Attacks on Geographic Routing Protocols for Wireless Sensor Network

With the increase in the military and several other applications of Wireless Sensor Network, provisions must be made for secure transmission of sensitive information throughout the network. Most of the routing protocols proposed for ad-hoc networks and sensor networks are not designed with security as a goal. Hence, many routing protocols are vulnerable to an attack by an adversary who can disrupt the network or harness valuable information from the network. Routing Protocols for wireless sensor networks are classified into three types depending on their network structure as Flat routing protocols, Hierarchical routing protocol and Geographic routing protocols. Large number of nodes in a wireless sensor network , limited battery power and their data centric nature make routing in wireless sensor network a challenging problem. We mainly concentrate on location-based or geographic routing protocol like Greedy Perimeter Stateless Routing Protocol. Sybil attack and Selective forwarding attack are the two attacks feasible in GPSR. These attacks are implemented in GPSR and their losses caused to the network are analysed

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial