One of the primary constraints in the design and deployment of WSNs is energy, as sensor nodes are powered by batteries. In such networks, energy efficiency can be achieved by reducing the use of the onboard radios, for instance limiting packet transmissions. The broadcast nature of the wireless channel surely represents an advantage in this respect: each node has to send a single broadcast packet to simultaneously reach all its neighboring nodes, thus reducing the number of required transmissions. We present an integrated optimization framework leveraging on this advantage to improve the convergence speed of a distributed consensus algorithm, by means of topology design. We evaluate the effectiveness of the proposed framework in terms of overall energy savings and worst-case algorithmic complexity of the optimization task, on different classes of network topologies, and compare such results with those obtained by a pure greedy strategy recently proposed in the literature. We prove that our framework can slightly reduce the average nodes’ energy cost with respect to its greedy antagonist, as well as reducing the computational overhead of the optimization task to a small fraction of the latter. These unique features make it suitable to tackle the problem also over large scenarios.

An Integrated Topology Control Framework to Accelerate Consensus in Broadcast Wireless Sensor Networks

Massimo Vecchio;Gennaro Amendola;Pietro Ducange
2018

Abstract

One of the primary constraints in the design and deployment of WSNs is energy, as sensor nodes are powered by batteries. In such networks, energy efficiency can be achieved by reducing the use of the onboard radios, for instance limiting packet transmissions. The broadcast nature of the wireless channel surely represents an advantage in this respect: each node has to send a single broadcast packet to simultaneously reach all its neighboring nodes, thus reducing the number of required transmissions. We present an integrated optimization framework leveraging on this advantage to improve the convergence speed of a distributed consensus algorithm, by means of topology design. We evaluate the effectiveness of the proposed framework in terms of overall energy savings and worst-case algorithmic complexity of the optimization task, on different classes of network topologies, and compare such results with those obtained by a pure greedy strategy recently proposed in the literature. We prove that our framework can slightly reduce the average nodes’ energy cost with respect to its greedy antagonist, as well as reducing the computational overhead of the optimization task to a small fraction of the latter. These unique features make it suitable to tackle the problem also over large scenarios.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/25876
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