2009
DOI: 10.1016/j.comnet.2008.12.001
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Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms

Abstract: Abstract-A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and nonreplenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in-… Show more

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Cited by 135 publications
(57 citation statements)
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“…Grid-based Routing and Aggregator Selection Scheme (GRASS) is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques [11]. Data aggregation aims to combine and summarize data packets from several sensor nodes so that communication bandwidth and energy consumption are reduced [12].…”
Section: Related Workmentioning
confidence: 99%
“…Grid-based Routing and Aggregator Selection Scheme (GRASS) is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques [11]. Data aggregation aims to combine and summarize data packets from several sensor nodes so that communication bandwidth and energy consumption are reduced [12].…”
Section: Related Workmentioning
confidence: 99%
“…Proposed techniques are mainly focused on limiting data aggregation by using several techniques on compression and aggregation, as well as for optimal routing of minimum hops from mobile to sink nodes [45] [46].…”
Section: Comparison Of Ir-uwb With Other Wireless Short-range Protocolsmentioning
confidence: 99%
“…Variations of this problem are encountered in the literature, in (Erciyes et al, 2008;Huang et al, 2006). A joint routing and data aggregation problem is also discussed in (Karaki et al, 2009) for a two-tier network, and some heuristic algorithms such as GA and greedy are proposed. From a distributed perspective, adapted versions of Prim's and Kruskal's algorithms have been proposed in (Attarde et al, 2010).…”
Section: Shortest Path and Spanning Tree Based Modelsmentioning
confidence: 99%