Over-the-air federated learning (OTA-FL) is a communication-efficient paradigm that leverages the superposition property of wireless channels to aggregate client updates simultaneously, significantly reducing uplink latency and bandwidth usage. While OTA-FL offers advantages in scalability and speed, it poses challenges in energy efficiency and delay management. This paper proposes a multi-attribute client selection framework that addresses these challenges through a multi-objective optimization approach. We analytically model selection attributes: energy efficiency, communication delay, loss, and fairness, and formulate three optimization problems to capture different trade-offs. To solve them, we employ the Multi-Objective Grey Wolf Optimizer (MOGWO), a nature-inspired metaheuristic algorithm that effectively balances exploration and exploitation. Experiments on MNIST, Fashion MNIST, and CIFAR-10 demonstrate that our approach outperforms baseline and loss-aware methods, achieving up to 13% energy savings while improving model accuracy, fairness, and reliability.

The rapid growth of the Internet of Things (IoT) has triggered the need for scalable and energy-efficient communication solutions. While LoRaWAN is widely used for long-range wireless access, its Aloha-based MAC protocol struggles with high collision rates in dense networks. Existing solutions such as irregular repetition slotted ALOHA (IRSA) and contention resolution diversity slotted ALOHA (CRDSA) have improved network performance by using packet repetitions and successive interference cancellation. However, they do not fully leverage the unique properties of LoRaWAN Spreading Factors (SFs). To address this gap, we propose a new approach called SF-IRSA, where IoT devices transmit replicas using different SFs, enabling the decoder to apply an SF-IRSA-SIC process that leverages both temporal and spatial dimensions for efficient packet decoding. Our theoretical analysis and simulations show that SF-IRSA outperforms IRSA and CRDSA in terms of throughput and reliability. Specifically, using up to two SFs results in a 16.2% increase in the asymptotic throughput compared to standard IRSA. When extending to three SFs, the throughput gain reaches 116.9%, with a maximum of $\mathbf{2 2 4. 5 2 \%}$ while using $\mathbf{6}$ SFs.

Federated learning (FL) faces significant challenges due to the non-independent and identically distributed (non-IID) data and the heterogeneous nature of clients’ characteristics. Clustered federated learning (CFL) addresses these issues by grouping similar clients and creating cluster-specific models. However, CFL introduces additional challenges, such as determining the optimal clustering criteria and managing the dynamic nature of client availability and data distribution. This paper proposes a novel CFL approach that integrates a full spectrum of relevant factors where clients are clustered based on data distribution, device type, and geographical location. Each group selects a subset of clients based on the information’s age, the client’s motivation, and the availability of resources to participate in the learning process. Unlike previous approaches that focus on a limited set of criteria, our method considers a holistic view of client attributes to improve clustering performance. The experimental results demonstrate the efficiency and effectiveness of the proposed method, highlighting significant improvements in communication efficiency and model quality. Furthermore, our approach adapts dynamically to changes in client availability, ensuring robust learning over time. By optimizing client selection and leveraging cluster-specific characteristics, the proposed approach enhances the scalability, robustness, and overall performance of FL systems.

Unmanned aerial vehicles (UAVs) are emerging as key enablers for extending coverage and reliability in next-generation wireless networks using millimeter-wave (mmWave) and terahertz (THz) links. However, their narrow directional beams make initial cell search and alignment challenging under stringent latency and reliability demands. We study a risk-aware beam alignment problem where both the expected access delay and its variability are minimized under strict reliability constraints. To tackle this, we develop the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), designed to balance convergence speed and computational efficiency. Simulations confirm that LSRFDA achieves faster alignment, lower latency, and higher reliability compared to Particle Swarm Optimization (PSO) and random search, making it suitable for UAV-assisted mmWave/THz URLLC and HRLLC scenarios.

Federated Learning (FL) presents a promising paradigm for decentralized model training; however, its real-world adoption is hindered by several critical challenges, including non-independent and identically distributed (non-IID) data across clients, heterogeneous computational capabilities, and significant communication overhead. To address these issues, this paper introduces a novel multi-attribute client clustering and selection framework for FL. The proposed approach groups clients according to data distribution, device capabilities, geographic location, and model update behavior. Within each cluster, an adaptive client selection mechanism leverages dynamic attributes such as residual energy, data freshness, and client participation motivation to identify the most suitable participants. Experimental evaluations on standard FL benchmark datasets demonstrate that the proposed framework achieves faster convergence, higher global model accuracy, and improved energy efficiency compared to state-of-the-art approaches.

Ultra-Reliable Low-Latency Communication is the Fifth Generation (5G) use case with the most stringent requirements for latency and reliability. In Beyond 5G and future 6G systems, there will be a need to support a large number of URLLC devices, giving rise to a new use case known as massive URLLC (mURLLC). Addressing these demands requires efficient resource sharing among multiple devices. Non-Orthogonal Multiple Access (NOMA) emerges as an efficient solution to enhance spectral efficiency by allowing simultaneous transmissions from multiple devices over shared resources. In this paper, we propose a novel joint sub-channel allocation and power control framework that integrates Irregular Repetition Slotted ALOHA (IRSA) with Grant-Free NOMA (GF-NOMA). The resource allocation problem is formulated as a multi-agent reinforcement learning task, where each device acts as a learning agent and the gNodeB (gNB) broadcasts global feedback to meet the stringent reliability and latency requirements. The framework introduces new Quality Scores (QS) that guide agents in selecting resources more efficiently. Extensive simulations demonstrate that the proposed framework significantly outperforms existing techniques in meeting the stringent mURLLC requirements.

The exponential increase in decentralized data generated by heterogeneous devices presents critical challenges for federated learning (FL), particularly in handling nonindependent and identically distributed (non-IID) data, limited client resources, and the preservation of privacy. Traditional FL approaches often suffer from prolonged training times, reduced accuracy, and high energy consumption due to uncoordinated client participation and data diversity. In this paper, we propose an advanced FL framework that strategically clusters clients based on multiple attributes, including data distribution patterns, device types, and geographic locations, to address these challenges. By leveraging this multi-dimensional clustering, our framework optimizes client selection processes, resulting in faster convergence, improved model accuracy, and enhanced energy efficiency. We benchmark our approach against random, roundrobin, and accuracy-based client selection strategies through extensive experiments. The results demonstrate notable improvements, including up to a$\mathbf{1. 0 9 \%}$decrease in model loss, a$\mathbf{2. 8 \%}$boost in accuracy, a 20.6 % reduction in training duration, a 30.8 % cut in communication overhead, and a 25.5 % increase in energy efficiency compared to baseline methods. Our findings highlight the critical role of incorporating device heterogeneity and geographic context into client clustering for scalable, efficient, and privacy-preserving FL deployments.

As the fifth-generation (5G) mobile communication system continues its global deployment, both industry and academia have started conceptualizing the 6th generation (6G) to address the growing need for a progressively advanced and digital society. Even while 5 G offers considerable advancements over LTE, it could struggle to be sufficient to meet all of the requirements, including ultra-high reliability, seamless automation, and ubiquitous coverage. In response, 6G is supposed to bring out a highly intelligent, automated, and ultra-reliable communication system that can handle a vast number of connected devices. This paper offers a comprehensive overview of 6G, beginning with its main stringent requirements while focusing on key enabling technologies such as terahertz (THz) communications, intelligent reflecting surfaces, massive MIMO and AI-driven networking that will shape the 6G networks. Furthermore, the paper lists various 6 G applications and usage scenarios that will benefit from these advancements. At the end, we outline the potential challenges that must be addressed to achieve the 6G promises.

This paper proposes two unmanned aerial vehicles (UAV) trajectory planning solutions taking into consideration mission deadline, energy consumption, and communication constraints. The problem is mathematically formulated as a mult-iobjective optimization problem. The NP-hardness complexity is demonstrated then two heuristic solutions are proposed. Specifically, we first present a near-optimal UAV trajectory planning algorithm that reduces the number of stop/hovering points. Then, we propose an artificial bee colony-based algorithm with enhanced candidate selection. Extensive simulation results show that both our schemes outperform the well-known Successive Convex Approximation (SCA) technique, under low-moderate traffic demand. They perform as well as SCA under high demand.

Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme aimed at maximizing throughput while minimizing power consumption by considering the traffic and energy constraints of IoT devices. Our approach employs a stochastic geometry framework and mean-field game theory to model and analyze the mutual interference among active IoT devices. Additionally, we utilize a Markov chain model to capture and track the queue length of IoT devices, enabling the derivation of the transmission success probability at steady-state. The simulation results illustrate the optimal power allocation strategy and evaluate the proposed approach’s performance in terms of packet transmission success probability and average delay.

We present a game theoretic model that describes the competition between non-cooperating Service Providers (SPs) to purchase resources from the Infrastructure Provider (InP), and to offer paid services to their end-users. In the proposed model, the demand of a SP depends not only on its price and its quality of service (QoS), but also on the prices charged and QoS offered by its competitors. We consider that the InP owns the cloud facility to support the resources required by SPs to run the services of their end-users. When SPs access resources through virtually unlimited cloud capacity, resulting in competition to attract end-users with price required and QoS offered. We establish the existence and uniqueness of Nash equilibrium of the induced game. In addition, we design an online learning algorithm to allow SPs to reach the Nash equilibrium in a fully distributed fashion. Finally, we run simulations using Amazon EC2 instances to validate the proposed model’s consistency, investigate the impact of some parameters on the market price, and analyze the convergence of the equilibrium price learning algorithm.

With today’s proliferation of IoT and real-time applications, it has become crucial to properly handle traffic, optimize the quality-of-service (QoS) of wireless networks and design novel approaches to enable reliable communications with bounded latency and high throughput for future wireless services. In order to meet these stringent QoS requirements, there has been a recent surge in research that investigates a deep restructuring of the Radio Access Network (RAN). In particular, the open radio access network (O-RAN) framework promises to deliver flexible, scalable, and agile solutions for improving the handover process of a moving user equipment (UE), by considering factors related to the requirements of applications in terms of QoS, traffic load, signal quality and the diversity of multiple access technologies or radio frequencies of the environment. Building on this basis, this paper investigates the handover process of a moving vehicle, by exploring and comparing the prediction accuracy results of different machine learning (ML) techniques used for anomaly detection. In particular, the structure of one of the O-RAN modules is presented. This module relates to a traffic steering application, specifically designed and used to detect anomalies within the network. Several ML techniques are then implemented in the O-RAN traffic steering module to predict the handover. The results pertaining to the comparison of the implemented ML techniques show that the random forest algorithm gives the highest accuracy (up to 98%), which helps boosting the handover process.

Agriculture is a mainstay of the Moroccan economy and the breadwinner for millions of farmers, offering them a broad array of crop varieties. However, a lack of resources and expertise renders it difficult for farmers to properly diagnose plant diseases. Consequently, valuable time and resources are often wasted trying to save diseased crops. To tackle this issue, a revolutionary method that uses the most recent breakthroughs in computer vision and deep learning technology to identify plant diseases in real-time has been presented. Our system employs YOLOv8 (You Only Look Once), an advanced object identification approach that analyses leaf images at a rate of 70 FPS (Frames Per Second). This innovative image analysis technique consists of splitting an image into numerous grid cells and uses a single neural network to predict the bounding box coordinates and class probabilities. This leads to a more efficient and accurate image assessment, resulting in quicker and more exact disease identification. Our results demonstrate the exceptional efficiency of YOLOv8, which outperforms conventional object identification algorithms in both speed and accuracy. This solidified YOLOv8's position as a leading solution for object detection and recognition in real-world scenarios.

The fifth-generation (5G) mobile standard has been designed to support new use cases such as ultra-reliable and low-latency communication (URLLC). The future 6G is envisioned to support extreme URLLC with higher QoS requirements (e.g., remote surgery, autonomous driving, etc.). URLLC applications need higher QoS that require more power allocations. Consequently, QoS variance will increases, which is intolerable for URLLC. An important amount of energy can be saved through a power control scheme. In this work, we are interested in energy-aware self-organizing networks that provide satisfactory performance for URLLC. We propose a predictive QoS paradigm to enhance satisfaction and reduce power consumption under URLLC’s constraints. A predictive QoS is an intelligent paradigm that allows Mobile/IoT-device to adjust power allocation to the minimum required to achieve the target QoS. First, we model power control as a satisfactory game, where IoT-devices aim to meet their target demands instead of maximizing them. Next, we introduce a distributed satisfactory learning scheme, called Robust Banach-Picard (RBP), to allow devices to self-adjust their power allocation to maintain reliability and latency within the tolerated range of the URLLC application. The algorithm implements deep learning and a derivative concept of federated learning to account for channel variability in power control. Extensive simulations exhibit the advantages and drawbacks of the proposed scheme for URLLC applications. Results show that RBP can maintain instantaneous reliability and latency within the tolerated request at the minimum energy costs. Consequently, RBP can be safe to use for URLLC use cases compared to conventional Banach-Picard iterates.

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network’s performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include micro-services, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

The implementation of multiple radio access tech-nologies (multi-RATs) at the nodes constitutes a new emerging alternative to manage the exponential increase in traffic demand in an energy efficient manner in IoT networks. This research proposes an analytical model of a multi-RAT network which leverages multi-hop routing to provide connection, collect, and transfer data to an end system. This model considers the topology, sensor settings, and layer interactions (PHY, MAC). Using the rate balance equation, we were able to anticipate the average end-to-end delay (E2E delay) as well as the age of information (AoI). Matlab simulation was used to assess the suggested model. We present the model parameter interval values where the stability zone is identified.

Smal1 cell densification is recognized as one of the most significant characteristics in the fifth-generation of communication systems (5G) and beyond. A substantial capacity boost can be achieved at a low cost by supplementing macro networks with numerous small cells to create ultra-dense heterogeneous networks, which can serve as the foundation for the next generation of services. In this paper, we investigate a model that accounts for the location and channel quality of an enhanced Mobile Broadband (eMBB) user as well as the locations, density, and energy levels of a large number of Internet of Things (IoT) devices. More specifically, the eMBB user is randomly distributed in the coverage area of the MBS, and given its channel gain, it adjusts its transmit power to achieve an acceptable Quality of Service (QoS). In contrast, the IoT devices are gathered around SBS and regulate their transmission power in accordance with their energy budget to minimize energy-efficient utility function. Due to the coupling, the Stackelberg-Nash differential game is initially used to model the power allocation problem, with the eMBB user playing the role of the leader and the IoT devices playing the role of the followers. Then, we use the mean-field approximation to construct a hierarchical mean-field game from which we can recover a set of equations that may be solved iteratively to provide the optimal power allocation strategies. Simulation results illustrate the optimal power allocation strategies and show the effectiveness of the proposed approach.

The narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the third-generation partnership project (3GPP) to provide connectivity to a large number of low-cost Internet of Things (IoT) devices with strict energy consumption limitations. However, in an ultradense small cell network employing NB-IoT technology, intercell interference can be a problem, raising serious concerns regarding the performance of NB-IoT, particularly in uplink transmission. Thus, a power allocation method must be established to analyze uplink performance, control and predict intercell interference, and avoid excessive energy waste during transmission. Unfortunately, standard power allocation techniques become inappropriate as their computational complexity grows in an ultradense environment. Furthermore, the performance of NB-IoT is strongly dependent on the traffic generated by IoT devices. In order to tackle these challenges, we provide a consistent and distributed uplink power allocation solution under spatiotemporal fluctuation incorporating NB-IoT features, such as the number of repetitions and the data rate, as well as the IoT device’s energy budget, packet size, and traffic intensity, by leveraging stochastic geometry analysis and mean-field game (MFG) theory. The effectiveness of our approach is illustrated via extensive numerical analysis, and many insightful discussions are presented.

Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US$\$ $482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.

LoRaWAN (Long Range Wide Area Network) is one of the most popular low power wide area networks technologies for the Internet of Things (IoT). It provides higher coverage, lower energy consumption and cost. A key parameter of LoRa modulation is the Spreading Factor (SF), which can be tuned to achieve a desired tradeoff between data rate and range. The six LoRa spreading factors (SF7 through SF12) are inherently orthogonal, which implies that communications using different SFs can coexist simultaneously on the same frequency channel without impacting performance. However, multiple devices using the same SF must compete for channel access and packet collisions must be managed. In this work, we present a comprehensive framework for Slotted-Aloha-based LoRa with random SF selection. Detailed analysis of the steady state of the system allows numerical derivation of the optimal retransmission probability. Furthermore, we assess the system's achievable performance in terms of average throughput and expected delay.

Nowadays, unmanned aerial vehicles (UAVs) are being used in several novel applications, especially in the telecommunication domain. However, ensuring UAV communication and networking for the purpose of a specific application is still challenging. Indeed, due to the mobility of a UAV in a vast area, permanent connectivity over the backhaul is very sporadic and might be lost. In this article, we consider an aerial mesh network where each UAV can serve as a flying base station to boost terrestrial base station in case of damaged infrastructure case for example, or/and provide connectivity for uncovered or poorly covered nodes, and behaves as a relay to establish communication between two components owing to a lack of reliable direct communication link between them. We then detail a case study where a UAV-fleet is used to collect data from the ground Internet-of-Things (IoT) devices and forward it to the cloud for further processing passing by a remote gateway. We aim here to build a queueing framework, including network layer, MAC layer, and physical layer, and investigate both uplink and downlink communication links. Next, we derive some closed forms allowing us to predict the network performance in terms of traffic intensity at every UAV of the aerial mesh network, end-to-end (E2E) throughput, and E2E delay of ongoing streams. Next, we conduct extensive simulations to illustrate the benefit of our framework. Results discussion and numerous insights on parameter setting, target quality of service, and design consideration are also drawn.

Ensuring high reliability is one of the major goals of 5G systems. This work investigates the problem of cooperative relaying and the optimal number of devices to be directly connected to the base station, in order to meet best uplink performance in terms of throughput and reliability. We first propose a D2D-relaying system where devices cooperate forming groups of cellular devices serving as relays to other groups of D2D transmitters. Second we adopt a Markov chain framework, where the states are defined as the numbers of D2D-relays present in the network. Based on that, we derive the average network throughput and reliability. Next, we show that there exists an optimal device distribution, that maximizes the overall reliability and throughput. This number is strongly related to the switching probabilities of the devices and the network parameters such as the orthogonality factor, the cooperation level of D2D-transmitter, the network density and the cluster's radius. Simulation results illustrate the optimal switching probabilities and the average number of D2D-relays that maximize the overall throughput and reliability.

Food security has been always a critical challenge for humankind, especially for developing countries. Different players around the world are engaged at different levels to resolve and overcome this challenge. Smart farming is one of the areas of interest in which Internet of Things (IoT) is presented as one of many paradigms that can be explored to manage crops in real-time. Crop's Management that is practicable through introducing of new practices, surveying important parameters and eventually improving the quality of crops. In this paper, we present an IoT-based low-cost architecture for smart farming based on wireless sensors network technology. The architecture supports the plug-and-play nodes approach. The system is based on the implementation of the change point detection algorithm and leach protocol for network clustering. This solution supports near real-time monitoring, data processing, and aid to improve decision-making. Heterogeneous wireless sensor nodes timely survey parameters such as soil moisture, ambient temperature, air quality, etc. Generated data is periodically transmitted to the relevant cluster heads. The Base Station gathers data from the cluster heads for eventual processing and storing. Extensive simulation runs show an improvement of 137% in terms of first dead node and improvement of 123% in terms of network lifetime.

The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization approach can give more accurate localization performance based on a decentralized and low-complexity processing. However, the presence of Non-Line of Sight links between IoT devices detrimentally impacts the localization accuracy. In this paper, we propose a distributed localization algorithm based on a convex relaxation of the Huber loss function. Moreover, to reduce the algorithm convergence time, an iterative stochastic gradient descent algorithm is proposed. Through numerical simulations, we show that the proposed algorithm when used with optimal relaxation parameters of the Huber loss function achieves very low root mean square error and outperforms existing algorithms in the literature. Finally, we validate our proposed scheme using real experimental data.

In this paper, we investigate the uplink power allocation problem in a large scale environment for user devices with multi-homing capabilities. We introduce an analytical model for multi-homing ultra-dense heterogeneous networks, which takes into account spatial randomness and user device diversity. Coupling stochastic geometry analysis and mean-field approximation, we formulate the problem as a mean-field optimal control with two populations. Then, the optimality conditions are derived using Lagrangian dual formulation to obtain the mean-field equilibrium. Finally, by using a finite difference method, we illustrate the optimal transmit power for both uni-homed and dual-homed devices.

In an increasingly urbanized world, road transportation plays an important role in today's economy. To reduce congestion due to parking searches, the concept of smart parking was introduced. One of the essential and interesting features of smart parking is the guidance to a vacant spot. To do so, in such parking, each spot is generally equipped with an IoT sensor. These sensors form an IoT wireless sensor network (WSN). The battery lifetime remains one of the main limitations of such a network. Here, we propose an algorithm for indoor guidance to a vacant spot based on finding a battery level balance between the parking sensors. A comparison between five parking strategies including our proposal is presented. Although still under improvement, the results of the first simulations highlight two majors points. The first concerns the extension of the first sensor fall's point. The second regards the number of sensors' battery replacement. Indeed, starting with the same initial battery level, when using our proposal, every sensor will have the same number of battery replacement when the other strategies operate more replacement. The proposed lifetime-efficient guidance allows reducing significantly the number of interventions relating to the batteries replacement of IoT parking sensors and push back the fall of the first sensor so extend the parking overall lifetime.

Using millimeter wave (mmWave) bands in 5G self-organizing networks has a significant potential to provide high bandwidth. However, the major challenge lies in initial access beamforming where mmWave communications suffer from deafness problem that may cause a significant loss in the received power especially when narrow beams are adopted. This paper tackles the problem of beam alignment in mmWave 5G. The problem is formulated as a non-cooperative game between transmitter and receiver where each player tries to align its beamforming direction in a way to obtain maximum throughput. We first provide a full characterization of pure Nash equilibria. Then, we propose a gradient descent algorithm that allows users to learn their optimal beamwidth. Simulation results prove the performance of our beam alignment model since mmWave frequencies deliver narrow beams with high gain and significant capacity.

Cloud computing (CC) is the emerging technology in the world for hosting and delivering services over the internet. It offers a variety of benefits such as cost saving, access to different services without any installation from anywhere and any time by internet, etc. Despite all the advantages offered by CC, this technology still susceptible to security threats. From user point of view, CC seems to be very insecure due to security attacks which threaten it and limit its widespread adoption. More motivations are required to provide more trustworthy solutions to secure the cloud as much as possible and preserve users trust. For that, we propose a new model called "profiling and preventing security attacks", abbreviated to PPSA, to detect and prevent the known attacks as well as the unknown attacks before accessing the cloud services/resources. Unlike the existing solutions, such as intrusion detection systems (IDSs), the proposed solution is able to be deployed in a wide area network in the basis of internet and prevent unknown attacks. In this study, we propose a new solution to profile and prevent security attacks in CC. Then, we define new security factors based on keystroke dynamic. Afterwards, we integrate a machine learning algorithm (classification based on associations) to our proposal in order to profile and predict security attacks and optimize as well the PPSA scheme. Eventually, the proposal is illustrated by a realistic case study.

