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In recent years, tremendous advances in communication technologies coupled with the advent of the Internet of Things (IoT) have led to the emergence of the Big Data phenomenon. Big Data is one of the big IT challenges of the current decade. The amount of data produced is constantly increasing and makes it more and more difficult to process. Managing these masses of data requires the use of new data management systems with efficient access methods. Considered as one of the main sources of Big Data, wireless sensors used in networks offer a credible solution to the problem of Big Data management, especially its collection. Several solutions for Big Data collection based on large-scale wireless sensor networks (LS-WSN) are proposed, taking into account the nature of the applications. The hierarchical architecture is the one used for the deployment of these applications. In such an architecture, relay sensors play an important role in finding the balance of the network and maximizing its lifetime. In most LS-WSN applications, once deployed, the LS-WSN does not provide a mechanism to evaluate and improve the positions of the initially deployed relay sensors. This paper proposes, based on the growth model of physarum polycephalum and its ability to prune unnecessary links and retain only those deemed useful for food routing, a mechanism for evaluating and optimizing relay sensors in LS-WSNs. Simulation results indicate that the proposed approach significantly improves the network lifetime compared to the initial deployment and that can be a useful approach for LS-WSNs dedicated to Big Data collection. The effectiveness of the proposed technique is demonstrated by experimental results in terms of connectivity and network lifetime.

Video broadcasting in Vehicular Ad Hoc Networks (VANETs) is beneficial for traffic management, entertainment, and advertising services because video notifications in active safety applications provide more information regarding accident scenarios than simple text messages. However, broadcasting videos over urban VANETs is challenging because of specificities, e.g., dynamic topology, shadowing phenomena, node mobility, and network partition. Moreover, the delay, jitter, and packet loss ratio associated with video streaming should not exceed strict thresholds for an acceptable quality of experience. To meet video streaming requirements, we propose a receiver-based, line-of-sight-aware and reliable bi-directional broadcasting protocol that obtains a tradeoff between broadcast reliability and coverage capabilities. The road network is segmented into a set of straight sections and the bi-directional broadcast method is applied to each section to address the obstructed line of sight problem and the coverage capacity simultaneously. Our protocol selects a sub-set of forwarders likely to have the best line of sight in a fully distributed manner. Furthermore, we overcame packet loss by designing an enhanced version of the store-carry-and-forward method that prioritizes the retransmission of packets containing more important video blocks. The simulation shows that our solution outperforms two innovative video broadcasting protocols in terms of frame loss, peak signal-to-noise ratio, and mean opinion score while keeping the end-to-end delay within the video streaming requirement range.

With the rapid growth of the internet of things (IoT), an impressive number of IoT’s application based on wireless sensor networks (WSNs) has been deployed in various domain. Due to its wide ranged applications, WSNs that have the capability to monitor a given sensing field, became the most used platform of IoT. Therefore, coverage becomes one of the most important challenge of WSNs. The search for better positions to assign to the sensors in order to control each point of an area of interest and the collection of data from sensors are major concerns in WSNs. This work addresses these problems by providing a hybrid approach that ensures sensors deployment on a grid for targets coverage while taking into account connectivity. The proposed sequential hybrid approach is based on three algorithms. The first places the sensors so as to all targets are covered. The second removes redundancies from the placement algorithm to reduce the number of sensors deployed. The third one, based on the genetic algorithm, aims to generate a connected graph which provide a minimal path that links deployed sensors and sink. Simulations and a comparative study were carried out to prove the relevance of the proposed method.

The internet of vehicles (IoV) is getting a considerable amount of attention from different research parties. IoV aims at enhancing the driving experience with its wide range of applications varying from safety, road management to entertainment; however, some of such applications bring severe security and privacy issues; identity exposing, and location tracking are good examples. By enabling vehicles to send their statuses to themselves via beacon messages, this creates an environmental awareness for safety purposes but also exposes them to the aforementioned attacks. A lot of work has been done to mitigate the effect of such attacks but still does not provide a holistic solution. In this article, which is an extension to a prior work, the authors investigate the effects of changing the transmission range while sending beacons on the achieved level of location privacy based on two location privacy schemes: SLOW and CAPS. The authors use additional privacy metrics in addition to comparing the strategies in some well-known security attacks. The outcomes confirm the feasibility of using such a mechanism.

