Forecasting future trajectories of pedestrians and vehicles is necessary for safety and efficiency maximization in connected and autonomous vehicle (CAV) networks. Existing federated learning (FL) approaches face challenges related to scalability, communication overhead, and privacy preservation. To address these challenges, we introduce a multi-level Hierarchical Decentralized Federated Learning framework tailored for trajectory prediction.Our approach organizes clients into a layered hierarchy, where communication is restricted to parent and child nodes, and synchronization across layers is both controlled and periodic. This design reduces redundant message exchanges while maintaining model consistency. We evaluate our method on two real-world trajectory datasets, Intersection Drone Dataset (inD) and Highway Drone Dataset (highD), and show that it achieves prediction accuracy comparable to Centralized Federated Learning (CFL) while reducing communication costs by approximately 25%. Our results demonstrate that hierarchical structuring in decentralized FL offers a scalable, privacy-preserving, and communication-efficient solution for real-world trajectory forecasting.

One of the goals of a PKI (Public Key Infrastructure), which is ubiquitous in our systems and networks, is to be able to create a trusted association — represented by a certificate — between a public key and an entity; It is however, of the same importance, to be able to revoke this trust. A major challenge is to efficiently share the revocations that took place. Two existing and widely used methods are presented: certificate revocation lists (CRLs) and the Online Certificate Status Protocol (OCSP). Both mechanisms can be optimized but have limitations, especially due to their cumbersome nature. In this article, a new encoding scheme (LightyCoding) is proposed, which is no longer based on the historical ASN.1 standard, and which offers a lightweight structure and improved performance. We describe its structure and functioning before proceeding with tests. The results are a significant reduction in CRL size of around 50% for most uses and 30% for OCSP, but still containing the same useful data as an ASN.1-encoded revocation. We also describe techniques implemented in our encoding to ensure frontand backward-compatibility, facilitating integration and use.

Enforcing the forgetting (or unlearning) of specific portions of a neural network's (NN) memory is necessary for both security and ethical considerations. Moreover, targeted forgetting can enhance the network's performance by eliminating the learning derived from compromised data. Conventional approaches, such as retraining the model from scratch, are associated with significant time and energy costs, while alternative methods can degrade the neural network's performance in terms of complexity and predictive accuracy. To address these challenges, this work introduces two novel approaches that mitigate the drawbacks associated with traditional unlearning methods. Both approaches leverage the Long Short-Term Memory (LSTM) network, chosen for its superior learning quality and efficient processing time—crucial factors in such algorithms. The first approach involves modifying the LSTM architecture by coarse tune the weight matrix of the forget gate to facilitate controlled forgetting. The second approach follows the idea of intruders by inducing poluuted data to the system, and the idea is based on learning the model through corrupted/bad data, which causes the LSTM memory to be changed at specific time intervals that correspond to the data that should be forgotten. We present a performance evaluation of the second approach, with a future study planned for the performance assessment of the first approach. The evaluation demonstrates the superiority of the proposed solutions in terms of quality of service and implementation simplicity.

The emergence of 5G and the upcoming 6G wireless communication technologies driven the need for innovative network architectures, such as Heterogeneous Networks (HetNets), which integrate diverse components to boost network capacity and performance. Mobile Edge Computing (MEC) offers a promising solution for low-latency computation requirements for different application and use cases at the network edge, but effective task offloading in dynamic HetNet environments remains challenging due to factors like user mobility, channel conditions, and frequent handovers. This paper proposes a Graph Convolutional Network-Assisted Task Offloading (GCN-ATO) algorithm, which leverages a trained GCN model to predict Key Performance Indicators (KPIs) based on current network conditions and user positions, thereby optimizing task offloading decisions. By reducing unnecessary data transmission and enhancing resource utilization, the GCN-ATO algorithm improves energy efficiency and task execution latency. We evaluate our approach using real-world mobility and network traces, demonstrating significant reductions in energy consumption compared to baseline schemes, while preserving task execution deadlines and system performance.

Mobile phone metadata is widely used to extract socio-economic activity metrics at scale. Properly utilized, this data provides unique insights into downstream tasks and business value. This paper uses large scale multi-region service-level mobile data traffic to investigates geospatial temporal clustering, aiming to cluster similar urban grids sharing the same social signature. Leveraging deep learning, time series clustering transcends morphology, achieving more accurate clustering in the hidden space. However, most current algorithms struggle to extract both temporal and spatial information. We propose a novel method, Deep Spatial Temporal Clustering (DSTC), for clustering fine-grained urban grids based on geospatial temporal characteristics of mobile traffic data. DSTC employs a lightweight spatiotemporal autoencoder and graph construction, optimizing clustering and reconstruction objectives to learn representations and cluster assignments. Using data from the NetMob 2023 Data Challenge, we demonstrate DSTC’s feasibility and interpretability through extensive case studies on two French metropolitan cities and three applications. This work advances geospatial temporal clustering and offers practical insights and business value from mobile phone metadata, reflecting real-world industrial scenarios. The source code for this paper is available at https://github.com/ComplexNetTSP/DSTCourGitHubrepository

The rapid adoption of electric vehicles (EVs) is driving increasing demand for efficient and strategically placed charging stations. While numerous studies have explored optimization methods for the placement of EV charging stations, most focus on smaller geographic areas, leaving the challenge of optimizing station distribution across larger regions unresolved. This paper presents a novel approach for optimizing both the placement and capacity of EV charging stations using the H3 spatial grid system and queuing theory. By leveraging the hexagonal structure of the H3 grid, we accurately model spatial data and analyze EV charging demands in both urban and non-urban areas. Queuing theory is employed to predict station utilization and optimize the allocation of charging points, minimizing user wait times and ensuring efficient resource distribution. The proposed method is adaptable to future growth in EV adoption and addresses infrastructure needs in both high-demand and underserved regions. This paper outlines the framework developed for the 13th SIGSPATIAL Cup (GISCUP 2024), which achieved top-5 performance. Results based on real-world data demonstrate the model's effectiveness in enhancing the spatial distribution of charging stations, improving accessibility and efficiency in EV infrastructure.

Balancing sustainable energy with cellular deployment can be challenging. However, multi-user MIMO antennas for dynamic positioning can benefit both objectives. This study presents an optimization algorithm leveraging dynamic drone deployment to alleviate cellular communication congestion in densely populated areas. Two deployment strategies are examined: prioritizing energy efficiency by minimizing antenna footprint, and optimizing service quality by placing serving offloading drones. A novel Voronoi planning tool models both approaches, aiding in estimating energy consumption across various scenarios, particularly in the context of modern multi-user massive MIMO and beamforming techniques. A comparative energy analysis for different techniques is conducted, along with establishing a theoretical minimal coverage antenna footprint.

