vehicular edge computing (VEC) is a critical enabler of low-latency and computation-intensive vehicular applications by offloading tasks from vehicles to edge servers. However, the dynamic nature of vehicular networks introduces significant uncertainty in task characteristics and network conditions. This paper proposes a robust task offloading scheme for VEC within the open radio access network (O-RAN) architecture. The proposed scheme integrates large-timescale computational resource allocation (CRA) with small-timescale task partitioning and radio resource allocation (RRA) using O-RAN’s hierarchical control framework. Our scheme minimizes the network-wide resources under latency constraints that are subjected to demand uncertainty. We employ the cutting-set method to address the demand uncertainty in the large-timescale CRA. We obtain a closed-form solution to the optimal task partitioning problem and provide a heuristic approach for the small-timescale RRA. Simulation results show that the small-timescale RRA succeeds to counteract the demand uncertainty at the large-timescale CRA, that is, no outage occurs when demands are in the assumed uncertainty set, whereas the non-robust scheme exhibits as high as 50% outage probability. Moreover, our slotted scheme consumes about 50% less bandwidth than the conventional non-slotted robust solution.

This paper proposes an efficient path-planning scheme for unmanned aerial vehicles (UAVs) aimed at addressing coverage holes in wireless networks. Coverage holes can undermine the quality of service (QoS) of terrestrial cellular networks where they cause outage times longer than a threshold value dictated by the different application requirements. The proposed approach leverages the self-organizing map (SOM), an unsupervised machine learning technique, to design a UAV trajectory that minimizes the flight path length, while ensuring a coverage hole-free cell or guaranteeing a maximum outage time across the existing holes. The designed path also satisfies constraints on minimum and maximum UAV velocity. Simulation results show that for realistic scenarios, we can practically eliminate all coverage holes when one UAV travels over the designed path. For more extreme scenarios, we show that we need to deploy multiple UAVs to satisfy the QoS requirements where each UAV covers a partition of the holes. To achieve optimal partitioning, we utilize the ant colony algorithm.

With the upcoming 6G networks, integrating advanced access techniques, resource-allocation schemes, and causal artificial intelligence capabilities plays an important role in addressing the growing demands for efficient utilization of communication resources. Symbiotic Communication allows resources to be efficiently allocated by establishing a symbiotic relationship between primary and secondary networks. Hence, in this paper, we consider a resource-service mutualism symbiotic relationship between a secure primary network and a simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) aided device-to-device (D2D) secondary transmission. This relationship enables the secondary D2D network to share its STAR-RIS to help enhance the primary transmission secrecy rate. In exchange, the secondary D2D transmission gains access to the communication resources allocated to the primary transmission. We propose a multiobjective energy-splitting (ES) STAR-RIS optimization problem, simultaneously maximizing the primary transmission secrecy rate and the D2D sum rate. We propose a suboptimal block coordinate descent solution algorithm and evaluate the model through numerical analysis. The numerical results validate the effectiveness of the proposed model in improving the primary secrecy rate while granting a better sum rate for the secondary D2D transmission.

High-altitude platform stations (HAPS) have gained significant attention for their role in supporting intelligent transportation systems (ITS) due to their wide coverage and cost-effectiveness. Positioned at 20 km altitude, HAPS serve as aerial base stations, where terrestrial networks are unavailable or damaged due to disasters. However, wind disturbances can cause HAPS to drift, leading to coverage hole area and reduced reliability in ITS operations. To address this challenge, we propose the use of networked flying platforms (NFPs), specifically unmanned aerial vehicles (UAVs) as a backup system to dynamically restore coverage and ensure continuity and stability in ITS services during HAPS displacement. The study uses the ERA5 wind dataset for the year 2023 to analyze stratospheric wind behavior in Doha, Ottawa, and New York, confirming the global need for backup solutions during high-wind events.

