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Green Communication for Next-Generation Wireless Systems: Optimization Strategies, Challenges, Solutions, and Future Aspects
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. Although one of the most appropriate schemes for handling energy constraints issues is the appropriate energy harvesting technique, the optimal energy optimization strategies should be coupled together for effectively utilizing the harvested energy. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. We have also prepared the summarized view of various protocols/algorithms with their remarkable details. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era.
The current era is the witness of many emerging technological advancements including the Internet of Things (IoT), cloud computing along with wireless sensor networks, and further integration of these emerging technologies for collecting and deep analysis of monitored information [1–3]. Also, this analyzed factual information is needed for enhancing the efficiency of the particular industrial system by ensuring optimal resource consumption. Moreover, for monitoring various events pertaining to home/office, human activities, health, defense, agriculture, and industries, etc., wireless sensor networks are utilized extensively [4, 5]. In extreme circumstances, sensor nodes are remotely deployed in harsh environmental conditions with the limited capacity of the battery for particular monitoring applications in which long operational periods are needed even years or decades and battery power depletes regularly with the course of time, and in these undesirable situations, it is impossible to provide battery replacement facility or recharging activity; therefore, energy efficiency is considered a major challenging issue for sensor nodes [6, 7].
To amicably handle the energy scarcity issue of sensor nodes, there is a need for an alternative energy source to compensate for the energy requirement in case of battery drain out [8, 9]. One of the best resolutions to this issue is the use of an energy harvesting technique in which energy from natural sources available in the environment is transformed into power to resuscitate the batteries [10, 11]. The new advancements in energy harvesting techniques for WSNs have become a prominent research field. But, in the case of energy harvesting, the energy output may be insufficient due to streaky and spatial variations, and some mechanisms are needed for effective utilization of the harvested energy; further, these mechanisms are collectively termed as energy optimization strategies [12, 13].
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 . In this article, we will discuss the various challenging issues and corresponding suggested solutions in the subsequent sections, although our main focus is on energy efficiency. It is a noteworthy fact that although a large number of research have been completed and others are still going on with a proposal for enhancing energy efficiency, energy optimization has become an evergreen research challenge for EH-WSNs . For EH-WSNs, there is a need for some planned mechanism known as energy optimization strategies for optimum energy consumption in a sensor node by managing energy efficiently. There exist a large number of literature focusing on energy management schemes in the last two decades; the reason is obvious; for optimal performance of WSN, there is a need for energy optimization. The ultimate aim of energy optimization strategies is to manage the energy in the network for increasing the lifetime of tiny sensor nodes, and the network remains operational perennially. Special attention is needed for managing the energy optimization issue for sensor nodes .
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.
1.1. A Distinctive Approach and Motivation
In this article, we have done an extensive study for classifying the energy optimization strategies for EH-WSNs; the uniqueness in this research that distinguishes this work from others is the in-depth classification of energy optimization strategies for EH-WSNs by covering all possible aspects. Also, in this article, different energy optimization strategies have been discussed consisting of various algorithms along with further details of improvements in the coming future.
Although, there exist a large number of literature on energy management issue that mostly focus on a particular area and discuss energy management by considering only particular perspective and they do not concentrate on other areas affecting energy utilization in EH-WSNs, and we have tried our best to overcome this drawback of existing pieces of literature and try to cover as many as possible all perspectives in this article. We hope that the energy optimization strategies discussed in this article with a wide view definitely help the researchers working on this area to understand the key pillars of the energy optimization framework. Further, this article also provides space to explore new possibilities for energy efficiency in the field of EH-WSNs.
1.2. Salient Contributions towards Findings
In this systematic review article, we have conducted a detailed study of energy optimization issues in EH-WSNs. The detailed study helps in finding the facts (research gaps) in the existing review papers and guides the authors to write a systematic review article covering all aspects related to energy optimization for EH-WSNs.
The major contribution of this systematic review article can be pointed out as follows: (1)This systematic review article explains the energy optimization strategies for EH-WSNs by considering eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. The ultimate aim is to discuss the energy optimization schemes for EH-WSNs with a diversified view covering all possible areas having the sharp vision that affected the energy consumption in the network(2)Furthermore, this systematic review article provides the pointwise precise overview of various key pieces of literature in the last twenty years covering especially duty cycle schemes, MAC protocols, opportunistic routing schemes considering geographic characteristics, cluster-based routing, and also energy balancing schemes with key attributes(3)Next, this systematic review article provides complete coverage of challenging issues for formulating the optimization problems for EH-WSNs and also describes the solutions to optimization problems in the form of popular algorithms(4)Further, this systematic review article provides the paradigm shift for energy harvesting technologies considering the twenty years of an era starting from the year 2000 to 2020 and also describing the next-generation technologies and future of energy harvesting techniques
We have structured this systematic review article as per the following: Section 1 comprises the brief introduction of WSNs, the need and role of energy harvesting techniques, and WSNs with energy harvesting techniques (EH-WSNs), and in the last, this section describes the need of energy optimization strategies for EH-WSNs. Section 2 provides a deep insight into the adopted research methodology. In Section 3, this article provides details about the energy harvesting techniques with corresponding sources of energy. In Section 4, this article provides the classification of energy optimization strategies with a broad view by covering multiple factors; also, the precise tabular representations of duty cycle schemes with key attributes, MAC protocols, opportunistic routing schemes considering geographic characteristics, key features of clustering approaches, and also energy balancing schemes are illustrated. The optimization problems with the respective solutions for EH-WSNs are provided in Section 5. Also, a paradigm shift for energy harvesting technologies along with the future of energy harvesting techniques for WSNs and further research directions is provided in Section 6. In Section 7, this review article provides a conclusion.
2. A Deep Insight into the Adopted Research Methodology
This systematic review article focuses on the energy optimization strategies for EH-WSNs with a broad view by covering all possible factors that affected the energy efficiency in EH-WSNs.
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. We would like to highlight a few pieces of literature as [17–20]. We have tried our best level to select the appropriate literature for conducting the detailed study for collecting facts and thereby designing this systematic review article.
We have selected the era starting from 2000 to 2020 for selecting the appropriate good literature with good citations. Articles published in different journals/conferences have been studied for collecting the facts about energy optimization. Figure 1 illustrates the number of literature considered yearwise.
We have adopted a structured research methodology consisting of six major steps as depicted in Figure 2. In this systematic review article, we have structured the research into six major steps:
In step 1, we select the potential research questions, and further, we understand the need of exploring the research questions; next, we define the research objectives corresponding to potential research questions. Further, in step 2, we prepare the new database consisting of high-quality research papers from three already existing databases, namely, Web of Science, SCI/SCIE, and Scopus databases, and next, we conduct an extensive research study which leads to further retrieval of data for formulating systematic research design. Furthermore, in step 3, we prepare the summarized solutions corresponding to research questions with the help of an analyzed literature review. Next, in step 4, we perform the quality assessment activities for verifying the facts and figures which we have collected from the newly constructed database. This step is very important because this systematic review article will provide significant information to researchers working on the energy optimization issues of EH-WSNs. Moreover, in step 5, we prepare the summary of outcomes after analyzing the literature in the newly constructed database. Besides, in step 6, we mark the corresponding references.
The newly constructed database consists of various literature, and we have considered three major parameters while considering the particular literature for this systematic review and three parameters are the articles that should focus on our primary objective, i.e., energy efficiency, the journal in which literature has been published that should be peer-reviewed, and next, the selected literature that should have appropriate citations.
Initially, we have selected 610 pieces of literature. After, the analysis we have further selected 305 pieces of literature out of the database of 610 pieces of literature. Again, we perform further analysis and select 178 pieces of literature out of 305 pieces of literature previously selected.
3. Energy Harvesting Techniques with Corresponding Sources of Energy
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 and Table 1 illustrates the power density of various energy harvesting schemes.
Next, we are trying to illustrate the brief description with relevant references as per the following.