Interest in applying Reinforcement Learning (RL) to Autonomous Vehicles (AVs) is experiencing a rapid and substantial expansion. Proximal Policy Optimization (PPO), a well-known RL algorithm with two versions, is simple to implement and has a high level of generality. In this paper, we first analyze the issues in each of the original PPO versions: asymmetric penalty in the Adaptive KL Penalty Coefficient PPO version, gradient loss and pessimistic estimate in the Clipped PPO version. Therefore, we propose three improved PPO algorithms: Adaptive JS Penalty Coefficient PPO, Leaky PPO, and Parametric PPO. To validate the effectiveness of the proposed algorithm, we generated three autonomous driving scenarios in the Metadrive simulator. Experimental results demonstrate that Leaky PPO outperforms the other five PPO variant algorithms in various autonomous driving simulation scenarios. Furthermore, we demonstrate that the Leaky PPO outperforms other popular RL algorithms and achieves state-of-the-art performance.

Our main objective in this work is to address the challenge of enhancing the forecasting of agents trajectories for Connected and Autonomous Vehicles (CAVs) while prioritizing privacy. We introduce an innovative approach to Federated Learning tailored to the contextual aspects of trajectory prediction. We employ the Siamese Neural Network (SNN) to capture context similarities between clients’ environments. Subsequent cluster formation employs SNN to group clients with similar static contexts for federated training, enhancing learning efficiency.Results of our experiments on real-world datasets collected from the highway drone dataset (highD) and the intersection drone dataset (inD) combination, quantified by utilizing wellestablished metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE), validate the effectiveness of our approach, obtaining superior trajectory prediction capabilities, showcasing the successful alignment of Federated learning with the intricate challenges of trajectory forecasting, all while prioritizing privacy.

Trajectory prediction has been identified as a challenging critical task for achieving full autonomy of the connected and autonomous vehicles (CAVs). Despite the advancement of communication technologies, only few studies include the connectivity and data exchange aspects. Thus, we introduce a novel Edge-Assisted clustering architecture that takes advantage of recent deep learning models and the evolution of edge technologies to achieve better forecasting. First, the historical positions of the target vehicles are fed into the base models of all CAVs in the scene, resulting in multiple generated predictions. Then, each prediction is transmitted to an edge server where trajectories clustering is performed using DBSCAN algorithm to obtain multiple partitions with similar trajectories. The largest cluster is averaged then broadcast back to all CAVs in the scene. Our proposed method surpasses state-of-the-art results on the real world trajectory prediction nuScenes vehicles dataset, obtaining better predictions up to 21%. We also demonstrate the robustness of our method against single-agent system failures, succeeding to get very satisfactory results due to our ability to detect outliers. System practicality is studied under the current 5G/6G capabilities.

Two models are proposed to optimize unmanned aerial vehicles (UAVs) traffic offloading in cellular networks. The first model optimizes the realtime traffic service, while delaying non-realtime traffic in the cell buffers (BS and the currently serving drone). Delayed traffic is then transmitted later when free resources are available and has a maximum service delay limit. The proposed model provides a heuristic solution to minimize the losses in non-realtime traffic based on the maximum delay and the size of the cell buffers. The second model completes the work by providing an optimal Integer Linear Programming solution to minimize the number of needed UAV assuming no data loss and buffering availability. It is also parameterized with the same delay limits as the first model. The performance of the proposed models is studied using the call detail record (CDR) dataset of the city of Milan cellular network provided by Telecom Italia. The two models showed great QoS performance based on the maximum delay of non-realtime traffic and cell buffer size compared to models that do not include the buffering and the delay limits.

Due to the rapid increase of connected devices and network traffic, the data transport from end-user devices to destination (connected device, cloud, edge servers, etc) can be interrupted because of obstacles and problems. In this paper, we propose to integrate edge servers with the intelligent reflecting surface (IRS) in a vehicular edge computing (VEC) environment. The IRS is deployed in fixed places inside the city (fixed IRS-Edge Nodes) and in taxis and buses (mobile IRS-Edge Nodes), where it is used for both reflecting signals and executing the different client vehicles' tasks. We propose an Optimal IRS-Edge Selection (OIES) model to select the optimal IRS-Edge Node(s) that satisfy the client vehicles' requirements. Moreover, we propose an Efficient IRS-Edge Selection (EIES) algorithm to deal with the high number of client vehicles in dense networks. The numerical results demonstrate the efficiency and the feasibility of the proposed solution.

Cellular deployment of new generations faces a coverage challenge due to the non-line-of-sight (NLOS) between clients' devices and base station (BS). Therefore, relaying on using the emerging technology; intelligent reflective surface (IRS) to reconfigure wireless signal propagation is considered the best solution that can address the mentioned challenge. Additionally, choosing the position of the IRS is not an easy task as the clients are mobile. Hence, there is a need for an efficient model to elect the best positions of the IRSs for a better network performance. In this work, two fold model is proposed to provide an automated solution to optimize IRS positions. The first one is the mixed integer linear programming (MILP) that solves the IRS positions problem in a classical way. Whereas the second one is based on the reinforcement learning optimization (RLO) with complex encoder and decoder network architecture to provide fast learning of the MILP results with a low mean square error. The proposed RLO model's validity is studied using 10 days of mobile dataset and actual cellular BSs' positions in the city of Rome (Italy). This study is based on the use of long short term memory (LSTM) and gated recurrent unit (GRU). The results show a significant performance of the proposed model based on LSTM compared to GRU.

The development of vehicular technologies and infrastructures leads to development in mobility handling for wireless communications. Improving connectivity establishment and reliability became an issue, especially for vehicles that may move out of antenna coverage during connection establishment. This paper focuses on improving LoRaWAN connectivity for roaming devices by combining a machine learning predictor and DNS prefetching to gather information necessary for connection establishment before the device comes under coverage, thus reducing the overall latency for connection establishment. The paper also relates to other issues by comparing the solution with other approaches and studying antenna occupation.

Several reinforcement techniques are compared to take control of Unmanned Aerial Vehicles (UAVs) and optimize communication offloading in cellular networks. Navigation actions are calculated to send the drones to the required position and turn them back when not needed. First, a use case is expressed and solved in form of a linear programming problem (ILP). Then, a Q learning algorithm is designed and evaluated to solve the same problem. Finally, a deep neural network based on Long Short Term Memory recurrent networks is used. The results of the three approaches are obtained with a real dataset extracted from the CDRs (Call Detail Records) in Milan city, Italy. It is shown that Q learning needs long convergence times to succeed to approach the ILP optimal results. Also, we demonstrate that deep neural network techniques learn much faster and mimic the ILP with very high scores.

