Unmanned Aerial Vehicle (UAV) formations play a vital role in modern mission-critical applications, such as disaster response, surveillance, and Search and Rescue (SAR) operations. Ensuring the operational health of Unmanned Aerial Vehicles (UAVs) in the most often dynamic environments in which they are deployed requires a robust, scalable, and realtime fault detection mechanism to ensure mission integrity. In this paper, we propose an Edge-Enhanced Graph Attention Network (EE-GAT) model for fault detection in a dynamic UAV formation. The proposed method integrates node-level features such as altitude, velocity, battery level, with edge-level network proximity measures (RSSI and SINR) into an attention-based graph learning framework to enable accurate detection of UAV position status in a leader-follower topology. Through extensive simulations in NS3 v3.42, we demonstrate that our EE-GAT achieves a high fault detection accuracy of up to $\mathbf{9 6. 1 \%}$. Our approach outperforms baseline models, offering a lightweight, and cost-effective solution for real-time UAV health monitoring in dynamic and large-scale formations.

In 6G, the network is envisioned as a service embedded within a unified digital infrastructure that also provides computing, storage, and control. This infrastructure spans both horizontal and vertical planes and integrates with the Cloud Continuum, including edge data centers. The Internet of Edges aims to deploy 6G nodes with embedded micro data centers, forming a distributed edge cloud positioned near end-users or within terminal devices. Computing task requests are handled at the end-users whenever possible; otherwise, they are escalated hierarchically to higher layers. This paper addresses the challenge of identifying the best layer at which Resource-Intensive computation is to be processed, especially computation related to Machine Learning (ML) tasks. In particular, Federated Learning (FL) will enable edge devices to collaboratively train ML models while preserving data privacy. However, constrained devices face challenges such as high prediction errors due to limited dataset sizes and energy consumption cost associated with FL. To address these issues, this paper proposes to adequately characterise the computing placement depending on the device characteristics. More energy constraint devices need to choose between local learning (on device) or federated learning through an edge server. Conversely, devices doted with more capacity need to federate the ML cooperatively in learning coalitions. Both cases will be considered through the lens of game theory. Extensive numerical results highlight the effectiveness of our approach, showcasing its ability to enhance learning while minimizing energy cost.

Federated Learning (FL) enables edge devices to collaboratively train machine learning models while preserving data privacy. However, in IoT networks, constrained devices face challenges such as high prediction errors due to limited dataset sizes and the computational costs associated with FL tasks. To address these issues, this paper proposes a two-level resource management framework for IoT Federated Learning. The first level focuses on forming homogeneous learning clusters with mandatory minimal dataset size where devices with similar computational power and data distribution are grouped together to optimize learning performance. The second level employs a two-stage Stackelberg game to incentivize devices to contribute larger datasets by offering monetary rewards, balancing the trade-off between prediction accuracy, computational costs, and energy consumption. Our framework shows significant improvements in prediction accuracy and overall cost reduction compared to the flat network approach, where all devices are grouped together.

This work proposes a novel approach to channel sensing in cognitive radio systems, drawing inspiration from reinforcement learning theory. While the adversarial Multi-Armed Bandit framework is commonly used to manage diverse channel sampling, it struggles to accurately assess resource occupancy due to geographical variations in channel availability across devices. To address this limitation, collaboration among devices is essential and can be effectively achieved through Federated Learning (FL). FL integrated into a Multi-Armed Bandit framework addresses key challenges, including data heterogeneity, the high cost of data centralization, privacy concerns, and biased learning. We enhance the widely-used Exponential-weight algorithm for exploration and exploitation (EXP3) by incorporating federation, allowing learning devices to collectively identify channels with less interference. Our simulation results demonstrate the effectiveness of the federated EXP3 (F-EXP3) algorithm by comparing it with the traditional EXP3 and the federated Upper Confidence Bound (UCB). The experiments reveal that F-EXP3 overcomes the limitations of individual learning, leading to superior channel selection performance.

The advent of 5G technology has revolutionized network communication by introducing network slicing (NS) and virtualization to allow multiple network service providers (NSPs) to share infrastructure, thereby reducing deployment costs and accelerating 5G adoption. While this new open marketplace enables NSPs to trade resources dynamically, it also exposes the system to security concerns, such as front-running and selfish-validation attacks, which can lead to market manipulation and strategy leakage. This paper presents TRADE-5G, a secure blockchain-based marketplace for 5G resource trading that mitigates these attacks and ensures fair, transparent resource allocation while preserving the confidentiality of NSP strategies. Through extensive simulations, TRADE-5G demonstrates a substantial 18% improvement in user satisfaction and a 36% reduction in wasted resources compared to traditional models. Additionally, it opens new profit opportunities for NSPs through unused resources, establishing a more competitive, secure, and transparent 5G trading environment that exceeds the capabilities of traditional mobile networks.

Edge computing is an efficient way to help constrained IoT devices by offloading heavy tasks on edge servers, especially computing tasks related to Machine Learning (ML). Moreover, such devices can only store a limited amount of data because of their reduced capacity. Consequently, ML is bound to be smeared with relatively high error prediction as these devices resort to a small training dataset for their learning. To mend that issue, IoT devices can group in clusters and resort to Federated Learning (FL) with their pairs in the same cluster or coalition. However, the learned model needs to be transmitted repeatedly over a wireless access network, which is energy consuming. Hence, although learning collectively through FL can reduce the learned model variance, it inflicts a communication cost, dependent on the coalition size, that must be taken into account. Therefore, a cost function is devised astutely by factoring in both the prediction error and communication cost in a learning cluster. Then, a coalition formation game is conceived to minimize the devised cost function. Autonomous IoT devices will engage in the proposed game leading to coalitions of optimal size. Once clusters are formed, distributed FL is applied in any cluster in order to reduce the learning error of participating devices while curbing their communication cost.

