In energy harvesting wireless sensor networks (EHWSNs), the energy tension of the network can be relieved by obtaining the energy from the surrounding environment, but the cost on hardware cannot be ignored. Therefore, how to minimize the cost of energy harvesting hardware to reduce the network deployment cost, and further optimize the network performance, is still a challenging issue in EHWSNs. In this paper, an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to reduce the cost and optimize the performance of solar-based EHWSNs. There are two main innovations of the ECTRA scheme. Firstly, an energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with maximum energy consumption are rotated, balancing energy consumption between nodes and reducing the maximum energy consumption in the network. Therefore, the battery storage capacity of nodes and the cost on hardware. Secondly, the ECTRA scheme selects a larger transmission radius for rotation when the node can absorb enough energy from the surroundings. The advantages of using this method are: (a) reducing the energy consumption of nodes in near-sink areas, thereby reducing the maximum energy consumption and allowing the node of the hotspot area to conserve energy, in order to prevent the node from outage. Hence, the network deployment costs can be further reduced; (b) reducing the network delay. When a larger transmission radius is used to transmit data in the network, fewer hops are needed by data packet to the sink. After the theoretical analyses, the results show the following advantages compared with traditional method. Firstly, the ECTRA scheme can effectively reduce deployment costs by 29.58% without effecting the network performance as shown in experiment analysis; Secondly, the ECTRA scheme can effectively reduce network data transmission delay by 44-71%; Thirdly, the ECTRA scheme shows a better balance in energy consumption and the maximum energy consumption is reduced by 27.89%; And lastly, the energy utilization rate is effectively improved by 30.09-55.48%. The internet of things (IoT) [1] [2] [3] is becoming a pervasive paradigm and will have a significant impact on future applications in many fields, including environmental monitoring [4] [5] [6] , industrial manufacture [7] [8] [9] , telecommunications [10] [11] [12] , intelligent transportation [13] [14] [15] , e-health [15] [16] [17] , and social networks [17] [18] [19] . IoT composed of numerous ubiquitous sensor-based devices can enrich data awareness and acquisition [20, 21] , combined with current fast-growing computational intelligent technologies [22, 23] to make intelligent decisions [24, 25] , and thus, form smart IoT, which obtain extensive attention from academic and industrial researchers [26, 27] . Nowadays, smart sensors based on IoT are playing a more and more fundamental role in many applications [28, 29] . For example, they play a key role in long-term monitoring of a wide range of ecological environments [30], real-time monitoring and intelligent decision-making on traffic status [31] , and are also widely used in agriculture, military, industrial automation, and many other applications [32] [33] [34] [35] . However, there are several challenges related to the mass promotion for smart IoT applications, and some of these challenges are to make IoT work with low cost, as well as a green and an energy-efficient paradigm [36] [37] [38] . In the beginning, the sensor node is small in size and is powered by battery, so it requires a fine plan for energy use to extend its life as much as possible. With the development of microelectronics manufacture polytechnics, energy harvesting wireless sensor networks (EHWSNs) [39, 40] -whose nodes can charge energy themselves by harvesting the energy of the surrounding environment-also develop. In EHWSNs, sensor nodes equipped with equipment that absorbs energy from the surrounding environment can replenish energy from the surrounding environment, so that they can support long-term work in an unmanned environment that does not require an energy supply, making EHWSNs more widely applicable than the usual wireless sensor networks (WSNs) [40, 41] . For example, for an EHWSN based on solar energy, its sensor nodes add solar panels that can absorb solar energy, in order to absorb solar energy in time and to charge nodes. [42, 43] . At the same time, some sensor devices with a wind wheel can replenish energy through wind energy, while other sensor devices can replenish electric energy by heat energy and vibrational energy [44] . Since these sensor nodes can replenish electric energy from the surrounding environment and achieve long-term monitoring of areas requiring inspection, theoretically, after deployment, it also can be called a green network, green computing, or green IoT [45] [46] [47] [48] . Compared with WSNs, EHWSNs have a significant advantage in application scenarios, performance properties, and development prospects, thus, they are getting focused attention from