Vehicular Cloud Networks (VCNs) represent a promising paradigm for delivering dynamic, low-latency services by leveraging the resources of connected vehicles. However, the highly mobile and resource-constrained nature of VCNs poses significant challenges for reliable service allocation, particularly when considering user-specific preferences. In this paper, we propose a user-preference-based service allocation mechanism that optimizes three key Service Level Agreement (SLA) parameters: delay, availability, and price. Our approach utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate Pareto-optimal solutions, allowing users to select services that best match their preferences. We evaluate the proposed approach through simulation, comparing its performance against alternative methods in terms of execution time, SLA adherence rate, and service stability. The results demonstrate that our approach not only achieves higher SLA adherence but also maintains efficiency under various vehicular conditions.

Mobile Ad-hoc Networks face challenges in achieving efficient multicasting due to dynamic topology changes and unreliable links. Existing multicast approaches either suffer from low packet delivery ratio or high overhead. These approaches rely on simple metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it remains available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor packet delivery ratio, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, a Q-Learning Adaptive Multicast Ad-hoc On-Demand Distance Vector routing protocol is proposed. It is an adaptive and bandwidth-efficient solution that utilizes link reliability as a routing metric instead of hop count, aiming to build a more stable multicast tree. By leveraging Q-learning principles, the proposed protocol continuously updates path costs to detect any deterioration. Additionally, the protocol dynamically explores the network using periodic group hello messages, enabling the identification of alternative paths and proactively switches to them if path costs deteriorate. Simulations conducted in Network Simulator 3 demonstrate the superiority of the proposed protocol over the traditional Multicast Ad-hoc On-Demand Distance Vector protocol. Furthermore, it outperforms a modified version, called Multicast Ad-hoc On-Demand Distance Vector-Route Reliability, that uses link reliability as a metric, demonstrating enhanced packet delivery ratio and reduced multicast-related overhead.

With the increasing demands placed on geographically distributed Data Centers (DCs), recent studies have focused on optimizing performance from the perspective of both cloud providers and customers. These studies address a variety of goals, such as minimizing transmission time, reducing resource usage, and optimizing network costs. However, many existing models for workload transfers operate using a uniform time-slot approach, which limits their flexibility in handling variable data transfer requests with different deadline requirements. This lack of adaptability can negatively impact the quality of service for users. Additionally, these models often overlook the potential benefits of incorporating multiple data sources, which can lead to sub-optimal transmission times. To overcome these limitations, this paper introduces TCOMS, a Time-slot-aware, COst-effective, and Multi-Source-aware method for file transfers tailored specifically for geo-distributed DCs, leveraging a multi-source and dynamic time-slot strategy to accelerate transmission and enhance service quality. The proposed model identifies the optimal sources, paths, and time slot lengths required to efficiently transmit workloads to their destinations while minimizing costs. Initially, we introduced a Mixed Integer NonLinear Programming (MINLP) model and subsequently linearized it within our framework. Given the NP-hard nature of the proposed model, its applicability is limited in large-scale environments. To address this issue, we developed an efficient heuristic algorithm that can derive near-optimal solutions in polynomial time. The simulation results demonstrate the effectiveness of the proposed TCOMS model and the heuristic algorithm in terms of the reduction in cost and transmission time for file transfers between geographically distributed DCs.

Vehicular Ad-hoc Networks (VANETs) and in-vehicle networks offer complementary perspectives on Intelligent Transportation Systems (ITS), enabling communication between vehicles and within individual vehicles, respectively. While VANETs focus on vehicle-to-vehicle communication, the growing demand for dynamic resource sharing and data processing across a fleet of vehicles highlights the need for Vehicular Cloud Networks (VCNs). VCNs, despite their lack of fixed infrastructure and the continuous mobility of vehicles, provide a promising solution for improving resource management and data sharing, making them critical for achieving efficient Service Level Agreements (SLAs) in infrastructure-less environments. This paper addresses these challenges by employing a hierarchical clustering technique and proposing a novel mathematical formulation for resource provisioning in infrastructure-less vehicular clouds. The formulation considers diverse criteria, including provider and requester mobility, data volume, and service delay tolerance, to ensure SLA adherence. A customized genetic algorithm is used to solve the maximization problem, incorporating a grouping mechanism for efficient problem solving. Simulations using the NS2 network simulator and the IBM CPLEX optimization tool validate the feasibility of the proposed approach and demonstrate its superior performance compared to the other methods.

Virtual resource allocation, widely referred to as Virtual Network Embedding (VNE), has received increasing attention from both industry and academia. In fact, VNE has ubiquitously become a technological leap in Internet of Vehicles (IoV) which is a fundamental framework for the anticipated success of future intelligent transportation. The general VNE problem has been shown to be NP-hard [1], [2] and finding an optimal VNE in a dynamic environment like IoV is even more challenging. In fact, research on VNE in dynamic environments, where connected moving vehicles act as substrate nodes to provision requested services, is still in its nascent stages. As a result, this paper proposes a Genetic Algorithm (GA) assisted by a novel fitness function considering critical network topological attributes and resource constraints for dealing with the online VNE problem considering vehicle mobility. Simulation results based on the Random Waypoint (RWP) mobility model indicate that the proposed algorithm achieves better performance compared to several existing VNE algorithms.

