Wireless sensor networks are deployed without predefined infrastructure and are generally left unattended. Indeed, the vulnerability of the wireless sensor networks to attacks comes principally from their inherent characteristics. As the data are transmitted over the air, it is very easy for an adversary to spy on traffic. Also, to meet the strict budgetary requirements, the sensor nodes tend to not be tamperproof and thus offer no protection against security attacks. Alongside with these vulnerabilities, the human intervention is always not allowed to deal with adversaries who attempt to compromise the network. Therefore, security systems are mainly needed to secure the network and ensure the protection against security threats. Indeed, cryptographic based systems are generally used to ensure security. However, due to the lack of memory and power (low computing, limited energy reserves) of the sensor nodes, most of these approaches are not suitable. Therefore, providing security while respecting the specific constraints of the sensors, represents one of the most important research issue in wireless sensor networks. Indeed, several studies have been conducted these last decades to propose lightweight and efficient security protocols for wireless sensor networks. In this paper, we review the most leading protocols and classify them based the addressed security issue. Also, we outline the main security constraints and challenges and present the future research directions based on the emerged application fields.

Securing the network communication represents one of the most important challenges in wireless sensor networks. The key distribution problem has been widely discussed in classical wireless sensor networks contrarily to heterogeneous ones. HWSNs (Heterogeneous Wireless Sensor Networks) have optimized the network capability and opened new security opportunities by introducing high resource capacity sensor nodes in the network. In this paper, an efficient dynamic authentication and key Management scheme is proposed for heterogeneous WSN. The main idea is to provide a single lightweight protocol for both authentication and key establishment while optimizing the security level. The key distribution algorithm is based on preexisting information to generate dynamic keys and does not require any secure channel and sharing phase which improves the security, energy efficiency and reduces the memory consumption. Experimental results have confirmed the performances of our mechanism compared to some of the existing security protocols.

Recently, mobile crowd sensing (MCS) is captivating growing attention because of their suitability for enormous range of new types of context-aware applications and services. This is attributed to the fact that modern smartphones are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform more complex tasks besides their inherent calling features. Despite a number of merits, MCS confronts new challenges due to network dynamics, the huge volume of data, sensing task coordination, and the user privacy problems. In this paper, a comprehensive review of MCS is presented. First, we highlight the distinguishing features and potential advantages of MCS compared to conventional sensor networks. Then, a taxonomy of MCS is devised based on sensing scale, level of user involvement and responsiveness, sampling rate, and underlying network infrastructure. Afterward, we categorize and classify prominent applications of MCS in environmental, infrastructure, social, and behavioral domains. The core architecture of MCS is also described. Finally, we describe the potential advantages, determine and reiterate the open research challenges of MCS and illustrate possible solutions

The renewable energy field has attracted the attention of the researchers and the industrials communities these last decades, and a variety of optimization technologies have been introduced. Most of these technologies are based on improvements through mechanical engineering and physics. However, the continuous need for energy efficiency, productivity and cost-effectiveness have pushed towards the application of new renewable energy strategies inspired from other research fields. Recently, it has been proved that applying Internet of Thing (IoT) can optimize the management of the renewable energy resources by combining sensors and data analytics.

Indeed, the renewable energy systems based on IoT can facilitate the communication between the renewable energy appliances to optimize the energy efficiency. For example, the energy regeneration will be switched from solar to wind turbine in a particularly cloudy day with a lot of wind. On the other side, IoT analytics have a significant effect on renewable energy outperforming. For instance, extracting data from solar panels may help to speed up the maintenance process and reduce the downtime.

