Diffusion Based Distributed Detection in Wireless Sensor Network

Abstract

Distributed wireless sensor networks find various remote sensing purposes like battleground monitoring, target localization, environmental monitoring, accurate cultivation, mobile communication and medicinal applications. Due to a wide variety of applications of wireless data, suitable design and implementation of data detection become the modern field of study and research. The distribution of the nodes in the network provides a spatial diversity, which includes the temporal dimension for the purpose of increase the robustness of the ongoing tasks and enhance the probability of data and event detection. In this area, we study the distributed network that contain the collection of a node connected to each other in the distributed manner. The node connected to each other is called neighbor node. In the problem of distributed detection of data, nodes have to decide based on the binary hypotheses of the measured data. In this detection problem we find the fully distributed and adaptive approach where all the node have to make own real time decision by cooperating with their immediate neighbour only and for this implementation no central processing node is required.For this distributed detection, we used diffusion based strategies Diffusion least mean square (DLMS) and Diffusion recursive least mean square(RLS) to find out distributed estimation of the parameter of interest. Distributed detection suitable in the wireless sensor network due to their robustness to node and link failure as compare to centralized scheme,,scalability and ability to save power and communication resources.The algorithm utilized is adaptive and track the variation in the active hypotheses.After the use for detection we analyze the performance of the proposed algorithm in term of probability og detection and probability of false alarm and find out the simulation result. We use some nonlinear techniques(huber loss , bi-square ) to reduce the effect of impulsive interference on the systems.