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One of the main concerns of cybersecurity is the detection of hoaxes across social media. Hoaxers propagate such messages to mislead users and to promote violence. Several approaches exist in literature to address this issue. They are mainly limited to detect hoax activities by characterizing the message nature and detecting provenance of messages. However, unless hoaxes are detected, they continue to propagate across social media nodes. This work aims at reducing the dissemination of hoaxes across group of users. Relying on social graph structure, this research develops a mechanism based on smart contract logics to prevent a group to consume a fake post. To achieve this objective, we used a smart contract to exploit a trust index computed based on message characteristics and group features such as graph density, group status, group degree, group acceptability. Based on the value of trust index, the message is forwarded or blocked. Experiments realized on groups of different characteristics revealed that the proposed smart contract is even able to reactively block a fake post of the same nature than the group type. Results indicate that the proportion of targeted groups could be reduced even if their interests match with the message subject. This research is an important step forward to anti-promote hoaxes with the novelty of exploiting smart contract approach to contain their propagation.

The constant evolution of transportation systems and traffic in developing countries is nowadays confronted with a problem of road safety and therefore of a high accident rate, especially in the context of inter-urban road transport. In this work, we propose a communication architecture for an Intelligent Transport System (ITS) to provide surveillance in an inter-urban transport network in the context of developing countries. We introduce two types of control points: Relay Control Points (RCP) and Treatment Control Points (TrCP). We also designed two multi-objective models for the deployment of these points. In order to ensure good coverage, to minimize the cost of installation and to put more emphasis in the areas having a high number of accidents. To ensure optimal deployment of these points, we have used the Non-Dominated Sorted Genetic Algorithm II (NSGA-II) that will allow to realize the Pareto front of non-dominated solutions. The results of the simulations show the effectiveness of our proposal.

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.

The main purpose of designing vehicular ad-hoc networks (VANETs) is to achieve safety by periodically broadcasting the vehicle's coordinates with a high precision. This advantage brings a threat represented in the possible tracking and identification of the vehicles. A possible solution is to use and change pseudonyms. However, even by changing pseudonyms, the vehicle could still be tracked if the adversary has a prior knowledge about the potential start and end points of a particular driver who has social interactions (e.g., with neighbors) which introduces the concept of vehicular social networks (VSNs). This article extends the authors previous work, namely: “EPP Extreme Points Privacy for Trips and Home Identification in Vehicular Social Networks,” which exploits the nature of the end points that are common between VSN users in order to create shared zones to anonymize them. The extension is represented by (a) the evaluation of the enjoyed location privacy of VSN users after quitting the district in addition to (b) detailing the used environment during the evaluation.

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.

The file attached to this record is the author's final peer reviewed version. Open access article. Internet of Vehicles (IoV) has the potential to enhance road-safety with environment sensing features provided by embedded devices and sensors. This benignant feature also raises privacy issues: as vehicles announce their fine-grained whereabouts mainly for safety requirements, adversaries can leverage this to track and identify users. Various privacy-preserving schemes have been designed and evaluated, for example, mix-zone, encryption, group forming, and silent-period-based techniques. However, they all suffer inherent limitations. In this paper, we review these limitations and propose WHISPER, a safety-aware location privacy-preserving scheme that adjusts the transmission range of vehicles in order to prevent continuous location monitoring. We detail the set of protocols used by WHISPER, then we compare it against other privacy-preserving schemes. The results show that WHISPER outperformed the other schemes by providing better location privacy levels while still fulfilling the road-safety requirements.

Internet of Vehicles (IoV) had remarkably enhanced the road -safety. Thanks to the environment sensing feature provided by the embedded devices and sensors. Nevertheless, this benignant feature had also introduced privacy issues; as vehicles spread their fine-grained locations at the aim of fulfilling safety requirements, adversaries can use this latter to track and identify the IoV users. Different privacy schemes and techniques are designed and evaluated like the mix-zone, encryption, groups forming, and silent-period based techniques. However, the majority do suffer from serious limitations inherited from the technique itself. In this paper, we propose a safety-friendly location privacy-preserving scheme, WHISPER, that adjusts the transmission range of vehicles on-the-fly in order to, occasionally, escape the continuous location tracking. We detail the protocols used by WHISPER, then we compare it against other privacy- preserving schemes using different metrics. The results show that WHISPER outperformed the other schemes after giving better location privacy levels while still keeping the road-safety fulfilled.

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.

