Problem
Research questions and friction points this paper is trying to address.
energy efficiency
AI-enabled WSNs
mission-critical environments
smart grid
urban infrastructure
Energy Efficient AI-Enabled Wireless Sensor Networks for Mission Critical Environments: A Systematic Review across Smart Grid, AI, and Urban Infrastructure Applications
🤖 AI Summary
This study addresses the challenge of jointly optimizing energy efficiency, reliability, low latency, and security in mission-critical wireless sensor networks (WSNs), a problem often approached in isolation by existing research. Through a systematic review of 50 high-quality studies published between 2023 and 2026, the work employs qualitative thematic coding and comparative analysis to examine the application of reinforcement learning, fuzzy logic, metaheuristics, and AI-driven security techniques in routing, clustering, and edge computing. The paper proposes a novel, lightweight, interpretable, and field-validated AI-driven paradigm for WSNs that emphasizes multi-objective co-design. Findings demonstrate that AI significantly enhances both energy efficiency and overall system performance, offering robust theoretical foundations and practical guidance for the architecture of future mission-critical systems.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
energy-efficient WSNs
AI-enabled networking
mission-critical systems
edge AI
reinforcement learning
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