An AI based energy management system (EMS) for microgrids is proposed. It is composed of three modules: a strategy based module, a deep learning (DL) and a reinforcement learning module (RL). This framework determines heuristically the optimal actions for the microgrid system under different time-dependent environmental conditions. In essence, a main innovation is applied to the EMS. Our deep learning algorithm uses recurrent neural networks (RNNs) instead of the habitual State Action Reward (SAR) approach (whether classical or deep). Learning is hence guided by successful actions rather than by blind exploration. A large improvement in learning rates is hence observed when compared to classical Q-learning on real datasets that present a large diversity in energy consumption profiles, acquired in French premises over a long period. It leads to question about the best appropriate reinforcement policies to adopt when solving large state environments.

High Dynamic Unmanned Aerial Vehicles (UAVs) are introduced to assist V2X networking and communication that requires ultra low latency and safety requirements (ULLC). In this paper, we propose a Follow Me UAV (FMU) architecture that aids Vehicular Edge Computing for service offering. Then, a communication protocol is proposed and associated with placement, routing, and optimization algorithms in small and dense networks (OFMU and AFMU). We use deep learning techniques (LSTM and GRU) to predict the connected vehicles trajectory, then the results are used to feed the optimization models. Then, we clarify through Reinforcement Learning based implementations autonomous UAV path planning. Optimization approaches are implemented and evaluated under different quality and computing scenarios. Then, the models are quantified under UAV selection time and energy cost. Results prove the feasibility of the optimization algorithms and suggest the use of mobile UAV as low latency edge servers for service offering.

The success of the Internet of Things (IoT) depends on the ability to provide reliable communication to the billions of devices that are used in many applications. In essence, estimating the quality of wireless links ensures the optimization of several protocols, reduces the end-to-end latency, and increases the reliability and the network lifetime. In this paper, we study the link quality in the Time Slotted Channel Hopping (TSCH) network by analyzing the received signal strength (RSSI) and error rates. The objective is to understand the temporal properties of these parameters which is important to select the appropriate channels for the critical applications and to enhance the Quality of Service (QoS) of the network. We apply machine learning techniques to a real dataset collected from a testbed IoT network deployed at Grenoble, France. We define five classes and present a classification of the 16 channels by comparing the performances of KNN (k-Nearest Neighbor) and LSTM (Long Short-Term Memory) algorithms.

Transfer learning (TL) is a useful technique that enables the wide spreading of neural networks after re-adaptation of their weights. In ths paper, two methods are introduced for transfer learning of recurrent neural networks: D-LSTM (Long Short Term Memory with deep layers) and CNN-1D (Convolutional Neural Network of One Dimension). The first is used to improve the prediction of time series when datasets are too small to obtain satisfactory results. The second enables personalizing and hence re-adaptation of an already-trained network to a new class of time series. In fact, the CNN-1D classification is applied to those real datasets to classify different behaviors in a large city. We show that our architecture drastically improves prediction when transfer learning is used in the same class of behavior but also on different classes of behaviors.

Service Function Chains (SFC) orchestration is necessary to optimize the use of computing resources and improve the performance of the overall virtualized functions in terms of system resources cost reduction and high quality. It requires intelligent joint chaining and placement algorithm due to the evident huge amount of traffic to be delivered to end customers of the network. In this paper, a global SFC architecture and an exact approach for finding the optimal SFC components instantiation(s) (OPC) are proposed. Then, a deep reinforcement learning (DRL) approach is formulated to deal with a huge number of SFC instances. Moreover, several scenarios are considered to quantify the behavior of OPC and DRL approaches. We compare their efficiency in terms of processing cost and orchestration time. Then, different deployment flavors are implemented and assessed. To study the algorithm’s behavior and to quantify the impact of the system, novel use cases are considered. Results prove the feasibility of the exact approaches in small network scale. Still, the DRL techniques act as an heuristic approaches for chain placement in dense networks.

The Internet of Things (IoT) is overrunning different domains and applications, where the use of wireless sensors and mobile devices is indispensable in such a mobile environment. These heterogeneous devices may generate a tremendous amount of content. Information-Centric Network (ICN) paradigm has been proposed to meet today’s users and application requirements. The in-network caching is a fundamental feature supported by design in ICN that improves network performance by providing ubiquitous caching in the network layer. Since most IoT devices are resource-constrained with limitations in communication, processing, energy, and memory; the energy-efficiency is a prime concern in IoT deployment. Different factors may affect energy efficiency in ICN-based wireless IoT networks such as transport (communication), caching, and energy limitation. This research paper attempts to focus on the in-network caching in wireless IoT to maximize the energy-efficiency. We propose an Energy-aware caching placement scheme (EaCP) that aims to maximize the energy-saving by trading-off between content transmission energy and content caching energy. Compared to other strategies, the simulation results show significant improvements while ensuring low data replication and a high cache hit ratio.

The emergence of new vehicles generation such as connected and autonomous vehicles led to new challenges in the vehicular networking and computing managements to provide efficient services and guarantee the quality of service. The edge computing facility allows the decentralization of processing from the cloud to the edge of the network. In this paper, we design and propose an end-to-end, reliable and low latency communication architecture that allows the allocation of compute-intensive autonomous driving services, in particular autopilot, to shared resources on edge computing servers and improve the level of performance for autonomous vehicles. The reference architecture is used to design an Advanced Autonomous Driving (AAD) communication protocol between autonomous vehicles, edge computing servers, and the centralized cloud. Then, a mathematical programming approach using Integer Linear Programming (ILP) is formulated to model the autopilot chain resources Offloading at the network edge. Further, a deep reinforcement learning (DRL) approach is proposed to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Moreover, several scenarios are considered to quantify the behavior of the optimization approaches. We compare their efficiency in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated Edge Autopilots.

Internet usability is expanded form just human-to-human interactions toward different communication types, while the communication itself is shifting from the host-centric model to the content-centric paradigm. The 5G and beyond networks promise not only to support such changes but also to provide massive data exchange and connectivity with high reliability. The next-generation networking technologies are the key enabled for 5G that aim at building a new ecosystem. One promising piece of this ecosystem is the information-centric network (ICN), which is a future network architecture that tends to tackle the current host-centric model issues. It natively supports several features, including abstraction content naming and transparent in-network content caching that contribute to improve network performance, reduce traffic, and improve the latency. In this article, we first provide a potential road map by introducing different next-generation active technologies to enable the big picture of 5G, including mobile-edge computing (MEC), software-defined networking (SDN), and network function virtualization (NFV). Then, we discuss the need for ICN and its coexistence within this ecosystem. Later, we present an in-depth review of the recent content naming schemes and a comprehensive review of in-network content caching solutions. We classify these solutions into different classes based on the used technologies and their working principle. Finally, we highlight some research challenges and propose promising directions for the research community.

