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In the last years, Wireless Sensor Networks (WSNs) experienced a rapid growth with a huge interest from both academia and industry. Besides communication services, their applications include environmental monitoring, surveillance, logistics and process control in industrial scenarios, local and home area networks for health, assistance of elderly and disabled people, energy saving, smart homes, and/or smart city services. The widespread deployment of WSN nodes and their interconnection through personal, local, or metropolitan area networks pose several challenges in terms of privacy and security of the network and of the access to data. Moreover, some of the possible applications of WSN have stringent security issues. Notwithstanding, this is only a part of the problem: the nodes of a WSN have limited resources in terms of computational and storage capability and have strict constraints in terms of compact size, low-power consumption, and power management. Therefore, new models, protocols, and advanced architectures for WSN have to be devised. In the aforementioned context, the article by I. Coisel and T. Martin addresses the privacy concerns derived from the rise of wireless applications based on Radio Frequency Identification (RFID) technology. Indeed, nowadays when such an application is deployed, informed customers yearn for guarantees that their privacy will not be threatened. One formal way to perform this task is to assess the privacy level of the RFID application with a model. However, if the chosen model does not reflect the assumptions and requirements of the analyzed application, it may misevaluate its privacy level. Selecting the most appropriate model among all the existing ones is not an easy task. To this end, the article by I. Coisel and T. Martin investigates the eight most well-known RFID privacy models and thoroughly examines their advantages and drawbacks in three steps. Firstly, five RFID authentication protocols are analyzed with these models. This discloses a main worry: although these protocols intuitively ensure different privacy levels, no model is able to accurately distinguish them. Secondly, these models are grouped according to their features (e.g., tag corruption ability). This classification reveals the most appropriate candidate model(s) to be used for a privacy analysis when one of these features is especially required. Furthermore, it points out that none of the models is comprehensive. Hence, some combinations of features may not match any model. Finally, the privacy properties of the eight models are compared in order to provide an overview of their relations. This part …

Privacy and Security in Wireless Sensor Networks: Protocols, Algorithms, and Efficient Architectures