Emerging Intelligent Algorithms for Edge-of-Things Computing
The cloud-based Internet of Things (IoT) is used to connect a wide range of things such as vehicles, mobile devices, sensors, industrial equipments and manufacturing machines etc. In recent years, IoT is widely applied in many areas it includes a development of smart city and smart home, smart grid, smart vehicle, smart health and smart environmental monitoring. The IoT is leading a new era of computing where intelligent applications efficiently monitor and control connected devices, and transform IoT sensor data into knowledge representations. By intelligently investigating and collecting large amounts of data (big data), smart systems can enhance the decision making, business flows, automate industrial control processes, successful production, and economic results. Nowadays, cloud-based IoT sytems generate a large volume of data that cannot be processed by traditional data processing algorithms and applications. This would create more complexities to ensure scalability, mobility, reliability, low latency, network bandwidth consumption and energy efficiency of IoT devices while moving the IoT sensor data to the cloud. The emerging edge computing technologies, IoT and rich cloud services are used to create a novel technology called Edge-of-Things (EoT). In EoT, data processing occurs in part at the network edge or between the cloud-to-end that can best meet customer necessities, rather than entirely processing in a comparatively less number of massive clouds. The main challenge in EoT is how to manage with emerging IoT environments, where a large number of connected devices participate for restricted wireless resources and where heterogeneity is ever-increasing. In order to overcome this issue, there is an urgent need for more intelligent algorithms and architectures that lead to more interoperable solutions and that can make effective decisions in emerging EoT.
Edited by: Gunasekaran Manogaran, Naveen Chilamkurti, Ching-Hsien Hsu and Bharat S. Rawal