Mechanical energy harvesting [21, 22] is defined as the method in which energy conversion from mechanical to electrical occurs. The harvester consists of the spring-mounted mass component installed inside, and the mechanical energy from displacements and oscillations is converted into electrical energy. This entire process utilized mechanical stress, vibrations, rotational movements of waste, fluid, force, and high-pressure motors. There exist three forms of mechanical energy harvesting, namely, electromagnetic, electrostatic, and piezoelectric. In the piezoelectric energy harvesting method [23, 24], the two factors play a major role such as the use of piezoelectric material and the piezoelectric effect. Here, the piezoelectric material is strained, and further, energy from vibrations, pressure, and force is converted to electrical energy. The electrostatic energy harvesting mechanism  utilized the principle in which energy conversion occurs from mechanical to electrical with the capacitance change. Here, the key factor is changing the capacitance of a varying capacitor which is depending on the vibrations. Next, the popular law of electromagnetic induction which is known as Faraday’s law is used in the electromagnetic energy harvesting mechanism [26, 27]. Here, the electromagnetic harvester utilizes the inductive spring-mass system which is the backbone of this type of harvesting system. The stationary magnet creates the magnetic field, and further, movement of magnetic material via the magnetic field induces the voltage. Now, energy conversion takes place from mechanical energy to electrical energy.
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. Here, at the P-N junction, an electrical field is formed . The solar energy harvesting mechanism is illustrated in Figure 4.
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 . Pyroelectric energy harvesting is defined as the method in which voltage is generated by cooling or heating the pyroelectric materials. The basic core element of pyroelectric energy harvesting is the pyroelectric material’s crystal structure, and due to change in temperature, atom location in crystal structure changes and which further results in producing the voltage. Pyroelectric materials require time-varying changes in the temperature as compared with thermoelectric energy harvesting in which temperature gradient is needed .
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, and further, voltage is induced whenever that magnetic flux crosses the secondary coil . RF energy harvesting is depicted in Figure 5.
Wind energy harvesting is defined as the method in which the conversion of energy from airflow or wind energy to electrical energy occurs. In wind energy harvesting, a specified-sized wind turbine is used. Further, the wind turbine exploited the linear motion due to wind for the generation of electrical power . Wind energy harvesting is explained in Figure 6.
Biochemical energy harvesting is defined as the method in which electrochemical reactions are used for the conversion of endogenous substances and oxygen to electrical energy .
Acoustic energy harvesting is defined as the method in which a resonator or transducer is used for the conversion of acoustic waves to electrical power . Acoustic energy harvesting is illustrated in Figure 7.
Hybrid energy harvesting is defined as the method of combining any harvesting technologies and further concurrently using this hybrid model on a single platform. The concept of hybrid energy harvesting is illustrated in Figure 8.
In the current era of IoT, nanoenergy-based technology is booming which is known as nanogenerators. Further, nanogenerators are having the capability of harvesting energy from the surroundings and efficiently utilizing that harvested energy for facilitating the tiny sensors and other portable electronic devices. In a more general way, we can define the nanogenerators as the energy harvesting device for generating electrical energy from ambient mechanical energy. The nanogenerators are classified into four major categories such as piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs), thermoelectric generator (TEGs), and pyroelectric nanogenerators (PyENGs) which are based on piezoelectric, triboelectric, thermoelectric, and pyroelectric effects, respectively. Further, piezoelectric nanogenerators (PENGs) are based on the piezoelectric effect. Next, the mechanism of triboelectrification, as well as electrostatic induction, is utilized in triboelectric nanogenerators (TENGs). Whenever the two dissimilar materials are brought into contact, then the charge is generated on the surface; this complete mechanism is known as triboelectrification; on the other hand, in the electrostatic induction mechanism, the flow of electrons from one electrode to another electrode through an external load causes an electricity generation. Furthermore, thermoelectric generators (TEGs) utilized the phenomenon of converting the temperature difference into electric voltage. Besides, pyroelectric nanogenerators (PyENGs) utilized nanosized pyroelectric materials for converting thermal energy to electrical energy; therefore, pyroelectric nanogenerators (PyENGs) are considered as futuristic energy harvesting devices with enormous capability [36, 37].
Furthermore, in the case of a linear and rotatory generator for energy harvesting [38, 39], the motion of the wave provides irregular mechanical energy. Further, this form of mechanical energy is needed to convert into regular mechanical motion. Next, there exist two types of motions, namely, linear or rotational. The rotational motion is dedicated for driving a turbine, and then, the rotating electrical generator is driven. But the linear motion is responsible for driving a linear electrical generator. Figure 9 illustrates the role of linear generator in converting ocean wave energy to electrical energy.
In the energy harvesting mechanism, there exists specific hardware which is known as an energy harvester and it is responsible for converting the environmental energy to electrical energy. Further, there is a need for efficient conversion of harvested energy to electrical energy and then appropriately conditioned by the power management circuit to an appropriate form then energy is stored in the energy storage elements, and finally, electrical energy can be utilized either for energizing the batteries or directly providing the supply to the load.
3.1. Role of Energy Harvesting Techniques in WSNs
The energy harvesting techniques have played a significant role in uplifting the overall performance of WSNs. The summary of key benefits is elaborated in a pointwise fashion in the following section. (1)The energy harvesting technique in WSN provides an alternative source of energy to sensor nodes (SNs) by utilizing the harvested energy from the environment, and in this way, this technique plays a major role in reducing the dependency on battery power. One of the main research areas in the field of WSN is energy efficiency, and various mechanisms have been developed to reduce the power consumption of SNs. The harvested energy may be used sufficiently for sensor node operation and further this technique can eliminate the use of the battery(2)WSN is specially deployed in risky environments for sensing particular events in which regular access to sensor nodes is very difficult or even not possible after deployment; here, a continuous power supply is needed for the sensor node’s operation and this is only possible by utilizing energy harvesting technology in WSN. Sensor nodes can operate continuously by utilizing harvested energy from natural sources from environments(3)WSN is deployed with the aim for long-term uninterrupted monitoring of a particular event, and this is the primary goal of any already deployed WSN. The energy harvesting technique can fulfill this aim by providing a continuous supply of energy to sensor nodes till the harvested energy is available from the environment; with this technique, sensor nodes can perform their basic tasks for decades(4)Traditional WSNs are suffering from the high cost of maintenance. In these WSNs, the maintenance mechanisms consist of replacing a large number of batteries in due time and also regular visits to sites for checking the health of batteries. This maintenance cost can be significantly reduced by using energy harvesting techniques. No regular visits are needed in the case of EH-WSNs; also, energy harvesting eliminates the general need for batteries, and therefore, in this technique, the costs of maintenance are significantly decreased(5)The overall average cost of installation for WSN having an energy harvesting mechanism is low as compared with traditional WSN, although there is a need for harvester circuits and other hardware devices that add a particular cost to the installation. The overhead in the installation is also low in the case of EH-WSN
4. Classification of Energy Optimization Strategies for EH-WSNs
In traditional WSN, the remotely deployed energy constraint sensor nodes (SNs) require optimal energy optimization strategies for efficient utilization of capabilities of WSNs. Generally, traditional sensor nodes (SNs) are powered by limited capacity batteries that are attached to SNs and the use of SNs are particularly useful in an isolated and remote area, but the limited energy capabilities of SNs affected the performance in these remote areas. Further, in harsh challenging environmental conditions, it is very difficult or impossible to replace the batteries or energizing the batteries. Therefore, WSNs, which are powered by conventional batteries, are not able to provide long-lasting performance. The issue of the limited capacity of battery power has shifted the attention of researchers towards finding alternate sources of energy to energize the SNs by harvesting ambient energy. Therefore, the life span of WSN can be enhanced by utilizing the emerging technologies known as energy harvesting schemes, and further, energy optimization strategies can be used to effectively utilize the energy harvesting techniques. Also, these mechanisms achieve balance in the energy for the overall network.