researchers [49, 50] . However, in EHWSNs, there are three challenging issues worth studying: The first is reducing costs for EHWSNs. The second concerns energy-efficiency or green computing. Finally, the third involves network performance optimization, especially in reducing network delays. (1) First, the issue of how to reduce the cost of network deployment. The sensor node of EHWSNs need to be equipped with energy harvesting hardware to absorb energy from the surrounding environment, which will increase construction cost. For example, the solar collector of solar energy harvesting wireless sensor networks mainly consist of a power controller and solar panel, in which the solar panel is used to absorb solar energy. Obviously, the larger the solar panel, the more energy is absorbed per unit time, but the larger the solar panel, the greater the construction cost. Accordingly, the construction cost of the power controller is also increased. Therefore, from the perspective of economic cost, the deployment cost of the network should be as low as possible. Especially for EHWSNs, the number of sensor nodes is huge, so the small cost increase of each node will have a huge impact on the total network deployment cost. Based on this, although EHWSNs can absorb energy from a surrounding environment, cost-effective use of energy is still an important issue worth studying. In EHWSNs, the energy issue involves not only how to effectively use it to improve network performance, but also how to reduce the hardware cost of the sensor node and build the network with minimum deployment cost. Sensors 2018, 18, 2885 3 of 41 (2) The issue for energy-efficiency in EHWSNs. The principle of energy use in EHWSNs is very different from traditional sensor networks. In traditional sensor networks that cannot replenish energy from the surrounding environment, the principle of its energy use is to minimize energy consumption and extend network lifetime. Instead, in an energy harvesting network, the principle of its energy use is to maximize energy consumption, which is called the principle of energy neutral [39] [40] [41] [42] [43] [44] . For example, in solar energy harvesting wireless sensor networks, instead of reducing energy consumption, the key is how to utilize the absorbed energy as much as possible to improve network performance during the daytime, especially at noon when solar energy is sufficient. The node cannot conserve all the energy absorbed from solar energy when the battery is fully charged, for the battery volume is limited. Therefore, it is an effective way to utilize solar energy as much as possible [39, 43] . An important feature of energy consumption in wireless sensor networks is uneven energy consumption [51, 52] , because its data transmission is centered on the sink, which shows a many-to-one data collection feature. Therefore, the energy consumption around the sink is much higher than that of other regions in hotspots, and the energy consumption of the far sink region node is much smaller than that of hotspots, which causes premature death of hotspot nodes in ordinary WSNs, due to high energy consumption, thus forming a so-called "energy hole" and making the network die prematurely [11, 30, 36] . For EHWSNs, although energy can be absorbed to reduce energy stress, the imbalance of network energy consumption still has the following two effects on the network: (a) Since the energy consumption of nodes in a hotspot region is much higher than that in other regions, when the energy absorbed by the environment is insufficient to support energy consumption itself, the node will stop operating data to protect themselves (which is called outage of the nodes). For example, in a solar sensor network, when the solar panel is fixed, if the absorbed solar energy is too little to fully charge the battery during a continuously cloudy day, then the energy of the hotspot region nodes may be exhausted [39, 40] . (b) Although increasing the solar panel to make the nodes absorb enough solar energy on a continuously cloudy day can support large energy consumption, and ensure the sensor node does not undergo outage in order to maintain the normal network running [43, 44] , in a homogeneous network, the hotspot area is a relatively small area for the entire network. In other words, when the energy absorbed by the solar collector can meet the energy consumption of the high energy consumption hotspot nodes, the energy of the majority of non-hotspot nodes must have a large surplus. That is to say, the energy is not fully utilized. Also, according to related research, there is up to 90% of energy remaining in the whole network in such cases [30, 36, 51] . What is worse, the solar collector is designed to meet the maximum energy consumption requirement, but in fact, the solar collector of most nodes does not need to be so large, which also increases the network deployment cost greatly, and is economically uneconomical. (3) The issue of reducing delay. Data is routed from the