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the Non dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength pareto evolutionary algorithm-II (SPEA-II); and the pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

International audience; In recent years, the decentralized wireless Vehicular Ad hoc Networks (VANETs) have emerged as a key technology for Intelligent Transportation Systems (ITS). The need for an efficient and reliable broadcast protocol, mainly in urban VANETs, is of great importance to support different services such as road safety, traffic efficiency, entertainment and advertisement. This paper proposes two new routing broadcast protocols: the Enhanced Counter-based broadcast protocol in Urban VANET (ECUV) and the Enhanced distance-based broadcast protocol in Urban VANET (EDUV). Both of them improve the distribution of data on urban VANETs. ECUV and EDUV use a road-network-topology-based approach to select a set of relay nodes with enhanced coverage capabilities during the data delivery in urban Vehicle to Vehicle (V2V) scenarios. They also improve the performance of the receiver-based protocols by alleviating the negative effect of their stochastic behavior. We study the behavior of these protocols with an analytical model, which shows that the enhanced versions reduce the transmission probability in high vehicle density to avoid the broadcast storm problem. Moreover, the obtained results proved that these proposed protocols increase the transmission probability in low vehicle density to satisfy the reachability requirement of data broadcasting. The network simulation results show clearly that ECUV and EDUV outperform other methods in terms of coverage capacity and efficiency.

Wireless sensor networks (WSNs) are becoming increasingly utilized in applications that require remote collection of data on environmental conditions. In particular dense WSNs are emerging as an important sensing platforms for the Internet of Things (IoT). WSNs are able to generate huge volumes of raw data, which require network structuring and efficient collaboration between nodes to ensure efficient transmission. In order to reduce the amount of data carried in the network, data aggregation is used in WSNs to define a policy of data fusion and compression. In this paper, we investigate a model for data aggregation in a dense {WSN} with a single sink. The model divides a circular coverage region centered at the sink into patches which are intersections of sectors of concentric rings, and data in each patch is aggregated at a single node before transmission. Nodes only communicate with other nodes in the same sector. Based on these assumptions, we formulate a linear programming problem to maximize system lifetime by minimizing the maximum proportionate energy consumption over all nodes. Under a wide variety of conditions, the optimal solution employs two transmissions mechanisms: direct transmission, in which nodes send information directly to the sink; and stepwise transmission, in which nodes transmit information to adjacent nodes. An exact formula is given for the proportionate energy consumption rate of the network. Asymptotic forms of this exact solution are also derived, and are verified to agree with the linear programming solution. We investigate three strategies for improving system lifetime: nonuniform energy and information density; iterated compression; and modifications of rings. We conclude that iterated compression has the biggest effect in increasing system lifetime., Comment: 13 pages, 9 figures

International audience; In recent years, technological advances and the ever-increasing power of embedded systems have seen the emergence of so-called smart cities. In these cities, application needs are increasingly calling for Large-Scale Wireless Sensor Networks (LS-WSN). However, the design and implementation of such networks pose several important and interesting challenges. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities. Moreover, sensors are often required to cooperate in order to route the collected data to a single central node (or sink). The many-to-one communication model that governs dense and widely deployed Wireless Sensor Networks (WSNs) most often leads to problems of network overload and congestion. Indeed, it is easy to show that the closer a node is geographical to the sink, the more data sources it has to relay. This leads to several problems including overloading of nodes close to the sink, high loss rate in the area close to the sink, and poor distribution of power consumption that directly affects the lives of these networks. In this context, we propose a contribution to the problem of LS-WSN energy consumption. We designed a hierarchical 3-tier architecture of LS-WSNs coupled with a modeling of the activities of the different sensors in the network. This architecture that is based on clustering also includes a redeployment function to maintain the topology in case of coverage gaps. The results of the performed simulations show that our architecture maximizes the lifetime than compared solutions.

The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.

Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.

Due to the energy constraints of sensors owing to the limitation of their built-in batteries, the lifespan of Wireless Sensor Networks (WSNs) are significantly affected. These particular ad-hoc networks have a huge number of applications including surveillance and target tracking. Unfortunately, since sensor nodes are limited in terms of power resources, efficient utilization of these resources is an important goal to design power-aware WSNs. This led researchers to propose numerous methods, such as clustered WSNs, in order to effectively manage the power resources. In this work, we proposed a heuristic clustering based on the hypergraph theory, and called HyperGraph Clustering (HGC) that aims at optimizing the energy of sensor nodes. Theoretical evaluation highlighted that this clustering protocol consumed less energy during the cluster formation phase and the selection of the cluster head. In addition, we evaluated the performance of the proposed HGC and the results showed the effectiveness of our scheme to those we compared in terms of the number of nodes alive, residual energy and the total consumption of the network.

