Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.

With recent advancements in technology, energy storage for gadgets and sensors has become a challenging task. Among several alternatives, the triboelectric nanogenerators (TENG) have been recognized as one of the most reliable methods to cure conventional battery innovation’s inadequacies. A TENG transfers mechanical energy from the surrounding environment into power. Natural energy resources can empower TENGs to create a clean and conveyed energy network, which can finally facilitate the development of different remote gadgets. In this review paper, TENGs targeting various environmental energy resources are systematically summarized. First, a brief introduction is given to the ocean waves’ principles, as well as the conventional energy harvesting devices. Next, different TENG systems are discussed in details. Furthermore, hybridization of TENGs with other energy innovations such as solar cells, electromagnetic generators, piezoelectric nanogenerators and magnetic intensity are investigated as an efficient technique to improve their performance. Advantages and disadvantages of different TENG structures are explored. A high level overview is provided on the connection of TENGs with structural health monitoring, artificial intelligence and the path forward.

Triboelectric nanogenerators (TENG), which convert mechanical energy (such as ocean waves) from the surrounding environment into electrical energy, have been identified as a green energy alternative for addressing the environmental issues resulting from the use of traditional energy resources. In this experimental design, we propose rolling spherical triboelectric nanogenerators (RS-TENG) for collecting energy from low-frequency ocean wave action. Copper and aluminum were used to create a spherical frame which functions as the electrode. In addition, different sizes of spherical dielectric (SD1, SD2, SD3, and SD4) were developed in order to compare the dielectric effect on output performance. This design places several electrodes on each side of the spherical structure such that the dielectric layers are able to move with the slightest oscillation and generate electrical energy. The performance of the RS-TENG was experimentally investigated, with the results indicating that the spherical dielectrics significantly impact energy harvesting performance. On the other hand, the triboelectric materials (i.e., copper and aluminum) play a less important role. The copper RS-TENG with the largest spherical dielectrics is the most efficient structure, with a maximum output of 12.75 V in open-circuit and a peak power of approximately 455 nW.

Early clinical diagnosis and treatment of disease rely heavily on measuring the many various types of medical information that are scattered throughout the body. Continuous and accurate monitoring of the human body is required in order to identify abnormal medical signals and to locate the factors that contribute to their occurrence in a timely manner. In order to fulfill this requirement, a variety of battery-free and self-powered methods of information collecting have been developed. For the purpose of a health monitoring system, this paper presents smart wearable sensors that are based on triboelectric nanogenerators (TENG) and piezoelectric nanogenerators (PENG), as well as hybrid nanogenerators that combine piezoelectric and triboelectric nanogenerators (PTNG). Following the presentation of the PENG and TENG principles, a summary and discussion of the most current developments in self-powered medical information sensors with a variety of purposes, structural designs, and electric performances follows. Wearable sensors that generate their own electricity are crucial not only for the proper development of children and patients with unique conditions, but for the purpose of maintaining checks on the wellbeing of the elderly and those who have recently recovered from illness, and for administering any necessary medical care. This work sought to do two things at once: provide perspectives for health monitoring, and open up new avenues for the analysis of long-distance biological movement status.

Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog-based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution-Map-Transfer-Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision-Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.

Triboelectric nanogenerators (TENG) have gained prominence in recent years, and their structural design is crucial for improvement of energy harvesting performance and sensing. Wearable biosensors can receive information about human health without the need for external charging, with energy instead provided by collection and storage modules that can be integrated into the biosensors. However, the failure to design suitable components for sensing remains a significant challenge associated with biomedical sensors. Therefore, design of TENG structures based on the human body is a considerable challenge, as biomedical sensors, such as implantable and wearable self-powered sensors, have recently advanced. Following a brief introduction of the fundamentals of triboelectric nanogenerators, we describe implantable and wearable self-powered sensors powered by triboelectric nanogenerators. Moreover, we examine the constraints limiting the practical uses of self-powered devices.

With the increasing improvement of wearable gadgets, nanogenerators have received significant attention in recent years. Herein, a hybrid piezoelectric and triboelectric nanogenerators (PTNG) for generating energy and monitoring is developed. The PTNG uses magnetic force to implement the opposing force in the sliding mode between the Kapton and copper/aluminum layers for the triboelectric part and polyvinylidene fluoride strips. The triboelectric part with copper set up in PTNG in mode 2 (capsule with electrode layer) is found with the maximum voltage in the open circuit and the peak power of approximately 12 μW for the triboelectric part. The piezoelectric part in PTNG is found with the maximum voltage Vmax=20.8V in the open circuit and the peak power of approximately 70 μW. In this design, a self‐powered walking sensing system is developed utilizing the PTNG for analyzing behavior of the human by walking on the treadmill. A test is conducted with different speeds of the treadmill, and the maximum hybrid open‐circuit voltage at 8 km h−1 is 21.9 V. This approach may present an innovative purpose for creating high‐performance and manageable energy harvesting gadgets with improved power output from human motions.

Triboelectric nanogenerators (TENG) have been reported with the advantages of high adaptability and easy integration in recent years, which, however, have been facing with the challenge of effective stimuli in different applications. In this study, we develop magnetic lifting triboelectric nanogenerators (ml-TENG) for energy harvesting and active sensing under cyclic loading, such as traffic. The ml-TENG take advantage of magnetic force to provide repulsive force to trigger for relative displacement between the electrode and dielectric layers in the sliding mode. Experimental and numerical studies are conducted to investigate the electrical performance of the ml-TENG under cyclic loading. The open-circuit voltage of 4 V and output power of 340 µW per capsule are obtained. In the end, we develop a self-powered velocity active sensing system using the ml-TENG, and the field test is conducted to obtain the maximum open-circuit voltage of 7.2 V at the velocity of 15 km/h. The reported ml-TENG provide a powerful tool to develop active sensing systems for real-world applications, such as velocity detection.