Adaptive Embedded IoT Platform for Intelligent Data Processing and Communication in Distributed Cyber-Physical Systems

Abstract

The fast pace of the development of the Cyber-Physical Systems (CPS) and Internet of Things (IoT) has driven the increased pressure on having intelligent, adaptive and resource efficient embedded systems that can be utilised in distributed data processing and communication. The presented paper suggests an adaptable embedded IoT platform, which may be adapted to the use of edge intelligence, which can produce better real-time data processing, minimise latency, and establish a more efficient means of communication within distributed CPSs. The architecture proposed combines embedded edge node with dynamic resource allocation, which allows context-aware decision-making as well as efficient use of both computational and energy resources. It relies on a hybrid processing model in which it processes some most vital data on-premises through lightweight machine learning methods, and it sends the rest of non-time-sensitive data to the cloud to be analysed even further. Also, the adaptive communication framework, dynamically choosing the protocols and transmission parameters depending on the network conditions and the needs of the application is presented, guaranteeing both the quality and energy-efficient communication. The embedded hardware platforms are used to implement the system and tested in different working environments. The experiment outcomes show that it has significant gains such as a shorter latency, improved energy, better bandwidth usage, and expanded network reliability as compared to traditional cloud-centric IoT architectures. The suggested platform is capable of meeting the main issues of the distributed environment like scalability, real-time responsiveness, and resource limitations. It has also been utilised specifically well in upcoming applications such as in smart cities, in industrial automation as well as remote healthcare monitoring where the timely and efficient processing of data is essential. This piece of work is a step towards the next generation intelligent CPS development by incorporating adaptive computing and communication in the edge.