source node to the sink via multi-hop routing, and one of the important performance parameters is delay [7, 9, 15, 28, 30, 31, 52] , which generally refers to the time that the data is generated from the source node until the sink successfully receives the data packet. Obviously, the smaller the delay, the better, because in some delay-sensitive applications, it makes sense to route data to the sink quickly because delayed data transmission may cause disastrous consequences [7, 9, 15] . For example, in the monitoring of emergency events or of industrial sites, such as boilers, metallurgical furnaces, and automatic assembly lines, rapid data transmission enables the control center to adopt corresponding countermeasures to avoid disasters, while dragged data transmission will result in the control center not having adequate time to take measures to avoid disasters [10, 31, 52] . In summary, it is still a challenging issue in EHWSNs to design an optimal solar collector with minimal cost in order to optimize network performance. In the current study, the previous research mainly focused on the effective use of energy. However, there are few strategies that can optimize the network deployment cost at the same time, and further improve the network performance. Therefore, Sensors 2018, 18, 2885 4 of 41 an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to make full use of energy, reduce delay, as well as network deployment costs for EHWSNs. The main contributions in this paper are listed as follows: (1) An energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with the largest energy consumption in the network are rotated, balancing the energy consumption between the nodes and reducing the maximum energy consumption. Thus, the battery volume can be reduced, correspondingly, and the cost on hardware can be further reduced. In EHWSNs, in order to avoid outage for the nodes, energy harvesting hardware is designed according to the energy requirement of the node with maximum energy consumption when deploying the network. In general, nodes in hotspot areas consume the most energy, meaning that our battery volume should be designed to meet energy consumption of the nodes in hotspot areas. However, in fact, the energy consumption of most non-hotspot regional nodes is much smaller than the energy consumption of hotspot nodes, and the designed battery volume is much larger than the actual demand. Therefore, the above design results in substantial waste of hardware and energy and further increases the network cost. What is worse, the larger the difference in energy consumption between hotspot regional nodes and non-hotspot nodes, the more hardware and energy is wasted. Therefore, the most ideal solution is to make the energy consumption of the entire network uniform, so that the hardware and energy of each node can be fully utilized. Based on this, an energy conserving approach is proposed in this paper. The detailed idea is as follows: When the network sends data with a certain transmission radius, the energy consumption in the network is uneven. Thus, there is a region with largest energy consumption which determines the network lifetime. Considering the regions of maximum energy consumption are not coincident under different transmission radii, we propose to send data with different transmission radii, and then, the largest energy consumption after multiple radius transmitting data by turns is lower than the largest energy consumption using a single transmission radius. Thus, node requirements for energy harvesting hardware and network deployment costs can be reduced. (2) The second innovation of the ECTRA scheme is that we only choose larger transmission radii than the previous radius to transmit data by turns, when the nodes can absorb enough energy from the surrounding environment. Then, the network performance can be further optimized in these two aspects: (a) Reduce network delay effectively. When the network uses a larger transmission radius, the hops in data transmitted to the sink needs are fewer, which can reduce delays effectively. (b) With larger radii, hotspot nodes near the sink area can conserve enough energy to transmit data when there is not sufficient energy. Thus, the battery volume or solar panel size can be reduced, and further, the cost can be reduced. (3) After deep theoretical analyses, the results show the following advantages compared with the traditional method: (1) The ECTRA scheme can effectively reduce deployment costs. The analysis results show that the network performance is better than the previous strategy, even when the energy collection hardware cost is reduced by 29.58%; (2) The ECTRA scheme can effectively reduce network data transmission delay by 44-71% compared to the traditional method; (3) The ECTRA scheme shows a better balance in energy consumption, and the maximum energy consumption is reduced by 27.89%; (4) The energy utilization rate is effectively improved by 30.09-55.48%. The rest of this paper is organized