LoRa (Long Range) is increasingly catching the attention of researchers, industry leaders and business influencers as one of the most promising standard for Internet of Things (IoT) ecosystem. LoRa uses slotted Aloha protocol allowing connected IoT devices to send data in a sporadic way and a fully distributed fashion without syncing with other devices. Under a massive IoT environment, LoRa experiences too many collisions which decreases the average throughput drastically and increases the expected delay. In this paper, we propose a scalable slotted Aloha access method for LoRa-empowered Massive IoT environments. Our scheme uses the option field of the LoRa frame, and proposes to iteratively reduce the number of contending devices till this field expires. The remaining devices will access the channel using the legacy slotted Aloha. Our approach does not require any change of the LoRa frame which is of great interest from implementation perspective. Analytic results and extensive numerical examples show clearly how our scheme outperforms the legacy LoRa. We also provide some insights on how to select the network parameters so as the whole network performance is maximized.

This paper presents an uplink energy-efficient power control mechanism for massive cellular Internet of Things (IoT) devices using a Mean-Field Game (MFG) approach. In this setting, IoT devices are clustered around closed access small base stations supporting massive connectivity, in order to avoid data traffic congestion. Each IoT device adapts its transmit power to its energy level and selfishly attempts to satisfy its quality of service expressed in terms of signal to interference plus noise ratio, while reducing the power consumption. The power control is first modeled as a differential game then extend to a MFG considering two cases: Large scale network and ultra dense IoT network. The mean field interference are derived using stochastic geometry analysis. Therefore, the IoT devices can predict their optimal transmit power policies based only on their initial energy distribution. A finite difference algorithm is then developed to obtain the mean field equilibrium. The simulations illustrate the optimal transmit power and the mean field at the equilibrium for both orthogonal and non orthogonal multiple access.

Streaming service is continuously growing, which makes it the killer application of current 4G networks, as it demands more resources from mobile networks. The cellular network receives a large number of requests, most times for the same content, consuming the spectrum, energy, in addition to monetary costs inefficiently. In order to optimize spectrum utilization and reduce the induced costs, we propose a noncooperative game framework allowing to understand the user's behaviors. We observed a volunteer Dilemma-like situation when a mobile user could stream the requested video to its neighbors over a D2D link. Afterward, we provide a full description of both pure and mixed Nash equilibria (NE). Furthermore, to ensure convergence to NE points, we use linear reward-inaction and Gibbs Boltzmann learning algorithms. Finally, we show how our scheme could be exploited through extensive numerical simulation. Our framework capture the user's selfish behavior and provides a solution regarding setting and parameters allowing to reach high performance in terms of spectrum utilization, energy efficiency, and overall cost.

Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors (k, l and m), second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.

Offloading the network, minimizing the power consumption as well as reducing interference are important issues in wireless networks. These requirements mandates that future cellular networks need to use Device-to-Device communication as a key enabler. To harness this solution, we propose a two-device system that combines cellular and Device-to-Device (D2D) communication in an uplink communication. We model this system as a quitting game where devices choose simultaneously either to continue or to quit transmitting over the cellular network. The devices will strategically choose whether to compete or to cooperate through mobile relaying. We first calculate the throughput and the outage probability in a fading channel, then we find the Sub-game Perfect Equilibrium of this game by determining the pure and mixed Nash equilibrium of each subgame. Results show that the outage probability depends on the transmission power and the distance separating a device from its serving BS. The quitting decision of devices depends on the fraction of throughput they would get after quitting, on the quitting frame and on the quitting regret.

In this paper, we are interested in building a joint availability and access cost policy for Unmanned Aerial Vehicles (UAVs)-empowered flying access networks. Indeed, we build a duopoly model to capture the adversarial behavior of UAVs operators in terms of their pricing and availability strategies. That is UAVs operators need to decide about the optimal beaconing period (period needed to send short messages advertising the existence of a UAV) while saving energy. Furthermore, they need to decide about the best price strategy in order to maximize their respective market share. Therefore, a tractable analysis for the game's Nash Equilibrium, both in terms of pricing and availability is derived. We show that this special game exhibits some very interesting properties as it is sub-modular with respect to the availability policy, whereas it is super-modular with respect to the service fee. Furthermore, we implement a learning scheme using best-response dynamics that allows operators to learn their joint pricing-availability strategies in a fast, accurate yet completely distributed fashion. Extensive simulations show the convergence of the proposed schemes to the joint pricing-availability Nash equilibrium and provide attractive insights on how the game parameters could be set to control the duopoly.

Recently, UAV has provided a significant role to support the wireless network, thanks to the several advantages that can offer in comparison with the terrestrial base station. In this paper, we aim to deal with data collection and charging depletion ground IoT devices through UAV station, which is used as a flying base station. To extend the network lifetime, we present a novel use of UAV with energy harvesting module. Thus, the UAV can be used as an energy source to serve depleted IoT devices. On one hand, the UAV charges the depletion ground IoT devices starting initially with those with battery level under a certain threshold. On the other hand, the UAV station collects data from IoT devices that have sufficient energy to transmit their packets, and in the same phase, the UAV exploits the RF signals transmitted by IoT devices to extract and harvest energy. Furthermore, and as the UAV station has a limited coverage time due to its known energy constraint, we investigate in this work the trade- off between both times that the UAV reserves to devices in order to recharge them and to collect data. Numerical results evaluate different metrics of performances in which we examine the added value of UAV with energy harvesting module.

Information Centric Network emerges a paradigm shift from host centric to information centric communication model. ICN deployment faces a significant challenge in poorly developed telecommunications infrastructures where end-to-end paths are not guaranteed. In this context, delay-tolerant networks (DTN) were introduced as an initiative that efficiently handles network interruptions. This paper proposes a new incentive approach for content caching, by exploiting ICN and DTN advantages while avoiding their shortcomings. The proposed caching solution intends to achieve a logical trade-off between the dissemination rate and the energy efficiency in such environments. To this end, we propose a reputation-based content caching mechanism where DTN stations (relays) attempt to deliver content stored in ICN stations. Our approach is designed to ensure an efficient equilibrium between the overall delivery probability and the energy consumption. Furthermore, the network performance is exhibited through a numerical investigation. We conclude that the proposed mechanism can significantly enhance the caching and the energy efficiency by achieving an optimum dissemination rate.

Due to the exponential booming of wireless traffic demands on vehicular networks, the 79 GHz band has been allocated to automotive radar applications since 2013. In the mining Internet-of-Things (IoT) environment, this millimeter wave band enhances the detection capability of targets and accuracy in extreme conditions. This paper provides a new design of part of radar sensor used in mining industry; the antenna. The proposed design preserves expressly the performance aspects required for IoT communications, such as the gain and the radiation pattern. We present a novel lx4 H-slotted patch antenna array operating in the 79 GHz band using inset-feeding line. It is characterized by its small size (10.8 mm x 2.083 mm). The simulated results on CST microwave studio software including the return loss, voltage standing wave ratio make this suggested design an attractive candidate to be deployed for medium-range radar and short-range radar in 77-81 GHz.

Cognitive radio (CR) is a form of dynamic spectrum management that can be programmed and configured intelligently to optimize the use of spectrum resources. In this paper, a decentralized scenario of spectrum leasing has been investigated through negotiation between the PU and SU networks. We propose a pricing-based spectrum leasing framework between the PU and a certain number of SUs, which allows the Pu and SUs to enhance their network performances, and additionally PUs maximize their respective monetary gains. The spectrum leasing problem can be depicted by a non-cooperative game where: on one hand, the PU plays the seller and attempts to maximize its own utility by setting the price of spectrum. On the other hand, SUs (the buyers) have two possible strategies, either to accept the leasing offer or to reject it, while maximizing their utilities. The game Nash Equilibria for both pure and mixed strategies is investigated. Some adaptive learning schemes are introduced aiming to enable cognitive agents to learn their optimal actions and rewards. Numerical investigations illustrates the accuracy of convergence of the proposed schemes.

