Edge Computing for IoT-Enabled Smart Cities: A Review of Middleware, Explainable AI, and User Behavior Analytics

This review looks at cloud–IoT edge middleware for analyzing user behavior in smart cities, with a particular focus on linking behavioral analytics to energy-aware and mission-critical urban services. The topic sits at the crossroads of smart-city platforms, IoT sensing, edge/fog/cloud computing, service-oriented architectures, machine learning, explainable AI, privacy-aware governance, and energy-critical applications such as EV charging, energy demand, environmental exposure, crowd safety, and the operational resilience of urban infrastructure. As such, this paper maps out the main categories of user-behavior data, examines five application domains, compares several case studies, and puts forward a new XAI-EdgeSOA Middleware Framework called X-EdgeCity Middleware. This framework brings together IoT sensing, edge preprocessing, fog coordination, event-driven communication, cloud analytics, XAI explanations, privacy-by-design, and benchmark logging. The goal is to support not just general smart-city applications, but also energy-sensitive or mission-critical services, where latency, reliability, explainability, and accountability have a direct impact on urban safety and continuity of operations.