Privacy models in wireless sensor networks: a survey

Wireless Sensor Networks (WSNs) are attracting attention from the research community. One of the key issues is to provide them with privacy protection. In recent years, a huge amount of contributions has been focused on this area. Surveys and literature reviews have also been produced to give a systematic view of the different approaches taken. However, no previous work has focused on privacy models, that is, the set of assumptions made to build the approach. In particular, this paper focuses on this matter by studying 41 papers of the last 5 years. We highlight the great differences appearing among related papers that could make them incompatible to be applied simultaneously. We propose a set of guidelines to build comprehensive privacy models so as to foster their comparability and suitability analysis for different scenarios.This work was supported by the MINECO Grant TIN2013-46469-R (Security and Privacy in the Internet of You (SPINY)) and the CAM Grant S2013/ICE-3095 (Cybersecurity,Data, and Risks (CIBERDINE)), which is cofunded by EuropeanFunds (FEDER). Furthermore, J.M. de Fuentes and L. González-Manzano were also partially supported by the Programa de Ayudas a la Movilidad of Carlos III University of Madrid

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Fortified Anonymous Communication Protocol for Location Privacy in WSN: A Modular Approach

Wireless sensor network (WSN) consists of many hosts called sensors. These sensors can sense a phenomenon (motion, temperature, humidity, average, max, min, etc.) and represent what they sense in a form of data. There are many applications for WSNs including object tracking and monitoring where in most of the cases these objects need protection. In these applications, data privacy itself might not be as important as the privacy of source location. In addition to the source location privacy, sink location privacy should also be provided. Providing an efficient end-to-end privacy solution would be a challenging task to achieve due to the open nature of the WSN. The key schemes needed for end-to-end location privacy are anonymity, observability, capture likelihood, and safety period. We extend this work to allow for countermeasures against multi-local and global adversaries. We present a network model protected against a sophisticated threat model: passive /active and local/multi-local/global attacks. This work provides a solution for end-to-end anonymity and location privacy as well. We will introduce a framework called fortified anonymous communication (FAC) protocol for WSN.http://dx.doi.org/10.3390/s15030582

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

Optimizing Source Anonymity Of Wireless Sensor Networks Against Global Adversary Using Fake Packet Injections

Wireless Sensor Networks WSNs have been utilized for many applications such as tracking and monitoring of endangered species in a national park, soldiers in a battlefield, and many others, which require anonymity of the origin, known as the Source Location Privacy (SLP). The aim of SLP is to prevent unauthorized observers from tracing the source of a real event (an asset) by analyzing the traffic of the network. We develop the following six techniques to provide anonymity: Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD), Controlled Dummy Adaptive Distribution (CAD), Exponential Dummy Adaptive Distribution (EDAD), Exponential Dummy Adaptive Distribution Plus One (EDADP1), and Exponential Dummy Adaptive Distribution Plus Two (EDADP2). Moreover, an enhanced version of the well-known FitProbRate technique is also developed. The purpose of these techniques is to overcome the anonymity problem against a global adversary model that has the capability of analyzing and monitoring the entire network. We perform an extensive verification of the proposed techniques via simulation, statistical, and visualization approaches. Three analytical models are developed to verify the performance of our techniques: A Visualization model is performed on the simulation data to confirm anonymity. A Neural Network model is developed to ensure that the introduced techniques preserve SLP. In addition, a Steganography model based on statistical empirical data is implemented to validate the anonymity of the proposed techniques. The Simulation demonstrates that the proposed techniques provide a reasonable delay, delivery ratio, and overhead of the real event's packets while keeping a high level of anonymity. Results show that the improved version of FitProbRate massively reduces the number of operations needed to detect the distribution type of a data sequence despite the number of intervals when compared to the original. A comprehensive comparison between EDADP1, EDADP2, and FitProbRate in terms of the average delay, anonymity level, average processing time, Anderson-Darling test, and polluted scenarios is conducted. Results show that all three techniques have a similar performance regarding the average delay and Anderson-Darling test. However, the proposed techniques outperform FitProbRate in terms of anonymity level, average processing time, and polluted scenarios. WSN applications that need privacy can select the suitable proposed technique based on the required level of anonymity with respect to delay, delivery ratio, and overhead