PSO-based sink placement and load-balanced anycast routing in multi-sink WSNs considering compressive sensing theory

This paper deals with the sink placement and anycast routing to increase the lifetime of multi-sink wireless sensor networks. Two algorithms are proposed, namely “Multi-sink Placement and Anycast Routing (MPAR)” and “Extended Multi-sink Placement and Anycast Routing (EMPAR)”, to jointly address the...

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Vydáno v:Engineering applications of artificial intelligence Ročník 100; s. 104164
Hlavní autoři: Jari, Anis, Avokh, Avid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.04.2021
Témata:
Ant colony optimization
Anycast routing
Clustering
Compressive sensing
Particle swarm optimization
Sink placement
Wireless sensor networks
Ant colony optimization
Wireless sensor networks
Compressive sensing
Sink placement
Anycast routing
Clustering
Particle swarm optimization
ISSN:0952-1976, 1873-6769
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Shrnutí:This paper deals with the sink placement and anycast routing to increase the lifetime of multi-sink wireless sensor networks. Two algorithms are proposed, namely “Multi-sink Placement and Anycast Routing (MPAR)” and “Extended Multi-sink Placement and Anycast Routing (EMPAR)”, to jointly address the problems of clustering, multi-sink placement, and load-balanced anycast routing. MPAR and EMPAR rely on a two-level architecture in which sensors are clustered at the lower level. Each sensor transmits its data to the corresponding Cluster Head (CH) via a load-balanced data aggregation routing tree. At the upper level, both schemes use a modified particle swarm optimization algorithm to determine the best location of sinks. For each sink, a high-level anycast routing tree is developed using the ant colony optimization algorithm. Each anycast tree uses the hybrid Compressive Sensing (CS) method to forward the aggregated data from CHs to sinks. Extensive simulations are conducted to illustrate the efficiency of the proposed algorithms in terms of energy consumption, energy consumption variance, and network lifetime. The results show that EMPAR has a better performance than MPAR due to its CH selection strategy. As an advantage, EMPAR considers both remaining energy and distance criteria along with a rest factor to select the best CH for each cluster. For an average number of clusters, EMPAR reduces the energy consumption by 5.98% and 12.20%, respectively, compared to the MPAR algorithm and the energy-aware CS-based data aggregation algorithm. It also increases the network lifetime in comparison with the aforementioned algorithms by 12.26% and 30.38%, respectively.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2021.104164