Unusual Spatio-Temporal fluctuations in cellular network traffic may lead to drastic network management misbehaviors and at least abnormal drops in quality of experience. It is also expected that the management of future cellular networks will mostly rely on machine learning and automation. In this article, we present a dynamic on-line data mining technique to detect these network anomalies allowing, network operators to pro-actively monitor and control a variety of real-world phenomena with less damage to the overall experience. To overcome the network performance degradation that can occur in real time, the network manager must imperatively and instantly identify abnormalities and hence provide a better continuous quality of service for the subscribers. Based on real cellular communication traces, we propose an automated framework, called STAD, ensuring spatio-temporal detection outliers using a combination of machine learning techniques including One-class SVM (OCSVM), Support Vector Regression (SVR) and recurrent neural networks, Long Short-Term Memory (LSTM). STAD is double checked with two real datasets of CDRs where results show high accuracy compared to the Isolation Forest and Auto-Regressive Integrated Moving Average (ARIMA) models.

Maximum Concurrent Flow techniques try to combine a maximum flow graph resolution and the satisfaction of queries on this graph. They are proven to give better performance over classical heuristic data-source placement methods combined with greedy query resolution. A new version of the MCF approach is proposed and examined. It is tailored to provide MCF properties for large IoT networks. The results found from evaluation and comparison with other algorithms show that it has high efficiency and outperforms many recent solutions.

We propose to utilize rideshare taxis as infrastructure for both communication and computation. Rideshare overlays become hence Mobile Edge Nodes. End-users utilize near rideshare taxis as edge servers to receive video chunks for live video streaming. The set cover problem (SCP) is used to formulate the rideshare taxis coverage optimization inside the city. It provides the maximum number of rideshare taxis that cover end-users routes which guarantee the efficiency of communication services. Simulation results show that the proposed architecture dramatically enhances the quality of service and the overall communication performance in terms of execution time and energy consumption.

This paper presents a framework based on the data analysis concept to automate the management of resources in cellular networks. Three processes are defined: identifying and detecting anomalies, analyzing the causes, and triggering adequate recovery actions. First, the proposed solution executes Deep Learning algorithms to forecast the normal behavior of the network and defines dynamic thresholds. Then, it identifies cells with peak demands and raises alarms if the measured real-time data exceeds the threshold values. Second, we define QoS optimization methods to proceed with suitable design for resource allocation as well as fault detection and avoidance. Hence, we distinguish three cases and define two classes of data: Real-time and non-real-time traffic. This solution is applied to a pre-analyzed semi-synthetic real dataset extracted from the CDRs (Call Detail Records) in Milan city, Italy. This dataset contains the Internet activity records of two months in three areas. The preliminary results elucidate the feasibility and preeminence of our proposed anomaly detection framework.

With the emergence of the Internet of Things (IoT) applications, a huge amount of information is generated to help the optimization of operational cellular networks, smart transportation, and energy management systems. Applying Artificial Intelligence approaches to exploit this data seems to be promising. In this paper, we propose a dual deep neural network architecture. It is used to classify time series and to predict future data. It is essentially based on Long Short Term Memory (LSTM) algorithms for accurate time series prediction and on deep neural network, classifiers to classify input streams. It is shown to work on different domains (cellular, energy management, and transportation systems). Cloud architecture is used for IoT data collection and our algorithm is applied on real-time energy data for accurate energy classification and prediction.

Many research efforts have been proposed from physical, networking, to application layers over Vehicular Ad hoc Networks (VANETs) to provide more safety and convenience to passengers. However, due to the highly dynamic topologies and frequent disconnections in VANET, various challenges are faced due to the use of IP that effects the data delivery and user experiences. Therefore, a new paradigm namely Information-Centric Networking (ICN) has been proposed aiming to replace the traditional Internet Protocol by using the content name as the pillar element and providing a distributed in-network caching to enhance the data dissemination & access, and reduce the network load & response latency. The use of ICN in a vehicular environment may require different caching placement strategies and replacement policies. To this end, we study, in this paper, the effectiveness of in-network caching for VANET, we simulate and compare various strategies in different scenarios. Furthermore, we provide different research guidelines to enhance the use of caching in such a challenging network.

The emerging of the internet of things (IoT) led to increasing the computation resources required to satisfy a large number of requests from the connected devices, for this the Cloud Computing (CC) allows the processing of requests in the cloud to guarantee the efficiency of services for end-users. The main problem of the current CC architecture is the latency in real-time applications such as video streaming, which require a distributed architecture to support the future generation of applications. The Mobile Edge Computing (MEC) provides a fully distributed architecture where a part of processing executed in the edge of network which supports the requirements of IoT applications. In this paper, we propose to use the connected devices as on-demand virtual edge servers to provide computation services close to endusers where each submitted task is divided into a set of sub-tasks, each one can be executed by any other device which is a part of the virtual edge server according to the available resources in the selected device. In this context, we have formulated the partitioned and the offloading problem in MEC environment using linear programming techniques. Optimal Partitioned and Offloading (OPO) algorithm that allocates network, storage and computing resources to user application sub-tasks with respect to MEC constraints and user quality requirements is modeled, implemented, and evaluated. Results show the feasibility and efficiency of the proposed algorithms.

The tremendous growth of today's connecting devices and generated content lead to an increasing load of current Internet infrastructure with challenging requirements. 5G technology promises to provide high bandwidth and ultra reliable low-latency communications, while Information-Centric Networking (ICN) promises to replace the current host-centric paradigm. ICN provides ubiquitous and transparent in-network content caching in order to enhance network performance and reduce content retrieval latency. In this regard, several cache strategies have been proposed, most of them are neither distributed in nature nor scalable in large-scale networks. In this paper, we design a distributed and efficient content caching scheme for 5G-enabled ICN networks, namely Cache and Split (CnS). CnS is designed to make a trade-off between the cache utilization and the content delivery time based on the number of received demands and content popularity. We evaluate our scheme using various performance metrics against different caching strategies. The obtained results prove an improvement in the cache utilization, with fast data retrieval, and enhancements in the content cache distribution.

The Internet of Things (IoT) is a network of billion of objects. Data streaming over IoT network is a tedious task that requires intelligent flow management and steering. In this paper, we propose a Distributed Maximum Concurrent Flow (DMCF) algorithm to solve the problem of distributing massive IoT video/data to large consumers over IP/data-centric networks. We propose two approaches based on graph theories, and using reinforcement learning techniques. The proposed approaches are implemented and evaluated over different complex graphs. Results show that in large graphs, reinforcement learning methods outperform classical graph theoretic ones.

The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.