as follows: In Section 2, the related works are reviewed. The system model is described Section 3. In Section 4, a novel ECTRA scheme is presented. Performance analyses of ECTRA are provided in Section 5. We conclude in Section 6. Sensors 2018, 18, 2885 5 of 41 This section is divided into three parts to discuss the three main works related to the research in this paper. The three works are listed respectively: (1) the related work on reducing and balancing energy consumption; (2) the related work on transmission radius, energy hole, and delay; (3) the related work on energy harvesting wireless sensor networks. (1) The research on energy balance and energy hole-related issues [11, 30, 36] . In wireless sensor networks, since nodes are generally powered by a battery, the battery capacity of a node is generally limited, due to limitations in cost and size. Therefore, it is an important research content of designing to reduce energy consumption of nodes and extend the lifetime of network in wireless sensor networks (WSNs), and many strategies to reduce energy consumption are put forward. The research on reducing energy consumption can expand from each layer, such as, Medium Access Control (MAC) layer, network layer, application layer, and so on [2, [7] [8] [9] [10] [11] [12] [13] [37] [38] [39] . The operations of energy consumption in WSNs mainly have the following categories: sleep, sending data, receiving data, idle, computing, etc. Lots of research demonstrates that the main energy consumption in these operations comes from data communication, which includes sending and receiving data. In order to save energy, researchers have proposed duty cycle-based WSNs to reduce node energy consumption [7] . In this network, the time of the node is divided into fixed cycles, and in one cycle, the node is divided into two states: sleep and awake. In the state of sleep, the node turns off the wireless communication device to conserve energy [7] . However, this method increases the delay of event monitoring and data transmission. In general, the longer the time that a node is awake, the smaller the delay in event monitoring and data transmission in one cycle, and the larger the energy consumption. In short, the delay is inversely proportional to the length of awake time. Based on this, the research on how to minimize the time that nodes need to be awake and reduce delay, at the same time, is further expanded, and there are many strategies proposed. In the early studies, researchers used an optimized duty cycle, which reduced the time the node was awake, to save energy as much as possible when the application requirements were met [7] . In this way, the duty cycle in the whole network is the same, which contradicts the unbalanced requirement for the duty cycle, due to unbalanced data volume at different distances from the sink. Since the time that nodes need to wake is much more in the near-sink area, these nodes bear a large amount of data compared with the far-sink area nodes that bear a small amount of data. In other words, nodes in the far-sink area do not require such a big duty cycle, although it is an optimal duty cycle. To solve this problem, researchers put forward an adaptive duty cycle scheme to optimize network performance [7] . They propose to adaptively adjust the size of the duty cycle based on the amount of data the node is in charge of. When the amount of data undertaken by the node is large, a large duty cycle is adopted, and when the amount of data undertaken by the node is small, a smaller duty cycle is used, to save energy. However, this method seems reasonable in only some aspects. Considering that the network lifetime is determined by the node with the largest energy consumption in the network, this method cannot improve network lifetime, because it concentrates on reducing the energy consumption of nodes in the far-sink area, which is small for the small amount of data load. What is worse, decreasing the duty cycle of nodes in this area results in a worse delay in monitoring and data transmission. Chen et al., in ref. [53], noticed this problem and proposed a dynamic adaptive duty cycle scheme that is almost opposite to the above scheme. Near-sink area nodes with a large amount of data use the optimal duty cycle, and far-sink area nodes increase the time they are awake on this basis, because they have residual energy for a small amount of data load. Thus, it can speed up data transmission and reduce delay. In summary, compared with the network that keeps the nodes awake, duty cycle-based WSNs can reduce energy consumption but this caused a longer delay at the same time. Thus, how to reduce the delay in duty cycle-based WSNs becomes an important issue. A duty cycle adjustment algorithm adaptively increasing the awake time to reduce the sleep delay effectively is proposed in ref. [53] . Sleep delay usually happens when sender nodes need to send data, and their next hop nodes are in sleep states. Obviously, synchronizing a sender