A FAULT TOLERANT APPROACH FOR DATA AGGREGATION IN WIRELESS SENSOR NETWORKS

Abstract

Wireless sensor networks (WSNs) have received significant attention in recent years due to their potential applications in military sensing, wildlife tracking, traffic surveillance, health care, environment monitoring, building structures monitoring, etc. WSNs can be treated as a special family of wireless ad hoc networks. Data aggregation is a basic operation in Wireless Sensor Networks. For an application which is aggregative in nature such as environment monitoring system or health monitoring system, the TDMA schedule based data aggregation is the most efficient technique, in terms of energy and data collection time. The TDMA schedule based algorithms works in two phases. First it constructs a tree by using the topology graph of network and then by using the tree a schedule is generated where data has to be transmitted along the tree. But, when one of nodes stops responding due to energy loss or hardware malfunction or external cause such as fire, flood etc., then the rest of the nodes under that node in the tree will not be able to send their data even if there is some other path. The present dissertation tries to fix this issue by using secondary parent, so that failure of a node does not affect the performance of the network. In this dissertation, a protocol has been proposed based on FEDA, which is the existing fault tolerance technique in wireless sensor networks and TDMA schedule based data aggregation. The approach we have used is a combination of the concept in FEDA and TDMA schedule based data aggregation method where a schedule has to be generated after assigning a secondary parent to each node, if there is any node other than the primary parent is in the range of the node. As a result, if the primary parent of a node goes down, it can still transmit data to the sink by using the secondary parent. Experimental results show that proposed approach will work fine without affecting the basic performance measures like energy consumption and time requirement to complete one round of data collection.