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This systematic review analyzes 50 studies from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) tailored for mission-critical applications, emphasizing their integration in power electronics and urban infrastructure. The findings reveal that AI techniques such as routing and clustering, edge AI, reinforcement learning, and metaheuristic optimization significantly enhance the energy efficiency of WSNs, but highlight that energy efficiency must be balanced with security, latency, and reliability. The authors advocate for a shift towards developing lightweight, explainable, and secure AI-driven WSN architectures that are validated in real-world settings rather than optimizing protocols in isolation.
AI techniques can significantly enhance the energy efficiency of wireless sensor networks, but must be integrated with security and reliability for mission-critical applications.
Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics, and urban infrastructure systems. The authors synthesise a corpus of 50 DOI indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimisation, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimising protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments.
