Abstract: |
The Internet of Things (IoT) continues to push the boundaries of traditional system architecture models, necessitating more agile, scalable, and efficient frameworks to handle complex, large, real-time applications. While conventional architectures, like layered, provide a structured approach, they often suffer from limitations in adaptability, latency, and dynamic resource utilisation. This paper presents a comprehensive design and implementation a Collaborative Computing Paradigms (CCP)-based IoT Reference Architecture, defined by five core attributes: interconnection and interplay across paradigms, dynamic distribution of data processing, computing fluidity enabling seamless transitions across layers, collaborative data storage and management, and scalability and extensibility of computational resources. To validate the proposed architecture, experiments were conducted in Data Center Management System, Automotive Telematics, Smart Building (Fire Safety, Environment) and Asset Tracking (Shipping carts in a mall). Comparative analysis against traditional layered IoT architectures reveals significant improvements in latency reduction, resource utilisation, scalability under variable workloads, and interoperability between computational layers. The CCP-based architecture leverages dynamic orchestration of Edge, Fog, and Cloud paradigms, allowing for adaptive load balancing, real-time analytics, and distributed decision-making, thereby overcoming the rigidity of layered models. The results highlight the superiority of CCP architectures in enabling low-latency processing, high fault tolerance, and dynamic resource optimisation in highly fluid and demanding IoT environments. We believe this work underscores the paradigm shift towards collaborative architectural models, establishing CCP as a benchmark for next-generation IoT system design, particularly in domains requiring high degrees of responsiveness and cross-layer integration. |