Abstract: The increasing integration of Wireless Sensor Networks (WSNs) and Internet of Things (IoT) technologies across domains such as smart cities, healthcare, and industrial automation demands sensor platforms that are not only efficient and scalable but also adaptable to intelligent and secure applications. While many earlier reviews focused on general specifications and often included outdated hardware, this paper presents an up-to-date and comprehensive review of Commercial Off-The-Shelf (COTS) platforms that are actively used in current research and industry. By systematically excluding nodes that are no longer produced and incorporating newly available sensor nodes, this study addresses critical gaps in previous surveys and ensures practical relevance. COTS platforms are categorized into Single Board Microcontrollers (SBMs) and Single Board Computers (SBCs), providing a clear and structured framework that considers performance, energy consumption, cost, and AI readiness. The review further highlights emerging trends such as the adoption of secure edge computing, real-time behavioral analytics, and on-device learning, which are increasingly vital for modern deployments. Moreover, the growing interest in Reduced Instruction Set Computer Five (RISC-V) architectures is discussed, offering open-source flexibility and enhanced performance as a promising alternative for future WSN implementations, although its supporting software ecosystem is still maturing. Finally, the paper introduces a data-informed decision- support framework to help practitioners select appropriate platforms based on applicationspecific constraints and performance trade-offs. By bridging the gap between traditional hardware classifications and evolving computational demands, this work provides a valuable and practical guide for researchers and developers navigating the rapidly changing WSN and IoT landscape.
Abdullah et al. (Tue,) studied this question.