An Approach for Fault-Tolerant Data Aggregation in WSN Based on Components and Self-Healing

PROJECT TITLE :

Component Based Self-Healing Approach for Fault-Tolerant Data Aggregation in WSN

ABSTRACT:

Battery energy drainouts are a common cause of node faults in wireless sensor networks (WSNs). Even though data aggregation is widely recognized as a tool for the conservation of energy, the nodes that perform the data aggregation are vulnerable to battery energy drain out faults. When a child node of an aggregator or intermediary node in a data aggregation tree experiences a failure, those child nodes become disconnected from the aggregation tree and form what are known as Affected Nodes (ANs) and/or subtrees. We use the concepts of component-based graph theory to tolerate such faults. We consider the subtrees to be components that need to be connected back to the root component tree. We use this information to develop two component-based self-healing fault-tolerant algorithms called SCR and SCR-DTRA. These algorithms take advantage of the inherent redundancy that is present in WSNs in order to determine alternate paths from affected components to the root node. Component-based algorithms, in contrast to the vast majority of other methods, are distinguished by the fact that they maintain precedence relations within the data aggregation hierarchy. This is the component-based algorithms' claim to fame. According to the findings of our simulations, both of the algorithms are successful in restoring more than 90 percent of the ANs and subtrees that were corrupted as a result of faults. When compared to other algorithms that were used for evaluation, these two algorithms have a lifespan that is greater than twice as long as the number of rounds that were tested.

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