International audience; Telecommunication network infrastructures in Africa and the Middle East regions, are deployed and operated in challenging environments that are highly scattered particularly in rural areas. Moreover, considerable number of cell sites are located in areas difficult to access. Furthermore, low income in rural areas does not allow a fast return on investment since the cost of deployment and operation of a cell site is considerable. These issues lead to a difficult human resource management, particularly, in the assignment of technicians to cell site for maintenance purpose. In this paper, an optimized scheme for costs of maintenance operations on cell sites is proposed. We used the k-means clustering algorithm for allocating field technician to a pool of cell sites. Moreover, to alleviate the k-means sensitivity to initialization, we proposed an initialization method that is based on the geometry of a sphere. We conducted series of experiments with sample of thousands of cell towers from OpenCellID and the results demonstrate the effectiveness of the proposal.

International audience; Nowadays, floods have become the widest global environmental and economic hazard in many countries, causing huge loss of lives and materials damages. It is, therefore, necessary to build an efficient flood forecasting system. The physical-based flood forecasting methods have indeed proven to be limited and ineffective. In most cases, they are only applicable under certain conditions. Indeed, some methods do not take into account all the parameters involved in the flood modeling, and these parameters can vary along a channel, which results in obtaining forecasted discharges very different from observed discharges. While using machine learning tools, especially artificial neural networks schemes appears to be an alternative. However, the performance of forecasting models, as well as a minimum error of prediction, is very interesting and challenging issues. In this paper, we used the multilayer perceptron in order to design a flood forecasting model and used discharge as input–output variables. The designed model has been tested upon intensive experiments and the results showed the effectiveness of our proposal with a good forecasting capacity.

With the emergence of Internet of Things (IoT) technology, a huge number of sensor based applications are going to be deployed. Thus, due to their valuable contribution, these sensors have to operate consistently and fulfill their task efficiently. To this end, the communications they involve must be optimal to comply with the expected requirements of IoT users. Bio-inspired computing paradigm has exhibited its capability to offer the best outcomes in information communications over wireless sensor network and vehicular ad hoc network environments. Providing mobility to sensor nodes in these networks enables to substantially improving ability of data sensing through energy saving, and data delivery through node proximity collecting. Indeed, this improvement is still also of great importance in IoT sensor environments. This work aims at increasing the knowledge of the readers by providing sufficient and comprehensive backgrounds in biologically inspired algorithms used for satisfactorily solving challenges posed by different sensors' mobility schemes in the context of IoT applications. Therefore, we give a global overview of static and mobile sensor node strategies with details related to Mobile Wireless Sensor Network (MWSN) enabling technologies. A set of routing protocols based on nodes' mobility will be succinctly examined according to classical domain as well as to bio-inspired one, in order to bring out what should be suitable for IoT applications depending on sensor nodes as building blocks.

Wireless sensor networks (WSNs) has recently drawn lots of attention due to its application in multiple domains. The sensors have limited power sources and in many applications they cannot be recharged or replaced due to hostile nature of the environment. Finding near optimal solutions for the energy problem is still an issue in WSNs. A new era is opened with algorithms inspired by nature to solve optimization problems. In this paper, we propose genetic algorithm based approaches for clustering and routing in WSNs. The objective of this mechanism is to prolong lifetime of a sensor and increase the quality of service. We perform extensive simulations of the proposed algorithms and compare the simulation results with that of the existing algorithms. The results demonstrate that the proposed algorithms outperform the existing algorithms in terms of various performance metrics including energy consumption and number of packets received by the base station.

One of the key targets of the upcoming 5G system is to build a mobile network architecture that supports not only classical mobile broadband applications (i.e., Internet and IMS), but also vertical industry services, such as those of automotive systems, e-health, public safety, and smart grid. Vertical industry is known to have specific needs that cannot be sustained by the current cellular networks. More notably, automotive systems require strict quality of service in terms of ultrashort latency for vehicle-toinfrastructure/ network (V2I/N) communications. In this paper, we introduce the Follow Me edge-Cloud (FMeC) concept, leveraging the mobile edge computing (MEC) architecture to sustain requirements of the 5G automotive systems. Assuming that automotive services are deployed on MEC entities, FMeC ensures low-latency access to these services by guaranteeing that vehicles (i.e., as well as user equipment on board vehicles) always connect to nearest automotive service. Besides the FMeC architecture, our contribution in this paper consists in presenting a projection of the FMeC solution on an automated driving use case that integrates automotive and Telco infrastructures, to realize the vision of future 5G automotive systems.We introduce the envisioned software defined networking/OpenFlow-based architecture and our mobility-aware framework based on a set of building blocks that permit achieving the automated driving requirements within 5G network. The evaluation results, obtained conjointly through theoretical analysis and computer simulation, showthat our proposed solution outperforms baseline approaches in meeting the automated driving latency requirement and minimizing the incurred global cost. This work was supported in part by the Academy of Finland Project CSN under Grant 311654 and in part by the European Union’s Horizon 2020 research and innovation program under the 5G!Pagoda project under Grant 723172 and EU H2020 5G-Transformer Project under Grant 761536.