The uniqueness in this systematic survey is that we have considered the two views while conducting the survey, first considering the current enhancements in energy optimization strategies while in parallel comparing with the traditional approaches for handling energy scarcity issues. This overall systematic survey broadly consists of two branches, namely, energy harvesting techniques coupled with energy optimization strategies for efficiently utilizing the harvested energy.
The aim of conducting the survey about energy optimization strategies is to handle the challenging issue of energy scarcity, and in this way, we can keep the sensor nodes alive making the network more operational and efficient. It must be noted that in this systematic survey, we have studied the internal design details of the protocol with energy-saving capabilities for understanding the limitations with abilities in dealing with the system with energy harvesting characteristics.
The role of energy optimization schemes for EH-WSNs is eventually to save energy which is considered as the primary issue for the EH-WSNs for achieving the specified objective. Extensive deep studies have been conducted for the classification of energy optimization schemes for EH-WSNs in the following categories by considering eleven factors, namely, radio optimization schemes, optimizing the energy harvesting process, data reduction schemes, schemes based on cross-layer optimization, sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power requirement, optimization of communication mechanism, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. The ultimate aim is to discuss the energy optimization schemes for EH-WSNs for achieving energy efficiency by decreasing energy consumption and also achieving energy balance among sensor nodes. Figure 10 depicts the deep classification of energy optimization schemes for EH-WSNs.
4.1. Radio Optimization Schemes
Radio optimization schemes are further divided into three categories such as schemes based on power control in transmission, cooperative communication schemes, and also modulation optimization schemes. A brief discussion about all these schemes is provided below.
In WSN, one of the main units in which heavy energy consumption occurs is the radio module. Therefore, some power control mechanism is needed for transmission. In the adaptive transmission power control framework, a separate model representing the relationship between power needed for particular transmission and corresponding quality of the link is built by the separate node for their neighbors. Further, these models help in maintaining link quality over a period of time dynamically with the help of a feedback-based algorithm for power control in transmission .
Next, for designing optimal WSN, optimization of energy is a crucial factor that needs to be carefully addressed. Also, in WSN, the energy consumption part in the circuit module as compared with actual energy consumption that occurs in the transmission part may not be negligible, and further, both these parts need to be recorded separately. Therefore, the general optimization techniques for energy may not be sufficient in the case of WSN, which are responsible for reducing the energy in the transmission process. Also, it has been proved in the literature that two prominent factors, namely, energy consumption and delay in transmission, can be minimized under the cooperative environment among the sensor nodes in WSN for transmitting and/or receiving information [41, 42].
Next, it should be noted that in communication link with point-to-point connection and for applications having shorter distance, one traditional fact that higher transmission duration results in minimum energy consumption may be false if energy consumption in circuit module is significantly considered. Also, the transmission time should be maximum in order to minimize the energy requirement in the transmission process. Further, there is a need of optimizing the transmission time for overall minimizing the total energy consumption in WSN [43, 44].
4.2. Optimization of Energy Harvesting Process
The research area towards the energy harvesting techniques is getting much higher attention by the researcher currently, and lots of research is going on exploring the optimal use of energy harvesting technique. The research activities on energy harvesting area can be broadly categorized into two categories such as optimal design and development of energy harvesting framework, and next category deals with efficiently storing the generated charge and this is possible only by proficiently designed capable electronic circuits. The microelectronic devices used in the harvesting process need to be optimized for reducing the overall energy consumption in the system. There exist a large number of literature discussing the optimization of energy harvesting mechanism; we are highlighting a few of the literature as follows.
Buchli et al.  have presented a unique scheme for dynamically adjusting the performance level of the system by designing an efficient power subsystem, and in this way, uninterrupted energy-neutral operation can be achieved in solar EH-WSNs. Next, Reis et al.  have discussed about the need of optimizing the extraction circuits for efficient utilization of the energy harvesting mechanism. Further, Tai and Zuo  have conducted a two-variable optimization analysis for deriving the exact optimization conditions for maximum power. Furthermore, Cai and Harne  have proposed an optimal optimization framework at the system level, particularly for a nonlinear vibration energy harvester. Besides, Fouad  has discussed the important role of the rectifier in energy harvesting applications and proposed an efficient CMOS rectifier.
4.3. Data Reduction Schemes
The data reduction schemes can be further classified as schemes based on aggregation, compression, adaptive sampling, and also network coding. A brief discussion about all these schemes is provided below.
In WSN, one of the ways to save energy is by efficiently handling the transmitted data. The aim of data aggregation schemes is to collect the sensitive data from the sensor nodes in the network and further sent it to the base station with lower data latency. Environment monitoring requires fresh data, and therefore, data latency is playing a crucial role in various monitoring applications . It is a very tedious step or even impossible to access all original data after performing the aggregation function. The success of gaining original data depends on the type of aggregation function. There are two approaches, namely, lossy and lossless. In the lossless approach, there exists a high possibility of gaining the original data without error. For selecting a particular approach, various factors need to be evaluated such as application area, rate of data transmission, and network characteristics .
Further, the data samples should be minimum for energy conservation schemes. The impact of reduced data samples also results in the decrement of the total number of communications and thereby maintaining energy efficiency. Further, spatiotemporal correlations can be utilized for reducing the data samples and such type of scheme is known as adaptive sampling. Here, a joint approach covering the features of both temporal and spatial correlations can be used to minimize the total amount of data that is needed to be acquired . Furthermore, in WSN, the number of transmissions can be further reduced by using the characteristics of multiple data packets coding scheme. This mechanism results in a reduced number of transmissions because multiple data packet coding occurs within a single transmission .
4.4. Schemes Based on Cross-Layer Optimization
The cross-layer optimization schemes can be further classified as MAC and network layers; application, transport, and network layers; MAC, network, and physical layers; and finally all layers. A brief discussion about all these schemes is provided as follows.
Bouabdallah et al.  have presented a joint cross-layer framework considering MAC and network layers. For enhancing the lifetime of the network, energy-efficient protocols are needed. They proposed the scheme for traffic balancing at the network layer, and further, they proposed another scheme for controlling the retry limit of retransmissions at the MAC layer. In the proposed framework, energy efficiency is achieved by controlling the retry limit and thereby contributing towards enhancing the lifetime of the network.
You and Liu  have formulated a problem for maximizing the lifespan of the network which is known as a cross-layer problem. The counterpart of the formulated cross-layer problem can be decomposed into several individual subproblems such as problem for the application layer with the focus on improving the lifetime, problem for transport layer with the focus on controlling the source rate, and problem for network layer with the focus on optimizing the routing efficiency. Further, the subgradient methods are used for solving the dual problem.
Lee et al.  have presented a cross-layer optimization framework for improving system performance. Further, they suggested that this joint optimization framework efficiently handles the communications especially in the case of delay-constraint applications. The mechanism of the proposed cross-layer optimization framework can be illustrated with consideration of different individual protocol layers such as MAC layer for power allocation, network layer for optimal routing, and physical layer for energy management.
Phan et al.  have presented a cross-layer design in the form of an optimization problem with two stages. The first stage of the optimization problem is dedicated for maximizing the total number of admitted sensor nodes. Further, the second stage is aimed at enhancing the network lifetime. Therefore, this cross-layer design is covering all layers. Next, they have also shown that the optimization problem having only one stage with a compact and precise mathematical framework can be derived from the two-stage optimization problem.
4.5. Sleep/Wake-Up Policies
The sleep/wake-up policies can be further classified as schemes based on topology control and also schemes based on duty cycling schemes. A brief discussion about topology control schemes is provided as follows.
Topology control framework is aimed at maintaining efficient connectivity or coverage in WSN. In this framework, each sensor node dynamically adjusts the power for optimal transmission. Also, each sensor node selects the appropriate neighboring sensor nodes set for direct communication. All these steps in the topology control framework contribute towards the conservation of energy at each sensor node and consequently enhance the network lifetime.
The optimal deployment of WSN requires careful handling of two crucial factors, namely, network coverage and connectivity. Further, area monitoring applications need proper coverage of networks with efficient connectivity; therefore, the success of area monitoring applications primarily depends on these two prominent factors. Also, there may exist a probability of coverage area redundancy issue and this redundancy should be minimum for optimal resource utilization by the resource constraints sensor nodes in the WSN . Next, the cooperative topology control framework has been presented in  and this framework is a distributed topology control scheme with the objective of maximizing the overall lifetime of the network.