Recent research developments have brought forward high-performance information technologies which include high-performance computing (HPC) and high-performance interconnection networks (HPINs). Resolving complex problems requires HPC, which involves use of parallel processing required to run complex application programs. HPC, or supercomputing, is a technology based on the use of supercomputers, which represent systems that perform at or near the current highest operational rate for computers. HPINs imply high-speed and large bandwidth data communication networks to optimise information exchange. Using HPINs, scientists can communicate, share experiments and results, and run simulations and programs on remote supercomputers at a high rate and capacity and with superior facilities. This special issue on ‘High-performance information technologies for engineering applications’ aims to present new advances and research results in the field of high-performance technologies and related areas. The topics covered in this special issue include: architectures for ubiquitous and pervasive HPC, cloud, distributed and grid computing, component technologies for HPC, HPINs, HPC in scientific, engineering, medical and social applications, internet and web computing and parallel and distributed algorithms. The special issue contains four papers. In the first paper, the authors Sundararaman and SubbuLakshmi address the job scheduling of scientific workflow applications in IaaS cloud and propose a job scheduling strategy called VMPROV to find the optimal number of virtual machines required to execute the jobs. Also, the authors introduce a priority-based round robin (PBRR) algorithm for resource mapping with minimal makespan and cost. The proposed algorithm considers the IaaS features such as pay per use, heterogeneous virtual environment and dynamic generation of virtual machines. Simulation experiments have been conducted with four real world scientific workflow applications to demonstrate the performances of VMPROV and PBRR algorithms compared with the classic algorithms in the schedule generation. The second paper, presented by Kortas and Arbia, points out how the cloud is less suitable for subcontracting components with real-time limitations and introduces a cloudlet architecture that offers a middleware framework to achieve minimal network latency. The authors evaluate the proposed cloudlet architecture based on the resource utilisation rate, the average execution time, the latency, the throughput and the lost packets within the local and distant connection. Additionally, a comparative performance study with similar services (MegaUpload and RapidShare) is presented. In the third paper, Manikandan and Subha present a parallel data processing approach for effective intensive care units (ICUs) with the internet of things. The main purpose is to improve efficiency and accuracy in data processing by applying a parallel decision tree algorithm in ICU data to take faster and accurate decisions on data selection. Indeed, the uses of parallelised algorithms optimise the process of collecting large sets of patient information. In addition, a decision tree algorithm is used for examining and extracting knowledge-based data from large databases. The IoT concept has been adopted by the authors for communicating the processing result to remote medical experts. The special issue is concluded with the paper by Bennour, Ettouil, Zarrouk and Abderrazak. In this paper, the authors point out the impact of the HPC and software parallelism in the optimisation meta-heuristics field. Also, the paper presents a study of the Java runtime performance on handling multi-thread PSO over general purpose multi-core machines and networked machines. In their analyses, the authors consider synchronous, asynchronous, single-swarm and multi-swarm PSO variants. We would like to thank all the authors for having submitted their research results for publication in this special issue. We believe that the papers published here reveal progress in the topic of high-performance information technologies. Also, we are very grateful for the collaboration of the reviewers and their influence of their expertise on the final quality. We sincerely thank Prof. Kuan-Ching Li, Editor of the International Journal of Computational Science and Engineering, for giving us the opportunity to prepare this special issue and his advice throughout this project. The support of the publisher with technical issues was also very welcome and assured good progress

Mobile Wireless Sensor and Actor Networks (MWSANs) can simply be defined as an extension of Wireless Sensor and Actor Networks (WSANs) in which the actor nodes are mobile. As such, in addition to challenges existing in WSAN, the mobility also imposes new challenges such as localization of actors, cooperative tracking of both actor-actor and actor-sensor collaboration, and communication infrastructure between distant actors. New communication protocols, specific to MWSANs, are needed. In this paper, we propose a self-organization and data collection protocol in order to provide energy efficiency, low latency, high success rate and suitably interactions between sensors and actors and take benefit from the mobility and resources existing on the network's actor nodes. The actor nodes move according to RWP mobility model. Each actor, during its pause time creates a temporary cluster, and is the head of it, collects and processes sensor data and performs actions on the environment based on the information gathered from sensor nodes in its cluster. Once an actor detects a base station it delivers the collected data to it. The simulations carried out (with TOSSIM tool), comfort us with good performances results.

In this paper, we propose a novel self-organization protocol for wireless sensor networks (WSNs) assisted by Unmanned Aerial Vehicle (UAV or Drone) called SSP (Self-organization Smart Protocol). In order to provide energy efficiency, low latency, high success rate and suitably interactions between sensors and UAVs while taking advantage of the air mobility (fly) and resource available on the UAVs in the network. The UAVs move according to RWP (Random Waypoint) mobility model. Each UAV, during its pause time at a known height, creates a temporary cluster, and acts as its head, collects and processes sensor data and performs actions on the environment based on the information gathered from sensor nodes in its cluster. Once an UAV detects a base station (BS) it forwards the collected data to it. The results of the simulations show the high performance of the proposed algorithm.

