Simulated annealing approach to optimizing the lifetime of sparse time-driven sensor networks

Santamaría, María Luisa; Galmés, Sebastià; Puigjaner, Ramon

doi:10.1109/mascot.2009.5366170

Cited by 6 publications

(8 citation statements)

References 30 publications

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“…This assumption gives the flexibility to focus on the upper layer devices. It has also been established that data sensing and processing requires lesser energy consumption than data transmission and reception [38,39]. Thus, energy consumed during data transmission and reception is only considered.…”

Section: Assumptionsmentioning

confidence: 99%

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

Ayinde

Hashim

2018

Int J Wireless Inf Networks

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Random deployment of sensor nodes is susceptible to initial communication hole, even when the network is densely populated. However, eliminating holes using structural deployment poses its difficulties. In either case, the resulting coverage holes can degrade overall network performance and lifetime. Many solutions utilizing Relay Nodes (RNs) have been proposed to alleviate this problem. In this regard, one of the recent solutions proposed using Artificial Bee Colony (ABC) to deploy RNs. This paper proposes RN deployment using two other evolutionary techniques -Gravitational Search Algorithm (GSA) and Differential Evolution (DE) and compares them with existing solution that uses ABC. These popular optimization tools are deployed to optimize the positions of relay nodes for lifetime maximization. Proposed algorithms guarantee satisfactory RNs utilization while maintaining desired connectivity level. It is shown that DE-based deployment improves the network lifetime better than other optimization heuristics considered.

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Section: Assumptionsmentioning

confidence: 99%

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

Ayinde

Hashim

2018

Int J Wireless Inf Networks

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“…In other words, the likely existence of a maximum transmission range is ignored at this stage. Under this assumption, the work presented in [3] showed that the problem of finding a data-gathering tree with optimal lifetime, by including in the analysis the workload of every node to a maximum extent, is NP-hard. Then, a heuristic method based on simulated annealing was proposed, which it was shown to yield, in general, a spanning tree with near-optimal lifetime in linear time.…”

Section: Network Planning Approach and Related Workmentioning

confidence: 99%

“…The energy consumption model used in the present paper was first developed in [3] (on the basis of a radio model described in [12]), under the following premises:…”

Section: Lifetime Modelmentioning

confidence: 99%

“…Under all these assumptions, Fig. 1 also shows the graph generated by the SA algorithm developed in [3]. This graph yields a lifetime of 722168 rounds, determined by node 8 -see expression (7).…”

Section: Illustrative Examplementioning

confidence: 99%

“…As part of this strategy, in [3] we developed a heuristic method based on simulated annealing (SA) that finds an optimal (or suboptimal) data-gathering tree in terms of lifetime expectancy. However, in general the overall link distance of this tree can be substantially improved (decreased), fact that would contribute to reduce the amount of relay nodes required to make the network operational.…”

Section: Introductionmentioning

confidence: 99%

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Resource Optimization Algorithm for Sparse Time-Driven Sensor Networks

2010

Self Cite

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Abstract. Time-driven sensor networks are devoted to the continuous reporting of data to the user. Typically, their topology is that of a data-gathering tree rooted at the sink, whose vertexes correspond to nodes located at sampling locations that have been selected according to user or application requirements. Thus, generally these locations are not close to each other and the resulting node deployment is rather sparse. In a previous paper, we developed a heuristic algorithm based on simulated annealing capable of finding an optimal or suboptimal data-gathering tree in terms of lifetime expectancy. However, despite the enhanced lifetime, the overall link distance is not optimized, fact that increases the need for additional resources (relay nodes). Therefore, in this paper we propose the Link Distance Reduction algorithm, with the goal of reducing link distances as long as network lifetime is preserved. The benefits of this new algorithm are evaluated in detail.

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Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm

Hashim

Ayinde

Abido

2016

Journal of Network and Computer Applications

107

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Simulated annealing approach to optimizing the lifetime of sparse time-driven sensor networks

Cited by 6 publications

References 30 publications

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

Resource Optimization Algorithm for Sparse Time-Driven Sensor Networks

Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm

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