P.M. Khilar and S. Mahapatra
Adhoc networks, clustering, heartbeat and comparison-based test-ing, distributed diagnosis, diagnosability, fault-tolerance, diagnosislatency, message complexity
We propose a scalable distributed diagnosis approach for large-scale self-diagnosable wireless adhoc networks that form an arbitrary network topology. The diagnosis strategy assumes multiple initiators for the diagnosis process in contrast to a single initiator centralized bottleneck and also avoids a costly distributed diagnosis algorithm where every node is an initiator of the diagnosis process. Key results of this paper include realistic testing mechanism and fault models, an efficient and scalable distributed diagnosis algorithm using clustering, a global diagnosis strategy of all the nodes. The proposed approach has been evaluated analytically as well as through simulation. The diagnosis latency and message complexity of the algorithm was found to be O(ΔTx + lcTf + max(Tout1, Tout2)) and O(ncCs) respectively. The result shows that the diagnosis performance is better using the proposed clustering approach than non-clustering approaches.
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