This review systematically analyzes Reinforcement Learning approaches for self-healing in energy-constrained secure edge IoT networks across 82 studies from 2020 to 2026. Unlike existing surveys that focus on general RL applications, the proposed review focuses on a three-level taxonomy that uniquely addresses edge IoT deployment realities through formulation-scope-hardware mapping. The work develops a novel three-level taxonomy classifying recovery scope (node, link, service, network), RL formulations (tabular, deep, multi-agent, model-based), and constraint integration (energy, latency, security, hybrid), revealing service migration dominance at 30% coverage and node recovery achieving 38% maximum energy savings. Normalized performance baselines establish energy gains up to 44%, latency compliance of 84% under mobility traces, and 35% security exposure reduction during failover windows. 10 evidence-based gaps emerge, including a complete absence of model-based node recovery and multi-agent network security orchestration spanning only 2 papers. 15 prioritized future directions target 70% sample efficiency gains, 35% exposure reduction under compromised agents, and 22% Pareto improvements through joint constraint optimization, providing researchers and practitioners structured roadmap for sustainable edge IoT resilience. Performance metrics are normalized against static policy baselines using logarithmic scaling and success ratios to ensure cross-study comparability.

Fault tolerance is essential for reliable and sustainable smart city infrastructure. Interconnected IoT systems must function under frequent faults, limited resources, and complex conditions. Existing research covers various fault-tolerant methods. However, current reviews often lack system-level critique and multidimensional analysis. This study provides a structured review of fault tolerance strategies across layered IoT architectures in smart cities. It evaluates fault detection, containment, and recovery techniques using specific metrics. These include fault visibility, propagation depth, containment score, and energy-resilience trade-offs. The analysis uses comparative tables, architecture-aware discussions, and conceptual plots. It investigates the impact of fault tolerance on decision-making in Supervisory Control And Data Acquisition (SCADA) systems, sensor networks, and real-time controllers. Simulation results and logic-based design support the relationships between evaluation metrics. Findings show a common reliance on redundancy and reactive methods. Many techniques fail to address cross-layer propagation, context-aware adaptation, and silent fault impact on user trust. The study combines these overlooked aspects into a system-level framework. This survey identifies performance bottlenecks and supports the design of adaptive, energy-efficient, and transparent IoT systems. The results contribute to bridging technical reliability with public trust, supporting scalable and responsible smart city development.