The emergence of a new generation of applications led to the appearance of new challenges that represent improvements in current communication technologies. For this, a new network paradigm's including edge computing that allows the process of data at the edge of the network. And the 5G network slicing that represents a new generation of communication increases the capacity of mobile networks by supporting the slicing technology that allows virtual “cutting” of a telecommunications network in several slices that provide high performance in terms of bandwidth and latency. Slice allocation and placement is an important networking optimization task that still painstakingly tune heuristics to get a sufficient solution. These algorithms use data as input and outputs near-optimal solutions. Thus, we are motivated by replacing this tedious process with the recent deep reinforcement learning algorithms. In this paper, we propose three approaches for Virtual Network Functions (VNFs) slices placement in edge computing (Integer linear programming (ILP), reinforcement learning (RL), and deep reinforcement learning (DRL)). Then they are implemented and evaluated. Several scenarios are considered to study the behavior of the algorithms and to quantify the impact of network size. The results show the feasibility and efficiency of the proposed techniques in terms of server utilization, placement time, and energy consumption.

Named Data Networking (NDN) uses the content name to enable content sharing in a network using Interest and Data messages. In essence, NDN supports communication through multiple interfaces, therefore, it is imperative to think of the interface that better meets the communication requirements of the application. The current interface ranking is based on single static metric such as minimum number of hops, maximum satisfaction rate, or minimum network delay. However, this ranking may adversely affect the network performance. To fill the gap, in this paper, we propose a new multi-metric robust interface ranking scheme that combines multiple metrics with different objective functions. Furthermore, we also introduce different forwarding modes to handle the forwarding decision according to the available ranked interfaces. Extensive simulation experiments demonstrate that the proposed scheme selects the best and suitable forwarding interface to deliver content.

The 5G networks are considered as new wireless technologies that promise to provides Ultra-Reliable Low Latency Communication. In doing so, it uses various coverage techniques with high access point density that rise various complexity in the handle and manage the mobility of users. Information-Centric Network (ICN) is an emerging paradigm that promises to replace the current IP network. The content in ICN is first-class citizens instead of the host. ICN names the content rather than the owner, and the demands are driven by the receiver. Although this change contributes to the ease of consumers' mobility, producer mobility is considered as a challenging issue in ICN. In this paper, we study the issue of producer mobility, discuss the existing issues and challenges, and then design a Label-based technique that takes the benefits of location-free naming to enhance the producer mobility in a seamless and easy manner. The simulation results show the high performance of our proposed architecture in terms of seamless handover and low data miss.

A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.

Time Slotted Channel Hopping (TSCH) is one of the most used MAC mechanisms introduced by the new amendment IEEE 802.15.4e. It combines both slotted access with channel hopping technique to allow multiple communications while exploiting the 16 available channels of 2.4GHz band. The channel hopping mechanism of 802.15.4e considers an interference-free environment and does not specify how to build and manage a schedule for communication purpose. In this paper, we propose a new distributed channel hopping scheme that exploits Latin rectangles to avoid interference and collisions. In essence, the scheduling of links is performed by Latin rectangles where rows are channel offsets and columns are slot offsets. Thus, the frequency of communication is derived using Latin rectangles. Consequently, interference and multi-path fading are mitigated with more reliability and robustness. The efficiency of the proposed scheme has been validated by extensive simulation.

Vehicular Named Data Network (VNDN) uses Named Data Network (NDN) as a communication enabler. The communication is achieved using the content name instead of the host address. NDN integrates content caching at the network level rather than the application level. Hence, the network becomes aware of content caching and delivering. The content caching is a fundamental element in VNDN communication. However, due to the limitations of the cache store, only the most used content should be cached while the less used should be evicted. Traditional caching replacement policies may not work efficiently in VNDN due to the large and diverse exchanged content. To solve this issue, we propose an efficient cache replacement policy that takes the quality of service into consideration. The idea consists of classifying the traffic into different classes, and split the cache store into a set of sub-cache stores according to the defined traffic classes with different storage capacities according to the network requirements. Each content is assigned a popularity-density value that balances the content popularity with its size. Content with the highest popularity-density value is cached while the lowest is evicted. Simulation results prove the efficiency of the proposed solution to enhance the overall network quality of service.

In contrast to the current host-centric architecture, Information-Centric Networking (ICN) adopts content naming instead of host address and in-network caching to enhance the content delivery, improve the data distribution, and satisfy users' requirements. As ICN is being incrementally deployed in different real-world scenarios, it will exist with IP-based services in a hybrid network setting. Full deployment of ICN and total replacement of IP protocol is not feasible at the current stage since IP is dominating the Internet. On the other hand, re-designing TCP/IP applications from ICN perspective is a time-consuming task and requires a careful investigation from both business and technical point of view. Thus, the coexistence of ICN and IP is one of the suitable solutions. Towards this end, we propose a simple yet efficient coexistence solution based on Network Function Virtualization (NFV) technology. We define a set of communication regions and control virtual functions. A gateway node is used as an intermediate entity to fetch and deliver content over regions. The simulation results show that the proposed approach is valid and allow content fetching and delivering from different ICN and/to IP regions in an efficient manner.

Drone-cell technology is emerging as a solution to support and backup the cellular network architecture. cell-drones are flexible and provide a more dynamic solution for resource allocation in both scales: spatial and geographic. They allow to increase the bandwidth availability anytime and everywhere according the continuous rate demands. Their fast deployment provide network operators with a reliable solution to face sudden network overload or peak data demands during mass events, without interrupting services and guaranteeing better QoS for users. With these advantages, drone-cell network management is still a complex task. We propose in this paper, a multiagent reinforcement learning approach for dynamic drones-cells management. Our approach is based on an enhanced joint action selection. Results show that our model speed up network learning and provide better network performance.

Stimulated by the emerging Internet of Things (IoT) applications and their massive generated data, the cellular providers are introducing various IoT functionalities into their networks architecture. They should integrate intelligent and autonomous mechanisms that are able to detect sudden and anomalous behavior issues. In this paper, we present an adaptive anomaly detection approach in cellular networks consisting of two parts: the detection of overloaded base-stations using machine learning algorithm (LSTM - Long Short-Term Memory) and the deployment of drones as mobile base-stations that support and back up the overloaded cells. The proposed approach is validated using real dataset extracted from the CDR of Milan combined with semi-synthetic eHealth data. Initially, The LSTM algorithm analyzes the impact of eHealth applications on cellular networks and identifies cells with peak demands. Then, drones are deployed to collect the requested data from these cells. The obtained results show that the use of drones improves the quality of service and provides a better network performance.