With the continuous evolution of networking technologies, multi-modal services that involve video, audio, and haptic contents are expected to become the dominant multimedia service in the near future. Edge caching is a key technology that can significantly reduce network load and content transmission latency, which is critical for the delivery of multi-modal contents. However, existing caching approaches only rely on a limited number of factors, e.g., popularity, to evaluate their importance for caching, which is inefficient for caching multi-modal contents, especially in dynamic network environments. To overcome this issue, we propose a content importance-based caching scheme which consists of a content importance evaluation model and a caching model. By leveraging dueling double deep Q networks (D3QN) model, the content importance evaluation model can adaptively evaluate contents' importance in dynamic networks. Based on the evaluated contents' importance, the caching model can easily cache and evict proper contents to improve caching efficiency. The simulation results show that the proposed content importance-based caching scheme outperforms existing caching schemes in terms of caching hit ratio (at least 15% higher), reduced network load (up to 22% reduction), average number of hops (up to 27% lower), and unsatisfied requests ratio (more than 47% reduction).

Data centers have undergone significant expansions in recent years, as cloud service providers seek to improve the quality of service and reduce operational costs. Cloud providers are investing heavily in inter-data center wide-area networks, which help to transport traffic between geographically distributed data centers. However, efficient workload management in complex large-scale networks with a dynamic environment is challenging. In this regard, researchers have developed various solutions to address different challenges for data transfer in inter-data center networks. In this paper, we present a comprehensive review of recent strategies and optimization schemes proposed in the literature to optimize data transfer in geographically distributed data centers. This review paper examines the challenges of data delivery and classifies recent existing solutions for addressing the issues based on communication patterns, objectives, proposed communication frameworks, and evaluation methods. In this study, we provide valuable insights into the current challenges and identify several promising research directions that require significant research endeavors in the future. The findings of this study are useful for researchers and practitioners interested in optimizing data transfer in inter-data center networks.

This paper proposes a reliable multicast routing protocol based on Q-learning for wireless ad-hoc networks. The proposed protocol has two goals: 1) enhance the reliability of data delivery and 2) reduce the overhead caused by multicast routing. To achieve these goals, the protocol uses link reliability as a routing metric. The protocol chooses the most reliable path for data transmission based on its Q-value. In addition, it continuously updates the Q-value of active paths and proactively switches to another path if the current path becomes less reliable. To evaluate the performance of the proposed protocol, simulations were conducted using Network Simulator 3 (NS-3). The performance of the proposed protocol was compared with the Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol. The simulation results show that the proposed protocol effectively enhances reliability as it outperforms the MAODV routing protocol in terms of Packet Delivery Ratio (PDR). Moreover, it reduces the control overhead caused by multicast routing.

The ever-growing number of connected User Equipment (UE), e.g., Internet of Things (IoT) devices, Connected Autonomous Vehicles (CAVs), has driven the evolution of Software-Defined Networks (SDN) and Fifth-Generation (5G) Networks to push the computing resources closer to the UE. Towards that end, Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) are promising solutions to facilitate the deployment of the required user’s service instances within an approximately one-hop communication range. However, discovering and assigning such service instances to the UE to maintain a high service availability is still an open challenge in the 3rd Generation Partnership Project (3GPP) standards due to the UE mobility and Telco network heterogeneity. This paper proposes an SDN-based dynamic service discovery and assignment framework for a distributed MEC infrastructure. The proposed framework considers various decision parameters such as UE’s location, the service instance’s demand (i.e., resource utilization), the network link status, and the service instance performance requirements (i.e., service profile) to offer a generic solution for discovering and assigning the service instances to the UE. The framework implementation results show an enhancement of the packet delivery ratio and a lower users’ perceived latency.

This paper proposes a heuristic model for the real-time task assignment problem in fog/cloud computing networks. Its purpose is to maximize profit by assigning tasks (either to fog nodes or the cloud) while simultaneously considering green computing practices (i.e., changing the states of fog nodes). Compared to the optimal model introduced in [1], the proposed real-time task assignment heuristic performs extremely well considering the fact that it only has access to partial information as opposed to the full historical data for the optimal model. The relative profit returned by the heuristic is consistently within the 79 to 89% range with a processing time that is significantly improved thus, enabling service providers to use such a model in larger network environments and in real-time.

Autonomous driving is a core application that greatly benefits from Internet of Vehicles (IoV). The calculation of the precise positions of Connected Autonomous Vehicles (CAVs) is mainly done using a Deep Neural Network (DNN) which requires significant computing power. Therefore, reducing the computational overhead and improving the efficiency are urgent problems to be solved. In this paper, we first propose a CAV cooperative learning architecture based on blockchain to improve the positioning accuracy of vehicles. Then, we introduce an error precision sharing model between CAVs. The proposed framework enables CAVs to train vehicle positioning accuracy models locally and exchange them via a blockchain network. Such a distributed training architecture further reduces the computing power required. Extensive simulation results show that the proposed scheme can also significantly improve the accuracy of the trajectory error compared to existing approaches.