with its next hop nodes is a feasible method to solve the above problem. When the sender needs to send data, its next hop nodes are in the awake state exactly, Sensors 2018, 18, 2885 6 of 41 which can effectively reduce the sleep delay. Wu et al. [7] proposed a local coarse synchronization method based on the above method. Instead of requiring nodes in the entire network to synchronize, it allows nodes in the same area to be asynchronously awake and only requires each sender node to be basically synchronized with its next hop nodes. Furthermore, not requiring that each node is strictly synchronized and making only a part of the nodes maintain a loose synchronization is easy to implement. In this way, it is a perfect method to effectively reduce delay. There are many schemes proposed to reduce energy consumption. Ref. [50] pointed out the complicated relationship between the energy consumption of transmitting unit bits by analyzing the relationship between the transmission power, the transmission success rate and the transmission distance of the node. In a wireless transmission environment, because the wireless signal is affected by all sorts of interference and signal attenuation, the receiver receives the data packet with a probability receiving relationship, instead of receiving successfully or not. Usually, when the distance between the sender and the receiver is close and the transmission power of sender is big, the receiver successfully receives the data with a high probability. Conversely, when the distance from the receiver exceeds a certain value, the acceptance rate of receiver drops sharply. The data transmission rate of the wireless link is defined as a probability less than 1. To ensure the accurate data transmission, certain strategies are taken to guarantee a good quality in wireless transmission with data loss, of which send-wait protocol is a simple and effective method [54] . There is a nonlinear relationship between the transmit power and the successful transmission rate of data, rather than a linear relationship. Therefore, finding a suitable transmission power that can minimize the energy required to successfully transmit unit-bit data is an important issue to study. Ref. [50] makes a further study and puts forward the algorithm for optimizing energy consumption across layers, which minimizes the energy required to successfully transmit unit-bit data and improve the energy utilization. Further research expands upon the above study. An optimal transmission power minimizes the energy consumption per bit data. However, the transmit data node with the basic unit data packet includes an overhead, such as a data header, besides valid data. An effective way to reduce overhead, such as a data header, is to increase packet length, thus reducing the ratio between the header and the valid data. The problem is that a longer packet length may increase packet error rate, although it can reduce overheads like data header, because there is a certain loss and packet error rate in wireless transmission. Even worse, the increase of packet error rate will increase the numbers of packet retransmission, which will increase the overhead of the unit's valid data instead. Therefore, Chen [55] et al. show the theoretical analysis results of the optimized packet size to minimize the energy consumption when the node transmits the valid data of the unit bits. Reducing energy consumption is one of the most direct and effective methods to improve network lifetime, but is not an effective way. In the process of routing, some nodes chosen as relay nodes will die early for their higher energy consumption than other nodes. At this time, an important factor in improving energy efficiency is the balance in energy consumption of nodes. Thus, there are lots of energy consumption balanced routing algorithms proposed by researchers, as shown in refs. [27, 28, 30, 31, 39, 41, 52, 56] . Summarizing some common features of the current energy consumption equilibrium routing strategies in ref. [56], we propose the design principle of this type of routing strategy. In fact, the key to the design principle of an energy consumption equilibrium routing strategy is to design what kind of cost function will choose the next hop node. When the energy consumption of a node is lower than the neighboring nodes of its period, if the characteristic of lower energy consumption can be reflected from the cost function, then the designed cost function can better guide the selection of the next hop, and thus, achieve a more balanced energy consumption. (2) Related work on the transmission radius of the node, energy hole, and delay. The above research only concentrates on reducing energy consumption of the node. However, in fact, there is a particular phenomenon called energy hole [2, 11, 30, 36] , which is also seen in EHWSNs. Specifically, because the sink is the final node that other nodes transmit data to in the whole network, and the data of the node is generally routed to the sink