Future generations of radio-based networks promise new timeliness for collaborative low-power sensing schemes in wireless sensor networks. Due to the hostile and inaccessible environment in which sensors are deployed, collect and transfer data in such networks is not an easy task. An effective data gathering can be improved by introducing unmanned aerial vehicles called drones, which act as mobile sinks and can autonomously fly over the network with the primary goal of collecting data from sensors. This paper presents a biologically inspired scheme of collaborative mobile sensing. The proposal has been designed in such a way that the coverage, the energy efficiency and a high network availability are maintained. Social foraging behaviors of the Escherichia coli bacteria modeled in the bacterial foraging optimization have been used to achieve these goals, especially the chemotaxis and the swarming features that allow bacteria to move. After a description, a formalization of the problem of mobile sensing is presented. Then, models that allow mobile sinks to move in a self-organized and self-adaptive way is proposed. In order to highlight the impact of mobility on energy consumption, delay, network coverage and successful amount of delivered data, intensive experiments have been done. Results demonstrate the effectiveness of the approach.

To accommodate the limited resources of sensors and specially energy capacity, researchers are increasingly interested in their improvement by developing new aware energy protocols to relay data to the concerned application. Finding near optimal solutions for the energy problem is still an issue in Wireless Sensor Networks (WSNs). A new era is opened with algorithms inspired by nature, which are meta-heuristic imitating living systems, to solve optimization problems. For this purpose, the Low Energy-Efficient Clustering and Routing Based on Genetic Algorithm (LECR-GA) mechanism is proposed. LECR-GA aims to prolong the WSN life-time and enhance its quality of service (QoS). Extensive simulations of the proposed solution were performed and their results were compared with those of literature.

The recent fifth generation (5G) system enabled a highly promising evolution of Cloud Radio Access Network (C-RAN). Unlike the conventional Radio Access Network (RAN), the C-RAN decouples the baseband processing unit (BBU) from the remote radio head (RRH) by allowing BBUs from multiple Base Stations (BSs) to operate into a centralized BBU pool on a remote cloud-based infrastructure and a scalable deployment of light-weight RRHs. In this paper, we propose an efficient resource allocation scheme for 5G C-RAN called Bee-Ant-CRAN. The challenge addressed is to design a logical joint mapping between User Equipment (UE) and RRHs as well as between RRHs and BBUs. This is done adaptively to network load conditions, in a way to reduce the overall network costs while maintaining the user QoS and QoE. The network load has been formulated as a mixed integer nonlinear problem with a number of constraints. Then, the formulated optimization problem is decomposed into two stage resource allocation problem: UE-RRH association and RRH-BBU mapping. Therefore, a modified Artificial Bee Colony is developed as a swarm intelligence based approach to build the UE-RRH mapping (resource allocation). Moreover, an ameliorated Ant Colony Optimization algorithm is proposed to solve the RRH-BBU mapping (clustering) problem. Computational results demonstrate that our proposed Bee-Ant-CRAN scheme reduces the resource wastage and significantly improves the spectral efficiency as well as the throughput.

Currently, we are experiencing a beginning of a new industrial revolution. The growth of machines and connected objects known as the “Internet of Things” (IoT) will rival the technological marvels of the past. To do this, researchers are no longer limited to conventional solutions, but, open up new promising prospects, the use of what we called “Biological inspirations”. This research aims to review the current bio-inspired IoT state-of-the-art by examining the literature, describing the challenges that threaten IoT, introducing future directions and open research with giving a base reference list to assist researchers.

Part 4: Optimization; International audience; Increasingly, the adoption of mobile sensors becomes imperative in the context of target tracking applications, especially for reliable data collection purpose. However, the design of a strategy that allows mobile sensors suitably to move in an autonomous, distributed and self-organized way is not evident to achieve by a deterministic polynomial algorithm. Solutions that are biologically inspired by the collective behaviour of individual social communities provide alternative tools and efficient algorithms that emerge from many interesting properties applicable to sensor technology. These solutions implement highly efficient systems that are structurally simple, powerful, highly distributed and fault-tolerant. Some biological societies, like colonies of the Escherichia coli bacteria, offer prospects to certain mobile sensors to acquire an artificial intelligence allowing them to move autonomously through the network. In this paper, we proposed a bio-inspired protocol named SMBFOA (Sink Mobility based on Bacterial Foraging Optimization Algorithm). The main idea of this protocol was inspired by the autonomous movement of the Escherichia coli bacterium. Based on the simulation results, we concluded that our proposed SMBFOA protocol increases the throughput data rate and prolongs the network lifetime duration for 30% and 5% respectively compared to Clustering Duty Cycle Mobility aware Protocol (CDCMP).

