Wireless sensor networks (WSNs) have emerged as a backbone technology for the wireless communication era. The demand for WSN is rapidly increasing due to their major role in various applications with a wider deployment and omnipresent nature. The WSN is rapidly integrated into a large number of applications such as industrial, security, monitoring, tracking, and applications in home automation. The widespread use in many different areas attracts research interest in WSNs. Therefore, researchers are taking initiatives in exploring innovation day by day particularly towards the Internet of Things (IoT). But, WSN is having lots of challenging issues that need to be addressed, and the inherent characteristics of WSN severely affect the performance. Energy constraints are one of the primary issues that require urgent attention from the research community. Optimal energy optimization strategies are needed to counter the issue of energy constraints.
The energy harvesting wireless sensor networks (EH-WSNs) are having a large number of challenging issues based on their inherent characteristics, and these issues have a direct impact on the performance.
This systematic and taxonomical survey focused on discussing the energy optimization strategies for EH-WSNs. There are certain specific reasons behind this. The energy optimization strategies in EH-WSN help in achieving the desired balance between processing and transmission of data.
By effectively utilizing the optimization strategies, the optimum performance can be achieved in EH-WSNs with the existence of prevailing various limitations. The term optimization in mathematical form can be defined as discovering the minimum or maximum for a particular defined function based on multiple constraints. For an optimization problem, a feasible solution is developed with the help of multiple values that satisfies all the prevailing constraints. The ultimate aim of a well-defined optimization technique is to carefully observe the feasible solutions and then proposing the optimum solution. Considering the EH-WSNs scenario with multisource energy harvesters, the design of power management circuits can be optimized for enhancing the overall efficiency. One of the most appropriate schemes for handling energy constraints issues of WSNs is the energy harvesting technique, and the proper optimization of EH technologies and devices is needed; further, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy.
There exist a large number of literature with a focus on performance enhancement for EH-WSNs; further, it should be noted that researchers have already put lots of efforts towards proposing various strategies for EH-WSNs for enhancing the performance by covering the energy scarcity issue.
The major issue that has a severe impact on the performance of WSNs is the evergreen energy scarcity issue that needs to be handled with a focused approach. Traditionally, sensor nodes are operating with limited capacity batteries and batteries are having their limitations such as leakages of current, breakdown issues, and limited energy density issues. Also, the sensor nodes energy profile is regularly depleted with the time; thereby, sensor node is not able to provide the assigned duties especially in longer duration, but there exist many applications which require a much higher lifespan of sensor nodes ranging from months to several years, so in these emergencies, there exist only two usual solutions in terms of replacing the batteries or harvesting the energy to fill the gap that arises due to depleted energy. Also, battery replacement is sometimes not possible especially in cases of difficult remote deployment. So, the most general solution for enhancing the life of sensor nodes is by providing a regular power supply and further this is possible by utilizing the energy harvesting mechanism and this scheme enables the sensor nodes to work continuously without interruption, since, in most cases, sensor nodes need a continuous power supply. Further, Figure 3 describes the different types of energy harvesting mechanisms with different energy types available for harvesting.
Photovoltaic energy harvesting is defined as the method in which conversion of photons to electrical energy occurs, and here, photons are coming from sources such as artificial or solar light. In this harvesting mechanism, photovoltaic cells are exploited.
Thermal energy harvesting can be implemented in two ways as pyroelectric and thermoelectric energy harvesting. Further, thermoelectric energy harvesting consists of power generators that are used for electrical energy generation based on the Seebeck effect and known as thermoelectric power generators. The thermopile is the main element of a thermoelectric power generator. Further, two dissimilar conductors’ arrays are used in the formation of the thermopile.
Wireless energy harvesting exists in two major forms such as resonant and RF energy harvesting. Further, RF energy harvesting consists of a rectifying antenna or rectenna which is used for converting electromagnetic waves into electrical energy. In the RF energy harvesting mechanism, there are two sources from which energy can be harvested either through RF power or through electromagnetic signals with a specific wavelength. Further, sources, namely, microwaves, cell phones, broadcasting of television and radio, and Wi-Fi, are considered as ambient RF power. Resonant energy harvesting or resonant inductive coupling can be defined as the method of transmitting and accumulating electrical energy specifically between the two coils. There exist two coils named primary and secondary coil; further, an inductive transformer is attached to a primary coil. At the same frequency, these two coils are resonant. Next, through the air, power is sent to a specified device that is attached to a secondary coil. The magnetic flux is produced by the primary coil which is time-varying in nature,
Reference: Next generation wireless system communication and Internet