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.

The rapid evolution of the Internet of Things (IoT) and the development of cloud computing have endorsed a new computing paradigm called edge computing, which brings the computing resources to the edge of the network. Due to low computing power and small data storage at the edge nodes, the task must be assigned to the computing nodes, where their associated data is available, to reduce overheads caused by data transmissions in the network. The proposed scheme named task priority-based data-prefetching scheduler (TPDS) tries to improve the data locality through available cached and prefetching data for offloading tasks to the edge computing nodes. The proposed TPDS prioritizes the tasks in the queue based on the available cached data in the edge computing nodes. Consequently, it increases the utilization of cached data and reduces the overhead caused by data eviction. The simulation results show that the proposed TPDS can be effective in terms of task scheduling and data locality.

This paper proposes an exact model for the planning and design problem of fog networks. More precisely, a mathematical model is proposed to simultaneously determine the optimal location, the capacity and the number of fog node(s) as well as the interconnection between the installed fog nodes and the cloud. The goal of the model is to minimize the delay in the network and the amount of traffic sent to the cloud data center. To address this multi-objective optimization problem, three optimization techniques are used: the weighted sum, the hierarchical and the trade-off methods. The weighted sum method aggregates all the lone objective functions into a single objective by applying a weighted vector. The hierarchical method takes a sequential approach by tightly constraining the more important objective function. The trade-off method solves a single objective function and translates all other objective functions into constraints. These methods are then compared in terms of average delay, amount of traffic sent to the cloud and amount of CPU time required to find optimal solution(s). Since we are dealing with a multi-objective optimization problem and that multiple optimal solutions can be found, the fuzzy-based mechanism and the hypervolume indicator have been used. Computational results show that as the problem size increases, the delay and the traffic also increase in a linear form; whereas, the solution time increases in non-polynomial time. The weighted sum method was able to achieve the best trade-off results for the delay and the traffic, whereas the hierarchical method was able to return minimum delay but with worse traffic going to the cloud. As the model considers realistic edge device traffic parameters, constraints, and various topology aspects, it can be helpful for the planning and deployment of fog networks and how they operate within a cloud infrastructure.

Boolean AND queries (BAQ) are one of the most important types of queries used in text searching. In this paper, a graph-based indexing technique is proposed to improve the search latency of BAQ. It shows how a graph structure represented using a hash table can reduce the number of intersections needed for the execution of BAQ. The performance of the proposed technique is compared with one of the most widely used index structures for textual documents called Inverted Index. A detailed performance analysis is performed through prototyping and measurement on a system subjected to a synthetic workload. To get further performance insights, the proposed graph-based indexing technique is also compared with an enterprise-level search engine called Elasticsearch which uses Inverted Index at its core. The analysis shows that the graph-based indexing technique can reduce the latency for executing BAQ significantly in comparison to the other techniques.

This paper introduces a new graph-based indexing (GBI) technique for big data systems. It uses a directed graph structure that effectively captures the simultaneous occurrence of multiple keywords in the same document. The objective is to use the relationship between the search keywords captured in the graph structure to effectively retrieve all results of Boolean AND queries at once. The performance of the proposed technique is compared with the conventional inverted index-based technique. This paper highlights that, irrespective of the intersection algorithm used to evaluate Boolean AND queries, GBI always returns Boolean AND search results faster than the inverted index. This is due to the fact that GBI always performs a smaller number of intersection operations and avoids intersection if search keywords do not have a common document. A preliminary performance analysis is performed through prototyping and measurement on a system subjected to a synthetic workload. The analysis shows that GBI improves search latency when executing Boolean AND queries by an average of 69% to 99.9% in comparison to the inverted index.

The considerable increase of multimedia services, such as video-on-demand (VoD) services, is a significant contributor to the total Internet traffic. Software-defined networking (SDN) and information-centric networking (ICN) are two promising technologies that can be combined to facilitate video delivery and to reduce network delays. In this paper, we first formulate the caching decision problem as a 0-1 integer linear programming (ILP) problem. Second, in contrast to existing approaches that solve the formulated ILP problem by assuming all future video requests are known, we consider the impact of the time scale, which transforms the static 0-1 ILP problem into a dynamic problem. By solving the dynamic 0-1 ILP problem, we find more accurate optimal solutions compared to existing approaches. Third, since the formulated 0-1 dynamic ILP problem is NP-hard, we leverage the in-network caching of ICN and the global view of the SDN controller to propose a novel SDN-based caching decision policy. Finally, extensive evaluations are performed, and the results demonstrate that the proposed SDN-based caching decision policy provides solutions that are close to the optimum in substantially less computation time. The SDN-based caching decision policy also outperforms existing practical ICN caching decision policies in terms of the cache hit ratio and the average number of hops, which are directly related to the video delivery latency. Moreover, the SDN-based caching decision policy can substantially reduce the number of generated and broadcasted interest packets, which is a shortcoming of the current ICN.