Offloading computationally intensive tasks from Autonomous Driving Vehicles (ADVs) to aerial platforms offers the potential to enhance real time performance and enable more complex functionalities. This paper investigates the trade offs between communication latency and computational capacity when offloading the command prediction task, specifically the prediction of steering angle, acceleration, and braking force, to High Altitude Platform Stations (HAPS) and Unmanned Aerial Vehicles (UAVs). We develop a theoretical framework to analyze the total delay, for both communication and processing latencies, associated with offloading a Deep Cascaded Neural Network (DCNN) model. Comparing a HAPS equipped with an NVIDIA Jetson Nano Graphics Processing Units (GPUs) against a UAV utilizing a Raspberry Pi 5 Single Board Computer (SBC) equipped with 64-bit quad-core Arm Cortex-A76 Multi-Core Processor (MCP), our results demonstrate a significant performance advantage for the HAPS in terms of overall latency. However, the UAV exhibited lower communication latency due to its closer proximity to the ADV.

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.

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.

Coverage holes directly affect the quality of service (QoS) and reliability of wireless networks and should be avoided as much as possible. In this paper we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations and we propose proper UAV path planning. While most of the works in the literature define holes based on statistical sense, i.e., when the coverage probability for a point on cell is below a certain threshold, e.g., 95%, in this paper, we target applications that allow only for short time disconnections, and define a location that is not covered for a certain amount of time to be in a coverage hole. To minimize such holes, we use optimal UAV path planning based on the two families of trajectories, namely, spiral and oval curves. We show that the proposed oval curves will result in a better performance in addressing the coverage holes and can guarantee a minimum signal to noise ratio over the whole coverage area, and over a guaranteed amount of time.

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.

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.

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.

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.

To realize intelligent connection of everything and the digital twin (DT) of the physical world in 6G and beyond, new communication and sensing solutions are demanded. The potential of multiple spectrums is maximized for various applications and scenarios. In such a context, an accurate, efficient, and pervasive multi-spectrum propagation model is needed as a critical and unified baseline for testing the performance of the solutions in various scenarios. This work presents ray-tracing (RT) oriented methods for the DT presentation of radio propagation at multiple frequency bands from microwave to visible light. The material- and field-measurement-based approaches are proposed to characterize the electromagnetic properties of materials. On that basis, the propagation mechanisms are developed and validated, and the corresponding parameters are inverted. For the real-time simulation demand, RT and artificial intelligence (AI) algorithms are fused to develop a super-resolution modeling method. The experimental results indicate that the proposed method outperforms the baseline model regarding stability and accuracy. It can significantly reduce the computation time with comparable accuracy to the RT-only approach. The proposed methodologies and the in-depth discussions in this work are expected to pave the way to realize the DT of multi-spectrum propagation for evaluating 6G and beyond technologies.

We propose a fixed-wing unmanned aerial vehicles (UAV)-based millimeter wave (mmWave) backhaul architecture that is offered as a cost effective and easy to deploy solution, to connect a disaster or remote area to the nearest core network. First, we fully characterize the single relay fixed-wing UAV-based communication system by taking into account the effects of realistic physical parameters, such as the UAV’s circular path, critical points of the flight path, heights and positions of obstacles, flight altitude, tracking error, the severity of UAV’s vibrations, the real 3D antenna pattern, mmWave atmospheric channel loss, temperature and air pressure. Second, we derive the distribution of the signal-to-noise ratio (SNR) metric, which is based on the sum of a series of Dirac delta functions. Using the SNR distribution, we derive analytical expressions for the outage probability and the ergodic capacity of the considered system as a function of all system parameters. To provide an acceptable quality of service for longer link lengths, we extend the analytical expressions to a multi-relay system. The accuracy of the analytical expressions are verified by Monte-Carlo simulations. Finally, by providing sufficient simulation results, we investigate the effects of key channel parameters such as antenna pattern gain and flight path on the performance of the considered system; and we carefully analyze the relationships between those parameters in order to maximize the average channel capacity.