Recently, telecommunication industry has witnessed an explosive growth in the number of connected devices equipped with more than one wireless interface. These multimode devices are able to connect to different wireless technologies at once to benefit from a higher throughput, a better energy efficiency (EE) and an extended battery lifetime. In this work, we analyze the behavior of devices equipped with cellular and device-to-device (D2D) interfaces with multihoming capability. We investigate the energy efficiency maximization problem under cellular and D2D communications. Yet, we propose a game theoretic framework where the interaction among cellular devices is modeled as a non-cooperative game. Next, we analyze the Nash equilibria and derive conditions of existence. An energy efficient mode selection is then discussed. Furthermore, we provide some numerical examples.

In this paper, we address the problem of energy-aware user satisfaction in self-organizing networks. Our main objective is to meet with the users requirements while reducing energy consumption. Accordingly, we aim at seeking satisfaction equilibria, mainly the efficient satisfaction equilibrium (ESE). We first define conditions of existence and uniqueness of ESE. Considering information-theoretic transmission rate based measure, we fully characterize the ESE and prove that, whenever it exists, it is a solution of a linear system. We show that, at the ESE, no player can increase its Quality of Service without degrading the energy performance. Finally, in order to reach the ESE, we propose a fully distributed scheme based on the Banach-Picard algorithm and show, through simulation results, its qualitative properties.

To cope with the energy performance concern of pervasive and Internet-of-Thing (IoT) devices, current pervasive systems require intelligent algorithms that can change the behavior of the devices and the overall network. Making the devices aware of their states and able to adjust their operative modes using context information has the potential to achieve better energy performance. Context information is typically obtained from environmental sensors, device sensors, and from external sources. In this work, we study the marketing of context-aware services through an adaptive context negotiation model. The negotiation process between context consumers and one or several context providers aims to satisfy the preferences of each the negotiating party concerning the quality-of-context (QoC) levels required by the context consumer. The proposed negotiation model uses an asymmetrical "Power Bargaining" model in which each negotiating party can influence the other party. It implements a learning algorithm for a symmetrical bargaining model. Numerical evaluation of the model shows the convergence of the Nash Bargaining Solution (NBS) by using a learning algorithm.

In this paper, we deal with the store-and-forward paradigm for self-organizing Delay Tolerant Networks (DTN). To overcome the decentralized nature and the infrastructureless constraint of such a network, highly distributed design and efficient incentive mechanisms are needed in order to convince relay nodes to disseminate the content. Here, we exhibit a new way to set the store-and-forward scheme based on the emerging matching game theory. This approach serve to match between on one hand different kinds of files generated by a source node and on the second hand relay nodes that may forward these files. In order to make incentive for cooperation, the source node offers a strategic reward to relay nodes that have accepted to forward a given file. Moreover, each relay and file can be defined by a context, i.e. its characteristics. Based on that, the source would prefer maximize the overall delivery probability at the same time as the relay would try to guarantee the highest possible reward while considering its battery status. Our matching-game-based scheme promises an efficient tradeoff between the overall delivery probability and the energy consumption. For practical considerations, we propose an algorithmic solution to achieve a stable matching between the sets of source files and the set of relay stations. Extensive simulations show that our scheme outperforms the legacy two-hop routing and illustrate the impact of preferences of each set involved in the game, and how such a tool can meet a high delivery rate at a reasonable energy budget.

Nowadays, practitioners as well as researchers skilled in the field of Information Technology (IT) attach great importance to the security of IT services. However, the traditional authentication techniques based on single factors such as passwords and tokens suffer from problems related to their robustness. In a recent work, we proposed a new technique called “Multi-factor Authentication based on Multimodal Biometrics (MFA-MB)” to overcome the drawbacks related to these techniques and also the problems related to the biometrics using single traits. Thus, this paper aims to model and develop an Autonomous Multimodal Biometric Authentication System called “AMBAS” using discrete-time Markov chains in order to decrease the complexity of the multimodal biometric system developed and used in the MFA-MB scheme. Eventually, giving the self-control to the AMBAS, offered thus as a new and unique solution, will improve therefore one user experience and achieve as well a good performances in terms of authentication time. This system aims to identify users according to different methodologies, likelihood, last-sate, random and weighting. The system already finds its application in both cloud and mobile computing. While giving a case study with three-modal biometrics, a simulation of different algorithms performed for the four methodologies is done to test the system performances and usefulness.

While trying to get a service from the Base station it happens that many devices will find themselves competing to get the cellular service, which means each device will use the maximum power possible to win the competition, but if there is another solution that will allow the device to get what it wanted with minimum of energy, how the device will react to it? In other words, we know that the communication through a Device-to-Device (D2D) link is done with a minimum of transmitted power, so why not optimize the power in a cellular communication by introducing D2D communication (i.e. switching from competition to cooperation). In this paper we will focus on studying the players'behavior based on quitting games(i.e. a non cooperative game theory), specifying both the pure and mixed Nash equilibrium during a competition between two devices while communicating with the base station, as well as calculating the probability of quitting and the average quitting time.

Founded in 2005 YouTube has became the most fast growing stream service, that has a great impact on the internet traffic distribution. Thus understanding YouTube's characteristics is crucial to network providers. In this survey we are going to present the factors that impact user's satisfaction, in other meaning the impact on the Quality of experience(QoE). Furthermore we will discuss how to determine the influencing factors on YouTube, how to measure its quality and how to improve it. Tomorrow YouTube will have to adapt to the changes that will occur because our environment is changing from regular to smart digital one.

Cooperative sensing enables secondary users to combine individual sensing results in order to attain sensing accuracies beyond those achieved by consumer RF devices. However, due to sensing costs, secondary users may prefer not to cooperate to the sensing task, leading to higher false alarm probability. In this paper, we study how information about the presence of cooperators affects the dynamics of cooperative sensing schemes. We consider two scenarios, namely the case when SUs cannot detect the presence of other potential cooperators, and the case when SUs have prior information on the presence of other SUs in radio range. Using an evolutionary game framework, we demonstrate that protocols delivering such type of information to SUs reduce cooperation and ultimately lead to degraded network performance. Finally, a learning process based on the replicator dynamics is proposed which is capable to drive the system to the evolutionary stable solution. The results of the paper are illustrated through numerical simulations.

This paper presents some interesting findings about consumer confusion vis-a-vis real price for offered services and its impact on Telecommunications market dynamics. Noting that pricing strategies have a critical influence on consumer choice, customer satisfaction and customer retention in the telecommunications sector, our study focuses on the analysis of the service providers' strategic behavior. Yet, we construct a simple oligopolistic model to capture the interactions among service providers and end users. Next, we analyze the behavior of service providers in terms of their pricing strategies within the framework of non-cooperative game theory. It aims to evaluate the impact of consumer confusion on the competition and operators profitability. Here, rational service providers are competing with each other to maximize their respective payoffs in presence of both a fraction of confused consumers while others are non confused. We provide some interesting results regarding the Nash equilibrium of this game. More precisely, we showed existence and uniqueness of the Nash equilibrium under some conditions. Furthermore, we introduce two learning algorithms that may lead operators to learn their strategic pricing schemes in a complete distributed manner. Extensive simulations show convergence of a proposed scheme to the Nash equilibrium and give some insights on how the game parameters may vary the oligopoly outcome.

In cognitive radio networks, secondary users (SUs) face two conflicting objectives. Each SU seeks to minimize the sensing duration while maximizing the detection probability of primary users (PU) to avoid interfering with their transmissions. Both objectives have a substantial effect on energy efficiency. This paper investigates a noncooperative setting for selecting the sensing duration when multiple SUs operate in the same network. Here, each SU has a certain throughput requirement. The interaction among SUs is captured via a satisfaction strategic game with explicitly stated throughput demands. We prove that depending on the throughput requirements, either zero, one or two Satisfaction Equilibria (SE) exist. We then provide a fully distributed learning algorithm (SELA) to discover them. Extensive simulation results show the validity of the proposed SELA and illustrate the relationship between the throughput demand and the sensing duration.