International audience; The Internet of Vehicles (IoV) has got the interest of different research bodies as a promising technology. IoV is mainly developed to reduce the number of crashes by enabling vehicles to sense the environment and spread their locations to the neighborhood via safety-beacons to enhance the system functioning. Nevertheless, a bunch of security and privacy threats are looming; by exploiting the spatio-data included in these beacons. A lot of privacy schemes were developed to cope with the problem like CAPS, CPN, RSP and SLOW. The schemes provide a certain level of location privacy yet the strength of the adversary, e.g., the number of eavesdropping stations, has not been fully considered. In this paper we aim at investigating the effect of the adversary's eavesdropping stations number and position on the overall system functioning via privacy and QoS metrics. We also show the performances of these schemes in a manhattan-grid model which gives a comparison between the used schemes. The results show that both the number and the emplacement of the eavesdropping stations have a real negative impact on the achieved location privacy of the IoV users.

The majority of older persons are challenged by chronic illnesses, so more innovations are needed for geriatric care. Therefore, technological and modern techniques have been adopted to improve the elderly's health. Different computing solutions have been deployed to store and process the health data such as cloud computing which provides powerful computing resources. However, connecting different kind of things directly to the cloud is inefficient. Fortunately, fog computing has emerged as a new computing solution to complement cloud, it allows computation and data storage closer to the IoT devices. However, managing health data stored in fog computing presents a major issue. It is necessary to consider the importance of performance, availability, storage, and privacy of data in fog computing. In this paper, we propose architecture based on Fog to Cloud computing which is a novel solution and innovative approach that contribute to enhance the synergy between the cloud and fog computing to facilitate the management of elderly health data. To analyze the performance of our model in Fog to Cloud environment, FogWorkflowSim toolkit has been used.

The Internet of Things (IoT) invention has taken the growth of sensors technology to a completely high step. New challenges in terms of data delivery have emerged due to strict QoS conditions. Among the solutions proposed in the literature is the subdivision of the large-scale network into several clusters. Except that most of these solutions are conventional. However, prior research generally confirms that bio-inspired paradigms are more flexible and effective compared to traditional methods. When it comes to a heterogeneous network, additional constraints appear. Nodes have different buffer sizes. Then, data captured must be sent before their buffers are full, otherwise, some data will be lost. This is not suitable for a real-time application where time and information are crucial elements. In this study, a comprehensive overview of the use of sensors in IoT contexts is performed. Two algorithms as Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA), combined with the Imperialist Competitive Algorithm (ICA) based Cluster Head (CH) selection with a novel approach for heterogeneous networks are proposed. These algorithms can support data exchange over a heterogeneous Wireless Sensor Network (WSN) infrastructure with taking into consideration the buffer overflow problem. Simulation results are presented and discussed in different network designs. The research demonstrated that knowing well how to manage buffers using bio-inspired techniques, leads to a significant reduction in data loss.

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.

Wireless sensor networks (WSNs) face many security issues. When external attacks can be prevented with traditional cryptographic mechanisms; internal attacks remain difficult to be eliminated. Trust and reputation have been recently suggested by many researches as a powerful tool for guaranteeing an effective security mechanism. They enable the detection and the isolation of both faulty and malicious nodes. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies especially in the case of dishonest recommendations attacks, i.e. badmouthing, ballot-stuffing and collusion attacks. In this paper, we propose a novel bio inspired trust model for WSNs namely Bee-Trust Scheme (BTS) based on the use of both a modified cloud model and a cognitive chronometry parameter. The objective of the scheme is to achieve both a higher detection rate and a lower false positive rate of dishonest recommendations attacks by allowing the distinction between erroneous recommendations and dishonest ones which has thus far been overlooked by most research work. Simulation results demonstrate that the proposed scheme is both effective and lightweight even when the number of dishonest recommenders is large.

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.

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.

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.

The fall is a crucial issue for elderly people. With the strong growth of information and communication technologies and the development of light and low-cost wearable technology, elderly fall detection has gained much attention. These technologies can help the elderly get timely assistance to reduce further injury. In this paper, we propose to develop a real-time aging friendly fall detection system based on acceleration and angular velocity values produced by the accelerometer and gyroscope sensors of the mobile device. We describe an effective and simple threshold-based solution to implement a real-time method to monitor the elderly and detect falls using a mobile phone. In case of a fall, our system will transmit an alert with location information to the contacts list via a notification. Thus, medical attention can be provided with minimal delay. The system was tested by volunteers and achieved high sensitivity, specificity and accuracy.