Computational offloading is an efficient way to help constrained IoT devices by performing heavy tasks on Edge servers, especially tasks related to Machine Learning. Moreover, due to their limited learning capacity and memory size, such devices can only store a limited amount of data as a training set for their learning. Consequently, learning prediction is bound to be smeared with relatively high error. To mend that issue, IoT devices can federate the learning process with their pairs via an Edge server. However, offloading repeatedly the learning model through a wireless access network is time consuming. Hence, although learning collectively can reduce the learned model variance, it inflicts a communication cost depending on the selected Edge server. Therefore, in this paper, we model the Edge Selection problem as a non-cooperative game where devices autonomously and efficiently select an Edge server to reduce both their learning error and their communication cost. Depending on the characteristics of the dataset, we discern two different types of games. For each game type, we implemented and compared a semi-distributed algorithm based on Best Response dynamics. We compared the obtained results with the optimal centralized approach and with a less computationally intensive meta-heuristics, to assess the price of anarchy. Our numerical analysis shows that the Best Response algorithm strikes a good balance between efficiency and swift convergence.

Computational offloading is a pivotal solution to several Internet of Things (IoT) issues as it helps subdue the constrained nature of IoT devices. By harnessing the large capacity at the Edge, IoT devices with limited battery and storage can delegate certain tasks, especially those related to Machine Learning. Because of their restricted capacity, such devices can only store a limited amount of data as a training set for their learning, leading to a faulty prediction with high error rate. To tackle that issue, IoT devices can federate the learning process with other devices while the Edge server acts as an aggregator. However, selecting the appropriate Edge is a significant challenge. In fact, although learning collectively can reduce the prediction error, it also brings about a communication cost that depends on the selected Edge. Thus, in this paper, we propose a Non-Cooperative game where devices autonomously and efficiently select an Edge server in order to reduce both their learning error and communication cost.

In the context of wireless networked robotics, complex missions, such as autonomous Search and Rescue, may require the use of multiple Unmanned Aerial Vehicles (UAVs) to achieve higher efficiency and Quality of Service (QoS) while assuring mission-specific imperatives like the maximization of area covered in a single passage of the fleet over area subsections. In this paper, we present a learning-based formation control protocol that adapts the principle of Q-learning to pilot an autonomous fleet of networked UAVs to maintain formation throughout a mission where large quantities of data need to be exchanged. Also, the protocol tries to ensure rotational formation control by leveraging only the signal strength extrapolated from the UAV communications. A leader-follower model is used to control the fleet. One UAV serves as the leader, and the remaining as the followers. The followers use the Received Signal Strength Indicator (RSSI) values obtained from their neighbors to autonomously determine the leader's direction of movement and maintain formation orientation to avoid area coverage overlapping. We carried out several simulation experiments to evaluate the performance of the proposed scheme in terms of QoS and convergence time of the formation under varying velocities.

Federated learning provides access to more data which is paramount for constrained LoRaWAN devices with limited memory storage. Learning on a larger data set will reduce the variance of the learned model, hence reducing its error. However, federating the learning process incurs a communication cost among learning devices that must be taken into account. In this paper, we formulate a Cooperative Hedonic game and introduce a new cost function that captures both the learning error and communication cost. LoRaWAN devices engage in the devised game by identifying if they should keep their learning local or federate with other devices in order to reduce both their learning error and communication cost. We compute the optimal size of formed coalitions and assess their stability. Then, we show through extensive simulations that devices have incentive to form learning coalitions depending on the data characteristics at hand and the communication cost in LoRaWAN.

For a seamless deployment of the Internet of Things (IoT), lightweight self-organizing solutions are needed to overcome the challenges of IoT, mainly stemming from their constrained nature. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via pure ALOHA-type access. In fact, the conception of LoRaWAN advocates simplicity in order to reduce drastically the battery consumption. This simplicity in the contention access method severely degrades reliability. To address that shortcoming, the adoption of Successive Interference Cancellation (SIC) in conjunction with packet repetition has been lately considered as a promising solution for the particular setting of IoT. However, the proposed solutions were devised for synchronised systems and particular care is needed to fit these solutions for the asynchronous LoRaWAN networks. In this paper, we put forward such a tailored mechanism, coined RALI (Repetition in ALOHA for LoRaWAN with Iteration), assess its performances analytically and demonstrate through extensive simulations its notable efficiency.

Dense deployment of small cells operating on different frequency bands based on multiple technologies provides a fundamental way to face the imminent thousand-fold traffic augmentation. This heterogeneous network (HetNet) architecture enables efficient traffic offloading among different tiers and technologies. However, research on multi-tier HetNets where various tiers share the same microwave spectrum has been well-addressed over the past years. Therefore, our work is targeted towards novel multi-tier HetNets with disparate spectrum (microwave and millimeter wave). In fact, despite the huge capacity brought by millimeter-wave technology, the latter will fail to provide universal coverage, especially indoor, and so mmWave will inevitably co-exist with a traditional sub-6GHz cellular network. In this work, we propose a coordinated user association and spectrum allocation by resorting to non-cooperative game theory. In fact, in such an arduous context, efficient distributed solutions are imperative. Extensive simulation results show the precedence of our coordinated approach in comparison with state-of-the-art heuristics. Moreover, we evaluate the impact of various network parameters, such as mmWave density, cell load, and user distribution and density, offering valuable guidelines into practical 5G HetNet design. Finally, we assess the benefit brought by massive MIMO for mmWave in such a highly heterogeneous setting.

With the development of various technologies, the modern industry has been promoted to a new era known as Industry 4.0. Within such paradigm, smart factories are becoming widely recognized as the fundamental concept. These systems generate and exchange vast amounts of privacy-sensitive data, which makes them attractive targets of attacks and unauthorized access. To improve privacy and security within such environments, a more decentralized approach is seen as the solution to allow their longterm growth. Currently, the blockchain technology represents one of the most suitable candidate technologies able to support distributed and secure ecosystem for Industry 4.0 while ensuring reliability, information integrity and access authorization. Blockchain based access control frameworks address encountered challenges regarding the confidentiality, traceability and notarization of access demands and procedures. However significant additional fears are raised about entities' privacy regarding access history and shared policies. In this paper, our main focus is to ensure strong privacy guarantees over the access control related procedures regarding access requester sensitive attributes and shared access control policies. The proposed scheme called PDAMF based on ring signatures adds a privacy layer for hiding sensitive attributes while keeping the verification process transparent and public. Results from a real implementation plus performance evaluation prove the proposed concept and demonstrate its feasibility.