Blockchain emerges as a potential solution for enabling distributed management and accountability of wireless Internet of Thing (IoT) networks. In addition, blockchain offers increased IoT security, as it involves every single node around the network to verify and approve new transactions. On the other hand, IEEE 802.11ax, a recently concluded WiFi standard aimed at achieving high network efficiency, plays a key role for solving spectrum sharing and cross-technology coexistence among WiFi and 6G cellular users, one of the key challenges faced by next-generation wireless systems. In this paper, we leverage the power of blockchain and 802.11ax technologies to propose a medium access control (MAC) protocol design for future IoT wireless local area networks (WLANs), also referred to as wireless IoT-Blockchain networks setup. Our simulation results show that the proposed protocol design enhances transmission latency and achievable network throughput.

With the emergence of three new paradigms, namely the Internet of Things (IoT), cloud/edge computing and mobile social networks; Mobile Crowd Sensing (MCS) has emerged as a potential approach for data collecting in numerous applications, such as traffic management, infotainment, disaster management or public safety. MCS mechanisms are receiving a lot of attention, both from research and development areas, showing their impact and benefit. But their optimization is still under development, mainly due to the large number of involved parameters. A major field within MCS relates to crowd management for emergency situations, where the management and optimization mechanisms become crucial to local authorities. To tackle this problem, in this work, we propose an MCS hybrid worker selection scheme that operated various modes depending on the delay-cost requirements. Our scheme exploits the user behavior to achieve an optimal bi-objective for any delay-cost requirement. We use simulations to evaluate the performance of our proposal, and we show the optimal and different sub-optimal solutions that can match the delay-cost requirements.

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.

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.

Utilizing the terahertz (THz) band in wireless communication systems has been usually motivated by the unlimited data rates afforded. However, the small coverage area is a main limitation of adopting THz band in several applications and scenarios. Relaying concept has been widely brought up in the literature to extend the coverage area of different wireless systems. It implies placing a relay (or multiple relays) at the cell edge, which will act as an intermediate point to assist in delivering data to/from the central base station. In this paper, the relaying in THz-based transmission system is considered. Specifically, the outage probability is analyzed at a set of mobile users being served by a central base station (BS) and by the aid of distributed relays. The impact of all the involved system parameters, including the users’ mobility range, the outage threshold, the number of relays and the relay-BS distance, are thoroughly analyzed and discussed. Derived mathematical formulas of the outage probability are presented.

Non-Orthogonal Multiple Access (NOMA) has been widely considered as an efficient multiple access scheme for future wireless networks. This is due to the promising performance of NOMA in terms of the larger number of served users and better error performance as compared to traditional multiple access schemes. Therefore, NOMA has received significant research efforts in analyzing its performance under different scenarios and assumptions. In this paper, we analyze the performance of a downlink NOMA scheme considering mobile users, in order to characterize the impact of mobility on the system performance. Specifically, the average bit error rate at mobile users is derived in a closed form expression. The users’ mobility is considered to follow the well-known random waypoint mobility model. Mathematical formulas have been verified using Monte Carlo simulations and compared to different scenarios.

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.

Subcarrier Index Modulation (SIM) has recently received a significant research efforts analyzing its different performance aspects. In this paper, performance analysis of SIM over TeraHertz (THz) frequency band is conducted considering mobile users. The mobility model adopted is Random WayPoint (RWP) model with different numbers of mobility dimensions. Moreover, different resource allocation schemes are considered, including fixed, random and distance-aware resource allocation schemes. Closed form expression for the average bit error rate are derived for THz-based SIM considering mobile users and all considered resource allocation schemes. Simulation results with the molecular absorption effect on THz-based system validate the accuracy of the derived mathematical formulas.

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.