A wireless sensor network (WSN) is a set of sensor nodes devoted to sense sizes of physical phenomena of a geographical area of interest. The sensed data so collected are transmitted to a specific node called sink via cluster-heads (CHs). In most wsn's applications, CHs play an important role in aggregation and transmission of sensed data. Indeed, a failed CH may abort the overall mission of the sub-network. Due to their crucial functions, CHs must be designed and maintained to be robust enough in order to face trouble coming from the harsh environment. Therefore, each failed CH should be detected, recovered and a new one takes the relay without data loss. In this paper, we propose a novel algorithm called Cluster-Head Recovery Algorithm (CHRA). In this algorithm, a checkpointing technique is used to save CH's state in order to create a recovery path within each cluster. We compute the node's failure probability through a Bayesian network. The efficiency of the proposal is that can detect and recover the CH's failure online and in a distributed manner. The proposed scheme functionalities have been conducted by simulation and the obtained results demonstrate the effectiveness of the proposal.

Vehicular Ad-hoc Networks (VANETs), which are a subclass of Mobile Ad-hoc Networks (MANETs), received a widespread attention during the last decades. With these promising set of safety applications, which are the main reason why they were developed, VANETs are considered as a tremendous support for the Intelligent Transportation Systems (ITS). However, several key issues remain to be solved before VANET becomes fully applicable; one of them being privacy preservation. To fulfill safety-requirements in VANETs, the vehicle needs to broadcast its status wirelessly. Consequently, any adversary can hear the broadcast messages at the aim of analyzing them, identifying, tracking and generating profile of his target. In other words, privacy of individuals may be seriously breached in VANETs if no safety measures were been taken. Using pseudonyms instead of the real identities of individuals, and changing them periodically during the communication is a promising solution for such crucial problems. There is a significant body of research work addressing this issue and a lot of researchers proposed various privacy protections basing on pseudonym change strategies. In this survey paper, we present an introduction to the privacy problem and give a recent and detailed state of the art of the different suggested pseudonym change strategies and approaches. We also propose a novel taxonomy to classify these strategies to diverse concepts. Finally, we discuss, give future directions and open issues and mention some of the observations that lead to better identify this problem for better future strategies.

The wireless sensor networks (WSNs) suffer from several constraints such as harsh areas of deployment, the bad medium of communication and energy limitations. So, to guarantee the reliability and to enhance the quality of the measurements in this kind of networks, it is very crucial that the nodes of the network can detect and clean the sensed data which do not conform to an expected pattern. To reach this goal, we present in this paper a new algorithm, called Outlier Detection Algorithm based on Mahalanobis Distance (ODA-MD). In this algorithm, Mahalanobis theorem is used to compute and compare sensed data in a distributed fashion. The efficiency of this proposal that can detect outliers online even several data types exist in the network. The functionalities of the proposed scheme were performed by simulation using data sets from the Berkeley Intel Laboratory. The obtained results demonstrate the effectiveness of the solution compared to other algorithms in terms of energy consumption, false alarm rate and detection accuracy.

Increasingly, the technologies that are used in telecommunications contribute to improve intelligent transport systems. These technologies are systems that offer several concepts like vehicular ad hoc networks, which allow reduction of traffic accidents, traffic congestion and consequently increase road safety and the comfort of road users. Developing countries in general and those of sub-Sahara in particular do not have telecommunication infrastructures, even road infrastructures for the implementation of an intelligent transport system. Moreover, with the serious increase in vehicles in these countries, the problem of traffic regulation arise especially in the inter-urban context. One of the consequences of this problem is the high rate of road traffic accidents. Several models of traffic regulation as well as modeling systems of urban transport networks are presented in the literature. Unfortunately these models are intended for developed countries. The aim of this paper is to present, firstly the problems of the implementation of intelligent transport systems in developing countries like sub-Saharan Africa. Secondly, we present a survey of a road traffic model and an urban transport network models. Finally, we propose solutions, which provide methodologies for the modeling of a system and monitor traffic on inter-urban road transport networks in developing countries.

Failures are inevitable in wireless sensor network, and it is important to detect and recover faulty nodes. In this paper, we present an algorithm to recover faulty nodes called Fault detection and Recovery Algorithm (FDRA). The performance evaluation is tested through simulation to evaluate some factors such as: Packet delivery ratio, control overhead, memory overhead and fault recovery delay. We compared our results with referenced algorithm: Fault Detection in Wireless Sensor Networks (FDWSN), and found that our FDRA performance outperforms that of FDWSN.

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.

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.

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.

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.

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.