Next, a detailed review for duty cycling schemes is provided in tabular form as described in Table 2.
4.6. Schemes Based on Load Balancing
A brief discussion about various schemes based on load balancing is provided below.
Liu et al.  present various strategies for enhancing the performance of EH-WSNs with a focus on three areas, namely, efficient scheduling, energy-efficient relaying, and finally optimizing the medium access control layer protocols. Next, Cai et al.  have presented a new routing algorithm for EH-WSNs in which the flow of load is maximized under the life span of the network; also, in this framework, energy consumption is balanced for prolonging the lifetime of the network. Here, residual energy is considered as a prominent factor for updating the transmission capacity between any two particular sensor nodes. Further. Wu et al.  have proposed an efficient energy-balanced routing framework that is based on an autonomous load regulating scheme for rechargeable WSN. In this framework, significant steps are taken to control the relay radius, and consequently, nodes become capable of adjusting their load. Furthermore, Chai and Zeng  have presented a routing scheme for EH-WSNs with efficient load balancing characteristics. This framework can handle real-time traffic efficiently by providing optimal routes.
4.7. Schemes Based on Optimization of Power for the Hardware Devices
Schemes based on optimization of power can be further classified into two categories such as processor power management and device management. A brief discussion about these schemes is provided below.
The rapid development in the IC fabrication technology and current innovations in semiconductor technology results in a tremendous transformation in the field of high-performance computing such as starting from the development of single-core architecture to multicore architecture with homogeneous characteristics and then multicore architecture with heterogeneous and dynamic reconfigurable characteristics. This rapid development further imposes a challenge in terms of increment in power density as well as heat dissipation and consequently affected the system reliability and availability. Currently, research is going on achieving high performance with low power consumption. Nagalakshmi and Gomathi  have presented various effective techniques for overall reducing the power dissipation in multicore processing architecture. Further, there is an urgent need to handle power management issues for designing efficient microprocessors in current scenarios of high-performance computing. The ultimate goal is to maximize the performance of the processor with low power consumption. The role of power management techniques is to maintain the balance between higher performance and power consumption with aggressive thermal effects. Next, Attia et al.  have explored the various schemes for managing the power in multicore processing architecture.
The designing of an electronic system should be harvesting-aware from two perspectives such as the hardware and software perspectives for achieving optimal performance in the energy harvesting environment. Also, the efficiency of the harvesting system can be enhanced by utilizing a proper power management framework with harvesting-aware characteristics. Further, an efficient power management framework will enable the harvesting system to operate uninterruptedly and achieve the expected objectives. One of the prominent operating modes is the energy-neutral mode that can provide assurance about longer system operation . Further, an efficient and proper functional power management framework is the need of the hour in the current high-performance demanding environments for an energy harvesting device. The harvested energy is stored in the energy storage element until enough energy is currently available to enable the sensor nodes to complete the desired task .
4.8. Schemes Based on Optimization of Communication Mechanism
Schemes based on optimization of communication mechanism can be further classified into three major categories, namely, MAC protocols for EH-WSNs, transmission schemes classification, and also routing protocols for EH-WSNs. A brief discussion about all these schemes is provided below.
4.8.1. MAC Protocols for EH-WSNs
These are further categorized into three categories such as sender-initiated asynchronous protocols, receiver-initiated asynchronous protocols, and also sink-initiated asynchronous protocols. Tables 3–5 are used to briefly describe each respective category clearly as follows.
(2) Receiver-Initiated Asynchronous Protocols. Six MAC protocols are described in this category in Table 4, and these include ODMAC , EH-MAC , QAEE-MAC , ERI-MAC , LEB-MAC , and also ED-CR and ED-PIR MAC .
4.8.2. Transmission Schemes Classification
Transmission schemes are divided into three categories, namely, fixed transmission schemes, variable transmission schemes, and also probability model-based transmission schemes. These schemes are briefly described below.
(1) Fixed Transmission Schemes. Reddy and Murthy  have effectively addressed the power management issue in EH-WSNs. They have proposed the framework that is based on the approach of energy-neutral for managing the power efficiency issues in communication and therefore enhancing the utility of sensor nodes that are utilizing the harvested energy. The designing of this framework clearly incorporates three factors such as fixed power consumption in the circuits, inefficiencies of battery, and finally storage capacity.
(2) Variable Transmission Schemes. The energy harvesting mechanism is considered as the prominent solution for sustained WSNs. In EH-WSNs, two important factors need to be considered while designing the optimal transmission policies such as the mechanism of energy refreshing and the storage constraints for rechargeable batteries. Tutuncuoglu and Yener  have tried to address these issues with optimum solutions. The proposed framework specifically tried to determine the transmission policy that enhances the data transmission rate in a bounded timeline. Finally, the proposed framework determined the optimum transmission policies with constraints such as the mechanism of energy refreshing and the storage constraints for rechargeable batteries. The mechanism of energy refreshing considers the model having discrete packets for energy arrivals. Next, the energy harvesting technique usually suffers from two vibrant issues such as instability and random behavior in harvested energy and these two issues need to be tackled efficiently since these issues cause temporal death of sensor nodes and thereby making a negative impact on the quality of service and overall performance starts degrading. Tang and Tan  have proposed the framework which considers the case of temporal death and analyzes the behavior of energy harvesting devices for data transmission characteristics, and further, the proposed framework determines the optimal transmission policy.
(3) Probability Model-Based Transmission Schemes. Berbakov et al.  have presented the approach for determining the optimal joint transmission scheme in a particular scenario consisting of two sensor nodes in which one is battery operated and another one is operated purely based on the harvested energy. Also, in this framework, the common message is transmitted to the base station cooperatively. The ultimate aim is to find the optimal joint transmission scheme for maximizing the throughput with the bounded deadline. Next, Michelusi et al.  have proposed an approach for handling the issue of uncertainty in estimating the state of charge for rechargeable batteries since it is almost impractical or very costly to accurately estimate the state of charge for the rechargeable battery. Also, the proposed approach noted the impact of deficient information about the state of charge on WSNs.
4.8.3. Routing Protocols for EH-WSNs
The energy scarcity issue of sensor nodes is an evergreen research area for both traditional WSNs and also for EH-WSNs. In the case of traditional WSNs, sensor nodes operated with limited capacity battery usually start struggling for providing optimum performance after specified time duration, and as a result, the performance of the network starts degrading, and there is a need for efficient utilization and management of harvested energy in case of EH-WSNs. Therefore, the energy issue is considered as a primary factor in designing routing protocols for both traditional WSNs and for EH-WSNs. Further, routing protocols for EH-WSNs are divided into four major categories, namely, routing protocols based on the cost of routes, based on passive characteristics, based on geographic area, and also based on clustering. These are briefly described as follows.
(1) Routing Protocols Based on Cost of Routes. Pais et al.  have presented a newly designed function reflecting cost-benefit for EH-WSNs. Their research work consists of the main innovation in terms of a new routing cost metric. Further, this routing cost metric is utilized for prolonging the lifetime of the sensor network. Next, Martinez et al.  have proposed an efficient framework for selecting the routes. Special emphasis has been given to the amount of the wastage of network energy which is generally not considered in the previous literature while selecting the appropriate routes. The main source of this network energy wastage is due to overcharging of limited capacity batteries in the network, and this is the main innovation in this framework. Therefore, this framework achieves higher levels of residual energy by reducing energy consumption.
(2) Routing Protocols Based on Passive Characteristics. The routing protocols based on passive characteristics utilize the past information about the network and do not actively consider the current state of the network for constructing the routing tables. Further, Kollias and Nikolaidis  have implemented a seasonally aware routing protocol. In this routing protocol, two factors are considered for the construction of routing tables such as solar energy harvesting rates and information about the several routes created in the past years. These passively attributed routing protocols are having an advantage in the specific scenario in which they reduce the wastage of energy in routes establishment overhead in the network, but on the other side, if a particular sensor node dies, then these protocols unable to recover it due to their passive nature.
