**1. Introduction**

With the spread of Internet of Medical Things (IoMT) applications, more intelligent services are presently emerging in the healthcare and medical areas, such as remote patient monitoring [1,2], telemedicine [3], biometrics scanners [4] and vital signs monitoring [5,6].

**Citation:** Ben Ammar, M.; Ben Dhaou, I.; El Houssaini, D.; Sahnoun, S.; Fakhfakh, A.; Kanoun, O. Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches. *Electronics* **2022**, *11*, 383. https:// doi.org/10.3390/electronics11030383

Academic Editors: Shailendra Rajput, Moshe Averbukh and Noel Rodriguez

Received: 6 December 2021 Accepted: 19 January 2022 Published: 27 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In general, the IoMT comprises different and heterogeneous smart devices, such as wearables, wireless sensors, and medical monitors, which can be applied to the human body, at home or in hospitals to provide better and more efficient remote monitoring. By combining information technology with medical information, wearable devices can perform better monitoring of medical and healthcare applications, resulting in reduced complexity and enhanced efficiency. With the use of the IoMT, physicians and healthcare responsible are also able to access different and real-time medical databases, which ensures a better understanding and identification of their patients' health issues.

The IoMT presents an application of the Internet of Things (IoT) in the field of medical and healthcare. The IoT comprises physical network devices equipped with sensors, software, and network connections that facilitate data collection and transmission. It can integrate cloud services and fog centers, where complex and efficient data processing is carried out with high processing capabilities. Considering the basic concepts of the IoT, the general layer architecture of IoMT is illustrated in Figure 1. It comprises four main layers, namely the sensing layer, the edge layer, the fog layer and the cloud layer. In the sensing layer, the wireless sensors and medical devices are installed along with different actuators. They are responsible for sensing medical and physiological information, and executing specific controlling and monitoring requests such as laser positioning and equipment maintenance. The raw data collected from the end devices are collected and transmitted to the edge devices, where data processing, reduction and analysis are carried out. Devices with edge computing processors provide improved security while operating at a low power level. Within the fog layer, local area networks are installed, where the data are transmitted from endpoints to a gateway, where it is then transmitted to sources for processing and return transmission. By the end, data are transmitted to the cloud layer, which can access several IoT devices at the same time. It permits real-time and continuous data processing with higher computational capabilities. However, even though wearable devices are becoming more powerful and affordable, machine-learning-based tasks that typically require more computation resources may overload them with higher data communications and, therefore, higher energy consumption.

**Figure 1.** General layers architecture of IoMT system.

Therefore, it becomes imperative to offload some tasks from resource-constrained edge devices to co-located edge devices, such as the fog. Applications that require intensive computation resources are often offloaded to cloud servers to be processed, which improves IoT device capabilities. Cloud computing, by contrast, may cause high latency response times, privacy and security issues. As a solution, some studies proposed to offload tasks to a Mobile Edge Computing (MEC) server via edge devices that can be placed near end devices and process some computational capacity. Thus, transmission latencies are reduced, and reliability and security are enhanced. Even though computation offloading over fog edge computing or MEC has reduced the energy consumption of IoMT devices to a certain degree, their energy limitations remain a key concern. However, most devices are powered by batteries, which limits their energy resources and operating times. Similarly, computation performance may be affected if not enough battery energy is available for task transmission. A larger battery or more frequent recharging can address this problem. In contrast, the small size of IoMT devices makes it difficult to equip them with larger batteries or to recharge their batteries frequently. To address these challenges, energyharvesting technologies have been identified as promising techniques to increase battery life and achieve energy-autonomous systems. Figure 2 shows the general architecture of an IoMT system with the integration of EH-supplied systems and considering the taskoffloading aspect. The IoMT system includes various types of sensors used, most likely activity sensors (presented in red circles in Figure 2), physiological sensors (presented in green circles in Figure 2). Sensor are placed over the human body within a network, where each sensor is responsible for monitoring certain physical information. The sensor data are gathered in the base station to be transmitted to the next IoT layer, which can be either an access point, a gateway or a mobile device. Later, the collected data are transported to the fog layer and then the cloud. Communication can be established between different installed devices over the different layers. During the communication, information related to the actual status of the corresponding devices, such as the residual energy level, neighbor list and reception acknowledgement could be shared. This information care is used later to decide upon the most appropriate device for task offloading. Offloading involves sharing details about which device will be best suited to execute the current task, the type of task that will be executed, and how it will be executed. Task offloading can occur at different levels of the IoMT system, such as from the WBAN to the gateway and from the gateway to the fog, to the cloud.

**Figure 2.** General architecture of an IoMT system based on energy harvesting and with consideration of task offloading.

Within the framework of IoT for medical applications, continuous data transmission takes place over the different layers of the network. Therefore, different sensor and communication technologies are used for sensing and transmitting data in real time, enabling fast calculations and optimal decision-making. It is crucial to satisfy the trade-off between the energy consumption, computational capability and data transmission for a real-time and accurate operation. Several schemes for energy efficiency and management are required to respond to these challenges. In general they can be classified into four main categories:


In this direction, it is important to investigate energy-efficient solutions for IoMT system, where intensive tasks and data processing are realized in a strict execution time. In particular, the communication and data transmissions need more attention, especially in the case of limited energy sources and computation capabilities. In this direction, investigations into energy-harvesting solutions along with task-offloading concepts present a promising solution to deal with excessive demands for a stable communication and data transmission. The contributions of this paper are:


The paper is organized as follows. Section 2 surveys research efforts related to joint energy harvesting and task-offloading approaches in fog edge computing systems. Section 3 presents the task-offloading approaches for fog edge computing, and deep-reinforcement learning-based algorithms. Section 4 highlights the related design considerations and challenges for EH driven task offloading. Section 5 reviews possibilities of energy supply, energy-storage strategies and recent trends in energy harvesting. Section 6 presents requirements for patient-centered care system. Finally, Section 7 concludes the paper.