Blockchain technology has attracted the attention of many researchers in several different application fields. The use of blockchain in Internet of Things (IoT) and Wireless Sensors Networks (WSN) has been analyzed and considered as a promising solution to many of the IoT limitations. Communication is a very basic essence of the blockchain network and must be carefully planned while integrating with IoT, where an extremely large number of devices are interconnected and could have a serious impact on security and performance of the network. In this work, blockchain nodes are assumed to use wireless channels to communicate among themselves and other elements of the IoT setup. These communications can be in unicast and broadcast manner, where transmission latency and throughput are significant metrics that might jeopardize the overall system. This paper will propose a Medium Access Control (MAC) mechanism for wireless IoT-Blockchain system, while addressing these performance metrics. The proposed MAC protocol is based on the widely used IEEE 802.11ax MAC protocol, Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) basic access mechanism. The model is then simulated within NS-3 to observe the delay and throughput over several different setups.

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.

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.

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.

We propose a blockchain-based, distributed protocol for enabling the deployment of Internet-of-Things (IoT) networks on-demand on top of IoT devices. Specifically, the proposed protocol leverages blockchain technology to: 1) enable distributed and secure authentication, registration, and management of participatory IoT devices; 2) provide fast discovery of IoT resources and scalable and secure instantiation of IoT networks-on-demand; and 3) manage payment operations and ensure reliable fund transfers among the network entities. The proposed protocol relies on a peer-to-peer network communication infrastructure to allow communications among the IoT devices in a distributed manner and uses a self-recovery/self-healing mechanism to ensure robustness against device failure and maliciousness. The protocol also introduces and uses a reputation system to monitor registered devices to keep track of their service delivery quality so that their service delivery reputations could be leveraged for future device selection and mapping. We implemented and evaluated the proposed protocol intensively using simulations and showed that it scales well with network parameters, is resilient to faulty devices, and is robust to 51% attack.

This paper proposes a distributed resource sharing protocol for enabling dynamic deployment of IoT networks on-demand in smart cities. The proposed protocol leverages Blockchain technology to: (i) enable distributed and secure management of IoT devices; (ii) provide fast discovery of IoT resources and scalable on-demand networks; and (iii) ensure reliable fund transfers for service payment among the network entities. The protocol relies on a peer-to-peer network infrastructure to allow communication among the IoT devices in a distributed manner, and uses a self-recovery/self- healing mechanism to ensure robustness against device failure and maliciousness. The protocol also introduces and uses a reputation system to monitor and keep track of services delivered by registered devices for quality of service delivery assurance. We implemented and evaluated the proposed protocol intensively using simulations to assess its effectiveness in terms of scalability to network sizes and robustness to device failures.

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.

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.

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.

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.

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.

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.

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.

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.

Air pollution is a major issue contributing to global warming that threaten the quality of life on Earth. Numerous research disciplines are combining their efforts to combat air pollution by developing new methods to monitor and control pollution. For this to happen, researchers need to have instant access to new data. In this paper, we have developed a Semi-Autonomous Unmanned Aerial Vehicle (UAV) loaded with sensors to measure different quantities indicating air pollution, in particular: temperature, humidity, dust, carbon monoxide, carbon dioxide, and ozone. The purpose of this UAV is to automatically patrol high altitudes to obtain sensor readings, and transmit raw data to a centralized server via mobile network for visualization and storage. Actual measurements and data collection is carried out in Qatar. This combination of the UAVs' mobility, remote sensing, and networking facilities allows concerned parties such as researchers, smart city administrators and crowd managers, to view and visualize relevant data with significant ease via a web interface, or an android app.

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.

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.

There have recently been research efforts that leverage compressive sampling to enable wideband spectrum sensing recovery at sub-Nyquist rates. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar primary user traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different bands could present different occupancy patterns. In fact, applications of similar types are often assigned spectrum bands within the same block, dictating that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum and exploit its inherent block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ℓ1-minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches, while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a smaller number of sensing measurements when compared to the state-of-the-art approaches.

The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of dense Heterogeneous Networks (HetNets). At the same time, the content providers' rise has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of HetNet investments. To this end, we consider a HetNet deployment where MNOs offer a multitude of services with diverse pricing. We propose a heuristic, joint QoE-aware resource allocation and dynamic pricing algorithm with overall user satisfaction constraints to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a state of the art algorithm. Moreover, we demonstrate the benefits of our dynamic pricing scheme and its applicability on other resource allocation algorithms.

The rise of Over The Top (OTT) content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of 5G networks in 2020. At the same time, the rise of OTT providers has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of a 5G investment. To this end, we consider a 5G Heterogeneous Network (HetNet) deployment where MNOs use a QoE-based charging scheme. We propose a heuristic, QoE-aware user association algorithm to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a baseline SINR-based scheme.

In long term evolution advanced (LTE-A) networks, the mobile devices can concurrently participate in cooperative outband device-to-device (D2D) data exchange by virtue of user- or network-related parameters (e.g., interest in the same content and cooperative transmissions, respectively). In these scenarios, two major problems arise: 1) the coexistence of multiple devices creates channel access issues, demanding effective medium access control (MAC) schemes, and 2) cellular network factors (i.e., scheduling policy and channel conditions) affect the D2D communication, as the circulating information in D2D links is mainly of cellular network origination, stressing the need for cross network approaches. In this context, the contribution of this paper is threefold. First, exploiting idle devices as relays and the benefits of network coding (NC) in bidirectional communications, we propose an adaptive cooperative NC-based MAC (ACNC-MAC) protocol for the D2D data exchange. Then, we devise a cross-network model that captures the impact of cellular network characteristics on D2D communication. Finally, we evaluate the performance of the ACNC-MAC in terms of throughput, energy efficiency, and battery consumption. Our results show that the LTE-A parameters and the relays' participation significantly affect the D2D throughput, while the D2D performance deteriorates with the increase of cell congestion.

Wireless network virtualization enables efficient network sharing in Long Term Evolution Advanced (LTE-A) networks. For the accommodation of ever-increasing user demands, Mobile Virtual Network Operators (MVNOs) can lease and operate virtual resources in network infrastructure that belongs to Mobile Network Operators (MNOs). The coexistence of multiple MVNOs that serve users with different Quality of Service (QoS) requirements and spatial distribution in an LTE-A cell further complicates the arising resource allocation problem. Moreover, the MVNOs aim to satisfy the QoS demands for the maximum possible number of users with the minimum possible cost. This multifaceted context renders centralized optimization approaches for virtual resource allocation in shared LTE-A networks unsuitable. As an alternative, the framework of matching theory, thanks to its distributed nature, can achieve a proper allocation through a stable matching between resources and MVNOs. In this context, we introduce a matching-theoretic formulation for the virtual resource allocation problem and propose a distributed algorithm that properly matches the MVNOs with the available resources. Our simulation results show that the proposed algorithm reaches a stable matching that satisfies the QoS demands for more users in comparison with other approaches.

Device-to-Device (D2D) communication allows the direct connection of mobile devices in a cellular network. In D2D networking, cooperative communication is inherent due to the proximity of the devices that are able to overhear and forward information. Particularly, adjacent devices can act as relays and assist the communication of other devices. Network Coding (NC) can further increase the cooperation gains, since a number of packets can be encoded and transmitted together. However, the contention among multiple relays causes channel access issues that must be regulated by effective Medium Access Control (MAC) protocols. In this context, we propose an Adaptive Cooperative Network Coding-based MAC (ACNC-MAC) protocol that utilizes cooperative relaying and exploits NC opportunities in a D2D topology. Both analytical and simulation results show that the proposed protocol is advantageous in terms of energy efficiency without sacrificing the Quality of Service (QoS).

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.