(3) Routing Protocols Based on Geographic Area. Jumira et al.  have proposed an efficient geographic routing scheme for EH-WSNs. The salient features of this energy-efficient beaconless geographic routing consist of loop-free nature with minimum communication overhead and stateless, and also, this routing does not require any prior knowledge about the neighborhood for communication. Further, the mechanism of this routing framework is different from other geographic routing and it started by first sending the data packet in place of sending the usual control messages which is a general trend in geographic routing. Next, only those neighbors are selected for communication that initially received the data packet successfully and, in this way, appropriate neighbors are selected for efficient communication. Next, Hieu and Kim  have proposed a unique geographic routing scheme for EH-WSNs. In this routing framework, four parameters are considered for selecting the appropriate routes, namely, quality of link, residual energy, location information, and also energy harvesting rate. The ultimate aim of this geographic routing is to select reliable routes and thereby enhancing the lifetime of the network. Further, the detailed review of opportunistic routing utilizing the concept of geographic characteristics is provided in Table 6.
(4) Routing Protocols Based on Clustering. The clustering mechanism significantly contributes towards energy efficiency in EH-WSNs.
A detailed review of clustering-based routing is provided in Table 7.
4.9. Schemes Based on Optimization of Battery Operation
In this section, the main focus is on the issue of efficiently managing the operations of sensor nodes. The brief discussion is provided as follows.
In EH-WSNs, the currently available energy profile is considered for the transmission of data packets; these data packets are generated by the sensor nodes after periodically sensing the field and further stored in the queue. Sharma et al.  have presented the optimal energy management schemes for sensor nodes having energy harvesting capabilities. Further, these schemes are attributed as optimal throughput and mean delay. The proposed schemes are aimed at achieving the energy-neutral operation condition in the network. Further, Sinha and Chandrakasan  have proposed a scheme for enhancing the energy efficiency of nodes which is actually a power management scheme based on the microoperating system. Next, Chetto and Ghor  have analyzed the energy harvesting system having dynamic power management capabilities for selecting the appropriate scheduler on a uniprocessor platform with applicative conditions.
4.10. Mobility-Based Schemes
The mobility-based schemes are further classified into two categories, namely, mobile relay-based and also mobile sink-based schemes. A brief discussion about these schemes is provided below.
The mobility-enabled WSNs are suffering from higher latency factors in specific activities towards the collection of data. The mobile base stations are generally moving at a slower speed for collecting the data which further results in increasing the latency. This issue severely degrades the performance of mobility-enabled WSNs. Researchers explore the issue and find that energy efficiency can be achieved by resolving this issue. Xing et al.  have proposed a scheme and tried to address this issue with efficiency. The proposed scheme makes a significant contribution towards balancing the two factors such as energy efficiency and latency factor in the mobility-enabled WSNs. In the proposed scheme, few sensor nodes buffer and aggregate the data and play the role of rendezvous points for the base station for the collection of data.
Next, another challenging issue arises due to the fact that data should be transmitted to the base station only during the lifetime of the particular application. This requirement is hard to achieve in the environment of bounded supplies of power. Moukaddem et al.  have proposed a scheme and tried to resolve this issue by introducing the concept of mobile relays and thereby reducing the energy consumption in data-rich WSNs.
In WSNs, the energy holes issue can be resolved by using the concept of sink mobility. The sink mobility scheme efficiently balances the energy and consequently handles the energy hole issue. In the sink mobility scheme, sensor nodes consume less energy since data collection activities occurred in a single hop by the mobile sink which is generally moving throughout the network. In the sink mobility environment, one major issue arises in data forwarding to mobile sink only during time-bound emergency circumstances, and in these situations, sensed data start losing its relevance in the bounded course of time; this sensitive issue needs to be resolved carefully. Ghosh et al.  have proposed an efficient scheme in which the Moore curve trajectory motion is used by the mobile sink for the collection of data. The other salient features including any forwarding and also strict sleep/wake-up policy have been followed for sensor nodes.
Next, for specific applications, the unpredictable movement behavior of the mobile sink causes performance degradation since the source sensors tried to locate the continuous moving mobile sink before actual reporting of data. The ultimate aim is to use the minimum number of hops for sending the data to a continuous moving mobile sink and the reason is obvious with the minimum number of hops leads to energy efficiency. Cheng et al.  have proposed the framework that addresses both the concerns such as locating the mobile sink efficiently and also reducing hops count.
4.11. Energy Balancing Schemes
The tiny sensor nodes collect the information for the specific event, and further, this information is transmitted to the sink; this is considered as the basic mechanism of WSNs. But, the transmission range issue creates hurdles in monitoring the large area in WSNs and now the need for relay nodes arises further with the help of relay nodes information reached to the sink node. Again, this step creates an imbalance in traffic share which results in an imbalance in energy consumption. The imbalance of energy also results in variation in the lifetime of sensor nodes. The reason is clear as some relay nodes are highly occupied with a portion of traffic and therefore consume a high amount of energy and soon they become dead. Dead nodes cause network portioning and thereby collected information unable to reach the destination, and the basic objective cannot be attained. Now all these circumstances force us to think about the need for an energy balance framework in WSNs. The energy balancing framework efficiently manages the traffic load in WSNs and therefore balances the lifetime of sensor nodes. The aim is to improve the lifetime of WSNs and to try to maintain the ideal environment in which all sensor nodes share the same lifetime with no variation. But in multihop WSNs, this condition is not feasible to maintain since the traffic density is increased near the sink. Therefore, in multihop WSNs, the objective is to balance the energy consumption.
A detailed review of energy balancing schemes is provided in Table 8.
5. Challenges in EH-WSNs for Formulating Optimization Problems along with Solutions
EH-WSNs are having various challenging issues which are further formulated as optimization problems. These multiobjective optimization problems can be solved by utilizing various efficient optimization algorithms that are currently available. A detailed classification of various existing challenges for green WSNs is illustrated in Figure 11.
Next, with the help of another (Figure 12), we are trying to illustrate some key challenges in EH-WSNs for formulating optimization problems along with algorithms for providing the solution to optimization problems in EH-WSNs. Therefore, Figure 12 is having two blocks; further, in the first block, some key challenges are represented, and in the second block, algorithms are depicted for providing the solution to multiobjective optimization problems. In the following section, we are trying to address challenges as well as solutions in detail.
In , two important metrics such as aggregate utility and network lifetime are considered for performance evaluation. Further, to achieve enhanced network lifetime along with improved aggregate utility, a multiobjective stochastic algorithm is utilized. There exist a large number of real-life optimization problems in different fields, and the optimal solutions of these optimization problems require specially designed optimization models along with the algorithms having multiobjective computational solution capabilities with stochastic nature.
Further, in WSNs, the sensor node is having limited capabilities in processing the particular event and therefore a single node would require high processing energy to solve the particular problem and the only solution in these circumstances is the distributed approach. In the distributed framework, the part of the solution concerning the particular node is required to be sent to the respective nodes. The overall communication overhead in the distributed approach is less as compared to a centralized approach since in the centralized approach, transmission mechanism occurs from the sensor node (solving) to all the other nodes as compared to the distributed approach in which the transmission mechanism occurs to a subset of nodes . Further, in , for determining the trade-off between enhanced network lifetime and QoS, a multiobjective routing framework is presented. The framework focused on battery cost/budget along with maximizing the residual energy of the forwarding set. Both the abovementioned literature used the heuristic strategy. The heuristic approach is usually utilized for finding the solution to multiobjective optimization problems, and this fact is attested by currently available various kinds of literature. Further, for finding the solution in a relatively practical time, a heuristic algorithm can be used which utilized the concept of the trial-and-error approach. The heuristic method majorly provides reasonably accurate solutions.
