Efficiency  of a Hybrid  Energy Harvesting  for IoT: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Sirine Rabah
,
Aida Zaier
,
Jaime Lloret
,
Hassen Dahman

The Internet of Things (IoT) is a network of interconnected physical devices, vehicles, and buildings that are embedded with sensors, software, and network connectivity, enabling them to collect and exchange data. This exchange of data between the physical and digital worlds allows for a wide range of applications, from smart homes and cities to industrial automation and healthcare. However, a key challenge faced by IoT nodes is the limited availability of energy to support their operations. Typically, these nodes can only function for a few days based on their duty cycle. A solution that aims to ensure the sustainability of IoT applications by addressing this energy challenge is introduced. 

  • IoT
  • energy harvesting
  • solar
  • wind
  • HHO
  • PSO

1. Introduction

The increase of Internet of Things devices has a significant impact on society, particularly in the fields of healthcare, entertainment, personal communication, and environmental monitoring [1][2][3]. One of the significant challenges faced by these low-power devices is their reliance on batteries that require frequent charging or replacement. This challenge has led to the emerging technology of energy harvesting as an attractive alternative solution for IoT devices. Energy harvesting refers to the process of transforming various types of energy resources, such as temperature, vibration, pressure and electromagnetic radiation including light and RF waves into usable electrical energy. In recent years, there has been a growing need for sustainable energy sources due to the increasing demand for clean energy production with minimal environmental impact. One of the most promising alternatives to traditional fossil fuels is hybrid energy harvesting, which combines multiple renewable sources such as solar and wind power. It is anticipated that integrating both PV and wind energy harvesting can yield significant benefits in terms of enhancing the lifetime of IoT sensors, owing to their complementary nature [4]. In cases where one energy source is insufficient or unavailable, the other energy source can compensate to meet storage battery demands. However, the output of these two energy sources can be uncertain due to factors such as environmental conditions or day-night variations. To achieve a stable voltage output, these two systems are connected in parallel to one another, so that if one source is unavailable, the system can be balanced by the other. As such, both systems can function independently or simultaneously. Due to the variable nature of solar radiation and wind speed, the present PV/wind arrangement has limited conversion capability [5]. Factors such as radiation level, temperature, and wind speed all have a significant impact on the performance of PV panels and wind turbines. As a result, additional Maximum Power Point Tracking (MPPT) approaches are urgently needed to achieve higher efficiency, maximize power extraction from PV panels and wind turbines and ensure optimal performance. A range of techniques has been developed for this purpose [6]. The constant voltage method is used in hybrid solar-wind energy systems to achieve a consistent voltage during battery charging [7]. Nonetheless, this method produces significant oscillations and necessitates a lengthy convergence time. On the other hand, the Perturb and Observe (P&O) algorithm serves as an active power control in a hybrid system and demonstrates strong dynamic performance in response to variations in wind and solar irradiation [8]. P&O modifications involving step size changes have also been performed and implemented in the hybrid system [9]. The incremental conductance method is also utilized in hybrid systems [10]. Traditional MPPT approaches have drawbacks such as dynamic response concerns and steady-state oscillation issues, which make them ineffective in adjusting to changing environmental conditions [11]. To address these issues, academics have created clever and advanced processing systems that employ techniques such as genetic algorithms (GA), neural networks (NN), and fuzzy logic controllers (FLC). However, the use of FLC, GA, and NN techniques for intelligent MPPT controllers in hybrid solar-wind systems is frequently limited due to variables such as limited fuzzy deduction rules [12] small population numbers, and limited data availability. While these techniques function well under specific conditions, they may fail to monitor the Maximum Power Point (MPP) precisely when facing fluctuations in the power curve [13][14]. In PV-wind system, efficient extraction of the maximum power point is critical
The drawbacks of using batteries, such as environmental pollution, have prompted the exploration of alternative methods to power IoT sensors with the energy available in their surroundings, a process known as energy harvesting. The main advantage of this technology is the continuity of the power supply. Theoretically, it lasts to operate as long as there is energy in the environment [15][16][17]. The future society, which largely relies on energy, highly depends on energy harvesting technology. This is mostly due to the fact that energy may be obtained from a variety of sources, as seen in Figure 1, making it a ubiquitous and environmentally friendly process. Moreover, the maintenance costs associated with such systems are relatively low. There are numerous sources of ambient energy that can be used with energy harvesting technology, some of which occur naturally and others human-generated [18]. These energy sources are gaining increasing attraction in the literature, including vibrations energy, radio frequency energy, thermal energy, solar photovoltaic energy (indoor and outdoor), and wind energy.
Sustainability 15 10252 g001 550
Figure 1. Energy harvesting sources.