Energy is an important resource in wireless communications systems that has to be optimized in order to guarantee low interference in the network as well as efficient use of the available resources. One of the prominent options within the Long Term Evolution (LTE) standard is the Time Division Duplexing (TDD) option where both Uplink (UL) and Downlink (DL) share the same bandwidth and they are allocated on different times. One of the major problems in the practical implementation of TDD systems is that adjacent cells may have different loads for their UL and DL, so that the resources are allocated in a different portion between UL-DL. Such an unsynchronization among cells generates interference that downgrades the system performance. This paper calculates the optimum amount of energy that should be allocated to each entity in the system to mitigate the interference effect and to guarantee minimum Quality of Service (QoS) satisfaction at all receivers. Closed form expressions and computer simulations show the advantages of the proposed energy allocation scheme.

In this paper we evaluate the energy efficiency of MIMO technologies introduced in the emerging IEEE 802.11n standard for infrastructure based WLANs. The main focus is on exploiting multiuser capabilities of MIMO technologies, while studying the proper design of multiuser MAC schemes to achieve an energy efficient solution. Three different multiuser MAC schemes are presented and analyzed. Analytical models for energy efficiency are mathematically developed and verified with link-level computer simulations. The results show the feasibility of achieving higher energy efficiency with the proper adjustment of the system parameters, and without significant degradation of throughput performance.

Long Term Evolution (LTE) supports closed-loop MIMO techniques to improve its performance, but in order to exploit multiuser MIMO channel capabilities, the design of an efficient MAC scheme that supports Multiuser MIMO is still an open issue in the literature. This paper proposes a novel energy efficient MAC scheme for LTE, which aims to achieve simultaneous downlink transmissions to multiple users, through the deployment of a low complexity beamforming technique at the physical layer. Our proposed scheme takes advantage from the multiuser gain of the MIMO channel and the multiplexing gain of the Multibeam Opportunistic Beamforming (MOB) technique, not only to improve the system throughput but also to provide an energy efficient wireless network. We show that our proposed scheme can provide good energy saving performance at the eNB, where the mathematical expression for performance evaluation in terms of the saved energy is also presented.

Vehicle to vehicle (V2V) communications to enhance the downlink and uplink connectivity in vehicular networks are studied. Effective rate formulations for the uplink and downlink directions are presented and analyzed, while considering IEEE 802.11p for short range communications between vehicles, and the Long Term Evolution (LTE) for communications between the vehicles and base stations over long range cellular links. Simulation results show the importance of V2V communications, and describe the performance tradeoffs between the transmit power, number of cooperating vehicles, and LTE resource allocation.

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.

Multiple Input Multiple Output (MIMO) adds a new dimension, the spatial one, to be optimized in Cognitive Radio (CR) by offering service simultaneously to more than one user. In this paper, statistical optimization techniques are applied to assess the performance of the Quality of Experience (QoE) in CR systems, where each user has different demands. A Multiuser scenario is considered where the transmitter can accomplish either a random or an opportunistic scheduling approach. Closed form expressions are derived for different scenarios and obtained for three QoE indicators in the system. The performance of primary and secondary users in such scenarios are mathematically formulated and compared their results to computer simulations.

Over the last years, cooperative communications have been studied from both fundamental and practical points of view. However, most of the existing studies of cooperative communications rely on math or computer simulation. In this paper, we describe the implementation of a cooperative communication strategy for wireless networks in a testbed based on Click Modular Router. We describe the methodology we have followed to reprogram the drivers controlling the data layer functions of off-the-shelf commercial Wireless Network Interface Cards (NICs). More precisely, we have implemented a Cooperative Automatic Retransmission (C-ARQ) scheme to show that the performance of wireless communications can be boosted by using an intermediate relay when the wireless channel between the source and the destination is in bad conditions. The results presented in this paper are promising, as they show that previous theory and simulation results presented in the literature can become true in a real implementation.