Faced with the limitations of the Aloha random access scheme and spread spectrum techniques, LoRaWAN is yet to realize its potential as the flagship technology for large-scale Internet-of-Things applications. LoRaWAN allows for low power and long range communications. Nonetheless, concurrent transmissions on the same spreading factors (SFs), increased with the inevitable densification of device deployment, will lead to collisions and degradation in performance. The problem is further amplified due to the shortage in radio resources, with multiple operators utilizing the same unlicensed frequency bands. In this article, we investigate different interoperator cooperation schemes and devise multiple algorithms for SF assignment in a multioperator LoRaWAN deployment scenario. We start by proposing a proportional fair optimal formulation for the assignment with the objective of maximizing the logarithmic sum of the normalized throughput per SF. Under the assumption of partial operator cooperation, we propose a gradient ascent-based iterative algorithm for solving the SF assignment problem, and a game theory-based approach, wherein each network operator seeks to maximize its own normalized throughput. Finally, and with cooperation between different operators bound to be limited, we use recurrent neural networks to enable the prediction of the success rate per SF. This prediction allows the different operators to assign SFs with minimum cooperation. We simulate our proposals and compare them to the legacy LoRaWAN approach as well as others in the state of the art, highlighting the gains they produce in terms of total normalized throughput and packet delivery ratios.

Due to highly variant traffic in downlink (DL) and uplink (UL) in heterogeneous networks (HetNets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, downlink and uplink decoupled access (DUDA) is introduced to balance between UL and DL transmissions and to further improve the system performance. Rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. In this article, we analytically investigate a joint TDD and DUDA statistical model with multiple small cells deployment. This model is based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, coupled and decoupled cell associations strategies are investigated in details. We derive analytical expressions for the capacity and the interference, considering a network of one macro cell and multiple small cells. We build on the derived capacity expressions to measure the decoupling gain and thus, identify the location of the interferer small cell where the decoupled mode maintains a higher gain in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the statistical model.

Dynamic time-division duplexing (TDD) enables flexible adjustments of uplink (UL) and downlink (DL) resources according to the instantaneous traffic load. However, it also brings new challenges in heterogeneous cellular networks (HetNets) because of the introduction of cross-link interference i.e., uplink to downlink interference and downlink to uplink interference. One step further in the optimization of HetNet, is the interdependency between UL and DL and how the association policies affect the system performance on both links in a way to mitigate the cross-link interference. In classical HetNets, coupled UL/DL access (CoUD) mode is adopted, where each user is associated in downlink and uplink with a single cell. However, the power imbalance between the macro cells and the small cells motivates the decoupling of both links. In the next generation HetNets, instead of being connected to a specific cell, a mobile user can independently receive the downlink traffic from one base station (BS) and transmit uplink traffic through another BS. This situation is referred to as decoupled uplink and downlink (DeUD) access. The optimization of a HetNet based system according to time-variant traffic loads necessitates finding a system level simulator where we can present the motivation and accurately assess the role of both decoupling and dynamic TDD techniques. In this paper, we resort to a system level simulator under which we develop a new module that investigates the dynamic TDD technique along with multiple association policies in a dense HetNet deployment. We create appropriate simulation environment that is relative to real scenarios i.e. simulations where multiple small cells are deployed in a heavy loaded HetNet system and under various traffic loads.

For a seamless deployment of the Internet of Things (IoT), self- managing solutions are needed to overcome the challenges of IoT, including massively dense networks and careful management of constrained resources in terms of calculation, memory, and battery. Leveraging on artificial intelligence will enable IoT devices to operate autonomously by using inherently distributed learning techniques. Fully distributed resource management will free devices from draining their limited energy by constantly communicating with a centralized controller. The present work is devoted to a specific IoT context, that of LoRaWAN, where devices communicate with the access network via ALOHA-type access and spread spectrum technology. Concurrent transmissions on different spreading factors increase the network capacity. However, the bottleneck is inevitable with the expected massive deployment of LoRa devices. To address this issue, we resort to the popular EXP3 (Exponential Weights for Exploration and Exploitation) algorithm to steer autonomously the decision of LoRa devices towards the least solicited spreading factors. Furthermore, the spreading factor selection is cast as a proportional fair optimization problem used as a benchmark for the learning-based algorithm. Extensive simulations were run in a realistic environment taking into account physical phenomena in LoRaWAN such as the capture effect and inter- spreading factor collision, as well as non- uniform device distribution. In such a realistic setting, we evaluate the performances of the EXP3.S algorithm, an efficient variant of the EXP3 algorithm, and show its relevance against the fair centralized solution and basic heuristics.

While smart factories are becoming widely recognized as a fundamental concept of Industry 4.0, their implementation has posed several challenges insofar that they generate, process, and exchange vast amounts of security critical and privacy sensitive data, which makes them attractive targets of attacks and unauthorized access. Security requirements in such scenario include integrity, confidentiality, traceability and notarization of exchanged data in the one hand plus access control, privacy and trust in the other one. In this context, we design a distributed resource management framework using the emerging smart contracts technology for Industry 4.0 applications and more specifically for smart factories environments. This last, named DRMF, utilizes three Ethereum smart contracts specifically a Governance Contract (GC), an Access Contract (AC) and a Lookup Contract (LC) that are respectively responsible for the registration of new joining entities as well as those requesting consensus partaking permissions, second the dynamic access authorization and third the mapping between the required services and contracts ensuring their management. Using the blockchain technology, this framework is expected to achieve distributed, flexible, verifiable and trustworthy access control in addition to a transparent, traceable and notarized resource usage and sharing. Results from a real implementation plus performance evaluation prove the proposed concept and demonstrate its feasibility.

In recent years, studies have been conducted to evaluate the performance of blockchain-based technologies to solve various problems. In this paper, we present a proof of concept in which we evaluate the robustness of a blockchain-based application in an ad hoc network confronted with split merge problems that may be caused by node mobility. We highlight how a blockchain should behave according to the network state and we measure its cost in terms of network load. We show that the measurements depend greatly on the mining algorithm used to solve the consensus.

In this paper, an innovative approach based on a Q-learning algorithm to allow a single operator to control a fleet of Unmanned Aerial Vehicles (UAVs) is presented. To follow an independently-controlled UAV (the leader), all the other UAVs (the followers) use only the radio signal strength values received during wireless ad-hoc communications among themselves, without the need for any infrastructure, any localization technique or any additional device. By applying the proposed behavior-based control scheme in real-time, the fleet formation can be perfectly maintained. The solution has been implemented through the definition of a new protocol and has been tested using ns-3. Experiments highlight the efficiency and effectiveness of the proposed method.

Security presents a significant challenge for the implementation and the realization of IoT scenarios. Its requirements include data confidentiality, authentication, access control as well as privacy and trust among things and services. To evaluate entities trustworthiness, exchanging trust information is crucial to reach an accurate assessment. Secure sharing and storage of trust information is essential for its confidentiality, integrity and privacy. In this context, our objective is to propose a secure trust management system based on the blockchain technology so that we can take advantages of security features it provides regarding reliability, traceability and information integrity. Blockchain based trust management can provide tamper proof data, enable a more reliable trust information integrity verification, and help to enhance its privacy and availability during sharing and storage. For this purpose, we design and implement a blockchain based trust architecture to collect trust evidences, to define a trust score for each device and to securely store and share them with other devices within the network by embedding them into blockchain transactions. Results from performance evaluation demonstrate that our proposal provides security features including tamper-proof and attacks resiliency, reliability in addition to a low complexity for IoT scenarios and applications.

Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios.1

In 5G networks, Mobile Edge Computing (MEC) has been proposed to enable computation and storage capabilities at the edge of Radio Access Networks. Proactive content caching in MEC is crucial to guarantee users' Quality of Experience thanks to the reduction of traffic latency. Predicting content popularity plays a key role in the effectiveness of proactive caching. In this paper, we propose a generic and flexible recommendation framework which allows recommending suitable learning and prediction algorithms among available ones, in order to predict content popularity. The investigated algorithms are categorized into two main classes: tree-based regressors and recurrent neural networks. Through the study case of YouTube video solicitation profiles, our proposed method, called Imputation-Boosted Collaborative-Filtering based Recommending Prediction Method (IBCF-RPM) shows its effectiveness in the prediction of content popularity for various popularity profiles. By running only 30% of the prediction algorithms, randomly chosen, on a given content profile, the proposed recommending method is able to estimate the accuracy of the other predictors and recommend a well-suited predictor for content popularity.

Time-division duplexing (TDD) systems can allow a dynamic adjustment of uplink and downlink resources according to the time-variant traffic loads. The interference generated by the concurrent uplink and downlink transmission in different cells, brings a new challenge to interference modeling in cellular networks. In this paper, an analytical framework is developed to evaluate the performance of a TDD system in heterogeneous networks (HetNets) which considers a concurrent uplink and downlink transmission in two different types of cells, macro and small cells. Firstly, we derive an analytical expression for the distribution of the interferer location considering all possible interference scenarios that could occur in TDD- based networks while taking into account the harmful impact of interference. Secondly, based on the latter result, we derive the distribution and moment generating function (MGF) of the uplink and downlink inter-cell interference considering a network consisting of one macro cell and one small cell. Finally, we build on the derived expressions to analyze the average capacity of the reference cell in both uplink and downlink transmissions. Monte- Carlo simulation results are provided to demonstrate the accuracy of the derived analytical expressions, in various realistic scenarios.

Recently, physical layer security has emerged as a promising security scheme for wireless networks, in contrast to traditional solutions that mainly rely on upper network layers. As such, several physical layer encryption algorithms that benefit from the random characteristics of physical channels have appeared in the literature. However, the majority of these schemes lack the notion of secrecy and dynamicity. In this paper, we focus on enhancing the physical layer encryption for wireless machine-to-machine devices, which share the same channel, with the aim of striking a good balance between performance and security robustness. The main idea is to perform encryption at the physical layer after symbol modulation. The cipher scheme is based on one round and one operation that reduces the encryption overhead in terms of latency and required resources. Furthermore, we propose a dynamic key approach that combines a pre-shared/stored secret key with a dynamic nonce extracted from the channel information to generate a dynamic key. The main advantage of the dynamic key approach is that it achieves a high-security level with minimal overhead. The dynamic key can be changed frequently upon any change in channel parameters or upon starting a new session. In addition to data encryption, a preamble encryption scheme is also proposed to prevent unauthorized synchronization or channel estimation by illegitimate users. Finally, security and performance analyses are performed to demonstrate the validity, efficiency and robustness of the proposed approach.

Heterogeneous Cloud Radio Access Networks (H-CRANs) are a promising cost-effective architecture for 5G system which incorporates the cloud computing into Heterogeneous Networks (HetNets). We consider in this work the joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink H-CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to address the beamforming and user association process over time by taking into account the user mobility as a key factor to tune the solution's parameters. More precisely, based on the mobility profile of users (mainly velocity), we propose an advanced Mobility-Aware Beamforming and User Clustering (MABUC) algorithm which selects the best Channel State Information (CSI) feedback strategy and periodicity to achieve the targeted sum-rate performance while ensuring the minimum possible cost (complexity and CSI signaling). MABUC inherits the behavior of our previously proposed Hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. MABUC algorithm, however, takes into account the user mobility by using a CSI estimation model which can improve the algorithm performance compared to reference schemes. Our proposed algorithm has the benefit to meet the targeted sum-rate performance while being aware and adaptive to practical system constraints such as mobility, complexity and signaling costs.