Identification schemes based on multivariate polynomials have been receiving attraction in different areas due to the quantum secure property. Identification is one of the most important elements for the IoT to achieve communication between objects, gather and share information with each other. Thus, identification schemes which are post-quantum secure are significant for Internet-of-Things (IoT) devices. Various polar forms of multivariate quadratic and cubic polynomial systems have been proposed for these identification schemes. There is a need to define polar form for multivariate dth degree polynomials, where d≥4 . In this paper, we propose a solution to this need by defining constructions for multivariate polynomials of degree d≥4 . We give a generic framework to construct the identification scheme for IoT and RFID applications. In addition, we compare identification schemes and curve-based cryptoGPS which is currently used in RFID applications.

Due to the intrinsically geo-distributed subcontracting maintenance activity practice, the maintenance scheduling has for a long time been a major challenge in the industry. This research work presents a methodology to schedule the maintenance activities of geo-distributed assets. A multi-agent system based approach is proposed to enable the decision-making for the subcontractors in a distributed industrial environment under uncertainty. An auction based negotiation mechanism is designed to promote competition and cooperation among the different agents, and to obtain global good schedule.Compared to the Weighted Shortest Processing Time first–Heuristic–Earliest Due Date (WSPT-H-EDD) method, the experimental results show that the proposed approach is able to provide scheduling scheme with good performances in terms of Global Cost, Total Weighted Tardiness Cost and makespan

Wireless sensor technology has been recognized as one of the emerging technologies of this century widely used for intelligent data sensing. A wireless sensor network (WSN) is composed of several sensor nodes, where the main objective of a sensor node is to collect information from its surrounding environment and transmit it to one or more points of centralized control, called base stations or sinks, for further analysis and processing. Limitations in WSN exist due to the restriction in its resources. Several approaches exist to increase the efficiency in communication and data acquisition in WSN. In this chapter, we describe data aggregation methods to optimize the data collection process and the sensor nodes. Additionally, we explain some interesting routing protocols that optimize communication among nodes in a WSN. Finally, we describe some strategies that would be useful to incorporate for sensor node mobility.

The interest shown by some community of researchers to autonomous drones or UAVs (unmanned aerial vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we proposed a flocking-based routing protocol for UAVs called BR-AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. Moreover, an automatic ground base stations discovery mechanism has been introduced for a proactive drones and ground networks association needed for the context of real-time applications. The performance of BR-AODV was evaluated and compared with that of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs

Examines the market for wireless sensor networks in the era and expansion of the Internet of Things. Over the past decade, the fast expansion of the Internet of Things (IoT) paradigm and wireless communication technologies has raised many scientific and engineering challenges that call for ingenious research efforts from both academia and industry. The IoT paradigm now covers several technologies beyond RFID and wireless sensor networks (WSNs). In fact, the number of potential application fields has already exceeded expectations. According to Cisco IBSG, more than 50 billion devices are expected to be connected to the Internet by 2020, with around 20 percent from the industry sector. Therefore, integrating the IoT concept and industrial WSNs (IWSNs) is an attractive choice for industrial processes, which may optimize operational efficiency, automation, maintenance, and rationalization. Moreover, IoT ensures large-scale interconnection between machines, computers, and people, enabling intelligent industrial operations. This emergent technological evolution has led to what has become the Industrial IoT (IIoT). IIoT will bring promising opportunities, along with new challenges

The interest shown by some community of researchers to autonomous drones or UAVs (Unmanned Aerial Vehicles) has increased with the advent of wireless communication networks. These networks allow UAVs to cooperate more efficiently in an ad hoc manner in order to achieve specific tasks in specific environments. To do so, each drone navigates autonomously while staying connected with other nodes in its group via radio links. This connectivity can deliberately be maintained for a while constraining the mobility of the drones. This will be suitable for the drones involved in a given path of a given transmission between a source and a destination. This constraint could be removed at the end of the transmission process and the mobility of each concerned drone becomes again independent from the others. In this work, we have proposed a bio-inspired routing protocol for UAVs called BR- AODV. The protocol takes advantage of a well known ad hoc routing protocol for on-demand route computation, and the Boids of Reynolds mechanism for connectivity and route maintaining while data is being transmitted. The performances of BR-AODV were evaluated and compared to those of classical AODV routing protocol and the results show that BR-AODV outperforms AODV in terms of delay, throughput and packet loss.