Multiple Input Multiple Output (MIMO) adds a new dimension, the spatial one, to be optimized in Cognitive Radio (CR) by delivering service simultaneously to more than one user. In this paper, statistical optimization techniques are carried out to assess the Quality of Experience (QoE) performance where each type of users has different demands. The Multiuser scenario is considered where the transmitter can accomplish an Opportunistic Scheduling approach. Closed form expressions are derived for different scenarios and obtained for the QoE indicators in the system regarding the minimum guaranteed throughput and maximum allowed scheduling delay. The primary users and secondary users performance within such scenarios are mathematically formulated and compared their results to simulations.

Multiple Input Multiple Output (MIMO) adds a new dimension to be exploited in Cognitive Radio (CR) by simultaneously serving several users. The spatial domain that is added through MIMO is another system resource that has to be optimized, and shared when possible. In the current paper, we present a spatial sharing that is carried out through Zero Forcing beamforming (ZFB). The power consumption in such a scenario is mathematically formulated and compared to single user case, to check the feasibility of employing spatial cognition from the power's perspective. Interesting conclusions are obtained about the utility of spatial cognition thanks to the derived closed form expressions for the data rate and consumed power. To provide a comprehensive measure of the expediency of spatial cognitive scenario, a joint power and rate metric is also proposed and analyzed.

The emerging 802.11n standard establishes the integration of MIMO technology in WLANs with the goal of achieving high data rates. However there are still many open issues regarding MAC protocol design for MIMO based systems, especially in order to exploit the multiuser capabilities of the MIMO channel. In this paper we propose a novel MAC scheme that considers an opportunistic channel-aware scheduling policy to achieve simultaneous downlink transmissions to multiple users. In an effort to offer a complete and practicable proposal, our MAC scheme is combined with a low-complexity beamforming technique at the Physical layer in a system where multiple antennas are employed at least at the transmitter side (Access Point). A mathematical model for the throughput performance of the proposed scheme is presented and validated through link-layer simulation results.

The recently approved 802.11n standard establishes the integration of MIMO technology in WLANs with the goal of achieving high data rates. However, it does not exploit the multiuser capabilities of the MIMO channel. In this paper we present a multiuser downlink transmission scheme that combines low-complexity beamforming at the Physical layer with an opportunistic channel-aware MAC scheduling policy. A mathematical model for the throughput calculation of the proposed scheme is presented and validated through link-layer simulation results.

Multiple Input Multiple Output (MIMO) opens new challenges to be exploited in Cognitive Radio (CR) by simultaneously serving several users. The spatial domain that is added through MIMO is another system resource that has to be optimized, and shared when possible. In this paper, we present a spatial sharing approach that is carried out through Zero Forcing beamforming (ZFB). Statistical assessment of the primary user's (high priority user) performance is carried out through two indicators: Quality of Service (QoS) and Probability of Satisfaction (PoS). Closed form expressions are derived for the primary user's behaviour in the developed scenario and compared its performance to extensive simulations. Interesting results are obtained regarding the number of simultaneous serviced users that can share the spatial domain.

The emerging 802.11n standard establishes the integration of MIMO technology in WLANs with the goal of achieving high data rates. However there are still many open issues regarding MAC protocol design for MIMO based systems, especially in order to exploit the multiuser capabilities of the MIMO channel. In this paper we investigate practical solutions to implement multiuser downlink transmission in infrastructure 802.11n based WLANs. A low-complexity beamforming transmission technique is employed at the physical layer and four MAC schemes that vary in complexity and efficiency are presented and evaluated through computer simulations.

A cross-layer based dynamically tuned queue length scheduler is presented in this paper, for the Downlink of multiuser and multiantenna WLAN systems with heterogeneous traffic requirements. An opportunistic scheduling algorithm is applied, while real time classes are prioritized. A trade-off between the throughput maximization of the system and the guarantee of the QoS requirements is obtained. Therefore the length of the queue is dynamically tuned to select the appropriate conditions based on the operator requirements.

