Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks

Luo, Dijun; Zhu, Xiaojun; Wu, Xiaobing; Chen, Guihai

doi:10.1109/infcom.2011.5934947

Cited by 94 publications

(40 citation statements)

References 13 publications

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“…We find that this problem can be reduced to a general version of semi-matching problems [Fakcharoenphol et al 2010;Harvey et al 2003] and show that it can be solved in polynomial time. Our main contributions are as follows (a preliminary version of this work appeared in Luo et al [2011]):…”

Section: Introductionmentioning

confidence: 99%

Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks

Shan

Chen

Luo

et al. 2014

ACM Trans. Sen. Netw.

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In wireless sensor networks, the spanning tree is usually used as a routing structure to collect data. In some situations, nodes do in-network aggregation to reduce transmissions, save energy, and maximize network lifetime. Because of the restricted energy of sensor nodes, how to build an aggregation tree of maximum lifetime is an important issue. It has been proved to be NP-complete in previous works. As shortest path spanning trees intuitively have short delay, it is imperative to find an energy-efficient shortest path tree for time-critical applications. In this article, we first study the problem of building maximum lifetime shortest path aggregation trees in wireless sensor networks. We show that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time. We present a centralized algorithm and design a distributed protocol for building such trees. Simulation results show that our approaches greatly improve the lifetime of the network and are very effective compared to other solutions. We extend our discussion to networks without aggregation and present interesting results.

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Section: Introductionmentioning

confidence: 99%

Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks

Shan

Chen

Luo

et al. 2014

ACM Trans. Sen. Netw.

Self Cite

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“…Lifetime maximization in shortest path data collection trees with aggregated traffic was considered in [21] and reduced to semi-matching. A distributed probabilistic load-balancing approach called Local-Wiser was introduced in [22].…”

Section: B Lifetime Of Data Collection Treesmentioning

confidence: 99%

“…Local-Wiser divides the nodes in the network according to their levels in the shortest path tree rooted at the sink and switches nodes from their parent to another node at the same level of the tree based on a local cost function. In contrast with [21] and [22], our approach makes no assumption on the properties of the routing tree, thus allowing more flexibility in rerouting the messages toward the sink.…”

Section: B Lifetime Of Data Collection Treesmentioning

confidence: 99%

Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree-Based Wireless Sensor Networks

Imon

Khan

Francesco

et al. 2015

IEEE/ACM Trans. Networking

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In most wireless sensor network (WSN) applications, data are typically gathered by sensor nodes and reported to a data collection point called sink. To support such a data collection pattern, a tree structure rooted at the sink is defined. Depending on various factors, including the WSN topology and the availability of resources, the energy consumption of nodes in different paths of the data collection tree may vary largely, thus affecting the overall network lifetime. This paper addresses the problem of lifetime maximization of WSNs based on data collection trees. Specifically, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai), that aims at extending the lifetime of WSNs through load balancing. Given an initial data collection tree, RaSMaLai randomly switches some sensor nodes from their original paths to other paths with lower load. We prove that, under appropriate settings of the operating parameters, RaSMaLai converges with a low time complexity. We further design a distributed version of our algorithm. Through an extensive performance evaluation study that includes simulation of large-scale scenarios and real experiments on a WSN testbed, we show that the proposed RaSMaLai algorithm and its distributed version achieve a longer network lifetime than the state-of-the-art solutions.Index Terms-Data collection tree, load balancing, network lifetime, randomized algorithm, wireless sensor networks.

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“…Moreover, if carefully used, routing along these alternative nodes will not add signi¿cant delay to the packet delivery, when compared with the original route. While several papers target at the maximization of the network lifetime [9], [10], [11], it is sometimes desirable to minimize the energy cost.…”

Section: A Setupmentioning

confidence: 99%

Determining an Efficient Family of Trees in Large Scale Wireless Sensor Networks

Wan

2015

2015 International Conference on Cloud Computing and Big Data (CCBD)

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In our previous work, we have demonstrated that multiple trees, as well as split trees, can provide additional lifetime extensions when certain nodes are deployed in a wireless sensor network. The main motivation of this paper is to explore how the number of the family-set of trees influences the lifetime gain, and we work on the problem of constructing data aggregation trees that minimizes the total energy cost of data transmission under diverse set of scenarios and network query region. Through query region division, we have compared and analyzed an angular division routing algorithm and query region division routing with LEACH. Theoretical and experimental results show that the query region division algorithm based on LEACH have better performance in terms of energy cost.

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Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks

Cited by 94 publications

References 13 publications

Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks

Building Maximum Lifetime Shortest Path Data Aggregation Trees in Wireless Sensor Networks

Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree-Based Wireless Sensor Networks

Determining an Efficient Family of Trees in Large Scale Wireless Sensor Networks

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