A cluster-based sensor network with data aggregation on cluster heads is the most popular approach for optimising the energy consumption, in order to maximise the overall network lifetime. Clustering is also used for optimising the quality of service and scalability in sensor networks. In large-scale networks, the management of nodes becomes a challenge. It is therefore necessary that, these sensors act in a self-organised manner to perform tasks. A number of protocols has been proposed. Swarm intelligence based models, inspired by social insects behaviours, provide the most powerful tools that lead to a global intelligence through simple actions in a self-organised manner. In this paper, we proposed a distributed clustering approach called NEST, based on the nest-sites selection process of a honeybee. Extensive experiments have been conducted and the results demonstrated that our algorithm delivers better performance in terms of network lifetime, delivered packets, end-to-end delay, energy consumption and efficiency.

Cloud Computing has emerged as a popular technology that support computing on demand services by allowing users to follow the pay-per-use-on-demand model. Minimizing energy consumption in cloud systems has many benefits that enable green computing. Energy aware task scheduling in cloud to the users by service cloud providers has non negligible influences on optimal resources utilization and thereby on the cost benefit. The traditional algorithms for task scheduling are not well enough for cloud computing. In such environment, tasks should be efficiently scheduled such a way that the makespan is reduced. In this paper, we proposed a biologically inspired scheduling scheme, which is a based on a modified version of the ant colony optimization that aims at reducing the makespan time while ensuring load balancing among resources in order to enable green computing. Experiments of the proposed scheme in various scenario have been conducted in order to elaborate the impact of proposed models in the reduction of makespan. The obtained results demonstrate the effectiveness of the proposal in regards to the compared algorithms.

Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

In Wireless Sensor Networks, mobile sensing refers to the presence of one or more mobile sinks or mobile sensors, which have the main role of collecting the gathered data by sensor nodes. This paper describes a new scheme of mobile sensing that aims at providing a good coverage and throughput while maintaining better energy efficiency and high network availability. To achieve this, some features of the social foraging behavior of the Escherichia coli bacteria have been used, especially the chemotaxis and swarming processes that allow bacteria to move. Particularly, a description and a formulation of a mobile sensing scheme based on an approach inspired by the Bacterial Foraging Optimization have been provided. Models that allow mobile sinks to move over the network in a self-organized and self-adaptive way have been proposed. The proposal has been experimented in order to elaborate the impact of mobility on delay, network coverage and successful amount of collected data. The obtained results demonstrate the effectiveness of the proposal.

The design of low-power scalable wireless sensor networks remains a key challenge of the research. Clustering and routing have been widely studied for extending the lifetime of a network, which is a critical issue in sensor networks. Routing involves non-negligible operations, which considerably affect the network lifetime and the throughput. The clustering technique with data aggregation on cluster heads has an influence on the overall performance of the network since it is favoring a maximum network lifetime. This paper presents a novel cluster-based routing protocol called ABC-SD. The proposed protocol exploits the biologically inspired fast and efficient searching features of the Artificial Bee Colony metaheuristic to build low-power clusters. For the choice of cluster heads, a multi-objective fitness function is designed by using a Linear Programming formulation. The routing problem is addressed by a cost-based function that makes a trade-off between the energy efficiency and the number of hops of the path. The clustering process is achieved at the Base Station with a centralized control algorithm, which exploits energy levels and the neighborhood information of location-unaware sensors. As for the routing of gathered data, it is realized in a distributed manner. Furthermore, unlike the existing protocols in the literature, a realistic energy model is adopted in the considered network model. The proposed protocol is intensively experimented with a number of topologies in various network scenarios and the results are compared with the well-known cluster-based routing protocols that include the swarm intelligence based protocols. The obtained results demonstrate the effectiveness of the proposed protocol in terms of network lifetime, network coverage and the amount of packets delivered to the Base Station. HighlightsLP formulation of the clustering problem.Routing problem addressed by a CF.ABC-based clustering algorithm with a tradeoff between the energy consumption and the quality of the communication link.Pre-established routing mechanism in which routing paths are less costly in terms of power consumption.Integration of a realistic energy model and realistic network settings and simulation of the proposed protocol to demonstrate its performance compared to some existing protocols.

Designing an energy-efficient and scalable sensor network while maximizing the lifetime remains a challenge. A number of protocols have been proposed in order to provide a good management in Wireless Sensor Networks (WSNs). Routing involves non-negligible operations that considerably affect the network lifetime and throughput. The clustering technique with data aggregation on cluster heads provides an efficient scalability in WSNs, favoring a better network lifetime. In this paper, we present an energy efficient biologically inspired clustering protocol that uses the efficient and fast searching features of Artificial Bee Colony (ABC) algorithm. In the proposed protocol, a centralized clustering process is adopted while the data gathering and routing operations are realized in a distributed manner. The proposed protocol is intensively experimented and the results are compared with the some of the well-known clustering and routing protocols. The obtained results demonstrate the effectiveness of the proposed protocol in terms of network lifetime and the amount of transferred packets.