2. Solar Energy Harvesting

Light is a plentiful energy source that can power a variety of indoor and outdoor applications, including wireless sensors and devices in the burgeoning IoT industry. To capture sunlight, solar or photovoltaic (PV) cells made of semiconductor materials such as silicon can be used. Through the photovoltaic effect, this technology directly converts sunlight into usable electricity. The semiconductor absorbs the energy of the photons, causing electrons to move out of their regular positions and create holes. This generates a current flow for the electric circuit. A photovoltaic cell forces the electrons and holes to move forward in opposite directions, creating a voltage and current between the two parts, like a battery. Sunlight is the most suitable energy source for harvesting, estimated to provide 1.4 kW/m22 [19]. While the theoretical efficiency of a PV cell is 90%, the practical efficiency is around 40% [20]. PV cells can maintain high performance for up to 25 years, after which the generated power decreases. Recently, numerous solar energy harvesting (SEH) prototypes have been developed for WSN and IoT networks. In building management, a WSN system designed with a Tyndall 25 mm mote uses energy harvesting from indoor light via a photovoltaic cell to reduce energy consumption [21]. An intelligent SEH system for WSN nodes is proposed in [22], utilizing a hardware-based comparator circuit to manage battery charging for increased system robustness. The potential of solar cells to supply energy for a single node is examined in detail in [23], including discussions on cell characteristics and control circuit design for battery voltage regulation. Sharma et al. conduct a comprehensive survey on SEH for WSN nodes in [24], covering solar cell efficiency, DC-DC converters, MPPT, energy prediction algorithms, and design challenges and solutions. An efficient SEH system with MPPT and PWM techniques is proposed in [24], with simulation, optimization, and hardware implementation. A wireless sensor system utilizing solar energy harvesting for agriculture development is proposed in [25]. The HaLOEWEn solar energy harvester charges a 4.6 AH Li-ion battery using tiny solar panels, and the quantity of energy harvested is determined by the prevailing light conditions [26]. A flexible wearable body sensor node with indoor photovoltaic energy harvesting is designed in [27], while solar micro panels placed in shirts tissue are used to harvest energy in [28].

3. Wind Energy Harvesting

Wind energy, similarly to solar energy, can be harnessed to produce usable electricity. While solar energy is dependent on sunlight, wind energy can be accessible during the day and night, as well as in rainy and cloudy conditions. Windy regions, including civil structures such as bridges and high buildings, are potential sources for wind energy harvesting for wireless sensor nodes. Researchers have explored the feasibility of wind energy harvesting, and numerous studies in this field have been carried out. For instance, Park et al. designed a bridge health monitoring system in [29] that used a wireless sensor node powered by a wind turbine generator (WTG). With a maximum output power of 27.3 mW at 3.0 m/s wind speed, the WTG generated enough electricity to power the wireless sensor node. Another work in [30] addressed the development of a small wind energy harvester with MPPT for monitoring wildfires with wireless sensor nodes. In [31], an anemometer-based solution was used to harvest wind energy, where the motion of an anemometer shaft was used to generate energy. The harvested energy was converted to battery potential using a pulsed buck-boost converter, and trickle charging was used to power the battery. The analysis showed that the energy harvesting capability ranged from ten to hundreds of micro-watts up to approximately one milli watt, which could significantly extend the lifetime of sensor devices.

