Multi-hop routing in IoT/WSN: Critical review and proposal of a conceptual framework for hybrid, multi-objective, and explainable protocols

Abstract

Energy remains the most critical and limiting resource in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) systems, directly constraining network lifetime, scalability, and real-world deployability. Although multi-hop routing is widely adopted to reduce transmission energy and balance traffic load, recent solutions increasingly rely on metaheuristic optimization and machine learning techniques whose computational, control, and learning overhead is rarely accounted for. This leads to a fundamental energy–intelligence trade-off that challenges the sustainability of intelligent routing in resource-constrained environments. This paper presents a critical, energy-centric review of multi-hop routing approaches for IoT and WSNs proposed between 2018 and 2025. Heuristic, metaheuristic, dynamic and Heterogeneous routing, reinforcement learning, deep reinforcement learning, and explainable AI-based protocols are systematically analyzed with an emphasis on net energy efficiency, scalability, feasibility on constrained devices, and model realism, rather than reported performance gains alone. The analysis reveals that energy is predominantly treated as a secondary optimization objective rather than as a governing system constraint. To address this limitation, we propose a hybrid and explainable routing framework governed by energy awareness, in which intelligence activation is explicitly conditioned on its net energy benefit. This perspective provides a principled foundation for sustainable and trustworthy intelligent routing in next-generation IoT and WSN systems.