In this paper, we propose a Proxy Mobile IPv6 (PMIPv6)-based architecture for Follow Me Cloud (FMC), a concept whereby mobile users are always connected to their optimal Data Centers via their optimal data anchor gateways, enabling not only data but also services to follow users. The approach envisioned in this paper consists of two parts: (i) a PMIPv6-based inter-domain mobility management support based on Distributed Mobility Management and (ii) a control plane based on Software Defined Networking - SDN/OpenFlow, which exploits the mobility information delivered by the PMIPv6 inter-domain mobility management approach to decide on triggering the migration of services/sessions within the cloud. Integrating SDN/Openflow rules with PMIPv6 permits removing the complexity and workload associated with tunneling in mobility management. Results obtained via analysis are encouraging and show the advantages of PMIPv6-based FMC in comparison to the state of the art mobility management protocols, such as Inter-domain PMIP (I-PMIP) and DMM-based PMIP (D-PMIP).

The Enhanced Coordination Channel Access (EDCA) mechanism is a 802.11e WG's initiative to provide Quality of Service (QoS) guarantees in 802.11-based wireless networks. Although EDCA introduces new mechanisms for differentiated medium access, it falls short in ensuring (i) intra-class QoS guarantees since flows belonging to the same service class (AC) are assigned the same MAC parameters regardless of their respective bit rate, which leads to throughput fairness rather than perceived QoS fairness, and (ii) sustained QoS guarantees by developing some sort of admission control mechanism to protect QoS of flows being served. In this paper, we propose a new MAC protocol featuring a dynamic channel reservation by using dynamic TXOPlimit parameter assignment along with a fully distributed admission control algorithm. By monitoring the dynamics MAC's AC queues, each AC figures out during runtime the TXOPlimit's value that best accommodates the application's data rate. TXOPlimit values are determined based on instantaneous network availability modelling taking into account key factors (e.g., contention level, network load, etc.) that influence flows' QoS performances. We generalize this model so that each AC active in the network may a priori assess the achievable QoS. This later model is used to design a fully-distributed admission control algorithm that regulates the network load to protect the already admitted flows from new entering flows. Simulation results show that compared to both EDCA and AEDCF (Adaptive EDCF), our protocol excels, in terms of network utilization and ability to guarantee the same QoS metrics' performances to flows of the same AC (intra-class). The admission control mechanism ensures high protection to the admitted flows while maintaining high network utilisation.

Follow Me Cloud (FMC) concept has emerged as a promising technology that allows seamless migration of services according to the corresponding users' mobility. Meanwhile, Software Defined Networking (SDN) is a new paradigm that permits to decouple the control and data planes of traditional network, and provides programmability and flexibility, allowing the network to dynamically adapt to changing traffic patterns and user demands. While the SDN implementations are gaining momentum, the control plane, however, is still suffering from scalability and performance concerns for a very large network. In this paper, we address these scalability and performance issues by introducing a novel SDN/OpenFlow-based architecture and control plane framework tailored for mobile cloud computing systems and more specifically for FMC-based systems where mobile nodes and network services are subject to constraints of movements and migrations. Contrary to centralized approach with single SDN controller, our approach permits to distribute the SDN/OpenFlow control plane on a two-level hierarchical architecture: a first level with a global controller G-FMCC, and second level with several local controllers L-FMCC(s). Thanks to our control plane framework and Network Function Virtual-ization concept (NFV), the L-FMCC(s) are deployed on-demand, where and when needed, depending on the global system load. Results obtained via analysis show that our solution ensures more efficient management of control plane, performances maintaining and network resources preservation.

The recently developed H.264 video standard achieves efficient encoding over a bandwidth ranging from a few kilobits per second to several megabits per second. Hence, transporting H.264 video is expected to be an important component of many wireless multimedia services, such as video conferencing, real-time network gaming, and TV broadcasting. However, due to wireless channel characteristics and lack of QoS support, the basic 802.11-based channel access procedure is merely sufficient to deliver non-real-time traffic. The delivery should be augmented by appropriate mechanisms to better consider different QoS requirements and ultimately adjust the medium access parameters to the video data content characteristics. In this article we address H.264 wireless video transmission over IEEE 802.11 WLAN by proposing a robust cross-layer architecture that leverages the inherent H.264 error resilience tools (i.e., data partitioning); and the existing QoS-based IEEE 802.11e MAC protocol possibilities. The performances of the proposed architecture are extensively investigated by simulations. Results obtained indicate that compared to 802.11 and 802.11e, our cross-layer architecture allows graceful video degradation while minimizing the mean packet loss and end-to-end delays.

