Detection of malicious activity for mobile nodes to avoid congestion in Wireless Sensor Network.

Applications of Wireless Sensor Network (WSN) are widely used in different areas. Packet drop occurs due to congestion-related issues like limited bandwidth, link failure, and interference also misbehaving node drops the packet to harm the network thus, provisioning of quality of service (QoS) for routing in mobile nodes for WSN is a challenging issue. Securing the mobile nodes from attackers has become one of the crucial aspects of providing QoS since nodes are weak to different kinds of attacks and threats that impact network connectivity and functionality. In WSN differentiating packet loss due to congestion or malicious node is a tedious job. The black-hole attack is examined to be an epidemic and popular passive attack that degrades overall reliability and network performance by dropping all the incoming packets. In the course of Black-hole node pretends that it has the shortest route to destination and intent to deceive every node in the network. In this paper we differentiate packet loss due to congestion or by malicious node, our scheme utilizes on-demand link and energy-aware dynamic multipath (O-LEADM) routing scheme for WSN to detect black-hole node by integrating bait method, the behavior of node is analyzed using control messages destination-sequence (des-Seq) and reply-sequence (rep-Seq) while accessing the channel. During route discovery, each intermediated node in the network sends the des-Seq message to all its neighbor nodes, and then neighbor nodes intern replies to the intermediate node by sending a rep-Seq message. If des-Seq and req-Seq from the neighbors do not match, then the node is said to be malicious. Connection to the network layer is allowed to an intermediate node if des-Seq and rep-Seq match. Channel availability and link quality parameter estimate the link stability thus nodes select forwarding based on their behavior and are capable of achieving QoS parameters such as link quality, residual energy, and higher packet delivery. Simulation under various network conditions is experimented with using the Network simulator tool (NS2) and using parameters the performance metric is in terms of delay, packet delivery ratio, overhead, and energy are evaluated.