In this paper, we present the Enhanced Learning Based Random Walk (ELBRW) recommender system for Places of Interest (POI), which leverages contextual information for providing more relevant POI recommendations. The ELBRW considers a model of contextual factors namely POI crowdedness based on a discrete-time Markov chain and combines user interests and "mobility homophily" for POI recommendation in Location- Based Social Networks (LBSNs). By comparing it to the Learning Based Random Walk (LBRW), a context- free recommender system, the performed experiments using LBSNs data provide promising results in terms of POI recommendation quality.

In this paper, we propose a green solution for cellular users located in hyper dense co-channel deployments of LTE small cell networks (SCNs), randomly distributed within LTE macro cell networks (MCNs). Our solution is based on a distributed sharing time access algorithm executed by small base stations (SBSs), and multi-homing capabilities of macro cellular users to improve the energy efficiency of cellular users and to satisfy their QoS throughput requirements. The theoretical analysis is validated by simulations. Our results demonstrate the improved energy efficiency of cellular users compared to the other access control mechanisms.

Cognitive radio is emerging as a promising technique to improve the utilization of the radio frequency spectrum in wireless networks. In this paper, we propose a hybrid CR (cognitive radio) system where underlay and overlay CR approaches are combined under SINR (Signal to Interference plus Noise Ratio) constraints. This new access type allows to optimize the spectrum sensing time with throughput improvement. The goal of this paper is to provide a new access mode in order to enhance the cognitive system performance while reducing the power spent for detecting the presence of the primary user and the spectrum holes. We consider the problem of spectrum sharing among primary (or “licensed”) users (PUs) and secondary (or “unlicensed”) users (SUs). In this scheme, the spectrum allocation problem is modeled as a non cooperative game, with each CR user acting as a player. Nash equilibrium is considered as the solution of this game.

Cooperative sensing is a promising technique that enables secondary users (SUs) to combine their channel observations in order to improve the spectrum sensing accuracy. However, in an adversarial environment where the secondary nodes are particularly selfish and the spectrum sensing is energy costly, selfish SUs can easily exploit the spectrum sensing result without participating in the sensing process. In this paper, we model the cooperative sensing as a volunteer dilemma where SUs have the choice to volunteer in order to sense and share the spectrum sensing results, or to free ride the spectrum sensing and achieve possibly a higher profitability. We mainly give a full characterization of the Nash equilibria and prove a counterintuitive property that claims: the probability of volunteering decreases when the number of SUs increases. Additionally, we propose a practical negotiation algorithm in order to select efficiently one SU to access to the channel. Finally, we propose a distributed algorithm that converges to Nash equilibria and show its performance through simulation results.

This paper introduces a new incentive mechanism for content caching in Delay Tolerant Network (DTN) aiming to improve the performance under relays energy cost. We model this distributed network problem as non-cooperative game, we focus on the source-relay interaction to investigate how far data transmission could be sustained. For instance, due to a limited capacity storage and battery lifetime the relay could abstain from cooperation. Thus implementing such a mechanism is crucial, the source offers the relay some positive reward in order for this latter to accept caching and forwarding the content to the final destination. However, the relay may either accept or reject this offer, depending on the reward value and the expected energy consumption due to this operation. Next, we exhibit some sufficient conditions ensuring existence of Nash equilibria for this game. Further, we discuss their efficiency using the concept of price of anarchy. Moreover, we propose two fully distributed algorithms to reach the equilibria (both for pure and mixed equilibria). We validate our proposal using extensive numerical examples and numerous simulation runs, and draw some conclusions and insightful remarks.

Recent advances in short range wireless communications along with exponential growth of the number of mobile devices are key factors for the emergence and development of Mobile Social Networks. Indeed, using proximity based approach to disseminate social content among individuals will reduce the load on the telecommunication operator infrastructure and will speedup the availability of the content among communities with common interests. Since content dissemination in Mobile Social Networks (MSNs) is intrinsically related to the mobility pattern of the users. Efficient dissemination protocols become a decisive issue. Mobile devices are battery powered, hence, energy consumption optimization is a critical issue to consider when deploying MSNs. Duty cycling is a well investigated approach to reduce energy consumption and prolong lifetime of mobile devices. In this paper we study the scheduling of active/idle cycles under Two-Hop social content dissemination protocol. We provide a game theoretical formulation of the problem and investigate the proposed activation game structural properties. We also study existence and uniqueness game solution and provide learning algorithm that ensure convergence of the studied MSN to this equilibrium operating point.

In this paper, we design a cooperative without explicit coordination framework for Delay Tolerant Network (DTN). The aim of our scheme is to provide incentives for nodes to deliver a message from a source node to a destination. In order to extend the reward-based incentive mechanism already discussed in related literature, we allow nodes to form coalitions. The relay nodes that belong to the same coalition share obtained reward after a successful delivery, and/or undergo some penalty in case of a failure delivery. Namely, we propose a coalitional game wherein relays of the same coalition cooperate while competing with relays of other coalitions to deliver the message. In this game, when a message is generated by a source, each candidate relay has two strategies: either to participate to forward the message, we refer to this by the “accept” `A' strategy. On one hand, when a relay chooses strategy `A', it seeks to receive a reward in case it is the first to get in contact with the destination. Otherwise, a penalty in terms of maximum energy consumption is applied. We consider that transmission, reception, storage and coalition formation operations are all energy consuming. On the other hand, i.e., when all relays of a coalition decide not to participate by choosing strategy `R', possibly to save energy, all of them undergo some penalty in form of a regret. Our approach is designed to ensure an efficient trade-off between the overall delivery probability and the energy consumption. Next, we propose two algorithms to allow relay node to converge to the stable partition. Extensive simulation runs illustrate the impact of many parameters on the coalitions' configuration at the equilibrium. We also exhibit how an efficient energy-delivery trade-off can be met.

This paper introduces a reward-based incentive mechanism for file caching in Delay Tolerant Networks (DTNs). In DTNs, nodes use relay's store-carry and forward paradigm to transmit data till final destinations under intermittent connectivity. However, the relays are not always available to assist data transmission due to limited energy or low storage capacity. Our proposal is based on a reward mechanism to sustain cooperation among relays. We model this distributed network problem as a non-cooperative game. On one hand, the source offers to the relays a positive reward if they accept to cache and to forward a given file successfully to a target destination. On the other hand, the relays can either accept or reject the source offer, depending on the reward value and the expected energy consumption of the caching-forwarding operation. Next, a full characterization of the equilibria of this game is provided. Then, we propose two fully distributed algorithms to discover the game Nash equilibria, both for pure/mixed equilibria and discrete/continuous strategy sets. We validate our proposal using extensive numerical examples and numerous learning simulations, and draw some conclusions and insightful remarks.

It is expected that in two-tier LTE heterogeneous networks, an extensive deployment of small cell networks (SCNs) will take place in the near future, especially in dense urban zones; hence a hyper density of SCNs randomly distributed within macro cell networks (MCNs) will emerge with many overlapping zones of neighboring SCNs. Therefore, the problems of interferences in co-channel deployment will be more complicated and then the overall throughput of downlink will substantially decrease. In order to mitigate the effect of interferences in a hyper density of SCNs scenarios, a solution based on a fully distributed algorithm for sharing time access to SCNs and multi-homing capabilities of macro cellular users is proposed to improve the overall data rate of downlink and at the same time to satisfy QoS throughput requirements of macro and home cellular users. Our tentative scheme will also reduce the signaling overhead due to the absence of coordination among small base stations (SBSs) and macro base station (MBS). Results validate our solution and show the improvement attained in a hyper density of SCNs within MCNs compared to open, closed and shared time access mechanisms based on single network selection.

During the last years, both academicians and professional researchers attribute an interest to the future of cities. They conclude that the technological leap will influence the both architecture and infrastructure, which will give birth to the smart cities vision. This essay aims to provide a comprehensive understanding of the movement towards smartness by providing a study on divers smart city definitions which depend on geographical, environmental, economical and social constraints of each city, next to presenting dimensions that let smart city a 3D concept and highlighting some smart city Models. It gives an overview of smart city characteristics: Smart Economy, Smart Environment, Smart Governance, Smart Mobility, Smart Living and Smart Human Level and shows some big pictures of the components of each paradigm and how they been illustrated. People usually moves to cities in order to fulfil their needs in job, relationships tpand enjoy the modern life, the urbanization phenomenon, climate change and resources depletion took place and addressed a significant number of Smart cities challenges were appeared in urban areas. However, thanks to ICT, Smart City provides opportunities for people to create, invent, test and experience new things in order to optimize their quality of life.

In the context of emerging technologies, Cloud Computing (CC) was introduced as a new paradigm to host and deliver Information Technology Services. In such an environment, privacy and security issues are critical areas that still require to be deeply explored. Yet, highly secured systems are generally met by computationally expensive systems. Such a system may also degrade the user experience and its willingness to adopt it. The aims of this paper are threefold: First, to integrate a Class-Association Rules (CARs) into the process of Multi-factor Authentication Based on Multimodal Biometrics (MFA-MB) for CC; Second, defines a new metric to measure the User Experience and; Third, exhibits an algorithm to authenticate cloud SaaS/PaaS Users with an enhanced MFA-MB scheme. Since, CARs are used to predict the most expected Multimodal Biometric Authentication (MBA) in the basis of Users' authentication habits, mined from their historical authentication data sets, which guarantees continuously improving their Experience. The integration of CARs in the CC authentication process allows also to identify the actual context (Time, Place, Device, etc.) which impacts the choice of biometrics used in MBA according to one User's situation. Therefore, this will help to increase the authentication security level using MBA at a decreased time and improved user experience. Integration of CARs is illustrated by a realistic case of Bimodal Biometric Authentication.

Abstract Traditional vertical handover schemes postulate that vertical handovers (VHOs) of users come on an individual basis. This enables users to know previously the decision already made by other users, and then the choice will be accordingly made. However, in case of group mobility, almost all VHO decisions of all users, in a given group (e.g., passengers on board a bus or a train equipped with smart phones or laptops), will be made at the same time. This concept is called group vertical handover (GVHO). When all VHO decisions of a large number of users are made at the same time, the system performance may degrade and network congestion may occur. In this paper, we propose two fully decentralized algorithms for network access selection, and that is based on the concept of congestion game to resolve the problem of network congestion in group mobility scenarios. Two learning algorithms, dubbed Sastry Algorithm and Q‐Learning Algorithm, are envisioned. Each one of these algorithms helps mobile users in a group to reach the nash equilibrium in a stochastic environment. The nash equilibrium represents a fair and efficient solution according to which each mobile user is connected to a single network and has no intention to change his decision to improve his throughput. This shall help resolve the problem of network congestion caused by GVHO. Simulation results validate the proposed algorithms and show their efficiency in achieving convergence, even at a slower pace. To achieve fast convergence, we also propose a heuristic method inspired from simulated annealing and incorporated in a hybrid learning algorithm to speed up convergence time and maintain efficient solutions. The simulation results also show the adaptability of our hybrid algorithm with decreasing step size‐simulated annealing (DSS‐SA) for high mobility group scenario. Copyright © 2015 John Wiley & Sons, Ltd.

Cloud Computing (CC) was introduced recently as a new paradigm to host and deliver Information Technology Services. Despite its advantages and maturity, security and privacy issues in CC remain an open challenge. Usually, cloud-based systems use login and password combination, PINs, smart cards, or unimodal biometrics for users authentication; Multimodal biometrics can be considered as an alternative solution and additional factor to increase CC authentication security level. First, the paper deals with the authentication security in CC and proposes a new approach to implement a multimodal biometric systems for authentication and identity management using user's physiological and/or behavioral traits. Second, combining the advantages of multi-factor and multimodal biometric techniques we develop a hybrid scheme called Multi-factor Authentication based on Multimodal Biometrics (MFA-MB) in order to authenticate and allow access for cloud consumers. Further, a classification of different practical multiple biometrics combinations is given for a wide number of MFA-MB real applications.

In heterogeneous network environments, users need to have mechanisms in place to decide which network is the most suitable at each moment in time for every application that the user requires. Always Best Connected is considered as a special concept to allow users to get connected to Internet using the access technology that best suits their needs or profile at any point in time. Clearly, this concept provides multiple access simultaneously for mobile users moving in heterogeneous access network environment. In this paper, we introduce a signaling game approach to achieve an always best connected service in vehicular networks. Under the considered scenario, we consider two smart vehicles named player 1 and player 2 moving in a road network area equipped with heterogeneous access networks. We assume that player 1 (super player) only has complete information on the road network, whilst player 2 has not any information. Player 1 plays first and sends a signal to player 2 which can be accurate or distorted. Based on the received signal and his belief about that signal, player 2 chooses its own action: it computes its suitable path which provides it an always best connected service.

Nowadays, Small-Cells are widely being deployed to assist and improve performance of mobile networks. Indeed, they are a promising solution to improve coverage and to offload data traffic in mobile networks. In this paper, we propose a signaling-less architecture of the heterogeneous network composed of one single Macro Base Station and a Single Small-Cell. First, we construct a game theoretic framework for channel-state independent interaction. We present many conditions for the existence of Pure Nash equilibrium. Next, and in order to capture the continuous change of the channel state, we build a random matrix game where the channel state is considered to be random (potentially ruled by some given distribution). A characterization of Nash equilibrium is provided in terms of pure strategies and mixed strategies. Convergence to Nash equilibrium is furthermore guaranteed using a variant of the well-known Combined fully distributed payoff and strategy learning. Our algorithm converges faster (only 10–20 iterations are required to converge to Nash equilibrium) and only need a limited amount of local information. This is quite promising since it says that our scheme is almost applicable for all environments (fast fading included).

In recent years, Delay Tolerant Networks (DTNs) have successfully presented as a possible extension of the Internet architecture in order to provide communication support to existing networks. However, these networks have a major issue which is the coordination among relays. In this work, we study the cooperative transmission for DTNs using coalitional game theory. We design a new incentive mechanism for heterogeneous system to induce coordination among DTN relays. In particular, we focus on the source packet dissemination to a destination using tow-hop relaying scheme, considering networks resource constraints: the relay buffer, the packet life time, and the energy consumption according to the mobile technologies. Rational mobiles are organized into separate coalition structures to meet a trade off between the source reward and the energy conservation. We discus the Nash equilibria for our game and the stable strategy state in which no mobile can get a higher payoff through changing unilaterally its coalition. Then, we use the distributed imitative Boltzmann-Gibbs learning algorithm enabling relays to learn the Nash equilibrium strategy; grand coalition. The improvement of the global system performance is examined, and a comparison between different inter-node collaboration states is presented.

A Long Term Evolution-Frequency Division Duplexing (LTE-FDD) small cell is one of the promising solutions for improving service quality and data rate in both the uplink and downlink of home users. Small cell (e.g., femtocell, picocell, microcell) is short range, low cost and low power base station installed by the indoor consumers. However, the avoidance of interferences is still an issue that needs to be addressed for successful deployment of small base stations (SBS) within existing macro cell networks mainly in co-channel deployment. Moreover, interferences are strongly dependent on the type of access control of small cells. Closed and open access are in conflict interests for macro users and home users in the uplink and downlink. To mitigate this conflict, we propose a fully distributed algorithm based on the shared time access and executed by LTE-FDD small cells, in order to reduce the effect of interferences, improve QoS of users, and maximize the overall capacity of downlink and uplink in two-tier LTE networks when small cells are deployed randomly. Simulation results validate our algorithm and show the improvement attained in offloading macro cell and satisfying QoS requirements of home users compared to the closed and open access mechanisms in both the uplink and downlink.

