In this paper we focus on the application of reinforcement learning to obstacle avoidance in dynamic Environments in wireless sensor networks. A distributed algorithm based on reinforcement learning is developed for sensor networks to guide mobile robot through the dynamic obstacles. The sensor network models the danger of the area under coverage as obstacles, and has the property of adoption o...

Sybil attacks pose a serious threat for Wireless Sensor Networks (WSN) security. They can create problems in routing, voting schemes, decision making, distributed storage and sensor re-programming. In a Sybil attack, the attacker masquerades as multiple sensor identities that are actually controlled by one or a few existing attacker nodes. Sybil identities are fabricated out of stolen keys, obt...

Advances in low-power electronics design and wireless communication have enabled the development of low cost, low power micro-sensor nodes. These sensor nodes are capable of sensing, processing and forwarding which have many applications such as underwater networks. In underwater wireless sensor networks (UWSNs) applications, sensors which are placed in underwater environments and predicted ena...

 Mobile sensor networks rely heavily on inter-sensor connectivity for collection of data. Nodes in these networks monitor different regions of an area of interest and collectively present a global overview of some monitored activities or phenomena. A failure of a sensor leads to loss of connectivity and may cause partitioning of the network into disjoint segments. A number of approaches have be...