Since multimedia services will become fundamental in the upcoming 5G networks, how to improve the user quality of experience (QoE) is becoming a major challenge. In this paper, we integrate the concept of Information-Centric Networking (ICN) to the infrastructure of 5G networks. Due to the in-network caching feature of ICN, proactive caching can be beneficial in 5G networks. More precisely, this paper introduces a novel proactive caching approach (called smart caching) which leverages the non-negative matrix factorization (NMF) technique to predict the future ratings of user preferences on all videos for 5G-ICN networks. To solve the shortcoming of the NMF technique that generates inaccurate predictions for high rated but unpopular videos, we also take video historical popularity into consideration. Thus, the user future demands can be predicted based on the user preferences (i.e. the predicted ratings) and the historical popularity of videos. Simulation results show that the proposed smart caching outperforms existing approaches in terms of hit ratio, average video retrieval delay, and user satisfaction.

Fog computing has risen as a promising technology for augmenting the computational and storage capability of the end devices and edge networks. The urging issues in this networking paradigm are fog nodes planning, resources allocation, and offloading strategies. This paper aims to formulate a mathematical model which jointly tackles these issues. The goal of the model is to optimize the tradeoff (Pareto front) between the capital expenditure and the network delay. To solve this multiobjective optimization problem and obtain benchmark values, we first use the weighted sum method and two existing evolutionary algorithms (EAs), nondominated sorting genetic algorithm II and speed-constrained multiobjective particle swarm optimization. Then, inspired by those EAs, this paper proposes a new EAs, named particle swarm optimized nondominated sorting genetic algorithm, which combines the convergence and searching efficiency of the existing EAs. The effectiveness of the proposed algorithm is evaluated by the hypervolume and inverted generational distance indicators. The performance evaluation results show that the proposed model and algorithms can help the network planners in the deployment of fog networks to complement their existing computation and storage infrastructure.

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

In earlier works [1], [2], we proposed to utilize a centralized broker-node to perform task scheduling for the resource augmentation of a large number of mobile devices. The task scheduler model focused on energy optimization was proposed for the centralized task scheduling problem. In this paper, the model extends the optimization process by including an economic element to it. Thus, we propose an energy and monetary cost-aware mathematical task scheduler model. Compared to the previous model, this model, can allow mobile devices to offload multiple tasks to cloud resources. The results in this paper are more thorough and more aspects of task offloading have been analysed. For instance, the model is evaluated under two different resource augmentation environments for mobile cloud computing: a local private cloud and public clouds. More precisely, the task scheduling problem is optimally solved to minimize: (i) the total energy consumption when applied to a local private cloud, and (ii) the total energy consumption and monetary cost when applied to public clouds. Our proposed model at the centralized broker-node finds optimal solutions for task assignment problem, and provides a significant reduction in the total costs compared with the task assignment by the centralized scheduler without optimization.

Information centric networking (ICN) is one of the emerging Internet paradigms proposed to overcome the shortcoming of the current host-centric Internet. With ubiquitous in-network caching, ICN can facilitate content delivery and reduce network delay. In this paper, we propose a novel collaborative caching scheme based on routers' position to cache popular videos on the edge routers which are closer to users. A priori knowledge of videos' popularity is not required as the proposed scheme adapts itself to the user requests. The benefits of our proposed approach are: light-weight, short content delivery time, and reduced network usages and publisher load. We use a simple topology to show how the proposed scheme works. Then, we use a realistic topology with real data traces to evaluate the performance of the proposed scheme. Simulation results show that our scheme outperforms existing schemes in terms of average number of hops and reduced publisher load ratio for both scenarios.

Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. Emergency, healthcare, and latency sensitive services require real-time responses. Also, it is necessary to decide what type of data has to be uploaded to the cloud, without burdening the core network and the cloud. For this purpose, the cloud on the edge of the network, known as Fog or Micro Datacenter (MDC), plays an important role. Fog resides between the underlying Internet of Things (IoTs) and the mega datacenter cloud. Its purpose is to manage resources, perform data filtration, preprocessing, and security measures. To achieve this, Fog requires an effective and efficient resource management framework, which we propose in this paper. Fog has to deal with mobile nodes and IoTs, which involves objects and devices of different types having a fluctuating connectivity behavior. All such types of service customers have an unpredictable relinquish probability, since any object or device can stop using resources at any moment. In our proposed methodology for resource estimation and management through Fog computing, we take into account these factors and formulate resource management on the basis of fluctuating relinquish probability of the customer, service type, service price, and variance of the relinquish probability. With the intent of showing practical implications of our method, we implemented it on Crawdad real trace and Amazon EC2 pricing. Based on various services, differentiated through Amazon's price plans and historical record of Cloud Service Customers (CSCs), the model determines the amount of resources to be allocated. More loyal CSCs get better services, while for the contrary case, the provider reserves resources cautiously.

Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.