In contrast to the host-centric model where the communication is directed using the destination address, Information-Centric Networking (ICN) adopts the content name as the pillar network element to provide data discovery and delivery process, as well as in other network functionalities. Named Data Networking (NDN) is an active ICN project that uses hierarchical unbounded names. These names are used in both interest and data packets and other data structures that may consume more memory with long lookup time. This paper targets the naming aspect in vehicular named data networks and proposes a Name-to-Hash Encoding scheme. The idea consists of hashing each name components separately to a fixed length, then perform a heuristic Wu-Manber-like algorithm lookup process. The former process enhances the NDN to consume less memory compared to hierarchical names, the latter process provides a fast lookup time. We have evaluated the proposed scheme against different related solutions using real domain datasets. Both theoretical analysis and experiments prove that the proposed scheme is efficient in terms of complexity, memory consumption, and lookup time.

We propose a solution for Electric Vehicles (EVs) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajectory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy management.

Information-Centric Networking (ICN) is a new communication paradigm that replaces the host addresses by the name of content; Named Data Networking (NDN) is a promising ICN architecture that has attracted research attention in recent years. NDN is a receiver-driven architecture implements pull-based communication in the form of one-interest-one-data. This model poses different challenges, especially from the transport layer perspective. In contact to IP-based networks where the congestion is handled in an end-to-end manner, NDN cannot apply the same concept, while most of the existing solutions are based on hop-by-hop connection. In this paper, we present a new congestion control mechanism for NDN based on link quality estimation. We focus our efforts to provide fast data transmission, decrease packet dropping rate, and maximize the link utilization. The simulation results show that our solution outperforms the NDN schemes in terms of throughput and drop packets.

Electrical vehicular (EV) energy management is a promising trend. Forecasting vehicular trajectories and delay is crucial for EV energy management. The presented work is devoted to the study and the application of deep learning techniques on specific road trajectories. First, exhaustive deep learning algorithms are considered. Second, road traces are converted to time series. Then, delays and road trajectories are analyzed. In fact, we consider two Recurrent Neural Networks (RNN): LSTM (Long Short Term Memory) and GRU (Gated Recurrent Units). Neural Networks are adapted and trained on 60 days of real urban traffic of Rome in Italy. We calculate the Loss function for both machine learning techniques which is defined by mean square error (MSE) and Root mean square error (RMSE). Experimental results demonstrate that both LSTM and GRU are adequate for the context of EV in terms of route trajectory and delay prediction.

Information-centric networking (ICN), a new networking paradigm in which the focal point is a named data, has been proposed recently as an evolving concept to the actual host-centric model of the Internet that relies mainly on host addresses. In vehicular networks, where vehicles are generally moving network elements and follow a content-oriented fashion, it will be fitting to use the ICN paradigm to improve the content dissemination and reduce the content retrieval latency. By applying this concept to such networks, we focus in this paper on the content delivery issue and propose an optimized caching scheme that proactively predicts the moving direction of a vehicle and brings into the next encountered RSU cache only the required content of interest to that vehicle. According to the obtained results from different measured metrics, the proposed solution outperforms in many ways other proposed schemes in the literature. For instance, our scheme improves drastically the cache utilization, enhances the network delay, and boosts the content diversity and distribution.

Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution.

Information-Centric Networking is considered as one of the most promising architecture for IoT. The use of content-centric approach may improve the content access & dissemination, reduce the content retrieval latency, and enhance the network performance. The use of in-network caching in ICN enhances the data availability in the network, overcomes the issue of single-point failure, and improves IoT devices power efficiency. In this paper, we present a Near-ICN Cache Placement (NCP) scheme for IoT taking traffic class into consideration. NCP is designed to select the optimal replica cache by minimizing: the cost of moving the data from content producer to replica nodes, the cost of caching the content in the replica and the cost of delivery the content to consumers. Hence, we presented a multi-objective optimization problem, with a heuristic caching selection algorithm. We evaluated NCP with various performance metrics against different caching schemes. The obtained results show improvement in the cache utilization, with fast data retrieval, and enhancement in the network cache distribution & diversity.

Internet of things (IoT) is an ever-growing technological paradigm that is expected to boost the development of a plethora of services and applications like eHealth services. The massive amount of data generated by eHealth applications will be handled by the cellular architecture. Due to the additional eHealth data, cellular networks may suffer from some anomalies which need intelligent and autonomic mechanisms to be avoided. Network operators must integrate to their architecture pro-active tools able to detect and signal these anomalous patterns and then mitigate the issue of overloaded base-stations. We address in this paper the issue of eHealth services by analyzing at first their impact on cellular networks. We propose also an on-line and efficient anomaly detection technique for eHealth data based on support vector regression (SVR). Moreover, we validate our model with a real dataset of cellular call detail records (CDR) combined with semi-synthetic eHealth dataset. A realistic testbed is provided on the context of a Marathon event where mobile users are running eHealth applications.

The performance of wireless body area networks (WBANs) may be degraded due to co-channel interference, i.e., when sensors of different coexisting WBANs transmit at the same time-slots using the same channel. In this paper, we exploit the 16 channels available in the 2.4 GHz unlicensed international band of ZIGBEE, and propose a distributed scheme that opts to avoid interference through channel to time-slot hopping based on Latin rectangles, DAIL. In DAIL, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and colunms are time-slots of its superframe. Subsequently, it assigns a unique symbol to each sensor; this latter forms a transmission pattern according to distinct positions of its symbol in the rectangle, such that collisions among different transnnssions of coexisting WBANs are minimized. We further present an analytical model that derives bounds on the collision probability of each sensor's transmission in the network. In addition, the efficiency of DAIL in interference mitigation has been validated by simulations.

Information-Centric Networking (ICN) is very promising for Hadoop-based distributed computing systems, where the data-centric approach is advantageous in reducing the data retrieval latency as well as the network traffic for Hadoop services. Moreover, the inherent in-network caching and computing features in ICN relaxes the end-to-end connectivity between consumers and producers (this helps networking, computation, and power efficiency as Hadoop nodes will use ICN services). Yet, building such a complex system needs new definitions and mappings on the architecture side. It needs also a flattening of the components and an optimization relative to data flow and computation performance. These issues are presented in this paper. Optimal optimization algorithms are then proposed, implemented and evaluated to improve the overall network performance. Experiments demonstrate that ICN support of Hadoop is a feasible architecture and show to improve the performance of Hadoop systems and reduce the end-to-end consumer delay.

The distributed Information-Centric Networking architecture has shown enormous potential to replace the host centric Internet architecture. A number of solutions such as Named Data Networking have become available. Building application services and integrating other technological design on top of ICNs is a challenging task, and has many open issues, hence an efficient distributed architecture needs to be developed. In this paper, we address the case of using IoT architecture targeted for ambient assisted living applications, on top of named data networking. We have proposed a complete architecture and implementation details for device & service networking, communication model, management, and naming. Within each model we have proposed mechanisms which support node mobility, hand-off, packet design, and push & pull data services without changing NDN data exchange model. This architecture is flexible, scalable, and can be adapted to other application specific IoT networks. We also have implemented the proposal on NDN simulator, and evaluated different services. The communication overhead and mobility implications have been studied to show effectiveness of new services with negligible cost to the network.

We formulate and study a broadcast problem arising in multi-channel duty cycling wireless body area networks (WBANs), where the sink needs to broadcast the control message to all sensor nodes. The objective is to design robust multichannel wake-up schedule with minimum worst-case broadcast delay while guaranteeing the full broadcast diversity regardless of clock drifts and asymmetric duty cycles. To that end, we first derive the lower-bound of worst-case broadcast delay with full diversity of any broadcast protocol and then design a multichannel broadcast protocol (MCB) that satisfies the performance requirement for the latency and diversity. Finally, the simulation results demonstrate the capability of MCB of ensuring successful broadcast delivery on every channel within the theoretical worst-case broadcast delay, even under asymmetric duty cycles and any amount of clock drifts.

Recent advances in microelectronics have enabled the realization of Wireless Body Area Networks (WBANs). However, the massive growth in wireless devices and the push for interconnecting these devices to form an Internet of Things (IoT) can be challenging for WBANs; hence robust communication is necessary through careful medium access arbitration. In this paper, we propose a new protocol to enable WBAN operation within an IoT. Basically, we leverage the emerging Bluetooth Low Energy technology (BLE) and promote the integration of a BLE transceiver and a Cognitive Radio module (CR) within the WBAN coordinator. Accordingly, a BLE informs WBANs through announcements about the frequency channels that are being used in their vicinity. To mitigate interference, the superframe's active period is extended to involve not only a Time Division Multiple Access (TDMA) frame, but also a Flexible Channel Selection (FCS) and a Flexible Backup TDMA (FBTDMA) frames. The WBAN sensors that experience interference on the default channel within the TDMA frame will eventually switch to another Interference Mitigation Channel (IMC). With the help of CR, an IMC is selected for a WBAN and each interfering sensor will be allocated a time-slot within the (FBTDMA) frame to retransmit using such IMC.

We formulate and study a broadcast problem arising in multichannel duty-cycling wireless body area networks (WBANs) which the sink needs to broadcast control information to all sensor nodes on or implanted in the human body. Despite its fundamental importance for the network configuration and secure key management, the multichannel broadcast problem is largely unaddressed in duty-cycling WBANs. In this paper, we devise novel 2-D scheduling specifying the rule of channel hopping and wake-up time slot selection, which achieves the order-minimal worst-case broadcast delay while guaranteeing the full broadcast diversity regardless of clock drifts and asymmetric duty cycles and channel perceptions. Specifically, we first employ the Chinese remainder theorem to design an effective multichannel broadcast (MCB) algorithm and further propose improved MCB that enhances the granularity of MCB in matching actual duty cycles and number of channels, reducing the theoretically worst-case broadcast delay of MCB by up to 75%. We demonstrate the performance of the proposed algorithms through theoretical analysis and extensive simulations.

Wireless communications are confronted to different kinds of interferences. Such interference has significant impact on the reliability of packet transmissions. Due to very low power communication, wireless body area networks are potentially susceptible to interfere with coexisting wireless systems, including other WBANs that might exist in their vicinity. We employ cognitive radios (CRs) for proactive interference sensing in such systems. Our approach insures the channel quality evaluation reducing the packet loss rate and mitigate interferences. On one hand, the intra-WBAN interference is managed through FTDMA protocol. On the other hand, if a high intensity of interference is detected, this solution allows switching channel transmission, thereby ensuring the coexistence of WBANs. Moreover, we modeled the functioning of this method using the Markov's chains in continuous time. The Transmission error rate is introduced as a measure to quantify the effectiveness of the proposed schemes.

When sensors of different coexisting wireless body area networks (WBANs) transmit at the same time using the same channel, a co-channel interference is experienced and hence the performance of the involved WBANs may be degraded. In this paper, we exploit the 16 channels available in the 2.4 GHz international band of ZIGBEE, and propose a distributed scheme that avoids interference through predictable channel hopping based on Latin rectangles, namely, CHIM. In the proposed CHIM scheme, each WBAN's coordinator picks a Latin rectangle whose rows are ZIGBEE channels and columns are sensor IDs. Based on the Latin rectangle of the individual WBAN, each sensor is allocated a backup time-slot and a channel to use if it experiences interference such that collisions among different transmissions of coexisting WBANs are minimized. We further present a mathematical analysis that derives the collision probability of each sensor's transmission in the network. In addition, the efficiency of CHIM in terms of transmission delay and energy consumption minimization are validated by simulations.

A Wireless Body Area Network (WBAN) provides health care services. The performance and utility of WBANs can be degraded due to interference. In this paper, our contribution for co-channel interference mitigation among coexisting WBANs is threefold. First, we propose a distributed orthogonal code allocation scheme, namely, OCAIM, where, each WBAN generates sensor interference lists (SILs), and then all sensors belonging to these lists are allocated orthogonal codes. Secondly, we propose a distributed time reference correlation scheme, namely, DTRC, that is used as a building block of OCAIM. DTRC enables each WBAN to generate a virtual time-based pattern to relate the different superframes. Accordingly, DTRC provides each WBAN with the knowledge about, 1) which superframes and, 2) which time-slots of those superframes interfere with the time-slots within its superframe. Thirdly, we further analyze the success and collision probabilities of frames transmissions when the number of coexisting WBANs grows. The simulation results demonstrate that OCAIM outperforms other competing schemes in terms of interference mitigation and power savings.

Duty-cycle has recently attracted significant research attention due to its paramount importance on energy conservation in Wireless Body Area Networks (WBANs). However, the additional delay resulted from applying duty-cycle is overlooked in most, if not all, of existing work, despite the fundamental importance of the delay in healthcare applications. In order to bridge this gap, we devote this paper to analyzing the delay of IEEE 802.15.6 CSMA/CA mechanism under duty-cycle. Technically, we first explicitly formulate the expressions of the random delay that a sensor node spends on transmitting packets under asynchronous duty- cycling protocol of IEEE 802.15.6 CSMA/CA. Moreover, we mathematically derive the probabilistic characteristics in terms of the expectation and variance of the delay. Furthermore, we conduct elaborate simulations to demonstrate the correctness of the theoretical analysis.

In this paper, we propose a distributed multi-hop interference avoidance algorithm, namely, IAA to avoid co-channel interference inside a wireless body area network (WBAN). Our proposal adopts carrier sense multiple access with collision avoidance (CSMA/CA) between sources and relays and a flexible time division multiple access (FTDMA) between relays and coordinator. The proposed scheme enables low interfering nodes to transmit their messages using base channel. Depending on suitable situations, high interfering nodes double their contention windows (CW) and probably use switched orthogonal channel. Simulation results show that proposed scheme has far better minimum SINR (12dB improvement) and longer energy lifetime than other schemes (power control and opportunistic relaying). Additionally, we validate our proposal in a theoretical analysis and also propose a probabilistic approach to prove the outage probability can be effectively reduced to the minimal.