Furthermore, the next important issue related to WSNs is regarding efficiently localizing the sensor nodes. An efficient optimization framework with multiobjective consideration is proposed in  for localizing the sensor nodes accurately to assess the geographical relevance of data. Next, coverage efficiency is also considered as one of the crucial issues for the performance evaluation of WSNs. Again, various literature have already been published with multiobjective formulations. They focus on enhancing the coverage with consideration of other desirable objectives also. The best solution is selected among multiple optimal solutions in the multiobjective optimization framework with consideration of a particular objective that is to be achieved . Both the abovementioned literature used the evolution-based strategy. A population-based approach is used in an evolutionary multiobjective optimization framework. Here, in a particular iteration, multiple solutions take part, and a further new set of solutions evolves in the subsequent iteration. Next, derivative information is not needed in the evolution-based optimization framework, and therefore, their implementation is easy. Evolution-based optimization frameworks have a wide area of applicability and also the capability for providing solutions to complex optimization problems with multiobjective nature.
Moreover, the deployment strategies in WSNs have an immense impact on the performance since the strategies adopted in deployment have a direct relationship with the power efficiency of sensor nodes and therefore deployment issue is also considered an important constraint for WSNs. Therefore, it can be concluded that optimal deployment strategies improve the energy efficiency of WSNs and consequently lifetime of the network enhanced; also, these strategies contribute towards enhancing the overall performance of the network. In , metaheuristic algorithms based on various approaches are presented for resolving the deployment issue in WSNs. The metaheuristic approach is considered better than the heuristics approach. The metaheuristic framework  has two primary components such as randomization and the selection of the optimal solution. The randomization components contribute to avoiding the trapping of solutions at local optima. The best solution selection step helps in ensuring that the solutions will converge to optimality.
Next, in WSNs, two conflicting objectives such as efficient connectivity and network lifetime are required to be optimized by optimization formulation with multiobjective consideration for enhancing the overall performance . The ultimate aim of the framework is to provide superior connectivity to the other schemes by considering the same energy conservation profile. Since superior connectivity along with enhanced lifetime are two prominent factors that greatly affected the performance, there is an urgent requirement of optimizing all objectives simultaneously by a single solution, but traditional multiobjective formulations are not capable to satisfy this requirement. The properly designed multiobjective optimization formulation can provide plenty of alternative solutions. Further, the location of these alternative solutions is nearby or on the Pareto optimal front. Also, only in a single run, plenty of Pareto optimal solutions can be discovered by nondominated sorting algorithm II.
Besides, in WSNs, energy-efficient or energy-aware routing frameworks are needed for the effective transmission of data packets. Various network attributes such as lifetime, overhead in communication, and availability of data are influenced by energy-efficient routing frameworks. Energy-efficient or energy-aware routing frameworks are aimed at maximizing system performance. For optimization and computational intelligence, bio-inspired algorithms are extensively used currently. Bio-inspired schemes consist of dual approaches such as reactive and proactive approaches. Bio-inspired schemes are capable enough for accomplishing adaptive routing, enhanced load balancing, and also discovering network topology .
Also, in WSNs, the bit error rate should be minimum, and on the other hand, signal-to-noise ratio should be maximum, and for achieving these conditions, transmission power should be increased. The increment in the transmission power has a severe impact on several key attributes of the network, namely, minimization of interference, energy efficiency, and lifetime. Hence, trade-offs among conflicting objectives can be attained by utilizing efficient multiobjective optimization frameworks. In , for minimizing the interferences and maximizing the throughputs, a multiobjective memetic algorithm is used to design efficient power allocation techniques along with a spectrum sensing module. Memetic algorithms are having the computational intelligence structures; further, the trial-and-error approach is used for discovering the Pareto optimal solution set.
Next, in WSNs, reliability and delay are also important parameters that need to be optimized for enhancing the system performance. In , an efficient routing framework is presented which utilized the multiobjective optimization approach. One of the most desired objectives is the implementation of quality of service, and this objective conflicts with other objectives such as lifetime, delay, and network cost. The routing optimization model utilized the fuzzy random variables for representing the randomness and fuzziness of the objectives and constraints. The mathematical notations are used for representing human reasoning in the Fuzzy logic approach. Fuzzy logic utilized the inference rules and linguistic variables for establishing the approximation for the truth value of a proposition.
It should be noted that high transmission power is required for increasing the transmission range of sensor nodes and consequently the degree of sensor node increases, but it has a high impact on energy efficiency and results in reduced performance in terms of energy-saving. Also, one of the attributes of WSN known as network reachability is usually low in case of poor connectivity of WSNs; on the other hand, strong connectivity in the network requires a high demand of power and consequently decreases the quality of service in WSNs. An extensive investigation is needed to analyze the optimal transmission power requirement of a sensor node. Further, for obtaining the energy-efficient clustering-based routing framework, an optimization formulation with multiobjective consideration is presented in . The trade-offs among total packets delivered to the base station, the lifetime of the network, energy utilization, and dead nodes are achieved in the proposed framework. Particle swarm optimization is used in the framework. Particle swarm optimization is based on swarm behavior. In recent years, various optimization formulations have been solved by the particle swarm optimization algorithm, and therefore, it has attained immense recognition as compared to other optimization algorithms.
Also, in WSNs, plenty of tiny sensor nodes are deployed densely; also, the spatial distribution of sensor nodes affected the overall performance which results generally in terms of the error in the reconstruction of the physical signal. In a distributed tracking framework, the sensors’ assignment to fusion centers in a dynamic fashion is presented in . A framework is proposed in which the original problem is decomposed into subproblems by taking the concept of a genetic algorithm approach for finding real-time suboptimal solutions. Over the last two decades, genetic algorithm has been proved as an efficient search technique for solving various optimization problems. The genetic algorithm is inspired by Darwin’s principle of natural evolution and starts with a set of randomly generated candidate solutions, denoted as population. Each individual in the population corresponds to a candidate solution to the problem, and it is represented by a genetic code.
6. Trendwise Technical Analysis of Energy Harvesting Technologies Development with Future Aspects
The development trends for various energy harvesting techniques can be majorly divided into four eras yearwise as illustrated in Figure 13. Development trends in the summarized form with the scientific progress for different energy harvesting technologies have been demonstrated in this figure.
The first era covers the development activities in a duration of ten years, i.e., from 2000-2010. The key attributes of this era in terms of significant development for energy harvesting technologies are the development of energy harvesting devices with enhanced fabrication technologies and further inclination of trends towards developing the hybrid model for energy harvesting systems.
The second era covers the development activities in the duration of five years, i.e., from 2010-2015. The significant key advancement towards energy harvesting technique can be highlighted as development towards triboelectric nanogenerators technology, development of multiple system architectures for energy harvesting devices and also the development of hybrid energy harvesting system model, and further inclination of trends towards integrating the Industry 4.0 with energy harvesting technologies.
The third era covers the development activities in the duration of five years, i.e., from 2015-2020. The important key attention-seeking developments for energy harvesting technique can be listed as the integration of energy harvesting technology with IoT; next, significant development is the use of energy harvesting technique with the cross-layer application, with the evolution of Industry 5.0 in December 2015 and further inclination of trends towards developing self-powered IoT.
Furthermore, the trends have started towards exploring the use of energy harvesting technologies for uplifting the current scenario of smart cities and societies as well and in the future; this trend will be further boosted with the acceleration towards the development of the technologies for small-scale self-powered devices with the system-level implementation. Next, machine learning will be used for analyzing the signal of the energy harvester for predicting the futuristic environmental conditions. The research is going on exploring new metamaterials for energy harvesting with machine learning. Further, research is going on exploring new emerging technologies for enhancing the performance of the energy harvesting system.