through multi-hop relay in WSNs, this means that the node Sensors 2018, 18, 2885 7 of 41 within one hop of the sink bears the data relay work of all other nodes in the network, and the energy consumption of nodes within one hop from the sink is much higher than that in other areas. Moreover, it is found that for normal WSN, the data amount of nodes within one hop from the sink bear more than 10 times the amount of data of nodes in the farthest area. Moreover, the energy consumption in this area is much higher than any other areas for its high data load, and thus, it is called a hotspot. When the energy in hotspots is exhausted, an area forms where all the nodes around the sink are dead, and this area is called an energy hole. The formation of the energy hole means the data of other nodes in the network cannot be forwarded to the sink, leading to early death of the network. However, at this time, there is lots of residual energy in the area whose distance from the sink is far (according to related research, up to 90% of energy is remaining in the network at this time). Obviously, this part of energy cannot be used effectively. An energy hole is formed due to the early death of nodes within one hop from the sink, so it has a close relationship with the transmission radius of the node. In general, when the transmission radius r is small, the energy consumption of the nodes in the near-sink area will be very high because they bear the data of the entire network, which causes early death of the network. Thus, increasing the transmission radius r can reduce the data load of nodes within one hop from the sink, and further decrease energy consumption in a certain area, which improves network lifetime. However, as the r increases, the energy consumption may not be reduced, although data load of the nodes decreases. This is because that the energy consumption of sending data from the node is proportional to the square or the fourth of the power of the transmission distance. When the transmission distance increases, the energy consumption of nodes will increase, although their data load is small [57, 58] . Based on this, researchers expand the study on how to reduce maximum energy consumption in the network, and attenuate the impact of the energy hole. The general methods are as follows: (a) use an optimized transmission radius r to achieve an optimized lifetime; (b) send data of the nodes in a part of the non-hotspot area directly to the sink without routing through nodes near the sink to reduce high energy consumption in hotspot area, thus improving lifetime; (c) change the data collection mode. If each node sends data directly to the sink, the energy consumption of the far-sink area node is the highest, while that of the near-sink area node is the lowest. On the contrary, if multi-hop data routing to the sink method is taken, the energy consumption of the near-sink area node is the highest, while that of the far-sink area node is the lowest. Hence, researchers propose mixing these two data collection modes to balance energy consumption and further improve lifetime. Transmission radius also has effects on the transmission delay of data. Since data packets are transmitted to the sink through multi-hop data transmission, the total delay of data transmission to the sink is the sum of each hop route delay, which indicates that increasing transmission radius can reduce delay. Of course, an optimal transmission radius should be gotten by considering delay, energy consumption, and lifetime together. (3) Energy Harvesting Wireless Sensor Networks (EHWSNs). EHWSNs bring profound changes to WSNs whose energy is tense [39] [40] [41] [42] [43] [44] . Since the sensor in EHWSNs increases hardware that can absorb energy, it can obtain energy from the surrounding environment. Theoretically speaking, sensor nodes can monitor the monitored objects for a long time without any guards. For example, solar-powered wireless sensor networks add a solar panel, rechargeable battery, and charge controller to normal sensor nodes. Among them, a solar panel is used to absorb solar energy and convert solar energy into electricity. A battery is used for storage and supply of power, and a charge controller is used to monitor the storage of the battery. When the battery power is below the threshold, the battery's power supply appliance is disconnected to protect the battery from excessive power loss. The solar panel usually adopts photovoltaics (PVs), and its size determines how much energy is absorbed per unit time. In general, the larger the size of the solar panel, the more energy is absorbed per unit time, so the more electric power the nodes can use. However, the big size of solar panel needs a bigger construction cost. At the same time, the volume of sensor nodes is greater, which limits the deployment of the network and other aspects. Similarly with the solar panel, the bigger the battery is, the more electric power the nodes can conserve. In this way, when solar power is