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Open Access

ARTICLE

Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Wireless Sensor Network Deployment

Kuo-Chun Tseng*, I-Chia Chen, Yu-Chieh Cho
Department of Computer Science and Information Engineering, National Ilan University, Yilan City, Taiwan
* Corresponding Author: Kuo-Chun Tseng. Email: email
(This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076965

Received 29 November 2025; Accepted 15 June 2026; Published online 09 July 2026

Abstract

Wireless Sensor Networks (WSNs) are important infrastructure for smart-city applications, such as environmental monitoring, public safety, and smart transportation. However, finding effective sensor locations is an NP-hard problem because a deployment must satisfy sensing coverage and communication connectivity while minimizing the number of sensors. Following the basic framework of a previous study, this study replaces the original optimization algorithm with the Amplitude-Ensemble Quantum-inspired Tabu Search (AEQTS) algorithm and retains the same entanglement-like initialization strategy, resulting in the proposed AEQTSwE (AEQTS with Entanglement) framework for the WSN deployment problem. AEQTSwE uses a quantum-inspired search mechanism and an ensemble update strategy to explore the solution space more efficiently, while the retained initialization strategy provides high-quality initial deployments. Experimental results show that AEQTSwE reduces the number of deployed sensors while satisfying the required coverage and connectivity constraints. It also converges faster and produces more stable solutions than existing approaches under different conditions. Sensitivity, ablation, statistical, and complexity analyses further show that AEQTSwE has low parameter sensitivity, stable performance, and potential for larger and more complex deployment scenarios.

Keywords

Wireless sensor network deployment; sensor deployment; quantum-inspired optimization; smart-city applications

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