This paper describes a cloud-based platform, called RP-SMARF, for supporting research collaboration in the area of smart facilities management. More precisely, the proposed system delivers sophisticated domain-specific services to users by linking together disparate privately-owned elements such as data repositories, specialized hardware resources and specialized software applications into a unified framework. This allows research collaborators within a knowledge domain to easily run complex applications developed by researchers within the domain on complex datasets. Both batch-type processing as well as interactive application access are supported by the platform. The processing of live data streams generated in one site by an application residing in another site can also be performed. An authorization framework allows element owners to precisely control which users can access various elements. RP-SMARF unifies geographically dispersed heterogeneous resources for research collaborators. This simplifies making software created by researchers more usable by other researchers thereby making many researchers more productive.

Long term user rate in cellular networks is the product of spectral efficiency achieved and the resources (time/frequency slots) allocated. The former is related to the received SINR, while the latter is limited by the load of the associated cell. The max-SINR cell association strategy has been used in cellular networks from GSM to LTE. This strategy maximizes the possible achieved spectral efficiency but fails to account for the load imbalance. Recently, there have been several investigations on load-aware cell association as an approach to match the traffic demand with the traffic supply, in which a user may associate to a less loaded cell, even though it does not necessarily provide the maximum SINR. In other words, a user is associated with a cell to get more share of resources at the cost of lower spectral efficiency. This paper goes beyond that by proposing a new load balancing approach that can simultaneously increase the user received SINR and the share of allocated resources. This is achieved by the user-in-the-loop (UIL) paradigm, which encourages the user to move to a new location that maximizes the utility function considering the received SINR, cell load and the probability of moving. Numerical results show that the UIL can increase the mean user rate substantially in comparison to the max-SINR or the load-aware cell association strategy, and also results in a more balanced load across the network.

In this paper, we present our work in the area of Cyber Foraging (CF), which enables mobile devices to offload heavy computations to resourceful computing nodes. The main focus of the paper is to minimize the total energy consumption across all the mobile devices in a large Cyber Foraging System (CFS). We use a centralized architecture where a broker node handles the task scheduling. We model the task scheduling problem for this centralized architecture and optimally solve the problem to minimize the total energy consumption, which is equivalent to maximizing the total energy saving across all mobile devices. Simulation results show that using a centralized broker able to optimally offload tasks can provide a significant reduction in energy consumption.

Unmanned Aeronautical Ad Hoc Networks (UAANETs) are a type of Mobile Ad Hoc Networks (MANETs) which are infrastructureless and self-organizing networks. The specificity of UAANETs is that they are formed by small and medium sized Unmanned Aerial Vehicles (UAVs) also known as drones. In UAANETs as well as in MANETs, geographic routing is widely used. Geographic routing relies on Greedy Forwarding (GF), also called Greedy Geographic Forwarding (GGF). GGF fails when a packet arrives at a node that has no neighbor closer to the destination than it is. The node in this situation is referred to as a void node. In this paper, we propose a strategy that salvages packets in void node situations. We thereafter append this strategy to a protocol that features GGF. Simulations in OPNET show an increase in packet delivery ratio of about 2% at virtually no additional cost.

This paper demonstrates that highly accurate radiometric identification of Long Term Evolution (LTE) transmitters is possible using commercial off-the-shelf hardware and support vector machines (SVM). The identification is based on unique modulation characteristics exhibited by the transmitters, resulting from minute imperfections introduced during radio hardware manufacturing. In these experiments, the Agilent Vector Signal Analysis (VSA) software and the Agilent PXA spectrum analyzer are used to extract radiometric properties from several LTE base stations, known as evolved Node B (eNB). The open-source SVM library libsvm performs the classification using 13 feature coefficients extracted by the VSA. When SVM parameters are optimized using grid search, and the training bin contains no less than 45 vectors, re-identification is shown to be in excess of 98%.

Proxy Mobile IPv6 (PMIPv6) has been developed by the IETF as a network-based mobility management protocol to support the mobility of IP devices. F-PMIPv6 and Localized Routing have been proposed to solve the issues of packet loss/handover delay and non-optimal routing of data packets respectively. Optimized PMIPv6 (O-PMIPv6), as proposed in [1], looked at combining all these features in one solution and was initially designed to work in predictive mode. However, when the mobile node moves very fast, the attachment with the target network may happen before the completion of the tunnel setup procedure between the previous and the new access network while using predictive mode. Moreover, the prediction of the attachment could fail for several reasons such as the lost of the detachment notification messages. All of these scenarios require support of reactive mode for O-PMIPv6. As a result, this paper extends the operation of O-PMIPv6 to the reactive mode. A comparative analysis is done with PMIPv6 and the reactive mode of F-PMIPv6 to study the effectiveness of the proposed extension in terms of route optimization handover delay, signaling cost and network utilization. Finally, the reactive O-PMIPv6 is compared to the predictive O-PMIPv6 to show differences in performance for both approaches.

As a result of high mobility of Unmanned Aerial Vehicles (UAVs), designing a good routing protocol is challenging for Unmanned Aeronautical Ad-hoc Networks (UAANETs). Geographic-based routing mechanisms are seen to be an interesting option for routing in UAANETs due to the fact that location information of UAVs is readily available. In this paper, a combined routing protocol, called the Reactive-Greedy-Reactive (RGR), is presented for UAANET applications, which combines the mechanisms of the Greedy Geographic Forwarding (GGF) and reactive routing. The proposed RGR employs location information of UAVs as well as reactive end-to-end paths in the routing process. Simulation results show that RGR outperforms existing protocols such as Ad-hoc On-demand Distance Vector (AODV) in search UAANET missions in terms of delay and packet delivery ratio, yet its overhead is similar to traditional mechanisms.

Due to the availability of location information in unmanned aerial vehicles (UAVs), we propose to use geographic routing mechanisms as a core forwarding protocol in unmanned aeronautical ad-hoc networks (UAANETs) for the purpose of reducing routing overhead. As a result, this paper investigates the performance of the core forwarding mechanism i.e. the greedy geographic part. Since the forwarding mechanism for dynamic UAANETs with many statistical inter-dependencies is complex, a closed-form model does not exist. Therefore, a quadratic polynomial estimation is proposed for computing the success probability of greedy geographic forwarding based on the results of a set of realistic Monte Carlo simulations. This mathematical model can later be used to predict and evaluate the performance of other greedy-based geographic routing protocols for UAV applications.

Proxy Mobile IPv6 (PMIPv6) has been developed by the IETF as a network-based mobility management protocol to support the mobility of IP devices. Although several proposals have been made for localized routing optimization, they don't take into account handover management and localized routing simultaneously. In fact, the localized routing state is only restored after the handover, leading to packet loss and signaling overhead. On the other hand, Fast Handovers for PMIPv6 (F-PMIPv6) protocol has been designed to mainly solve the issues of long handover delay and packets loss during handover. As a result, this paper looks at enhancing F-PMIPv6 by combining the handover with route optimization by proposing a new protocol called Optimized Proxy Mobile IPv6 (O-PMIPv6). The proposed protocol enhances the performance of PMIPv6 and F-PMIPv6 in terms of route optimization handover delay, signaling cost, and network utilization.

ABSTRACT Because of their specific characteristics, Unmanned Aeronautical Ad‐hoc Networks (UAANETs) can be classified as a special kind of mobilead hocnetworks. Because of the high mobility of Unmanned Aerial Vehicles, designing a good routing protocol for UAANETs is challenging. Here, we present a new protocol called Reactive‐Greedy‐Reactive (RGR) as a promising routing protocol in high mobility and density‐variable scenarios. RGR combines features of reactive MANET routing protocols such as Ad‐hoc On‐demand Distance Vector with geographic routing protocols, exploiting the unique characteristics of UAANETs. In addition to combining reactive and geographic routing, the protocol has a number of features to further improve the overall performance. We present the rationale and design of the protocol, discuss the specific performance improvements in detail and provide extensive simulation results that demonstrate that RGR outperforms purely reactive or geographic routing protocols. The results also demonstrate the impact of the various protocol modifications. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) are usually randomly deployed in a region of interest. As a result, algorithms that can compute the location of sensor nodes within a WSN are needed. In recent years, several localization algorithms have been proposed for stationary WSNs. However, most studies only provide simulation results and most algorithms have never been implemented on a real testbed. In this paper, we implement a localization algorithm called CCA-MAP on a real WSN testbed. To the best of our knowledge, the CCA-MAP algorithm is amongst the best performing algorithms proposed for stationary WSNs. The results obtained show that the implementation results are consistent with the simulation results.

In this paper, Wireless Personal Area Network (IEEE 802.15.4) wireless channel utilization using Memsic (formerly Crossbow) IRIS Motes is measured and compared to ns2.34 simulation and theoretical models. Results show divergence between calculated, simulated and experimental results at higher throughput. Experimental results also highlight the limitations of the MoteWorks framework with regard to channel throughput measurements. A set of mitigation strategies is outlined to improve future research efforts aiming to characterize the validity of simulation models using the Memsic wireless sensor nodes.

In this paper, we first survey cross layer architectures for Wireless Sensor Networks and Wireless Multimedia Sensor Networks (WMSNs). Afterwards, we introduce a novel framework for supporting multiple applications in WMSNs. The proposed framework supports heterogeneous flows and it classifies WMSN traffic into six classes. The framework partitions the network into hexagonal cells and it uses the seven cell frequency reuse pattern to enhance throughput and minimize interference. The framework uses a shared database to enable cross layer interactions. For supporting multiple applications on a single node, an area in the memory is reserved where each application can request its protocols parameters. Protocol parameters and interaction between the shared database and applications are done through cross layer optimization middleware. A TDMA-based distributed MAC is used to support heterogeneous traffic flows.

In this paper, we focus on the development of an efficient heuristic in order to solve the planning problem of 3rd Generation (3G) Universal Mobile Telecommunication System (UMTS) all-IP Release 4 networks. We also incorporate a realistic traffic profile taken from real live networks. Since the problem is NP-hard, an approximate algorithm based on the local search principles is proposed. Numerical results demonstrate that the local search algorithm produces solutions that are, on average, within 5.03% of the optimal solution, and in the best and worst cases at 0.87% and 9.29% of the optimal solution respectively.

In this paper, we present a scheme that employs TCP aware network coding with opportunistic scheduling to enhance TCP performance in wireless mobile ad hoc networks. Specifically, we consider a TCP parameter, congestion window size, and wireless channel conditions simultaneously to improve TCP throughput performance. We evaluate our scheme by using ns2 simulations in which the mobility and the traffic parameters are varied. The results show that our scheme gives approximately 35% performance improvement in a high mobility environment and about 33% performance improvement in no/low mobility environment as compared to traditional network coding with opportunistic scheduling. The results further show that when more TCP sessions are generated in the network, our scheme performance increases by approximately 6.9 Kbps per TCP session as compared to traditional network coding with opportunistic scheduling which only increases by roughly 5.9 Kbps per TCP session.

Biosensors are a very promising technology that will take healthcare to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by implanted biosensors may damage the tissues around them. Dynamic sensor scheduling is one way to manage the heat generated by implanted biosensors. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process. Not like previous works, the temperature increase in the tissues caused by heat is incorporated into the model. The solution of the model gives an optimal policy that when executed, it will result in the maximum possible network lifetime under a constraint on the maximum temperature tolerable by the patient's body. The optimal policy is compared with two policies one of which is specifically designed for biosensor networks. Numerical and simulation results show the validity of the model and superiority of the optimal policy produced by the model in terms of both network lifetime and temperature increase.

In this paper, we propose a new cooperative algorithm based on tabu search (TS) and genetic algorithm (GA) in order to solve the global planning problem of Universal Mobile Telecommunications System (UMTS) networks. This problem has been shown to be NP-hard as it is composed of three different subproblems (each one being NP-hard): the cell planning problem, the access network planning problem and the core network planning problem. As a result, approximate algorithms are necessary in order to solve larger instances of the problem. Numerical results show that the cooperative algorithm can find solutions with an average gap of 0.24% with respect to the optimal solution in a reasonable amount of computation time. By combining TS and GA, we show that better results can be obtained than using each algorithm separately.

The ultimate objective of network planning is to achieve the best performance and profitability with the minimum network investment. In this paper, we investigate the use of the genetic algorithm in order to find "good" feasible solution for the global topology planning problem of Universal Mobile Telecommunications System (UMTS) networks. The latter has been shown to be NP-hard as it is composed of three different subproblems (each one being NP-hard): the cell planning problem, the access network planning problem and the core network planning problem. As a result, we concentrate our effort on the development of an approximate approach based on the genetic algorithm. Numerical results show that quasi-optimal solutions (on average, within 2.56 ± 1.01% of the optimal solution) can be found by using the genetic algorithm with a relatively short computation time compared to CPLEX.

Existing network coding schemes such as COPE require exchange of information among neighboring nodes in order to correctly encode and decode data packets. However, this leads to high packet overhead and degrades system performance more especially when network traffic is high. Therefore, in this paper, we propose a new adaptive W scheme whose objective is to adaptively control waiting time of overheard packets that are stored in a buffer (called packet pool in) to achieve a tradeoff between throughput and overhead. Ns2 simulation results show that there is tremendous performance improvement in terms of throughput and overhead when adaptive W scheme is used as compared to fixed W scheme in high network traffic, densely populated network and low/high mobility environment.

Cross-layer design has proven to be a promising approach to achieving optimal performance in wireless networks. However, care must be taken when designing cross-layer protocols and frameworks so as to avoid excessive complexity and loss of standardization. In this paper, we propose a cross-layer framework for wireless sensor networks which preserves modularity and clearly defines the inter-layer interactions allowing for ease of standardization and long-term development. This Central-Networked framework consists of the centered physical and centered application models.

Biosensors are tiny wireless medical devices which are attached or implanted into the body of a human being or animal to monitor and control biological processes. They are distinguished from conventional sensors by their biologically derived sensing elements. Biosensors generate heat when they transmit their measurements and their temperature rises when recharged by electromagnetic energy. These phenomena translate to a temperature increase in the tissues surrounding the biosensors. If the temperature increase exceeds a certain threshold, the tissues might be damaged. In this paper, we discuss the problem of finding an optimal operating policy for a rechargable biosensor under a strict maximum temperature increase constraint. This problem can be formulated as a Markov decision process with an average reward criterion. The solution is an optimal policy that maximizes the average number of samples which can be generated by the biosensor while observing the constraint on the maximum safe temperature level. Due to the exponential nature of the problem, a heuristic policy is proposed. The performance of the policies is studied through simulation. A greedy policy is used as a baseline for comparison.