Due to load traffic disparity in downlink (DL) and uplink (UL) in heterogeneous cellular networks (Het-Nets), dynamic time-division duplexing (TDD) is proposed to dynamically allocate UL and DL resources. Under the same circumstances, decoupled UL/DL access is introduced to balance between UL and DL transmissions and to further improve the overall system performance. In classical HetNets, coupled UL/DL access (CUDA) mode is adopted, where each user is associated in downlink and uplink with a single cell. In the next generation HetNets, rather than belonging to a specific cell, a mobile user can receive the downlink traffic from one base station (BS) and send uplink traffic through another BS. This situation is referred to as downlink and uplink decoupled access (DUDA). In this paper, we analytically investigate a joint TDD and DUDA statistical model based on a geometric probability approach. Taking all possible TDD subframes combinations between the macro and small cells, four coupled and decoupled cell associations strategies are investigated in details. We derive inter-cell interference and capacity expressions for each of these association policies to assess the improvement brought by DUDA mode to TDD HetNets. In this particular DUDA mode, users are associated in UL with the small cell and in DL with the macro cell. Moreover, we identify the small cell offset under which the decoupled mode performs significantly better and maintains a higher spectral efficiency in both DL and UL. Monte-Carlo simulations results are presented to validate the accuracy of the analytical model.

Cloud Radio Access Network (C-RAN) is an evolution in the base station architecture, mainly composed of two elements: The Base Band Unit (BBU) and the Remote Radio Head (RRH). This new architecture separates the two elements (the BBUs from RRHs), to group all the BBUs into one cloud, while RRHs are distributed across many sites. Unlike in conventional architecture, many RRHs can be associated to one BBU when traffic load is low, allowing better exploitation of radio resources. A dynamic re-association between BBUs and RRHs is crucial, since coping with traffic load can enable reduction in power consumption and enhancement in energy efficiency. However, re-associations between BBUs and RRHs would cost frequent handovers for users connected to re-associated RRHs. Unlike the existing studies, the aim of this paper is to solve the dynamic aspect of the BBU-RRH association. To that end, we introduce a tunable bi-objective optimization problem formulated as a Set Partitioning Problem (SPP). The purpose is to jointly minimize the power consumption and the re-association rate of users experiencing a BBU change, subject to a minimum guarantee of Quality of Service (QoS). A heuristic approach is also introduced and provides close performance to the optimal solution.

Cloud Radio Access Network (C-RAN) is a promising technology to improve user quality of service and reduce network capital and operating costs. The key concept behind C-RAN is to break down the conventional base station into a Base Band Unit (BBU) and a Remote Radio Head (RRH), and to pool BBUs from multiple sites into a single geographical point. Moreover, to achieve statistical multiplexing gain, RRHs should be efficiently clustered: many RRHs may be mapped into a single BBU. In this article, RRH clustering is formulated as a coalition formation game where RRHs collaborate and organize themselves into disjoint independent clusters, in a way to optimize network throughput, power consumption, and handover frequency. An optimal centralized solution, based on exhaustive search, is presented. We also propose a distributed algorithm, based on the merge-and-split rule, to form RRH clusters. Simulation results show that our centralized solution adapts to network load conditions and outperforms the no-clustering method, where only one RRH is assigned to each BBU, and the grand coalition method, where all RRHs are assigned to a single BBU. More importantly, our distributed algorithm achieves very close performance to the optimal solution, with significantly lower computational complexity.

Internet of Things (IoT) is characterized by heterogeneous devices that interact with each other on a collaborative basis to fulfill a common goal. In this scenario, some of the deployed devices are expected to be constrained in terms of memory usage, power consumption and processing resources. To address the specific properties and constraints of such networks, a complete stack of standardized protocols has been developed, among them the Routing Protocol for Low-Power and lossy networks (RPL). However, this protocol is exposed to a large variety of attacks from the inside of the network itself. To fill this gap, this paper focuses on the design and the integration of a novel Link reliable and Trust aware model into the RPL protocol. Our approach aims to ensure Trust among entities and to provide QoS guarantees during the construction and the maintenance of the network routing topology. Our model targets both node and link Trust and follows a multidimensional approach to enable an accurate Trust value computation for IoT entities. To prove the efficiency of our proposal, this last has been implemented and tested successfully within an IoT environment. Therefore, a set of experiments has been made to show the high accuracy level of our system.

Cloud Radio Access Network (CRAN) is a promising network architecture for 5G to address the increasing demand for mobile data traffic. We consider a joint beamforming and clustering (user-to-Remote Radio Head (RRH) association) issue for downlink CRAN to solve the sum-rate maximization problem under fronthaul link capacity and per-RRH power constraints. The main objective is to investigate and analyze the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of imperfect Channel State Information (CSI). We propose a hybrid algorithm which periodically activates dynamic and static clustering strategies to manage the allocation process over time. This algorithm has the benefit to approach the optimal performance while being aware of practical system constraints. Furthermore, we present an analysis of major cost metrics for the proposed and reference dynamic algorithms. The simulation results show that our proposed algorithm reduces significantly the complexity and signaling costs while approaching the performance of the optimal solution.

Energy consumption has become a key issue in the design of communication systems. Indeed, both for economic and green reasons, the concept of energy saving appears at an early stage of projects, fully integrating the list of expected performance, as well as throughput or security. In multihop wireless networks, several energy-aware approaches have been proposed with specific goals, such as network's lifetime or stability. To reduce the global energy consumption of such networks, we proposed in a previous work an optimal solution with interference consideration, based on mixed integer linear programming, to route a set of flows over a minimal number of nodes. Thus, without degrading flow rates, inactive nodes can be put in a sleep mode or be turned off to maximize energy savings. Indeed, the energy consumption related to communication is just a fraction of the total consumption of a node In this article, we are going a step further, by providing a metric to efficiently aggregate flows with classical shortest path algorithms. Thanks to a theoretical comparison and simulations, we demonstrate the significant gains can be obtained with our approach. Finally, we discuss on ways to implement our solution in existing routing protocols, in a fully distributed manner, and other considerations that may need to take on.

