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.

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In last few years, the Internet of Things (IoT) platform, which comprises a significant number of devices equipped with sensors and utilized for monitoring and actuation operations, has seen significant growth. Massive volumes of data are collected by IoT devices, which are subsequently sent to the cloud for analysis and forecasting. IoT data that is directly sent to the cloud can congest IT infrastructure that is used for other purposes. As a result, data offloading has become a popular issue in academic and industrial sectors, specifically for traffic-intensive applications that can benefit from offloading to local edge and cloud infrastructure. A lot of in-depth studies have been undertaken in areas where sensor device mobility is high in order to ensure efficient data offloading. Nevertheless, very few studies have taken into account the issue of data offloading with sensor devices that have very limited mobility, like industrial IoT sensors, which really needs more attention as it is one of the most data-trafficking areas. To counter this problem, a data offloading-based heuristic technique using edge computing is proposed in IIoT-based applications. This will reduce data transferring quantity, resulting in lower data migration capacity, bandwidth usage, and load, lowering operational costs. It is being put through extensive testing to see how it compares to existing algorithms. The experimental results show that the proposed algorithm is superior in terms of energy consumption, dependability, and resilience under various situations.