## 2 Related Work  
The development of IoT offloading methods is a hot topic of study, and a lot of work has been done in this field. Therefore, depending on their characteristics, advantages, and weaknesses, we have listed the selected related works in this portion. Several Q-learning spin-off techniques are used in this field. [PERSON] and [PERSON] [3] suggested a task offloading approach to a particular fog or the cloud node to establish the best decision on where and when to offload a task. As an MDP, the topic is provided and analyzed. Two decision-makers were discussed in their suggested MDP, where IoT objects may choose the fog platform to which they need to offload their tasks, while fog servers can decide to offload those jobs to other fog or to cloud nodes to balance the tasks. A Q-learning-based model is applied to achieve the optimal policy to manage large-size state and action space. The findings showed that their technique achieves improved load balancing and decreases the latency relative to other works. Fog computing allows IoT systems to enable applications insensitive to latency and computational intensity with lower overhead in computation and energy consumption. Therefore, [PERSON], [PERSON] [31] investigated fog-enabled IoT-eHealth networks' offloading and migration problems. First, they described a total time allocation model that considers transmission, migrations, calculation, and a penalty for unreasonable offloading decisions. Thus, multi-stage stochastic programming was utilized to develop the offloading issue as a stochastic optimization to determine the effect of ambiguity on the offloading strategy in stochastic settings. Besides, they examined the efficiency of stochastic multi-stage programming, considering the limited potential of energy and computation. The results revealed that their method's performance can be improved relative to baseline systems, while their technique can lower total computational complexity. Additionally, privacy is crucial for IoT-fog computing schemes, and several strategies are applied to various offloading techniques that make the network reliable and powerful, such as blockchain, etc. To build a node access allocation policy based on the types of health information, [PERSON], [PERSON] [32] suggested a simulated annealing-based offloading algorithm. In this manner, the quickest response can be obtained by users. Certain individuals may eventually be recognized to suffer emergency injuries, which means these situations must be managed with the highest priority; otherwise, life threats may be posed. The findings showed that the algorithm for emergency measures would efficiently help patients find the best way to connect with competent medical units. Their strategy primarily evaluated offloading traffic to reduce latency in the healthcare system. Several DL spin-off techniques are used in fog/edge computing and industrial IoT networks for offloading. [PERSON], [PERSON] [33] suggested a Two-Stage Possible Game-based Computation Offloading strategy (TPOS) for Wireless Body Area Networks (WBANs). They divided the game space into two stages to solve the multi-user game problem. Initially, jobs with various priorities in WBANs were assigned offloading choices based on their utility and penalty functions. The MEC node allocated computational resources on the second level to offload tasks via the potential game. Even in heavy tasks and dense WBAN scenarios, their research revealed that the TPOS algorithm could satisfy low latency and low energy consumption. The estimate of various forms of uncertainty is required to create active learning paradigms, novelty detection, error detection, and data fusion, all of which are critical for developing successful and tailored activity recognition systems. Besides, [PERSON], [PERSON] [34] highlighted significant offloading difficulties in blockchain-enabled heterogeneity IoT-edge-cloud computing settings, where edge platforms can collaborate to reduce IoT object energy consumption while meeting delay limitations.