This entry is adapted from the peer-reviewed paper 10.3390/su151310252

References

  1. Zhang, D.G.; Wu, H.; Zhao, P.Z.; Liu, X.h.; Cui, Y.Y.; Chen, L.; Zhang, T. New approach of multi-path reliable transmission for marginal wireless sensor network. Wirel. Netw. 2020, 26, 1503–1517.
  2. Shafiq, M.; Tian, Z.; Bashir, A.K.; Du, X.; Guizani, M. IoT malicious traffic identification using wrapper-based feature selection mechanisms. Comput. Secur. 2020, 94, 101863.
  3. Yu, X.; Yang, X.; Tan, Q.; Shan, C.; Lv, Z. An edge computing based anomaly detection method in IoT industrial sustainability. Appl. Soft Comput. 2022, 128, 109486.
  4. Yazici, M.S.; Yavasoglu, H.A.; Eroglu, M. A mobile off-grid platform powered with photovoltaic/wind/battery/fuel cell hybrid power systems. Int. J. Hydrogen Energy 2013, 38, 11639–11645.
  5. Muñoz-Palomeque, E.; Sierra-García, J.E.; Santos, M. Wind turbine maximum power point tracking control based on unsupervised neural networks. J. Comput. Des. Eng. 2023, 10, 108–121.
  6. Khan, M.J.; Mathew, L. Comparative study of maximum power point tracking techniques for hybrid renewable energy system. Int. J. Electron. 2019, 106, 1216–1228.
  7. Vasant, L.G.; Pawar, V. Solar-wind hybrid energy system using MPPT. In Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 15–16 June 2017; pp. 595–597.
  8. Rezkallah, M.; Hamadi, A.; Chandra, A.; Singh, B. Design and implementation of active power control with improved P&O method for wind-PV-battery-based standalone generation system. IEEE Trans. Ind. Electron. 2017, 65, 5590–5600.
  9. Fathabadi, H. Novel standalone hybrid solar/wind/fuel cell power generation system for remote areas. Sol. Energy 2017, 146, 30–43.
  10. Ram, J.P.; Rajasekar, N.; Miyatake, M. Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review. Renew. Sustain. Energy Rev. 2017, 73, 1138–1159.
  11. Vardia, M.; Priyadarshi, N.; Ali, I.; Azam, F.; Bhoi, A.K. Maximum power point tracking for wind energy conversion system. In Advances in Greener Energy Technologies; Springer: Singapore, 2020; pp. 631–640.
  12. Qais, M.H.; Hasanien, H.M.; Alghuwainem, S. Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators. Appl. Soft Comput. 2020, 86, 105937.
  13. Kumar, G.A. Optimal power point tracking of solar and wind energy in a hybrid wind solar energy system. Int. J. Energy Environ. Eng. 2022, 13, 77–103.
  14. Mokhtari, Y.; Rekioua, D. High performance of maximum power point tracking using ant colony algorithm in wind turbine. Renew. Energy 2018, 126, 1055–1063.
  15. Chamanian, S.; Baghaee, S.; Ulusan, H.; Zorlu, Ö.; Külah, H.; Uysal-Biyikoglu, E. Powering-up wireless sensor nodes utilizing rechargeable batteries and an electromagnetic vibration energy harvesting system. Energies 2014, 7, 6323–6339.
  16. Kamalinejad, P.; Mahapatra, C.; Sheng, Z.; Mirabbasi, S.; Leung, V.C.; Guan, Y.L. Wireless energy harvesting for the Internet of Things. IEEE Commun. Mag. 2015, 53, 102–108.
  17. Annapureddy, V.; Na, S.M.; Hwang, G.T.; Kang, M.G.; Sriramdas, R.; Palneedi, H.; Yoon, W.H.; Hahn, B.D.; Kim, J.W.; Ahn, C.W.; et al. Exceeding milli-watt powering magneto-mechano-electric generator for standalone-powered electronics. Energy Environ. Sci. 2018, 11, 818–829.
  18. Williams, A.J.; Torquato, M.F.; Cameron, I.M.; Fahmy, A.A.; Sienz, J. Survey of energy harvesting technologies for wireless sensor networks. IEEE Access 2021, 9, 77493–77510.
  19. Sudevalayam, S.; Kulkarni, P. Energy harvesting sensor nodes: Survey and implications. IEEE Commun. Surv. Tutor. 2010, 13, 443–461.
  20. Atallah, R.; Khabbaz, M.; Assi, C. Energy harvesting in vehicular networks: A contemporary survey. IEEE Wirel. Commun. 2016, 23, 70–77.
  21. Wang, W.S.; O’Donnell, T.; Ribetto, L.; O’Flynn, B.; Hayes, M.; O’Mathuna, C. Energy harvesting embedded wireless sensor system for building environment applications. In Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, Aalborg, Denmark, 17–20 May 2009; pp. 36–41.
  22. Li, Y.; Shi, R. An intelligent solar energy-harvesting system for wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2015, 2015, 179.
  23. Samijayani, O.N.; Firdaus, H.; Mujadin, A. Solar energy harvesting for wireless sensor networks node. In Proceedings of the 2017 International Symposium on Electronics and Smart Devices (ISESD), Yogyakarta, Indonesia, 17–19 October 2017; pp. 30–33.
  24. Sharma, H.; Haque, A.; Jaffery, Z.A. Modeling and optimisation of a solar energy harvesting system for wireless sensor network nodes. J. Sens. Actuator Netw. 2018, 7, 40.
  25. Saxena, M.; Dutta, S. Improved the efficiency of IoT in agriculture by introduction optimum energy harvesting in WSN. In Proceedings of the 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, 13–14 February 2020; pp. 1–5.
  26. Zhao, P. Energy Harvesting Techniques for Autonomous WSNs/RFID with a Focus on RF Energy Harvesting. 2012. Available online: https://tuprints.ulb.tu-darmstadt.de/id/eprint/3102 (accessed on 19 June 2023).
  27. Toh, W.Y.; Tan, Y.K.; Koh, W.S.; Siek, L. Autonomous wearable sensor nodes with flexible energy harvesting. IEEE Sens. J. 2014, 14, 2299–2306.
  28. Lloret, J.; Garcia, M.; Catala, A.; Rodrigues, J.J. A group-based wireless body sensors network using energy harvesting for soccer team monitoring. Int. J. Sens. Netw. 2016, 21, 208–225.
  29. Park, J.W.; Jung, H.J.; Jo, H.; Jang, S.; Spencer, B.F., Jr. Feasibility study of wind generator for smart wireless sensor node in cable-stayed bridge. In Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, San Diego, CA, USA, 7–11 March 2010; Volume 7647, pp. 1241–1248.
  30. Tan, Y.K.; Panda, S.K. Self-autonomous wireless sensor nodes with wind energy harvesting for remote sensing of wind-driven wildfire spread. IEEE Trans. Instrum. Meas. 2011, 60, 1367–1377.
  31. Weimer, M.A.; Paing, T.S.; Zane, R.A. Remote area wind energy harvesting for low-power autonomous sensors. In Proceedings of the 2006 37th IEEE Power Electronics Specialists Conference, Jeju, Republic of Korea, 18–22 June 2006; pp. 1–5.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback