The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach.

A genetic approach for the maximum network lifetime problem with additional operating time slot constraints

D'Ambrosio C.
Membro del Collaboration Group
;
Iossa A.
Membro del Collaboration Group
;
Laureana F.
Membro del Collaboration Group
;
Palmieri F.
Membro del Collaboration Group
2020-01-01

Abstract

The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4742213
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