Multimedia streaming over wired network, such as the Internet, has been popular now for quite some time. Further, as the bandwidth of wireless technologies have increased, consideration has only recently turned to delivering video over wireless networks. Meanwhile, an emerging new video compression standard namely H.264 is widely accepted. The H.264 standard outperforms all previous video compression, and can deliver high video quality at low rate. However, due to wireless channel characteristics, it is insufficient to merely deliver video data; instead delivery must consider also an adaptation of the video data to the networks specific and particularly network friendliness. In this paper, we address H.264 wireless video transmission over IEEE 802.11 wireless LAN by proposing a robust cross layer architecture which exploits: (i) the new standard IEEE 802.11e MAC protocol; (ii) the H.264 error resilience tools, namely data partitioning. The performances of the proposed architecture are extensively investigated by simulations. Results obtained indicate that our architecture can reliably transport the H.264 video stream, while the actual IEEE 802.11 standard in the same conditions, degrades considerably the transmitted video.

With the widespread IEEE 802.11 networks use, strong needs to enhance quality of service (QoS) has appeared. The IEEE 802.11 medium access control (MAC) protocol provides a contention-based distributed channel access mechanism that allow for wireless medium sharing. This protocol involves a significant collision rate as the network gets fairly loaded. Although the contention window (CW) is doubled after each collision, active stations may randomly select a backoff timer value smaller than the preceding one. This is obviously sub-optimal since the backoff values should rather increase after each collision in order to further space between successive transmissions and thus absorbing the growing contending flows. In this paper, we propose a novel backoff mechanism, namely "determinist contention window algorithm" (DCWA), which further separates between the different backoff ranges associated to the different contention stages. Instead of just doubling the upper bound of the CW, DCWA increases both backoff range bounds (i.e., upper and lower bounds). On the other hand, after each successful transmission the backoff range is readjusted by taking into account current network load and past history. Simulation results show that DCWA outperforms both the distributed coordination function (DCF) and the slow decrease (SD) scheme in terms of responsiveness to network load fluctuations, network utilization, and fairness among active stations

The increase of IEEE 802.11's bandwidth led to a deployment of many multimedia applications over wireless networks. Nevertheless, these applications impose stringent constraints in QoS. In this context, a lot of works have been proposed in order to enhance the QoS-capable IEEE 802.11e MAC protocol. However, they settle for maintaining only an inter-QoS differentiation between the traffic classes, and neglect the intra-Qos differentiation. In fact, the flows belonging to the same service class are assigned the same MAC parameters regardless of their data rate, which leads to throughput fairness rather than perceived QoS fairness. On the other hand, the proposed schemes exhibit performance degradation when the number of flows increases. In this paper, we propose a new MAC protocol based on the reservation of the wireless channel through the use of transmission Opportunity (TXOPlimit) parameter. Each traffic class monitors the MAC queue and computes at runtime the TXOPlimit's value. Thus based on the class' priority and flow's data rate, we can ensure both intra and inter QoS differentiation. Additionally, we specify a distributed admission control mechanism that regulates the network load and protects the admitted flows from the new ones. Simulation results show that compared to the Enhanced Distributed Channel Access (EDCA) scheme of 802.11e, our protocol excels, in terms of network utilization and ability to maintain intra-QoS data rate differentiation. Further when introducing the admission control mechanism, we ensure high protection to the admitted flows, and maintain the network in steady state.

Today the great challenge to the IEEE 802.11 wireless LAN is to support real time and multimedia applications. However, the characteristics of wireless channel (low and fluctuating bandwidth link and the large error rate) post different problems in supporting the requirements on QoS (bandwidth, delays, jitters) of these sensitive applications. We focus on the JVC H.26L video encoder, the wireless LAN and their interaction in QoS. The interaction is made by the guarantee of a reliable H.26L video transport over the IEEE 802.11 wireless LAN. In this context, we propose a novel cross layer architecture. This architecture is based on a marking scheme, which exploits the new propositions of the IEEE 802.11 E group for supporting QoS.

