Consensus algorithms have generated a lot of interest due to their simplicity in computing globally relevant statistics exploiting only local communications among sensors. However, the inherent iterative nature of consensus algorithms makes them prone to a possibly large energy consumption. Because of the strong energy constraints of wireless sensor networks, it is then of interest to minimize energy consumption necessary to achieve consensus, within a prescribed accuracy requirement. In this work, we propose a method for optimizing the network topology and power allocation over each link, in order to minimize energy consumption, while ensuring that the network reaches a global consensus. Interestingly, we show how to introduce a relaxation in the topology optimization that converts a combinatorial problem into a convex-concave fractional problem. The results show how the sparsity of the resulting network depends on the propagation model. © EURASIP, 2010.

Average consensus with minimum energy consumption: Optimal topology and power allocation / SARDELLITTI, Stefania; BARBAROSSA, Sergio; A., Swami. - (2010), pp. 189-193. ((Intervento presentato al convegno 18th European Signal Processing Conference, EUSIPCO 2010 tenutosi a Aalborg; Denmark.

Average consensus with minimum energy consumption: Optimal topology and power allocation

SARDELLITTI, Stefania;BARBAROSSA, Sergio;
2010

Abstract

Consensus algorithms have generated a lot of interest due to their simplicity in computing globally relevant statistics exploiting only local communications among sensors. However, the inherent iterative nature of consensus algorithms makes them prone to a possibly large energy consumption. Because of the strong energy constraints of wireless sensor networks, it is then of interest to minimize energy consumption necessary to achieve consensus, within a prescribed accuracy requirement. In this work, we propose a method for optimizing the network topology and power allocation over each link, in order to minimize energy consumption, while ensuring that the network reaches a global consensus. Interestingly, we show how to introduce a relaxation in the topology optimization that converts a combinatorial problem into a convex-concave fractional problem. The results show how the sparsity of the resulting network depends on the propagation model. © EURASIP, 2010.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/403136
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