The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energy-constrained 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 energy-saving problem. This research covers several areas going from physical layer optimisation 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.
Unquestionably, communicating entities (object, or things) in the Internet of Things (IoT) context are playing an active role in human activities, systems and processes. The high connectivity of ...intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA (Defense Advanced Research Projects Agency) as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the IoT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the IoT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current standardization activities are surveyed and discussed to the ensure the security of IoT components and applications.
The Internet of Things (IoT) is a network made up of a large number of devices that collaborate to provide various service for the benefit of society. Two communication modes are required to enable a ...smooth collaboration. A device can send the same message to several other ones participating in the same service. It may also address a specific device in a Peer-to-Peer manner. The first mode of communication is called Group Communication, while we refer to the second as Device-to-Device Communication. One of the main challenges facing the IoT is how to secure these two modes of communication. Among all the security issues, the Key Management is one of the most challenging. This is mainly due to the fact that most of the IoT devices have limited resources in terms of storage, calculation, communication and energy. Although different approaches have been proposed to deal with this problem, each of them presents its own limitations and weaknesses. Moreover, they usually consider either the Group or the Device-to-Device Communication. In this paper, we propose a novel versatile Key Management protocol for the Internet of Things. To the best of our knowledge, this is the first protocol that secures both modes of communication at the same time. We then analyze the security and performance of our solution and compare it to the existing schemes. For Group Communication, we show that our solution ensures the forward and backward secrecy and, unlike most of the existing Group Key Management protocols, guarantees the secure coexistence of several services in the network. With regard to Device-to-Device Communication, we prove that our solution is flexible and provides a good level of resilience and network connectivity compared to the existing Peer-to-Peer Key Management schemes. We finally demonstrate that, by balancing the loads between the heterogeneous devices according to their capabilities, our solution is both efficient and scalable.
A mobile ad hoc network (MANET) is a wireless communication network which does not rely on a pre-existing infrastructure or any centralized management. Securing the exchanges in MANETs is compulsory ...to guarantee a widespread development of services for this kind of networks. The deployment of any security policy requires the definition of a trust model that defines who trusts who and how. Our work aims to provide a fully distributed trust model for mobile ad hoc networks. In this paper, we propose a fully distributed public key certificate management system based on trust graphs and threshold cryptography. It permits users to issue public key certificates, and to perform authentication via certificates' chains without any centralized management or trusted authorities. Moreover, thanks to the use of threshold cryptography; our system resists against false public keys certification. We perform an overall evaluation of our proposed approach through simulations. The results indicate out performance of our approach while providing effective security.
With the constant increasing of vehicular traffic around the world, especially in urban areas, existing traffic management solutions become inefficient. This can be clearly seen in our life through ...persistent traffic jam and rising number of accidents. Wireless sensor networks (WSN) based intelligent transportation systems (ITS) have emerged as a cost effective technology that bear a pivotal potential to overcome these difficulties. This technology enables a new broad range of smart city applications around urban sensing including traffic safety, traffic congestion control, road state monitoring, vehicular warning services, and parking management.
This manuscript gives a comprehensive review on WSN based ITS solutions. The main contribution of this paper is to classify current WSNs based ITS projects from the application perspective, with discussions on the fulfillment of the application requirements.
Connected cars offer safety and efficiency for both individuals and fleets of private vehicles and public transportation companies. However, equipping vehicles with information and communication ...technologies raises privacy and security concerns, which significantly threaten the user’s data and life. Using bot malware, a hacker may compromise a vehicle and control it remotely, for instance, he can disable breaks or start the engine remotely. In this paper, besides in-vehicle attacks existing in the literature, we consider new zero-day bot malware attacks specific to the vehicular context, WSMP-Flood, and Geo-WSMP Flood. Then, we propose AntibotV, a multilevel behaviour-based framework for vehicular botnets detection in vehicular networks. The proposed framework combines two main modules for attack detection, the first one monitors the vehicle’s activity at the network level, whereas the second one monitors the in-vehicle activity. The two intrusion detection modules have been trained on a historical network and in-vehicle communication using decision tree algorithms. The experimental results showed that the proposed framework outperforms existing solutions, it achieves a detection rate higher than 97% and a false positive rate lower than 0.14%.
•Reviews more than 45 recent solutions papers, and more than 40 different privacy-preserving deep learning techniques.•Proposes a multi-level taxonomy that classifies the privacy-preserving deep ...learning techniques.•Summarizes evaluation results of the reviewed solutions with respect to performance metrics.•Discusses and outline a number of learned lessons of each privacy-preserving task.•Presents solutions comparison, highlights open research challenges and provides some recommendations for future research.
Deep learning is one of the advanced approaches of machine learning, and has attracted a growing attention in the recent years. It is used nowadays in different domains and applications such as pattern recognition, medical prediction, and speech recognition. Differently from traditional learning algorithms, deep learning can overcome the dependency on hand-designed features. Deep learning experience is particularly improved by leveraging powerful infrastructures such as clouds and adopting collaborative learning for model training. However, this comes at the expense of privacy, especially when sensitive data are processed during the training and the prediction phases, as well as when training model is shared. In this paper, we provide a review of the existing privacy-preserving deep learning techniques, and propose a novel multi-level taxonomy, which categorizes the current state-of-the-art privacy-preserving deep learning techniques on the basis of privacy-preserving tasks at the top level, and key technological concepts at the base level. This survey further summarizes evaluation results of the reviewed solutions with respect to defined performance metrics. In addition, it derives a set of learned lessons from each privacy-preserving task. Finally, it highlights open research challenges and provides some recommendations as future research directions.
There has been a host of research works on wireless sensor networks 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. In this paper, we propose an innovative architecture for collecting and accessing large amount of data generated by medical sensor networks. Our architecture resolves all the aforementioned challenges and makes easy information sharing between healthcare professionals. 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 combines several cryptographic schemes to achieve high flexibility and performance.
The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contains much private information. Therefore, anonymization is required ...before publishing the data for data mining purposes (scientific research, marketing, decision support etc).Most of anonymization works focus on the privacy preserving techniques that allow publishing one instance of the social network without revealing sensitive information. However to analyze the evolution of the social network sequential releases are needed. In this paper we study the problem of privacy in sequential releases of social networks which are represented as labeled bipartite graphs to model the affiliation relationship between users and interests. We propose a solution that allows publishing sequential releases of the same social network while preserving the privacy of data. We consider a set of complex queries to study the utility given by our solution. The experiments demonstrate that the utility of data is preserved as the queries can be answered with reasonable accuracy over the anonymized data.
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 Attribute-based 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.
•We propose a new cloud-based architecture for medical wireless sensor networks.•We ensure the security of medical data without patients/doctors interventions.•We develop an access control that supports complex and dynamic security policies.•We extend our access control to support emergency situations.•Simulations show that our access control is efficient, fine-grained and scalable.
PDF