Actually, the quality of service (QoS) provisioning in IEEE 802.11-based wireless network is assured by the enhanced coordination channel access (EDCA) mechanism, which lacks the aptitude to ensure: (i) an intra-QoS differentiation between the access class (AC), where flows belonging to the same service class are assigned the same MAC parameters regardless theirs respective bit rate, which leads to throughput fairness rather than perceived QoS fairness; (ii) an admission control mechanism that manages the network traffic. In this paper we propose a new MAC protocol featuring a dynamic reservation of the wireless channel (by using the TXOPlimit parameter) along with a distributed admission control mechanism. By monitoring the MAC queue, each flow computes at runtimes the TXOPlimit's value that satisfies the application requested data rate. Meanwhile we specify a fully distributed admission control mechanism that regulates the network load and protects the admitted flows from the new ones. Simulation results show that compared to the EDCA scheme of 802.1 Ie, our protocol excels, in terms of network utilization and ability to maintain intra-QoS data rate differentiation. Further when introducing the admission control mechanism, we ensure high protection to the admitted flows, and maintain the network in steady state

During the past few years, wireless sensor networks witnessed an increased interest in both the industrial and the scientific community due to the potential wide area of applications. However, sensors’ components are designed with extreme resource constraints, especially the power supply limitation. It is therefore necessary to design low power, scalable and energy efficient protocols in order to extend the lifetime of such networks. Cluster-based sensor networks are the most popular approach for optimizing the energy consumption of sensor nodes, in order to strongly influence the overall performance of the network. In addition, routing involves non negligible operations that considerably affect the network lifetime and the throughput. In this thesis, we addressed the clustering and routing problems by hiring intelligent optimization methods through biologically inspired computing, which provides the most powerful models that enabled a global intelligence through local and simple behaviors. We proposed a distributed clustering approach based on the nest-sites selection process of a honeybee swarm. We formulated the distributed clustering problem as a social decision-making process in which sensors act in a collective manner to choose their cluster heads. To achieve this choice, we proposed a multi- objective cost-based fitness function. In the design of our proposed algorithm, we focused on the distribution of load balancing among each cluster member in order to extend network lifetime by making a tradeoff between the energy consumption and the quality of the communication link among sensors. Then, we proposed a centralized cluster-based routing protocol for wireless sensor networks by using the fast and efficient searching features of the artificial bee colony algorithm. We formulated the clustering as a linear programming problem and the routing problem is solved by proposing a cost-based function. We designed a multi-objective fitness function that uses the weighted sum approach, in the assignment of sensors to a cluster. The clustering algorithm allows the efficient building of clusters by making a tradeoff between the energy consumption and the quality of the communication link within clusters while the routing is realized in a distributed manner. The proposed protocols have been intensively experimented with a number of topologies in various network scenarios and the results are compared with the well-known cluster-based routing protocols. The results demonstrated the effectiveness of the proposed protocols., Au cours de ces dernières années, les réseaux de capteurs sans fils ont connu un intérêt croissant à la fois au sein de la communauté scientifique et industrielle en raison du large potentiel en terme d’applications offertes. Toutefois, les capteurs sont conçus avec d’extrêmes contraintes en ressources, en particulier la limitation de l’énergie. Il est donc nécessaire de concevoir des protocoles efficaces, évolutifs et moins consommateur d’énergie afin de prolonger la durée de vie de ces réseaux. Le clustering est une approche très populaire, utilisée pour l’optimisation de la consommation d’énergie des capteurs. Cette technique permet d’influencer fortement la performance globale du réseau. En outre, dans de tels réseaux, le routage génère un nombre assez élevé d’opérations non négligeables qui affectent considérablement la durée de vie du réseau ainsi que le débit offert. Dans cette thèse, nous nous sommes intéressés d’une part aux problèmes de clustering et de routage en utilisant des méthodes d’optimisation inspirées de certaines sociétés biologiques fournissant des modèles puissants qui conduisent à l’établissement d’une intelligence globale en se basant sur des comportements individuels très simples. Nous avons proposé une approche de clustering distribuée basée sur le processus de sélection des sites de nidification chez les colonies d’abeilles. Nous avons formulé le problème de clustering distribuée comme un processus social de prise de décision dans lequel les capteurs agissent d’une manière collective pour choisir des représentants au sein de leurs clusters respectifs. Le protocole proposé assure une distribution de l’équilibrage de charge entre les membres de chaque cluster afin de prolonger la durée de vie du réseau en faisant un compromis entre la consommation d’énergie et la qualité du canal de communication. D’autre part, nous avons proposé un protocole de routage basé sur des clusters en utilisant un algorithme inspiré du phénomène de butinage des abeilles. Nous avons formulé le problème de clustring comme un problème de programmation linéaire alors que le problème du routage est résolu par une fonction de coûts. L’algorithme de clustering permet la construction efficace des clusters en faisant un compromis entre la consommation d’énergie et la qualité du canal communication au sein des clusters tandis que le routage est réalisé de manière distribuée. Les protocoles proposés ont été intensivement expérimentés sur plusieurs topologies dans différents scénarios de réseaux et comparés avec des protocoles bien connus de clustering et routage. Les résultats obtenus démontrent l’efficacité des protocoles proposés.