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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.

Rapid population growth and the number of vehicles in cities have complicated urban mobility management. Digitalization supported by the internet of things and wireless communication has allowed some cities to mitigate the problem by taking advantage of the multiple benefits offered. These are cyber-physical systems (CPS), which are systems where a number of devices collaborate for the control of physical entities. This recent technology finds its application in urban mobility. However, in the context of developing countries, there are many local specificities one needs to consider. How could the integration of cyber-physical systems help urban decision makers to design sustainable urban mobility systems that meet the needs of the population? The paper proposed not only a recent review of the literature, but also a framework of CPS of urban mobility to guide decision makers. The challenges, opportunities, and barriers to innovation of CPS in urban environments in developing countries have also been identified.

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.

Nowadays, the cloud computing technology combined with the new generation networks and internet of things facilitate the networking of numerous smart devices. Moreover, the advent of the smart web requires massive data backup from the smart connected devices to the cloud. Unfortunately, the publication of several of these data, such as medical information and financial transactions, could lead to serious privacy breaches, which is becoming the most serious issue in cloud of things. For instance, passive attacks can launched in order to get access to private information. For this reason, several data anonymization techniques have emerged in order to keep data as confidential as possible. However, these different techniques are making the data unusable the most of time. Meanwhile, differential privacy that has been used in a number of cyber physical systems recently emerged as an efficient technique for ensuring the privacy of cloud of things stored data. In this exploratory paper, we study the guarantees of differential privacy of a multi-level anonymization scheme of data graphs. The considered scheme disturbs the structure of the graph by adding false edges, groups the vertices in distinct sets and permutes the vertices in these groups. Particularly, we demonstrated the guarantees that the anonymized data by this algorithm remain exploitable while guaranteeing the anonymity of users.

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.

Nowadays, the use of Wireless Sensor Networks (WSNs) is increasingly growing as they allow a large number of applications. In a large scale sensor network, communication among sensors is achieved by using a multihop communication. However, since the sensor is limited by its resources, sensors' operating systems are developed in order to optimize the management of these resources, especially the power consumption. Therefore, the hybrid operating system Contiki uses a low consumption layer called Rime which allows sensors to perform multihop sending with a low energy cost. This is favored by the implementation of lightweight processes called protothreads. These processes have a good efficiency/consumption ratio for monolithic tasks, but the management of several tasks remains a problem. In order to enable multitasking, Contiki provides to users a preemptive multithreading module that allows the management of multiple threads. However, it usually causes greater energy wastage. To improve multithreading in sensor networks, a Multithreading Model for an Efficient Data Delivery (MMEDD) using protothreads is proposed in this paper. Intensive experiments have been conducted on COOJA simulator that is integrated in Contiki. The results show that MMEDD provides better ratio message reception rate/energy consumption than other architectures.

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.

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.

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

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.

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.

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.

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.

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.

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).

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.

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.

WThe Virtual Machine (VM) technology has increased considerably within the last two decades and the performance of VMs seems closest to the host machine. This has motivated the use of VM in High Performance Computing (HPC). Running a scientific computation on VMs can severely impact the performance of applications. In addition, choosing the proper VM to run a specific application while minimizing the lost of performance is not an easy task. However, several virtualization solutions have been proposed. This paper presents the result of a comparison of the performance of the most commonly used VMs solutions: OpenVz, Linux-Vserver, LXC, XEN, KVM and VMware ESXi. The performance of these VMs are evaluated with the NAS benchmark, Lmbench, IOzone and Intel ® MPI Benchmark taking into account the consumption of resources such as CPU, memory, latency, disk and network communication. The result shows that some virtualization solutions present better performance in consumption of some of the previously mentioned resources than others. This work aims at helping a scientist in selecting the VM suitable to compute a specific task without a significant overhead on applications performance.