(untitled) (International Journal of Engineering and Computer Science, 2016)
IoT applications have now emerged as a dominant force across a wide spectrum of industries, including healthcare and smart cities, while at the same time, the corresponding issues arising from latency and resource issues have become extremely complex. It is expected that the IoT environment across the world will consist of over 30 billion connected devices, and these devices will seem to contribute more than 79 zettabytes of data by 2030. These volumes demonstrate the absolute necessity of seeking solutions which may be effectively implemented and provide reasonable response time, while being efficiently computationally-scalable. There is an opportunity from the edge computing to decrease the latency in the system because the data are processed closer to the devices while the cloud computing provides a vast capacity for storage and computational requirements. Nonetheless, none of these paradigms taken solely can effectively cope with the requirements of today’s IoT. This paper analyses how edge and cloud computation systems, when used as a combined system, operates and provides an in-depth survey of latency minimisation techniques, resource management, and the compromise that comes with it. A new micro/macro hybrid architecture is introduced, where edge nodes would be used to handle real-time tasks, and the cloud would be utilized for big data computations. A number of simulated experiments indicate that the presented architecture cuts the latency by a quarter and increases the efficiency of resource utilization by a fifth, when compared to standard single-edge and standalone-cloud systems. These results highlight how hybrid edge-cloud architectures can support the requirements of IoT applications and offer theoretical contributions, as well as directional advice for subsequent implementations.