A Comparative Analysis of Service Management Techniques in the Internet of Things

The Internet of Things (IoT) interconnects vast networks of heterogeneous devices across dynamic domains such as healthcare, industry, and smart cities, presenting critical challenges in real-time orchestration, resource efficiency, security, and scalability. To address these demands, diverse service management paradigms have emerged as essential frameworks. This review systematically analyzes existing IoT service management approaches, categorizing them into five core methodologies: (1) rule-based and model-driven control, (2) AI-driven learning techniques, (3) optimization-based strategies, (4) decentralized blockchain systems, and (5) edge-centric federated intelligence. We evaluate how these paradigms support adaptive resource allocation, QoS-aware orchestration, privacy-preserving computation, and resilient security in distributed IoT environments. Key challenges-including adaptability to dynamic