http://www.science.smith.edu/~jcardell/Courses/EGR328/Readings/SecureDataAggreg.pdf

Secure data aggregation in wireless sensor networks: A comprehensive overview

https://www.nics.uma.es/pub/papers/JavierLopezMunoz2010.pdf

Trust management systems for wireless sensor networks: Best practices

Much of the existing work on wireless sensor networks (WSNs) has focused on addressing the power and computational resource constraints of WSNs by the design of specific routing, MAC, and cross-layer protocols. Recently, there have been heightened privacy concerns over the data collected by and transmitted through WSNs. The wireless transmission required by a WSN, and the self-organizing nature of its architecture, makes privacy protection for WSNs an especially challenging problem. This paper provides a state-of-the-art survey of privacy-preserving techniques for WSNs. In particular, we review two main categories of privacy-preserving techniques for protecting two types of private information, data-oriented and context-oriented privacy, respectively. We also discuss a number of important open challenges for future research. Our hope is that this paper sheds some light on a fruitful direction of future research for privacy preservation in WSNs.

Privacy preservation in wireless sensor networks: A state-of-the-art survey

SUMMARY The range of applications of wireless sensor networks is so wide that it tends to invade our every day life. In the future, a sensor network will survey our health, our home, the roads we follow, the office or the industry we work in or even the aircrafts we use, in an attempt to enhance our safety. However, the wireless sensor networks themselves are prone to security attacks. The list of security attacks, although already very long, continues to augment impeding the expansion of these networks. The trust management schemes consist of a powerful tool for the detection of unexpected node behaviours (either faulty or malicious). Once misbehaving nodes are detected, their neighbours can use this information to avoid cooperating with them, either for data forwarding, data aggregation or any other cooperative function. A variety of trust models which follow different directions regarding the distribution of measurement functionality, the monitored behaviours and the way measurements are used to calculate/define the node’s trustworthiness has been presented in the literature. In this paper, we survey trust models in an attempt to explore the interplay among the implementation requirements, the resource consumption and the achieved security. Our goal is to draw guidelines for the design of deployable trust model designs with respect to the available node and network capabilities and application peculiarities. Copyright © 2010 John Wiley & Sons, Ltd.

Trust management in wireless sensor networks

http://www.agentgroup.ing.unimo.it/Zambonelli/PDF/PMC12.pdf

Fast track article: Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber-physical convergence

With the advent of the data age, wireless sensor networks that can collect data and monitor and control according to data will play a more and more important role. In wireless sensor networks, we can locate or perceive and identify the state of things according to the disturbance of received signal strength (RSSI). This paper analyzes the fluctuation of RSSI, The experiments in the real scene show that the RSSI under interference has the characteristics of sinusoidal or cosine fluctuation, and is symmetrical along the midpoint of the nodes of the transmitting signal and the receiving signal. The interference formed by the same interference source at the midpoint has the least disturbance to the RSSI.

Research on Law of RSSI Fluctuation of Wireless Sensor Networks

DDoS attacks can cause extreme performance degradation at network elements when a large number of malicious flows collaborate to cause congestion, resulting in a denial of service to legitimate users. The flows form a logical aggregate which is typically characterized by similar values in several fields in their packets. The fields and their similar values form a signature for the attack. The focus of this paper is on providing protection to legitimate users against such attacks by detecting significant signatures in network traffic and controlling aggregates of flows carrying these signatures. The paper proposes a system PISA* for deployment at a network element. The detection algorithm in PISA* is based on an improved version of our earlier randomized algorithm. A new control mechanism based on a drop probability function over an attribute named RED Drop Aggregate (RDA) is incorporated in PISA*. RDA is the normalized count of the number of RED (Random Early Detection) drops suffered by the flows carrying a signature. This paper discusses results from an implementation of PISA* that demonstrates that it is effective in detecting, isolating, and controlling offending traffic and providing protection to non-offending traffic.

Wei Zhang

Papers

Research on Law of RSSI Fluctuation of Wireless Sensor Networks

PISA*: A System for Control of DDoS Attacks

Machine Learning-Based Performance-Efficient MAC Protocol for Single Hop Underwater Acoustic Sensor Networks