Abstract:The human body contains a near-infinite supply of energy in chemical, thermal, and mechanical forms. However, the majority of implantable and wearable devices are still operated by batteries, whose insufficient capacity and large size limit their lifespan and increase the risk of hazardous material leakage. Such energy can be used to exceed the battery power limits of implantable and wearable devices. Moreover, novel materials and fabrication methods can be used to create various medical therapies and life-enh… Show more
“…Temperature differences between biological structures and the ambient environment are also measurable. Because the human body contains a near-infinite supply of thermal energy, TEGs are suitable power sources for wearable sensors and electronics which monitor health [80].…”
Section: Deriving Power From Biological Systemsmentioning
Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.
“…Temperature differences between biological structures and the ambient environment are also measurable. Because the human body contains a near-infinite supply of thermal energy, TEGs are suitable power sources for wearable sensors and electronics which monitor health [80].…”
Section: Deriving Power From Biological Systemsmentioning
Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.
“…However, the mobility and portability of devices have brought great challenges to secure device management [27][28][29]. In the midst of existing challenges, identifying an IoT device and detecting a change in its position are critical challenges in many IoT applications (e.g., intelligent logistics, intelligent transportation, and intelligent manufacturing [30,31]).…”
Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things (IoT). In this paper, a device management system is proposed to track the devices by using audio-based location distinction techniques. In the proposed scheme, traditional cryptographic techniques, such as symmetric encryption algorithm, RSA-based signcryption scheme, and audio-based secure transmission, are utilized to provide authentication, non-repudiation, and confidentiality in the information interaction of the management system. Moreover, an audio-based location distinction method is designed to detect the position change of the devices. Specifically, the audio frequency response (AFR) of several frequency points is utilized as a device signature. The device signature has the features as follows. (1) Hardware Signature: different pairs of speaker and microphone have different signatures; (2) Distance Signature: in the same direction, the signatures are different at different distances; and (3) Direction Signature: at the same distance, the signatures are different in different directions. Based on the features above, a movement detection algorithm for device identification and location distinction is designed. Moreover, a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity, authentication, and non-repudiation in the process of information interaction between devices, Access Points (APs), and Severs. Extensive experiments are conducted to evaluate the performance of the proposed method. The experimental results show that the proposed method has a good performance in accuracy and energy consumption.
“… 27 , 28 It can be seen that previous studies have focused more on the output power, conversion efficiency, wearability, and conversion mechanism while ignoring the impact of the device on the metabolic consumption of the host. 29 , 30 , 31 Figure 1 Hotspot analysis diagram based on CiteSpace …”
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