Smart Architectural Framework for Symmetrical Data Offloading in IoT

With new technologies coming to the market, the Internet of Things (IoT) is one of the technologies that has gained exponential rise by facilitating Machine to Machine (M2M) communication and bringing smart devices closer to end users. By 2025, it is expected that IoT will bring together 78.4 billion of devices, thus improving the quality of life beyond our imagination; however, there are multiple potential challenges, such as the exploitation of energy consumption and the huge data traffic being generated by smart devices causing congestion and utilizing more bandwidth. Various researchers have provided an alternative to this problem by performing offloading of data, the task and computational requirements of an application at edge and fog nodes of IoT, thus helping to overcome latency issues for critical applications. Despite the importance of an offloading approach in IoT, there is need for a systematic, symmetric, comprehensive, and detailed survey in this field. This paper provides a systematic literature review (SLR) on data offloading approaches in IoT network at edge and fog nodes in the form of a classical taxonomy in order to recognize the state-of-the art mechanism(s) associated with this important topic and provide open consideration of issues as well. All of the research on classified offloading approaches done by researchers is compared with each other according to important factors such as performance metrics, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed. Finally, an efficient smart architecture-based framework is proposed to handle the symmetric data offloading issues.

Automated summary

With the rapid development of Internet of Things (IoT) technology, it is expected to connect 78.4 billion devices by 2025, but will also face various challenges such as energy consumption and data congestion. Various research has proposed data offloading at edge and fog nodes to help overcome latency issues, but more systematic research is still needed in this field.