Abstract-We consider the scenario of wireless sensor networks where a given application has to be deployed and each application task has to be assigned to each node in the best possible way. Approaches where decisions on task execution are taken by a single central node can avoid the exchange of data packets between task execution nodes but cannot adapt to dynamic network conditions, and suffer from computational complexity. To address this issue, in this paper, we propose an adaptive and decentralized task allocation negotiation algorithm (TAN) for cluster network topologies. It is based on noncooperative game theory, where neighboring nodes engage in negotiations to maximize their own utility functions to agree on which of them should execute single application tasks. Performance is evaluated in a city scenario, where the urban streets are equipped with different sensors and the application target is the detection of the fastest way to reach a destination, and in random WSN scenarios. Comparisons are made with three other algorithms: 1) baseline setting with no task assignment to multiple nodes; 2) centralized task assignment lifetime optimization; and 3) a dynamic distributed algorithm, DLMA. The result is that TAN outperforms these algorithms in terms of application completion time and average energy consumption.
Due to the shortcomings of centralized approaches, such as being unsuitable for dynamic network conditions, being fragile to unstable communication links, and high computational complexity, in Pilloni et al., REF an adaptive and decentralized task allocation negotiation (TAN) algorithm is developed for clustered WSNs.
TAN: A Distributed Algorithm for Dynamic Task Assignment in WSNs
IEEE Sensors Journal
IEEE Sensors Journal
Computer Science