In this paper, we propose a hybrid cognitive radio (CR) system where underlay and overlay CR approaches are combined under SIRN constraints. This new access type allows to optimize the spectrum sensing time with throughput improvement. Our proposed access type is based on a merger of these two modes depending on the number of secondary users (SU) in the Underlay Mode and on the access probabilities for these two modes. The number of all users in the Underlay access mode should not exceed a certain number of users. When a new SU arrives and if the number of SUs in Underlay mode is already equal to the predifined max number, the SU decides to switch to the Overlay mode to access the canal using this method. The SU states will be modeled with a Markov chain, the aim of this work is attempting to show that this new method allows to increase the throughput of the SU with energy optimization. The exact outage probability of secondary system is also derived for this model. Simulation results show that the performance, especiallay the throughput can be significantly increased by the proposed hybrid system.

We investigate energy conservation and system performance of decentralized resource allocation scheme in cognitive radio networks thoroughly based on secondary users competitive behavior. Indeed, the contention on data channel unoccupied by licensed user leads to a single winner, but also involves a loss of energy of all nodes. In this paper, we apply minority game (MG) to the most important phase from the opportunistic spectrum access (OSA) process: the sensing phase. We attempt to carry out a cooperation in a non-cooperative environment with no information exchange. We study the Nash equilibrium solution for pure and fully mixed strategies, and we use distributed learning algorithms enabling cognitive users to learn the Nash equilibrium. Finally, we provide numerical results to validate the proposed approach. The resource allocation based on minority game approach improves secondary users battery life and the performance of the network.

To ensure connectivity in highly sparse Wireless Sensor Networks (WSNs), we consider a Ferry-assisted Wireless Sensor Network (FWSN). In our FWSN, message ferries moving along concentric annulus collect the static sensors generated packets and propagate them throughout a ferry-to-ferry forwarding schema to the sink. In this paper we present a queueing model to study and analyze the FWSN behavior. We will adapt a queuing model with finite queues which will allow us to analyze the network behavior in tens of packet loss using an analytic model. The objective of this work is to provide a way to optimize the energy consumption for each individual sensor. In our approach we consider a sensor Ferry, which is a mobile sensor with the capacity to provide the control operation upon the other fixed sensors within to network. Therefor, these fixed sensors will have to perform less control operations which will reduce their individual energy consumption. By doing so, this will automatically impact positively on to the lifetime of the network.

In this paper, we model a Virtual MIMO system using a game-theoretic approach. We are interested in the uplink, considering a non-coopertive game, where each user try to satisfy a quality of service. The uplink of a direct-sequence code division multiple access (DS-CDMA) data network is considered and a non-cooperative game is proposed in which users are allowed to choose their uplink receivers as well as to satisfy their quality of service. The utility function used in this framework is defined so that the throughput used by the user is divided into two components: the throughput received from cellular Network, and throughput received from Virtual MIMO System. In addition, this framework is used to study a constrained Nash equilibrium for the proposed game, and the impact of the interaction among users.

The concept of Participatory Sensing is centered on individuals that collect data using their smart phones (or other dedicated devices) to track the evolution of their work/living places. The main objective is to use the gathered data in order to enhance the offered quality of life. To maximize people involvement in this process we propose a ferry based architecture to leverage the contributors from charges associated with accessing service providers infrastructure to forward collected data. Opportunistic contact with message ferries will be exploited to gather data that will be carried from ferry-to-ferry till it reaches a centralized processing and decision-taking authority. We provide a closed formula for the End-to-End throughput of the proposed gathering network architecture.

Vehicular Ad Hoc Network (VANET) is considered as a special application of Mobile Ad Hoc Networks (MANETs) in road traffic, which can autonomously organize networks without infrastructure. VANETs enable vehicles on the road to communicate with each other and with road infrastructure using wireless capabilities. In the last few years, extensive research has been performed to extend Internet connectivity to VANETs. Indeed, several routing protocols have been proposed to determine routes between vehicles and gateways. In this paper, we propose a routing algorithm which is based on the Congestion Game to resolve the problem of network congestion in VANET and to provide the optimal Internet access paths. The simulation results show that the proposed routing algorithm has better feasibility and effectiveness for communicating VANETs.

The traditional vertical handover schemes postulate that vertical handover of each user comes on an individual basis. This enables the users to know previously the decision already made by other users, and then the choice will be made accordingly. However, in the case of a group vertical handover, almost all the VHO decisions - which will certainly choose the best network, will be made at the same time which will lead to system performance degradation or network congestion. In this paper, we propose a totally decentralized algorithm for network selection which based on the Congestion Game to resolve the problem of network congestion in GVHO. Therefore, the proposed algorithm named Fully Decentralized Nash Learning Algorithm with incomplete information is a prediction done by each mobile in the group that helps them to reach the Nash equilibrium. Simulation results validate the algorithm and show its robustness under two scenarios. In the first one, we examine the algorithm with a fixed number of mobiles in group to evaluate the mixed strategy and the average perceived throughput of mobiles in WIMAX and HSDPA on the basis of iteration. In the second one, we examine the algorithm with different number of mobiles in group for testing the average number of iterations needed to reach the Nash equilibrium. We also compare it with the traditional vertical handover algorithm.

In this paper, we consider a distributed opportunistic access (D-OSA), in which cognitive radio (CR) users attempt to access a channel licensed to a primary network. In this context, we formulate the problem of designing the equilibrium sensing time in a distributed manner, in order to maximize the throughput of CR users while guarantying a good protection to the primary users (PU). Next, we study the Nash equilibrium of the system, we also propose a combined learning algorithm for continuous actions that is fully distributed, and allows to the CR users to learn their equilibrium payoffs and their equilibrium sensing time. The simulation results show that the system can learn the sensing time and converge to a unique Nash equilibrium, which come to prove the theoretical study. A surprising feature is that there exists a correlation between the transmit probability and the sensing time. More precisely, lower transmit probability induces lower sensing times.

This paper deals with the problem of joint sensing and medium accessing in cognitive radio networks. We design a non-cooperative two-step game to describe the Sense-Transmit-Wait paradigm in an opportunistic point of view. We give a full characterization of the Nash equilibria and analyze the optimal pricing policy, from the network owner view, for both centralized setting and decentralized setting. Next, we propose a combined learning algorithm that is fully distributed and allows the cognitive users to learn their optimal payoffs and their optimal strategies in both symmetric and asymmetric cases. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in medium access cognitive radio networks in terms of sensing capabilities.

In wireless communication systems, the delay remains a crucial factor. In this work, we focus on the design of a new opportunistic cross-layer MAC protocol involving channels allocation and packet scheduling for cognitive networks in order to optimize system performance. Cognitive radio provides the opportunity for secondary users (unlicensed users) to use available portions of the licensed spectrum bands without interfering with primary users (licensed users). We consider that each secondary user is equipped with two transceivers. The role of the first transceiver is to obtain and exchange channels information on the control channel. The second transceiver is devoted to periodically detect and dynamically use the available data channels. The paper deals with channel allocation considering traffic characteristics of secondary users. So, we propose a mechanism of resource reservation to improve Quality of Service (QoS) requirements that favors successful secondary users to transmit data during x time slots without interfering with primary users. We develop a new analytical model, while taking into account the backoff mechanism. We analyze delay parameter for two scenarios with and without resource reservation. We show through simulations that our approach guarantees an optimal response delay.

This paper presents a cross-layered framework to study linear and circular ad hoc networks built on the IEEE 802.11e Enhanced Distributed Coordination Function. We investigate the intricate interactions among PHY, MAC and Network layers. Now, the carrier sense threshold, the transmit power, the contention window size, the retransmissions retry limit and the multi rates are jointly incorporated. We propose an equivalent network where each node is able to listen to other nodes, i.e., the effect of hidden nodes could be definitely eliminated. Then, we develop an analytical model that predicts the throughput of each connection as well as the stability of forwarding queues at intermediate nodes while each node is able to prevent the accumulative interference phenomenon. Further, performance of such a system is evaluated via simulation. We show that the performance measures of MAC layer are affected by the traffic intensity of flows to be forwarded and the average hop length. Moreover, attempt rate and collision probability are dependent on the traffic flows, PHY parameters and routing scheme.