In this systematic survey, we have conducted a deep study for the energy optimization issue of EH-WSNs. The ultimate aim is to enhance the life span of sensor nodes. In this high-level systematic and taxonomical survey, we have organized the energy optimization strategies for EH-WSNs into eleven main classes such as schemes based on optimization of radio, schemes based on optimization of the energy harvesting process, schemes based on reduction of data in the system, schemes based on sleep/wake-up policies, schemes based on load balancing, schemes based on optimization of power needed by the hardware devices in the system, optimization of communication mechanisms, schemes based on optimization of battery operations, mobility-based schemes, and finally energy balancing schemes. This systematic and taxonomy survey also provides a progressive detailed overview and classification of various optimization challenges for the EH-WSNs that require attention from the researcher followed by a survey of corresponding solutions for corresponding optimization issues. Further, this systematic and taxonomical survey also provides a deep analysis of various emerging energy harvesting technologies in the last twenty years of the era. From the study, it can be concluded that energy harvesting technology can be used as an alternative source of energy for sensor nodes, but environmental heterogeneity compels us to think about strategies for effective utilization of harvested energy and energy optimization strategies play a major role in achieving this objective. There is a scope of enhancing the efficiency of the energy harvesting system, and therefore, a lot of research is still going on optimizing the energy harvesting mechanism. Although optimization of the energy harvesting mechanism possesses several challenging issues that need to be handled effectively, further, it should be worth mentioning that we should explore the possibility of new sources of energy for energizing the tiny sensor nodes. Besides, a hybrid approach can be considered for handling the energy scarcity issue of tiny sensor nodes that comprising all three pillars of energy in WSN, namely, harvested energy from the environment, batteries, and finally wireless energy transfer mechanism, but again this approach is an open research area and sincere efforts are needed to counter several challenges and, in this way, a sustained WSN may be realized.
Conflicts of Interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
The research is carried out at the Deenbandhu Chhotu Ram University of Science and Technology, India, and is fully supported by the Nottingham Trent University, UK.
L. Farhan, R. Kharel, O. Kaiwartya, M. Quiroz-Castellanos, A. Alissa, and M. Abdulsalam, “A concise review on Internet of Things (IoT)-problems, challenges and opportunities,” in 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), pp. 1–6, Budapest, Hungary, 2018.View at: Publisher Site | Google Scholar
A. Khatri, S. Kumar, O. Kaiwartya, N. Aslam, N. Meena, and A. H. Abdullah, “Towards green computing in wireless sensor networks: controlled mobility–aided balanced tree approach,” International Journal of Communication Systems, vol. 31, no. 7, article e3463, 2018.View at: Publisher Site | Google Scholar
M. Prasad, Y. T. Liu, D. L. Li, C. T. Lin, R. R. Shah, and O. P. Kaiwartya, “A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system,” Journal of Artificial Intelligence and Soft Computing Research, vol. 7, no. 1, pp. 33–46, 2017.View at: Publisher Site | Google Scholar
F. Ullah, A. H. Abdullah, O. Kaiwartya, S. Kumar, and M. M. Arshad, “Medium access control (MAC) for wireless body area network (WBAN): superframe structure, multiple access technique, taxonomy, and challenges,” Human-Centric Computing and Information Sciences, vol. 7, no. 1, pp. 1–39, 2017.View at: Publisher Site | Google Scholar
O. Kaiwartya, S. Kumar, and R. Kasana, “Traffic light based time stable geocast (T-TSG) routing for urban VANETs,” in Sixth International Conference on Contemporary Computing (IC3), pp. 113–117, Noida, India, 2013, August.View at: Google Scholar
A. Khatri, S. Kumar, O. Kaiwartya, and A. H. Abdullah, “Optimizing energy consumption and inequality in wireless sensor networks using NSGA-II,” in Proceedings of the ICCCS, vol. 10, Boca Raton, FL, USA, 2016.View at: Google Scholar
B. Maamer, A. Boughamoura, A. M. F. El-Bab, L. A. Francis, and F. Tounsi, “A review on design improvements and techniques for mechanical energy harvesting using piezoelectric and electromagnetic schemes,” Energy Conversion and Management, vol. 199, article 111973, 2019.View at: Publisher Site | Google Scholar
B. A. Bhayo, H. H. Al-Kayiem, S. I. Gilani, and F. B. Ismail, “Power management optimization of hybrid solar photovoltaic-battery integrated with pumped-hydro-storage system for standalone electricity generation,” Energy Conversion and Management, vol. 215, article 112942, 2020.View at: Publisher Site | Google Scholar
S. Pandya, G. Velarde, L. Zhang et al., “New approach to waste-heat energy harvesting: pyroelectric energy conversion,” NPG Asia Materials, vol. 11, no. 1, pp. 1–5, 2019.View at: Google Scholar
R. Heer, J. Wissenwasser, M. Milnera, L. Farmer, C. Höpfner, and M. Vellekoop, “Wireless powered electronic sensors for biological applications,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 700–703, Buenos Aires, Argentina, 2010, September.View at: Google Scholar
F. M. Costa and H. Ochiai, “A comparison of modulations for energy optimization in wireless sensor network links,” in 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1–5, Miami, FL, USA, 2010, December.View at: Google Scholar
B. Buchli, F. Sutton, J. Beutel, and L. Thiele, “Dynamic power management for long-term energy neutral operation of solar energy harvesting systems,” in Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 31–45, Memphis Tennessee, 2014, November.View at: Google Scholar
S. Wang, A. Vasilakos, H. Jiang et al., “Energy efficient broadcasting using network coding aware protocol in wireless ad hoc network,” in 2011 IEEE International Conference on Communications (ICC), pp. 1–5, Kyoto, Japan, June 2011.View at: Google Scholar
F. Bouabdallah, N. Bouabdallah, and R. Boutaba, “Cross-layer design for energy conservation in wireless sensor networks,” in 2009 IEEE International Conference on Communications, pp. 1–6, Dresden, Germany, June 2009.View at: Google Scholar
K. Lee, H. Lee, and S. Lee, “A cross-layer scheduling technique for maximizing end-to-end video quality,” in The International Conference on Information Networking 2011 (ICOIN2011), pp. 410–413, Kuala Lumpur, Malaysia, January 2011.View at: Google Scholar
A. Kansal, D. Potter, and M. B. Srivastava, “Performance aware tasking for environmentally powered sensor networks,” in Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems, pp. 223–234, New York NY USA, June 2004.View at: Google Scholar
J. Hsu, S. Zahedi, A. Kansal, M. Srivastava, and V. Raghunathan, “Adaptive duty cycling for energy harvesting systems,” in Proceedings of the 2006 International Symposium on Low Power Electronics and Design, pp. 180–185, Tegernsee Bavaria Germany, October 2006.View at: Google Scholar
P. Lee, M. Han, H. P. Tan, and A. Valera, “An empirical study of harvesting-aware duty cycling in environmentally-powered wireless sensor networks,” in 2010 IEEE International Conference on Communication Systems, pp. 306–310, Singapore, November 2010.View at: Google Scholar
H. Li, N. Jaggi, and B. Sikdar, “Cooperative relay scheduling under partial state information in energy harvesting sensor networks,” in 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1–5, Miami, FL, USA, December 2010.View at: Google Scholar
D. Audet, L. C. De Oliveira, N. MacMillan, D. Marinakis, and K. Wu, “Scheduling recurring tasks in energy harvesting sensors,” in 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 277–282, Shanghai, China, April 2011.View at: Google Scholar
P. Györke and B. Pataki, “Application of energy-harvesting in wireless sensor networks using predictive scheduling,” in 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 582–587, Graz, Austria, May 2012.View at: Google Scholar
H. Kooti, N. Dang, D. Mishra, and E. Bozorgzadeh, “Energy budget management for energy harvesting embedded systems,” in 2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 320–329, Seoul, Korea (South), August 2012.View at: Google Scholar