Wireless Body Area Networks (WBANs) which offer a variety of promising applications in the areas of medical and consumer electronics have been paid lots of attention. However, very limited work has been done on the network reliability combined with energy conservation in spite of their fundamental importance. In order to bridge this gap, we devote this paper to developing a reliable and energy-efficient leader election (REELE) algorithm for WBANs. To this end, technically, we first partition a WBAN into regions and build the reliability and energy consumption models. By the reliability analysis, we then propose a novel communication strategy for nodes and further derive the total energy consumption of a region. With the reliability and residual energy of a node and total energy consumption considered jointly, REELE algorithm can considerably enhance reliability and conserve energy. Extensive simulation results demonstrate the effectiveness and the efficiency of REELE in terms of longer network lifetime, better energy characteristics as well as higher reliability.

This paper considers the problem of intra-interference in a Wireless Body Area Network (WBAN). The problem arises mainly because each bio-sensor collects different parameters with different data rate and alternation. Another source of interference is related to normal patient movement. Proposals in the literature usually assume that the interference can be handled using time multiplexing or by listening before transmission to avoid collision. However, these adaptive approaches, given the high-occupancy channels, bring with them major problems of collision and extra energy consumption. One solution could be a power control mechanism. Nevertheless, techniques of that kind are challenging in that they require periodic information on the condition of the wireless channels, conditions that are difficult to estimate. To address these concerns, a tree-based WBAN topology using a set of relay nodes with stable communication called a "virtual backbone" is proposed. As bio-sensors report data mainly in uplink traffic, it is assumed that they share a small number of wireless channels using the TDMA technique. Relay nodes, on the other hand, share the most number of channels in order to improve the fluidity of data across the WBAN. To mitigate co-channel interference among relays, two techniques from the literature called the adaptive data rate and the adaptive duty cycle are used. Simulation experiments showed that the proposed architecture, when combined with intra-interference mitigation techniques, improves energy efficiency and increases data rate.

In Wireless Body Area Networks (WBANs), the energy consumption determines the lifetime of the entire network. As a result, how to conserve the energy to prolong the network lifetime becomes a key problem in WBANs. In this paper, to address the energy conservation problem in WBANs, we develop an Energy-Efficient Leader Election mechanism, called EELE. In EELE, each node competes for the leader following the distributed leader election algorithm in which a utility function is constructed with the consideration of the residual energy and the location of the node. Moreover, a distance-aware hybrid communication mode is proposed such that a node can choose either direct communication or cooperative communication to alleviate the burden of the leader or the far node. Extensive simulation results demonstrate the effectiveness and the efficiency of EELE mechanism in terms of longer network lifetime, better energy characteristics and higher throughput.

Wireless body area network (WBAN) witness an upward interest in several domain. Mainly, the medical domain takes advantages from the health service facility, the high flexibility and the mobility. However, interferences from coexisting wireless networks may lose critical informations and greatly affect on network reliability. In this paper, we are conducting to improve coexistence between WBAN based IEEE 802.15.4 protocol and WIFI. We adopt multi-hop routing to ensure reliability, connectivity and battery life. Then we investigate different effects of transmit power, transmission frequency and packet size on WBAN performances under heavy and real interferences circumstances. We propose a well suited model and simple adaptive algorithm which adjust dynamically its parameters with received performances indicators.

This work further investigates paradigm of radiation awareness in WBAN network environments. We incorporate the effect of topology as well as the time domain and environment aspects. Even, if the impact of radiation to human health remains largely unexplored and controversial. In this paper, we propose a multi objectives flow model for WBSNs which allows describing a new optimal deployment model for WBAN sensor devices dynamic topology and the relevant possible trade-offs between coverage, connectivity, network life time and radiation to human health. We propose oblivious deployment heuristics that are radiation aware. Simulation results show that the algorithm balances the energy consumption of nodes effectively and maximize the network lifetime. It will meet the enhanced WBANs requirements, including better delivery ratio, less reliable routing overhead. Our proposed radiation aware deployment heuristics succeed to keep radiation levels low, while not increasing latency.

The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Sensor Networks (WBSNs). The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. WBSNs like any other sensor networks suffer limited energy resources and hence preserving the energy of the nodes is of great importance. Unlike typical sensor networks WBSNs have few and dissimilar sensors. In addition, an extremely low transmit power per node is needed to minimize interference to cope with health concerns and to avoid tissue heating which means that the existing solution for preserving energy in wireless sensor networks might not be efficient in WBSNs. Most of the attention has been given to the energy routing where energy awareness is an essential consideration. In this paper, we propose a Min-Max multi-commodity flow model for WBSNs which allows to prevent sensor node saturation, by imposing an equilibrium use of sensors during the routing process taking into account the specific characteristics of the wireless environment on the human body. The Min-Max objective is transformed to a Min objective by adding a set of constraints to the model. Based on the energy consumption for sending and receiving data and the available residual energy of nodes, the max-min based mathematical programming model is designed to find optimal routing. Simulation results show that the algorithm balances the energy consumption of nodes effectively and maximize the network lifetime.

WBSNs (wireless body sensor network) like any other sensor networks suffers limited energy and are the highly distributed network, in which its nodes are the organizer itself and each of them has the flexibility of collecting and transmitting patient biomedical information to a sink. When knowledge sent to sink from a path that doesn't have a definite basis, the routing is a crucial challenge in Wireless Body Area Sensor Networks, additionally reliability and routing delay are the considerable factors in these type of networks. Most of the attention should be given to the energy routing where energy awareness is an essential consideration in WBSNs and the frequent topology change increases the dynamics of network topology, and complicates the process of relay selection in cooperative communications. In this paper, we propose a Min-Max multi-commodity flow model for WBSNs which allows to prevent sensor node saturation and take best action against reliability and the path loss, by imposing an equilibrium use of sensors during the routing process. Simulation results show that the algorithm balances the energy consumption of nodes effectively and maximize the network lifetime. It will meet the enhanced WBSNs requirements, including better delivery ratio, less reliable routing overhead.

In this paper, we present our policy based rule representation for a Diffserv network and show how they are translated and downloaded into the network element. One common problem in specifying rules for policy-based networks is the conflict detection problem, where rules may contradict or duplicate each other. We explore the algorithmic issues related to conflict detection problem and present a conflict detection algorithm. Performance results are also presented. An interactive Java-based policy management tool editor was designed for specifying the rules and detecting the conflicts among rules