Recent advances in wireless communication have led to the introduction of a novel network of miniaturized, low power, intelligent sensors that can be placed in, on, or around the body. This network is referred to as Wireless Body Area Network (WBAN). The main purpose of WBAN is to physiologically monitor patient's vital signs and consequently route the related data towards a base station. Since the environment of such a network is principally the human body, data routing mechanisms used in traditional wireless networks (e.g. WSN, WANET) need to be revised, and more restrictions have to be addressed in order to adapt it to WBAN routing challenges. Compared to those dedicated to on-body WBAN, in-body WBAN routing protocols have more constrains and restrictions and are expected to be efficient and robust. As better as we know, only few routing protocols have been proposed in literature and the research field stills underexplored. Therefore, in this paper we present an overview of the main existing routing protocols proposed for wireless in-body sensor networks (WIBSN).

The volume contains the proceedings of the first edition of the International Conference on Internet of Things and Machine Learning (IML'17). The International Conference on Internet of Things and Machine Learning (IML'17) represents an ideal opportunity for the students and the researchers from both industrial and academic domains having as main objective to present their latest ideas and research results in any one of the IML'17 topics. IML'17 aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges, emerging technologies and progress in standards on topics relevant to today's fast moving areas of Internet of Things and Machine Learning.

This volume contains the proceedings of the second edition of the International Conference on Internet of Things, Data and Cloud Computing (ICC 2017). The ICC 2017 conference represents an excellent opportunity for the students and the researchers, from both industrial and academic domains, having as main objective to present their latest ideas and research results in any one of the ICC 2017 topics.

Due to the fast development of the cloud computing and the computer science technology, the researchers are more attracted to combine the Internet of Things (IoT) and the Data and Cloud Computing. The Internet of Things (IoT) paradigm is based on artificially-intelligent and self-configuring nodes (things) interconnected in a dynamic and global network infrastructure. Internet of things refers to the real world and to things with limited storage and processing ability. It also refers to the main problems concerning reliability, performance, security, and privacy. On the other hand, cloud computing provides huge capacities of storage and substantial processing power, that may solve the problems of most of the Internet of things technologies.

ICC 2017 aims to provide a forum for the scientists to present their latest research results and perspectives for future works in the IoT, Data and Cloud Computing fields. The ICC 2017 conference is a scientific event, which gathered leading researchers and practitioners, who presented, exchanged, and discussed their original ideas. The ICC 2017 conference paper authors have provided high quality contributions reviewed by an international program committee featuring renowned international experts on a broad range of IoT, Data and Cloud Computing topics. More than 590 papers were submitted to the ICC 2017 conference from authors of 58 countries. Two or three reviewers were assigned to each paper and according to these reviews, 213 papers were accepted, which provides an acceptance rate of 36 %.

In wireless sensor networks, one of the most important constraints is the low power consumption requirement. For that reason, several hierarchical or cluster-based routing methods have been proposed to provide an efficient way to save energy during communication. However, their main challenge is to have efficient mechanisms to achieve the trade-off between increasing the network lifetime and accomplishing acceptable transmission latency. In this paper, we propose a novel protocol for cluster-based wireless sensor networks called PEAL (Power Efficient and Adaptive Latency). Our simulation results show that PEAL can extend the network lifetime about 47% compared to the classic protocol LEACH (Low-Energy Adaptive Clustering Hierarchy) and introduces an acceptable transmission latency compared to the energy conservation gain.

WSNs consist of large number of small sensors densely deployed to monitor a phenomenon. Most of the data generated from the WSNs represent events happening at time intervals. Sometimes and according to the nature of the applications, this data stream is continuous and can reach high speeds. Therefore, adopting new techniques, platforms and tools to deal with this large amount of sensory data became necessary. Therefore, the Big Data paradigm can represent a good solution for the extraction, analysis, viewing, sharing, storage and transfer of such volume of data. This paper presents a survey on integrating Big Data tools for gathering, storing and analyzing the data generated by WSNs.