Heuristic and approximate Steiner tree algorithms for ensuring network connectivity in mobile wireless sensor networks

Connectivity problems are among the most challenging issues in Mobile Wireless Sensor Networks (MWSNs). Ensuring connectivity in such networks means finding network configurations in which all mobile sensors can connect to a base station during data gathering events. This paper considers MWSNs in wh...

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Veröffentlicht in:Journal of network and computer applications Jg. 238; S. 104155
Hauptverfasser: Son, Nguyen Van, Hanh, Nguyen Thi, Minh, Trinh The, Binh, Huynh Thi Thanh, Thang, Nguyen Xuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2025
Schlagworte:
Approximate algorithm
Connectivity
Mobile wireless sensor networks
Spanning tree
Steiner tree
Connectivity
Approximate algorithm
Steiner tree
Spanning tree
Mobile wireless sensor networks
ISSN:1084-8045
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Zusammenfassung:Connectivity problems are among the most challenging issues in Mobile Wireless Sensor Networks (MWSNs). Ensuring connectivity in such networks means finding network configurations in which all mobile sensors can connect to a base station during data gathering events. This paper considers MWSNs in which a minimal number of relay nodes need to be placed in order to maintain connectivity. Two algorithms are proposed: AST (Approximation Steiner Tree) is an approximate algorithm with a ratio of 2×opt+O(K×N) (where K×N is the number of nodes on the time-flattened domain) and CBAST (Cluster-Based on the Approximation Steiner Tree algorithm) is a highly effective heuristic. Both algorithms focus on optimal Steiner Tree construction to produce high-quality solutions. AST is an approximation based on 3-point Steiner Trees, while CBAST forms clusters of static components and uses a 2-approximation algorithm to maintain connectivity in each cluster. Experiments on a large number of generated instances are performed to compare AST and CBAST with existing state-of-the-art heuristics. Our results show that CBAST significantly outperforms baseline methods while also reducing computation time and energy consumption.
ISSN:1084-8045
DOI:10.1016/j.jnca.2025.104155