B. Akgün and I. Aykın, “EHPBS: Energy Harvesting Prediction Based Scheduling in Wireless Sensor Networks,” in 2013 4th Annual International Conference on Energy Aware Computing Systems and Applications (ICEAC), pp. 1–5, Istanbul, Turkey, December 2013.View at: Google Scholar
P. Sommer, B. Kusy, and R. Jurdak, “Power management for long-term sensing applications with energy harvesting,” in Proceedings of the 1st International Workshop on Energy Neutral Sensing Systems, pp. 1–6, Rome Italy, November 2013.View at: Google Scholar
Y. Liu, H. Yang, Y. Wang et al., “Power system design and task scheduling for photovoltaic energy harvesting based nonvolatile sensor nodes,” in Smart Sensors and Systems, pp. 243–277, Springer, Cham, 2015.View at: Google Scholar
D. Zhang, Y. Liu, X. Sheng et al., “Deadline-aware task scheduling for solar-powered nonvolatile sensor nodes with global energy migration,” in Proceedings of the 52nd Annual Design Automation Conference, pp. 1–6, San Francisco California, June 2015.View at: Google Scholar
A. Gomez, L. Sigrist, M. Magno, L. Benini, and L. Thiele, “Dynamic energy burst scaling for transiently powered systems,” in 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 349–354, Dresden, Germany, March 2016.View at: Google Scholar
J. Oueis, R. Stanica, and F. Valois, “Energy harvesting wireless sensor networks: from characterization to duty cycle dimensioning,” in 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 183–191, Brasilia, Brazil, October 2016.View at: Google Scholar
W. Housseyni, O. Mosbahi, M. Khalgui, and M. Chetto, “Real-time scheduling of sporadic tasks in energy harvesting distributed reconfigurable embedded systems,” in 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8, Agadir, Morocco, November 2016.View at: Google Scholar
A. Maeda, S. Kasahara, and Y. Takahashi, “Dynamic task scheduling for energy harvesting nodes in wireless sensor networks,” in Proceedings of the 11th International Conference on Queueing Theory and Network Applications, pp. 1–8, Wellington New Zealand, December 2016.View at: Google Scholar
P. Anagnostou, A. Gomez, P. A. Hager et al., “Torpor: a power-aware HW scheduler for energy harvesting IoT SoCs,” in 2018 28th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), pp. 54–61, July 2018.View at: Google Scholar
H. I. Liu, W. J. He, and W. K. Seah, “LEB-MAC: load and energy balancing MAC protocol for energy harvesting powered wireless sensor networks,” in 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS), pp. 584–591, Hsinchu, Taiwan, December 2014.View at: Google Scholar
K. Nagalakshmi and N. Gomathi, “Analysis of power management techniques in multicore processors,” in Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp. 397–418, Springer, Singapore, 2017.View at: Google Scholar
D. Pimentel and P. Musílek, “Power management with energy harvesting devices,” in CCECE 2010, pp. 1–4, Calgary, AB, Canada, May 2010.View at: Google Scholar
A. Herber, A. Hanisch, T. Gnoerrlich, D. Laqua, and P. Husar, “Design of power management in energy harvesting devices,” Biomedical Technician, vol. 57, Supplement 1, pp. 251–254, 2012.View at: Google Scholar
Y. Kim, C. W. Park, and T. J. Lee, “MAC protocol for energy-harvesting users in cognitive radio networks,” in Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, pp. 1–4, Siem Reap Cambodia, January 2014.View at: Google Scholar
X. Fafoutis and N. Dragoni, “ODMAC: an on-demand MAC protocol for energy harvesting-wireless sensor networks,” in Proceedings of the 8th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, pp. 49–56, Miami Florida USA, November 2011.View at: Google Scholar
Z. A. Eu and H. P. Tan, “Probabilistic polling for multi-hop energy harvesting wireless sensor networks,” in 2012 IEEE International Conference on Communications (ICC), pp. 271–275, Ottawa, ON, Canada, June 2012.View at: Google Scholar
S. C. Kim, J. H. Jeon, and H. J. Park, “QoS aware energy-efficient (QAEE) MAC protocol for energy harvesting wireless sensor networks,” in International Conference on Hybrid Information Technology, pp. 41–48, Berlin, Heidelberg, August 2012.View at: Google Scholar
J. Varghese and S. V. Rao, “Energy-efficient exponential decision MAC for energy harvesting wireless sensor networks,” in 2014 International Conference on Advances in Green Energy (ICAGE), pp. 239–244, Thiruvananthapuram, India, December 2014.View at: Google Scholar
C. Fujii and W. K. Seah, “Multi-tier probabilistic polling in wireless sensor networks powered by energy harvesting,” in 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 383–388, Adelaide, SA, Australia, December 2011.View at: Google Scholar
T. D. Nguyen, J. Y. Khan, and D. T. Ngo, “An adaptive MAC protocol for RF energy harvesting wireless sensor networks,” in 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, Washington, DC, USA, December 2016.View at: Google Scholar
N. Pais, B. K. Cetin, N. Pratas, F. J. Velez, N. R. Prasad, and R. Prasad, “Cost-benefit aware routing protocol for wireless sensor networks with hybrid energy storage system,” Journal of Green Engineering, vol. 1, no. 2, pp. 189–208, 2011.View at: Google Scholar
A. Kollias and I. Nikolaidis, “Seasonally aware routing for thermoelectric energy harvesting wireless sensor networks,” in 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), pp. 1–11, Lisbon, Portugal, May 2015.View at: Google Scholar
Z. A. Eu and H. P. Tan, “Adaptive opportunistic routing protocol for energy harvesting wireless sensor networks,” in 2012 IEEE International Conference on Communications (ICC), pp. 318–322, Ottawa, ON, Canada, June 2012.View at: Google Scholar
S. S. Beheshtiha, H. P. Tan, and M. Sabaei, OR-AHaD: An Opportunistic Routing Algorithm for Energy Harvesting WSN, River Publishers, 2013.
J. Meng, X. Zhang, Y. Dong, and X. Lin, “Adaptive energy-harvesting aware clustering routing protocol for wireless sensor networks,” in 7th International Conference on Communications and Networking in China, pp. 742–747, Kunming, China, August 2012.View at: Google Scholar
M. Xiao, X. Zhang, and Y. Dong, “An effective routing protocol for energy harvesting wireless sensor networks,” in 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2080–2084, Shanghai, China, April 2013.View at: Google Scholar
A. Mostafa and K. Hassan, “Robust energy harvesting aware clustering with fuzzy petri net reasoning algorithm,” in 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 378–383, Larnaca, Cyprus, October 2014.View at: Google Scholar
J. Li and D. Liu, “DPSO-based clustering routing algorithm for energy harvesting wireless sensor networks,” in 2015 International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1–5, Nanjing, China, October 2015.View at: Google Scholar
J. Li and D. Liu, “An energy aware distributed clustering routing protocol for energy harvesting wireless sensor networks,” in 2016 IEEE/CIC International Conference on Communications in China (ICCC), pp. 1–6, Chengdu, China, July 2016.View at: Google Scholar
R. S. Rathore, S. Sangwan, S. Prakash, K. Adhikari, R. Kharel, and Y. Cao, “Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs,” EURASIP Journal on Wireless Communications and Networking, vol. 2020, Article ID 101, 2020.View at: Publisher Site | Google Scholar
F. El-Moukaddem, E. Torng, and G. Xing, “Mobile relay configuration in data-intensive wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 12, no. 2, pp. 261–273, 2011.View at: Google Scholar
R. Yao, W. Wang, K. Sohraby, S. Jin, S. Lim, and H. Zhu, “A weight-optimized source rate optimization approach in energy harvesting wireless sensor networks,” in 2012 IEEE Global Communications Conference (GLOBECOM), pp. 1789–1793, Anaheim, CA, USA, December 2012.View at: Google Scholar
J. Andersson, A Survey of Multiobjective Optimization in Engineering Design, Department of Mechanical Engineering, Linktjping University, Sweden, 2000.
P. Krömer and P. Musilek, “Bio-inspired routing strategies for wireless sensor networks,” in Propagation Phenomena in Real World Networks, pp. 155–181, Springer, Cham, 2015.View at: Google Scholar
H. V. Dang and W. Kinsner, “Multiobjective memetic optimization for spectrum sensing and power allocation in cognitive wireless networks,” in 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–6, Toronto, ON, Canada, May 2014.View at: Google Scholar