Since Universal Mobile Telecommunications System (UMTS) networks are very expensive to deploy, network optimization is an important issue for service providers to remain competitive. As a result, this paper proposes a new mathematical programming model in order to plan UMTS Release 4.0 networks. Given realistic traffic profile as input, the model simultaneously considers voice and data services as well as network signaling. More precisely, the model finds the number, the type and the location of the network elements (NE), selects the number and the type of links and interfaces and finally designs the network topology. The objective of the model is to minimize network cost. An illustrative example of the model is described and a set of randomly generated problems are solved using a commercial implementation of the branch-and-bound algorithm.

A directional sensor network is formed by directional sensors which may be oriented toward different directions. The sensing region of a directional sensor can be viewed as a sector in a two-dimensional plane. Therefore, a directional sensor can only choose one sector (or direction) at any time instant. Planning of directional sensor networks has received very little attention in the literature. In this paper, we discuss directional sensor placement which is a critical task in the planning of directional sensor networks. We also present an integer linear programming model whose goal is to minimize the number of directional sensors that need to be deployed to monitor a set of discrete targets in a sensor field. Numerical results demonstrate the viability and effectiveness of the model.

In this paper, a simulated annealing algorithm is proposed and studied in order to find good feasible solution for the global topology planning problem of Universal Mobile Telecommunications System (UMTS) networks. The latter has been shown to be NP-hard as it is composed of three different subproblems (each one being NP-hard): the cell planning problem, the access network planning problem and the core network planning problem. As a result, we concentrate our effort on the development of an approximate algorithm based on simulated annealing. Numerical results show that quasi-optimal solutions (on average, within 5.26% of the optimal solution) can be found with a relatively short computation time compared to CPLEX.

Accurately determining the location of mobile wireless sensor nodes in real-time is essential for many purposes. This paper proposes a new and efficient algorithm for localization of mobile node(s) within a WSN. The proposed algorithm, called iterative CCA-MAP (iCCA-MAP), is based on the CCA-MAP algorithm which applies an efficient nonlinear data mapping technique. The latter has been shown to perform extremely well for localizing stationary nodes in WSNs. Simulation results show that the localization error results for both CCA-MAP and iCCA-MAP are similar. However, the computational time required for obtaining location results using the iterative CCA-MAP scheme is far smaller than that of the original CCA-MAP. The advantage of the proposed algorithm is that it can provide the mobile node's location information at near real-time, allowing for it to be applied at a much higher frequency in order to provide up-to-date estimations of the mobile node's position, which can result in a lower localization error.

Abstract In this paper, we present a literature review on the topological planning problem of third generation (3G) cellular networks based on the universal mobile telecommunications system (UMTS) standard. After describing the UMTS architecture, we introduce each subproblem and present major works that have been done. The cell, the access and the core network planning problems have all been considered as well as a more global approach (when more than one subproblems are considered simultaneously). Both planning and expansion algorithms are also included in this review. The goal of this paper is to present and classify the different research works that have been done so that it can be used as a starting point for future research on topological design of UMTS networks. Copyright © 2008 John Wiley & Sons, Ltd.

Unlike isotropic sensors, directional sensors have a finite angle of view and thus cannot sense the whole circular area around them. The sensing region of a directional sensor can be viewed as a sector in a 2D plane. A directional sensor network is formed by directional sensors which may be oriented toward different directions. In this paper, we present an integer linear programming model for the minimum cost sensor placement problem in directional sensor networks. The objective is to minimize the total cost of directional sensors by properly choosing the type and direction for each sensor to be installed in the sensor field. The model guarantees that all the targets are covered and sensor nodes can deliver their data to a sink node. Numerical results demonstrate the viability and effectiveness of the proposed model.

The directional sensor placement problem is an essential part of any planning model for directional wireless sensor networks. Its goal is to find an optimal subset of locations where directional sensors should be placed so that the total network cost is minimized while the requirements of coverage and connectivity are satisfied. Directional sensors are characterized by three important parameters: sensing range, field of view and orientation. These parameters have significant impact on the overall cost of the directional sensor network. In this paper, we present an integer linear programming formulation for the directional sensor placement problem in which the goal is to minimize the network cost by appropriately choosing the values of the above parameters for each sensor to be installed in the sensor field. We show the viability and effectiveness of the proposed model through numerical results.

In this paper, we propose a local search heuristic to find "good" feasible solutions in order to solve the global planning problem of universal mobile telecommunications system (UMTS) networks. The global problem is composed of three different subproblems: the cell, the access network and the core network planning subproblems. The global model is very complex to solve exactly and for this reason, we concentrate our efforts on the development of efficient heuristics. A detailed example illustrates the solutions obtained with the proposed approach. Computational results show that our heuristic produces results that are, on average, within 5.48% of the optimal solution.

In this paper, we propose a tabu search heuristic to find "good" feasible solutions for the global planning problem of universal mobile telecommunications system (UMTS) networks that is composed of three subproblems: the cell, the access network and the core network planning subproblems. Since this global planning problem has been proven to be NP-hard, we concentrate our efforts on the development of an efficient heuristic. Numerical results show that quasi-optimal solutions (on average, within 0.21% of the optimal solution) can be obtained with the proposed approach