The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energyconstrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energysaving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.

Internet of Things (IoT) is one of the promising technologies that has attracted a lot of attention in both industrial and academic fields these years. It aims to integrate seamlessly both physical and digital worlds in one single ecosystem that makes up a new intelligent era of Internet. This technology offers a huge business value for organizations and provides opportunities for many existing applications such as energy, healthcare and other sectors. However, as new emergent technology, IoT suffers from several security issues which are most challenging than those from other fields regarding its complex environment and resources-constrained IoT devices. A lot of researches have been initiated in order to provide efficient security solutions in IoT, particularly to address resources constraints and scalability issues. Furthermore, some technologies related to networking and cryptocurrency fields such as Software Defined Networking (SDN) and Blockchain are revolutionizing the world of the Internet of Things thanks to their efficiency and scalability. In this paper, we provide a comprehensive top down survey of the most recent proposed security and privacy solutions in IoT. We discuss particularly the benefits that new approaches such as blockchain and Software Defined Networking can bring to the security and the privacy in IoT in terms of flexibility and scalability. Finally, we give a general classification of existing solutions and comparison based on important parameters.

Nowadays, there is a trend to design complex, yet secure systems. In this context, the Trusted Execution Environment (TEE) was designed to enrich the previously defined trusted platforms. TEE is commonly known as an isolated processing environment in which applications can be securely executed irrespective of the rest of the system. However, TEE still lacks a precise definition as well as representative building blocks that systematize its design. Existing definitions of TEE are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related concepts, such as secure execution environment (SEE). In this paper, we propose a precise definition of TEE and analyze its core properties. Furthermore, we discuss important concepts related to TEE, such as trust and formal verification. We give a short survey on the existing academic and industrial ARM TrustZone-based TEE, and compare them using our proposed definition. Finally, we discuss some known attacks on deployed TEE as well as its wide use to guarantee security in diverse applications.

Given the sensitivity of the potential WSN applications and because of resource limitations, key management emerges as a challenging issue for WSNs. One of the main concerns when designing a key management scheme is the network scalability. Indeed, the protocol should support a large number of nodes to enable a large scale deployment of the network. In this paper, we propose a new highly scalable key management scheme for WSNs which provides a good secure connectivity coverage. For this purpose, we make use for the first time of the unital design theory. We show that the basic mapping from unitals to key pre-distribution allows to achieve an extremely high network scalability. Nonetheless, this naive mapping does not guarantee a high key sharing probability. Therefore, we propose an enhanced unital-based key pre-distribution scheme providing high network scalability and good key sharing probability lower bounded by 1 − e −1 ≈ 0.632. We conduct analytical analysis and simulations to compare our solution to main existing ones regarding different criteria including storage overhead, network scalability, network connectivity, average secure path length and network resiliency. The obtained results show that our approach enhances considerably the network scalability while providing high secure connectivity coverage and good overall performances. Moreover, the obtained results show that at equal network size, our solution reduces significantly the storage overhead compared to main existing solutions.

Rehabilitation supervision has emerged as a new application of wireless sensor networks (WSN), with unique communication, signal processing and hardware design requirements. It is a broad and complex interdisciplinary research area on which more than one hundred papers have been published by several research communities (electronics, bio-mechanical, control and computer science). In this paper, we present WSN for rehabilitation supervision with a focus on key scientific and technical challenges that have been solved as well as interdisciplinary challenges that are still open. We thoroughly review existing projects conducted by several research communities involved in this exciting field. Furthermore, we discuss the open research issues and give directions for future research works. Our aim is to gather information that encourage engineers, clinicians and computer scientists to work together in this field to tackle the arising challenges.

Human context recognition (HCR) from on-body sensor networks is an important and challenging task for many healthcare applications because it offers continuous monitoring capability of both personal and environmental parameters. However, these systems still face a major energy issue that prevent their wide adoption. Indeed, in healthcare applications, sensors are used to capture data during daily life or extended stays in hospital. Thus, continuous sampling and communication tasks quickly deplete sensors' battery reserves, and frequent battery replacement are not convenient. Therefore, there is a need to develop energyefficient solutions for long-term monitoring applications in order to foster the acceptance of these technologies by the patients. In this paper, we survey existing energy-efficient approaches designed for HCR based on wearable sensor networks. We propose a new classification of the energy-efficient mechanisms for healthrelated human context recognition applications and we review the related works in details. Moreover, we provide a qualitative comparison of these solutions in terms of energy-consumption, recognition accuracy and latency. Finally, we discuss open research issue and give directions for future works.

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There has been a host of research works on wireless sensor networks (WSN) for medical applications. However, the major shortcoming of these efforts is a lack of consideration of data management. Indeed, the huge amount of high sensitive data generated and collected by medical sensor networks introduces several challenges that existing architectures cannot solve. These challenges include scalability, availability and security. Furthermore, WSNs for medical applications provide useful and real information about patients' health state. This information should be available for healthcare providers to facilitate response and to improve the rescue process of a patient during emergency. Hence, emergency management is another challenge for medical wireless sensor networks. In this paper, we propose an innovative architecture for collecting and accessing large amount of data generated by medical sensor networks. Our architecture overcomes all the aforementioned challenges and makes easy information sharing between healthcare professionals in normal and emergency situations. Furthermore, we propose an effective and flexible security mechanism that guarantees confidentiality, integrity as well as fine-grained access control to outsourced medical data. This mechanism relies on Ciphertext Policy Attributebased Encryption (CP-ABE) to achieve high flexibility and performance. Finally, we carry out extensive simulations that allow showing that our scheme provides an efficient, fine-grained and scalable access control in normal and emergency situations.