In recent years, Cloud-assisted Internet of Things (Cloud-of-Things or in short CoT) has emerged as a revolutionary paradigm that enables intelligent and self-configuring (smart) IoT devices and sensors to be connected with the cloud through the Internet. This new paradigm enables the resource-constrained IoT devices to get the benefit from Cloud’s powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the IoT data as well as analysis of the processed information under context using inherently complex models. At the same time, cloud can benefit from IoT by allowing its users to build applications that can use and handle these smart IoT objects interconnected and controlled through software services using cloud infrastructure. Thus, the CoT paradigm can stimulate the development of innovative and novel applications to various areas such as smart cities, smart homes, smart grids, smart agriculture, smart transportation, smart healthcare, etc. to improve all aspects of people’s life.

However, currently the CoT paradigm is facing increasing difficulty to handle the Big data that IoT generates from these application use cases. As billions of previously unconnected devices are now generating more than two exabytes of data each day, it is challenging to ensure low latency and network bandwidth consumption, optimal utilization of computational recourses, scalability and energy efficiency of IoT devices while moving all data to the cloud. Hence, in recent times, this centralized CoT model is undergoing a paradigm shift towards a decentralized model termed as edge computing, that allows data computing, storage and service supply to be moved from Cloud to the local edge devices such as smartphones, smart gateways or routers and local PCs that can offer computing and storage capabilities on a smaller scale in real-time. Edge computing pushes data storage, computing and controls closer to the data source(s) instead of performing in a centralized local server or devices as in the case of Fog computing; therefore, enables each edge device to play its own role of determining what information should be stored or processed locally and what needs to be sent to the cloud for further use. Thus, edge computing complements CoT paradigm in terms of high scalability, low delay, location awareness, and allowing of using local client computing capabilities in real time.

While researchers and practitioners have been making progress within the area of edge computing, still there exists several issues that need to be addressed for CoT paradigm. Some of these issues are: novel network architecture and middleware platform for edge and CoT paradigm considering emerging technologies such as 5G wireless networks, semantic computing; edge analytics for Big data; novel security and privacy methods; social intelligence into the edge node to host CoT applications; and context-aware service management on the edge with effective quality of service (QoS) support and other issues.