The emphasis of this paper is put on 5G multi-cell networks which are composed of dense and mutually interfering evolved NodeBs (eNBs) sharing the scarce radio resources. Consequently, greater focus is given to resource management techniques that take Inter-Cell Interference (ICI) into account, in particular to power control. Beside power control, this paper tackles also user association and scheduling. Despite the relevance of the addressed problem, it has remained largely unsolved, mainly due to its non-convex and combinatorial nature. We address this multifaceted challenge in a distributed fashion for reduced complexity.

Traditional design of wireless networks mainly focuses on system capacity and spectral efficiency. As green networking is an inevitable trend, energy-efficient design for future wireless networks becomes paramount. In this paper, we address energy-efficient resource management in downlink orthogonal frequency division multiple access networks. The focus is targeted toward multi-cell networks, which are composed of multiple base stations (BSs) sharing the available radio resources. Consequently, greater emphasis is given to techniques that take inter-cell interference into account. Resource management in our context refers to the task of allocating the radio resources in order to maximize energy efficiency. We devise resource management techniques that jointly tackle the problems of scheduling and power control. Accordingly, we adopt two different approaches: a centralized approach, where BSs coordinate in order to reach a globally optimal energy-efficient solution, and a distributed approach, where BSs selfishly strive to maximize their own energy efficiency. We portray the centralized approach as a convex optimization problem, whereas we have recourse to non-cooperative game theory to model the distributed approach. In particular, we show that the non-cooperative game converges to a unique Nash equilibrium in low- and high-interference scenarios. We perform thorough numerical simulations to quantify the discrepancy between the centralized and distributed approaches, and identify the conditions where they have precedence over the state of the art. Moreover, the simulation results highlight the fast convergence of our algorithms, which is a precious asset for realistic deployments.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs and co-channel interference caused by resource sharing becomes a significant challenge. We consider both the total energy efficiency (EE) and individual EE optimization problems, which are fractional programming and generalized fractional programming problems, respectively, and are hard to tackle due to their non-concave nature. We first transform them into equivalent optimization problems in parametric subtractive forms, which fit in a class of non-concave optimization methods known as difference of two concave functions programming, and then solve them using Dinkelbach and branch-and-bound methods to give global optimal solutions. Due to the unaffordable complexity of the global optimal solution, we further propose sub-optimal schemes through adding constraints on the interferences to convert the non-concave problems into concave ones and to give sub-optimal solutions with reasonable complexity. The sub-optimal solution gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed schemes.

In this paper, we investigate the energy-efficient resource allocation problem for the device-to- device (D2D) communications overlaying LTE networks, where the D2D user equipment (UE) shares the spectrum with the cellular UE in an orthogonal way such that the interference between them is completely eliminated. We consider both the non- orthogonal and orthogonal resource allocation strategies for D2D communications, where the resources allocated to different D2D pairs are non-orthogonal and orthogonal respectively. In the non-orthogonal strategy, the interference exists among different D2D pairs, and the resource allocation only concerns the transmit power control for each D2D pair; whereas in the orthogonal strategy, there is no interference, and the resource allocation concerns both the resource block (RB) allocation and the transmit power control. In the two strategies, the related resource allocation problems are firstly formulated as a fractional programming (FP) problem and a mixed-integer nonlinear fractional programming (MINLFP) problem respectively, both of which are then transformed into equivalent optimization problems in parametric subtractive form by exploiting the property of FP. As the transformed equivalent problems are non-concave, we develop the sub-optimal energy-efficient resource allocation schemes by solving them based on Dinkelbach and Powell-Hestenes-Rockafellar augmented Lagrangian methods. Simulation results demonstrate the effectiveness of the proposed schemes and show that the non-orthogonal strategy outperforms the orthogonal one in terms of the energy efficiency.

The security mechanisms for Long Term Evolution (LTE) networks are essential between User Equipment and eNodeB to prevent diverse threats and attacks. Data confidentiality and Data integrity are the main inevitable features for any secure communication system. Thus the 3GPP has standardized till now three pairs of algorithms (EEA1, EIA1), (EEA2, EIA2) and (EEA3, EIA3) for LTE security. In this paper, a new efficient DI algorithm based on a keyed hash function called ERDIA is introduced. Accordingly, the use of key and message dependent diffusion layers ensure the key sensibility and the avalanche effect using only one processing round. The experimental results show that the proposed hash function is immune against most possible known attacks. Besides, a lower computational time is attained compared to EIA2. Furthermore, our proposition would be similarly well suited for other networks and applications such as wireless networks.

The focus of this paper is targeted towards multi-cell dense LTE and LTE-Advanced networks, which are composed of multiple evolved Node B (eNodeB) co-existing in the same operating area and sharing the available radio resources. In such scenarios, momentous emphasis is given towards the techniques that take Inter-Cell Interference (ICI) into account while allocating the scarce radio resources. In this context, we propose solutions for the problem of joint power control and scheduling in the framework of Inter-Cell Interference Coordination (ICIC) in the downlink of LTE OFDMA-based multi-cell systems. Two approaches are adopted to allocate system resources in order to achieve high performance: a centralized approach based on convex optimization and a semi-distributed approach based on non-cooperative game theory. The centralized approach needs a central controller to optimally allocate resources like in LTE CoMP (Coordinated Multipoint). In the semi-distributed approach, eNodeBs coordinate among each other for efficient resource allocation based on local knowledge conveyed by the X2 interface. It turns out that despite the lower complexity of the semi-distributed approach and its inherent adaptability, there is only a slight discrepancy of results among both approaches, which makes the distributed approach much more promising, in particular as a procedure of SON (Self Organized Network).