Scheduling in a downlink channel based on partial channel state information at the transmitter, is carried out through a multibeam opportunistic beamforming technique. Within a more practical perspective, this paper first presents a transmission strategy where a minimum rate per user is required, which in a wireless fading scenario, can be only guaranteed under a certain system outage constraint. An automatic repeat request (ARQ) is proposed to combat the system outage, so that it increases the system reliability, which stands as a possible Quality of Service (QoS) indicator for the system behaviour. Closed form expressions for the ARQ gain are obtained together with its maximum scheduling delay performance. The derived expressions are then tested via simulations in several transmission scenarios.

Scheduling in a Broadcast (BC) channel based on partial Channel State Information at the Transmitter (CSIT) is carried out in an opportunistic way, where several orthogonal beams are randomly generated at the Base Station transmitter to simultaneously deliver several users with their intended data. The paper presents a power allocation over the transmitting beams, where a minimum rate per user restriction is required for each scheduled user, standing as a potential Quality of Service (QoS) indicator for the system behaviour. However, in practical wireless scenarios the CSIT is imperfect due to non-accurate estimation, so that robust schemes are required to meet the system demands. Based on the allowed system outage in the QoS achievement, different robust power allocation schemes are proposed, which are efficiently solved through convex optimization tools. The presented strategies are later compared via simulations for the different scenarios and the system specifications.

Scheduling in a broadcast channel based on partial channel-state-information at the transmitter is carried out in an opportunistic way. In this paper, the number of generated beams is larger than the number of available base station antennas, where the beams are obtained through Grassmannian line packing to guarantee the largest orthogonality among them. Approximate expressions and bounds are derived for the sum capacity of the proposed approach together with its scaling law. For practical systems, we show that this scheme can increase the number of serviced users under a minimum-rate-per-user requirement.

Spatial scheduling in a heterogeneous WiMax Downlink channel, based on partial channel state information at the transmitter (CSIT), is carried out through a multibeam opportunistic beamforming technique. In contrast to existing literature, this paper considers a more practical perspective and presents a strategy where a minimum rate per user is required, which can only be guaranteed under a certain system outage restriction. Within this scenario, the paper then formulates the minimum rate and maximum/mean delay expressions in closed form expressions. Furthermore, in opportunistic schemes a high number of users is required to improve the sum rate system performance, but for practical considerations, a large number of available users induces a large service delay. A dynamic call admission control (CAC) is then proposed to regulate the number of users, and to guarantee the service to all the users in the cell under rate and delay requirements. The derived expressions are then tested via simulations in several transmission scenarios, where users running delay-constrained (DC) and delay-tolerant (DT) applications coexist.

Scheduling in a broadcast (BC) channel based on partial channel state information at the transmitter (CSIT) is carried out in an opportunistic way, where several orthogonal beams are randomly generated at the base station transmitter to simultaneously deliver several users with their intended data. Within a more practical perspective of the opportunistic systems, this paper first presents a transmission scheme where a minimum rate per user restriction is required for each scheduled user. This minimum rate is demanded by each user to properly decode and manage its received signal, which stands as a possible Quality of Service (QoS) indicator for the system behaviour. Then, the paper considers an imperfect CSIT situation where robust schemes are required to meet the QoS restrictions. Two robust opportunistic transmission philosophies are presented through a power allocation over the transmitting beams, and they are efficiently solved via convex optimization tools.

Scheduling in a broadcast (BC) channel based on partial channel state information at the transmitter (CSIT) is carried out through a multibeam opportunistic beamforming technique. Within a more practical perspective, this paper first presents a transmission strategy where a minimum rate per user is required, which in a wireless fading scenario, can only be guaranteed under a certain system outage constraint. This minimum rate is demanded within a given time interval to satisfy maximum delay restrictions for the user application. Closed form expressions for maximum scheduling delay and maximum jitter are obtained, standing as possible quality of service (QoS) indicators for the system behaviour. The derived expressions are then tested via simulations in several transmission scenarios.