**2. Related Works**

Recently, task offloading in fog edge computing systems has gained considerable attention due to the increasing development of IoMT devices. In [7], the authors developed a deep-learning-based, Internet of Medical Things-enabled edge computing framework for tackling COVID-19. It detects various COVID-19 symptoms and generates reports and alerts for medical decision support. Results indicate that the system can be used to effectively manage in-home health during a pandemic. Nevertheless, improvements to the system accuracy were needed as well as implementations with real subjects. In [8] a joint optimization framework was also proposed for IoT fog computing to achieve optimal resource allocation. The results show that the proposed framework enhanced the performance of IoT-based network systems. In [9] authors investigated delay-sensitive task offloading in edge-enabled healthcare services. A priority-aware service provisioning was proposed, allowing edge server computing resources to handle hard-deadline tasks earlier than soft-deadline tasks which have a lower priority and can tolerate longer delays over hard-deadline tasks. In contrast, the authors plan to examine how hard-deadline tasks can be placed in remote healthcare applications where ensuring high reliability is a crucial requirement.

When focusing on the increasing number of tasks that require high computational capability and consequently more energy, mobile devices need effective mechanisms to figure out which tasks to perform locally and which to migrate to the cloud. The authors in [10] discussed different computational offloading techniques. They consider the offloading either to a fog node or a cloud. They both have their trade-offs. The cloud, as an example, is rich in terms of resources, but offloading computational tasks to cloud servers can lead to security and privacy issues and it is also far away from mobile nodes. In contrast, fog is nearby but has limited resources. Hence, offloading to a cloud or fog consumes different amounts of energy and increases computation performance. In this context, the authors proposed an energy consumption-oriented algorithm to reduce energy consumption when offloading tasks. Initially, they compute the consumed energy when offloading the task to the fog compared to the cloud. Afterwards, they evaluated which entity would be preferred for the task based on the computation requirements. Based on these factors, the task is then offloaded to the desired entity.

Energy harvesting is a promising technology for converting ambient (solar, wind, etc.) and human energy (motion, breath) into electrical power, enabling communication systems to achieve energy-autonomous and efficient communications. In [11] the joint offloading and resource allocation issues in energy harvesting small cell networks is addressed to maximize the number of tasks performed by edge servers while reducing their energy and delay costs. In [12], the authors proposed a deep-reinforcement-learning-based framework for online offloading to reduce the computational complexity in large EH-driven networks. The proposed algorithm can successfully improve offloading behavior by implementing a deep neural network that learns binary offloading decisions based on past offloading experiences. In contrast, a distributed implementation of the proposed algorithm is still needed to enable the users to make offloading decisions in a distributed manner via a learning process. Similarly, a reinforcement-learning-based privacy-aware offloading scheme for a healthcare IoT device supplied by energy harvesting was proposed in [13]. The offloading policy applied on the edge device can be determined by considering the privacy level, energy consumption, and computation latency at each time slot. In [14], the authors investigated computation offloading and resource allocation issues with multiple energy harvesting supplied mobiles. All mobile devices initially harvest energy from RF signals and then use it to perform their own tasks locally or offload them to a MEC server. Some other offloading schemes can also achieve self-sustaining operations. In [15], for instance, the state-of-the-art of methodologies for task offloading in MEC and wireless power transfer to end nodes were recently described. The authors demonstrated the effective use of the Wireless Power Transfer (WPT) technique to charge end mobile phones which have gained more popularity in MEC. However, the increasing demand for computing resources may degrade the performance of MEC. Accordingly, they highlighted the influence of making decisions between task-offloading implementations and offloading locations on the power consumption of MEC devices.

Energy-efficient appliances have become prevalent in various fields and industries, including health care. Therefore, energy management is an effective technique for evaluating the energy efficiency of different devices. By contrast, the surveyed contributions lack discussions of joint energy-harvesting technologies, fog edge computing, and energy management techniques which are vital for IoMT devices.

Table 1 compares the state-of-the-art-related surveys based on specific key features.


**Table 1.** Comparison between state-of-the-art surveys.