In this paper, we investigate a multi-cell Long Term Evolution (LTE) uplink resource allocation problem to mitigate the inter-cell interference based on interference graph, in which the vertices represent the user equipments (UEs) and the edges represent critical interference relations between the UEs. Besides, to avoid increasing the overhead of LTE system, we derive the interference graph based on the channel statistics of cellular links. Given that the uplink resource allocation is NP-hard, two heuristic algorithms are proposed to give solutions with reasonable complexity. The first one is centralized algorithm based on the global interference graph, and the other one is distributed algorithm based on local interference graph. Both the centralized and distributed algorithms can mitigate the inter-cell interference evidently. We show by simulations that the centralized algorithm outperforms the distributed one in terms of throughput and fairness at the cost of higher overhead and complexity. As well, simulation results show that the cell-edge throughput improvements of the algorithms are more than 100% compared with improved riding peaks algorithm and the fairness factors are greater than 0.88.

Recently, several encryption schemes have been presented to Random Linear Network Coding (RLNC). The recent proposed lightweight security system for Network Coding is based upon protecting the Global Encoding Vectors (GEV) and using other vector to ensure the encoding process of RLNC at intermediate nodes. However, the current lightweight security scheme, presents several practical challenges to be deployed in real applications. Furthermore, achieving a high security level results in high computational complexity and adds some communication overhead. In this paper, a new scheme is proposed to overcome the drawbacks of the lightweight security scheme and that can be used for RLNC real-time data exchange. First, the cryptographic primitive (AES in CTR mode) is replaced by another approach that is based on the utilization of a new flexible key-dependent invertible matrix (dynamic diffusion layer). Then, we show that this approach reduces the size of communication overhead of GEV from 2 × h to h elements. In addition to that, we also demonstrate that besides the information confidentially, both the packet integrity and the source authentication are attained with minimum computational complexity and memory overhead. Indeed, cryptographic strength of this scheme shows that the proposed scheme has sufficient security strength and good performance characteristics to ensure an efficient and simple implementation thus, facilitating the integration of this system in many applications that consider security as a principal requirement.

We investigate the energy-efficient resource allocation problem for the downlink in long-term evolution heterogeneous networks through maximizing the energy efficiency (EE) under the per-user throughput and per-eNB power constraints in this paper. We demonstrate that EE is an increasing function in channel gain, and that EE is continuously differentiable and strictly quasiconcave in transmit power associated with each resource block (RB). Due to the non-convexity of the optimization problem, we develop a two-step resource allocation scheme composed by RB allocation and transmit power control. In the first step, we allocate RBs to users through maximizing the minimum EE of individual user and satisfying the throughput requirement of each user. In the second step, by enforcing the per-user throughput and per-eNB power constraints and exploiting the strict quasiconcavity of EE in transmit power associated with each RB, the power control algorithm is developed to maximize the EE. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme and show that it greatly improves the EE compared with conventional spectral-efficient scheme.

The security of Wireless Sensor Networks (WSN) is essential for effective deployment in various areas and applications such as military and business. The existing security solutions of WSN are based on multi-round function, which requires high computing complexity and energy consumption. WSN have, however, limited resources that prevent their efficient deployment for a long period. In this paper, a new kind of security system based on a cipher and authentication algorithm called EDCA is presented to ensure the necessary security requirements with low computation complexity. Furthermore, the proposed cipher is based on a dynamic binary diffusion layer. The contents of packets is divided into many blocks, which are mixed together to produce the cipher blocks. Likewise, an enhanced version of cipher is presented to attain a better statistical properties. Additionally, EDCA is evaluated by comparing it with AES, which is considered reliable and robust in several standards of sensor networks. The results show that the proposed algorithm has a reduced computation complexity, and is robust and could be adopted for different types of wireless or mobile networks.

Random Linear Network Coding (RLNC) is a promising technology of Network coding (NC) {that is} verified to be both sufficient and efficient. In this paper, we propose an efficient implementation of coding process, ensuring the security against active and passive attacks, in order to deploy RLNC in real networks, especially on battery constrained mobile devices with low computation capabilities such as mobile phones or sensors. We first present our flexible secure solution that can achieve simultaneously the information confidentiality, the packet integrity and the source authentication. It contains a new scheme of generation of invertible key dependent binary Global Encoding Matrix (GEM) with a complexity, a memory consumption and a decoding delay lower than the traditional $RLNC$. The effectiveness of coding process is proved by modifying the Galois field of calculation from integer (int8, int16) to binary in order to ensure low computational requirements that lead to high throughput and low energy consumption. Furthermore, theoretical and numerical results reveal that the proposed methods give an effectiveness of coding and a higher level of security compared to many recent works in this field.

Long Term Evolution (LTE) Networks were proposed for serving a multitude of high-speed data-rate services. For such systems, the Quality of Service (QoS) at the wireless part needs to be guaranteed by using smart, fast and strong resource allocation mechanisms. In order to build such algorithms, several parameters such as channel conditions, packet delays, queue length sizes and flow bitrates should be used to compute a maximization metric. Most authors compute such metric by obtaining the product of the afore-mentioned parameters. We consider this fact as a huge mistake. In this paper, we analyse the relevance of each parameter and give them different pounds. We propose a new resource allocation algorithm that presents a low complexity level. It works in a heterogeneous service scenario real time (RT) and non-Real Time (NRT). In order to evaluate the performance of our algorithm, several QoS constraints such as throughput, Packet Loss Ratio (PLR) and Fairness Index (FI) are used.

In this paper, a new ciphering algorithm is proposed for Long Term Evolution (LTE) data confidentiality. The proposed cipher scheme is based on a novel stream cipher framework which uses Substitution-Diffusion (SD) structure to provide key-streams that possess acceptable cryptographic performance (avalanche effect and key sensibility). Standards have been already adopted by 3GPP for LTE data confidentiality; EEA1, EEA2, and EEA3 which are based on Snow 3G, Advanced Encryption Standard (AES), and ZUC, respectively. Although the above mentioned algorithms have sufficient security strength against attacks, our solution is constructed to ensure less complexity with similar security strength. The proposed algorithm consists of an addition layer and a modified RC6 substitution layer which could require no memory and has stronger cryptographic properties compared to RC6. Furthermore, a new dynamic non-invertible diffusion layer technique is introduced, which is constructed from the output of the substitution layer. Theoretical and simulation results show that our algorithm is immune against liner, differential, chosen/known-plain-text, brute force and statistical attacks. Equally important to note, our proposed cipher algorithm has a lower computational time compared to AES, and could be adapted for other kinds of wireless networks.

Most of the works on Generation-Based Network Coding (GBNC) consider a fixed generation size. A large generation size maximizes the Network Coding benefits but leads to a long delay while a small generation size reduces the delay but decreases the throughput. This paper presents the DYnamic GEneration Size (DYGES) approach. Our network-aware method adjusts the generation size according to the network variations (network size, congestion, losses) for multicast flows to keep the delay steady. Our goal is to guarantee a Quality of Service (QoS) in terms of delay. The simulation results show the accuracy of DYGES.

Network Coding is a recent technique that has many advantages such as reducing the bandwidth consumption and increasing the throughput. IROCX [1] is a routing algorithm that applies Network Coding while considering the interference impact. However, it assumes a constant transmission power. We extend IROCX by allowing the nodes to transmit with several power levels. Our approach, TC-IROCX (Topology Control on Interference-aware Opportunistically Coded Exchanges) aims at maximizing the number of accepted flows. The simulation results show that TC-IROCX adjusts the transmission power according to the network state in order to enhance the acceptation rate.

Network Coding is a new field that aims at, notably increasing the throughput in a network. ROCX [1] is an algorithm that makes routing with the awareness of network coding. However, its major limitation is that it does not take into account the bandwidth limitation and the interference impact. If the interference is not considered in a wireless network, a flow requiring a certain bandwidth might be accepted and see its throughput decreasing due to interference. In this case, the Quality of Service (QoS) is not respected. Therefore, we use the clique-based model of I2ILP [2] to introduce constraints that consider interference. We present in this paper IROCX, a routing algorithm for wireless mesh networks. It uses Linear Programming for routing while maximizing the benefits of Network Coding and considering interference. The simulation results show the effectiveness of our algorithm.

This paper presents a theoretical estimation for idle channel time in a multi-hop environment. Idle channel time is the time proportion of a node during which the channel state is idle. Thus, it can be used to evaluate the available bandwidth. Major related work considers a cross-layer model, where the idle channel time measure is available at the MAC layer, but it is rarely implemented. Furthermore, this measure is not very flexible: it cannot differentiate the traffic's priorities and it works under the hypothesis that flows are strictly policed. Estimating instead of measuring the idle channel time prevents these drawbacks. This estimation is computed in three steps by (1) calculating the idle channel time bounds, (2) evaluating the probability of a given idle channel time value, (3) computing the expected value of this distribution and deducing the average idle channel time. We show by simulation that our estimation is accurate.

This paper studies the unfairness issues of network coding in multi hop wireless networks. Most of the work on network coding focuses on the obtained throughput gain. They show that mixing lineally the packets at the intermediate nodes is capacity-achieving. However, network coding schemes designed only to maximize the throughput could be unfairly biased. The reason is that by mixing different flows, packets destined to one destination in order to be decoded need to wait for the reception of the whole mixed set of encoded packets that may be totally independent in terms of final destination. This may lead to highly unfair delay for small block data. To mitigate this unfairness, relay nodes may mix only packets going to the same destination. We call this strategy FairMix. Although FairMix may limit the maximum attainable throughput, it aims to make distinct for decoding delay of each destination corresponding to the size of the data block. In order to investigate this trade off, we compare the FairMix performance with a naive network coding which mixes packets destined to different destinations. The simulation under lossy wireless links, limited memory and bandwidth resources, and different block sizes shows that FairMix is effective in improving fairness among destinations in comparison to naive network coding.

This paper presents an admission control algorithm based on dynamic constraints for multi-hop networks. Assuming each node knows the topology and flow reservations within its radio range, local constraints on flow rates can be computed. As long as these constraints are satisfied, flows are accepted. Since computing optimal constraints is not practical, existing approaches compute a system of either necessary or sufficient constraints. In practice, the approach based on necessary constraints tends to overload the network whereas in the latter approach, a significant part of the bandwidth remains unused. In addition, these works assume that the interference model and the sublayers are optimal. In this paper, we propose to take into account the channel state in the constraints computation and, thus, to adjust them according to model relevance. Therefore we give a probabilistic model to evaluate the time spent by the channel in the idle state. By comparing this estimation with the measure value, we evaluate the model accuracy and include the corresponding error rate in the constraints of the admission control. Simulations show that our admission control algorithm outperforms previous work.

We focus on applications with quantitative QoS (quality of service) requirements in their end-to- end response time. Two types of quantitative QoS guarantees can be delivered by a network: deterministic and probabilistic. The deterministic approach is based on a worst case analysis. The probabilistic approach uses a mathematical model to obtain the probability of the response time exceeding a given value. We assume that flows are scheduled according to non-preemptive FP/FIFO. The packet with the highest fixed priority is scheduled first. If two packets share the same fixed priority, the packet that arrives first on the node considered is scheduled first. We compare deterministic and probabilistic QoS guarantees and introduce the concept of p-schedulability: the QoS requested by all flows is met with probability p.

In this paper, we are interested in real-time flows requiring quantitative and deterministic QoS (quality of service) guarantees. We focus more particularly on two QoS parameters: the worst case end-to-end response time and jitter. We consider a FIFO (first in first out) scheduling of flows. The FIFO scheduling is the simplest one to implement and very used. We first establish a bound on the worst case end-to-end response time of any flow in the network, using the trajectory approach. We present an example illustrating our results. Finally, we show how to apply these results to the EF (expedited forwarding) class in a DiffServ (differentiated services) architecture

In this paper, we focus on the quantitative quality of service (QoS) guarantee in a differentiated services (DiffServ) domain that can be granted to an expedited forwarding (EF) flow in terms of end-to-end delay. This study shows that delays much smaller than the deterministic bound can be guaranteed with probabilities close to one. An admission control derived from these results is then proposed, providing a probabilistic QoS guarantee to EF flows.