sufficient, the nodes can conserve Sensors 2018, 18, 2885 8 of 41 enough energy to use when solar power is not sufficient. Of course, a big volume of battery requires a bigger construction cost. Therefore, reducing energy consumption is still a main goal in EHWSNs. Reducing the energy consumption of the node, effectively, can reduce the size of the solar panel and battery, thereby reducing deployment costs. Moreover, how to utilize absorbed energy is also a subject worth studying in EHWSNs. For example, in solar-powered WSNs, when the solar radiation is strong, the node can absorb a lot of energy through the solar panel, in addition to fully charging the battery's electricity, and there is still surplus. In such cases, the key is to study how to make full use of energy that can be absorbed from the environment, rather than to reduce energy consumption. In summary, there are two essential points when studying EHWSNs: (a) minimize energy consumption; (b) utilize energy absorbed from the environment effectively. The concept of reducing energy consumption of nodes is the same with normal WSNs, and what makes a difference in EHWSNs is the goal of reducing energy consumption. The main goal in WSNs is to increase lifetime as much as possible, while in EHWSNs it is to provide a benchmark for selecting the size of the solar panel and battery, in order to effectively reduce deployment costs. The design goal in EHWSNs is that the power supply system still has a high probability of providing energy normally in the harsh environment. For solar-powered wireless sensor networks, the power supply system should be designed to keep nodes working normally in the case of continuous low solar radiation intensity. If the energy consumption of nodes is low, then a smaller size of the solar panel and battery can be adopted, in order to reduce deployment cost. When deploying the network, the suggestion of utilizing as much energy as possible is designed under the premise of being able to survive in a harsh environment. Therefore, most time in the network, the energy absorbed by nodes from the periodic environment is greater than the minimum energy required by the network. Thus, using this energy as much as possible to improve network lifetime is an important but challenging issue. The main difficulty is making full use of energy absorbed from environment when the energy is enough because the maximum energy that a node can utilize is designed according to the worst situation. Therefore, the main problems are summarized as follows: in solar-powered wireless sensor networks, there is enough solar energy to use at daytime, while no there is no solar energy to charge the electric power at night. Thus, the principle of energy utilization in such a system is storing as much energy as possible at daytime, in order to avoid the outage of nodes in the evening, or on continuously cloudy days when the solar energy is not sufficient. A universal law is that the solar energy gradually becomes stronger from morning to noon at 12:00, and then gradually weakens after 14:00. In order to make full use of energy, we can try to utilize as much energy as possible, even using the residual energy of battery when solar energy is going up. As time goes by in the morning, the solar energy will gradually increase, and it is enough sufficient to fully charge the battery. Therefore, if the energy is not used in advance at this time, when the battery is fully charged, the time for efficient use of energy will be very short. Another reason is that as time goes by in the afternoon, the solar energy is decreasing. Hence, at this time, we must reduce the previous higher energy consumption to maintain a high level of stored energy in the battery, so that it can be used in the evening or on continuously cloudy days. In short, it is suggested to use energy in advance when the radiation intensity of solar energy increases, and to conserve energy in advance to prepare for insufficient energy supplementation when the radiation intensity of solar energy decreases. What is worth noting is that the radiation intensity of solar energy is unpredictable in advance, because it is changes according to weather conditions and seasons. Here comes the conflict: if the energy is used in advance, the sudden weakening of solar energy due to a weather change may result in insufficient storage of the battery and possible outage. On the contrary, if a conservative "make ends meet" strategy is adopted, the energy may not be fully utilized. Thus, accurate prediction of solar energy has an important impact on improving energy efficiency in such EHWSNs. A considerable number of predictive models have been proposed by many researchers to model the energy harvesting process [41] , such as the finite state Markov model and general stochastic model. Sensors 2018, 18, 2885 9 of 41 Although modeling the energy harvesting process can effectively predict the replenishment of solar energy, which helps to effectively design energy utilization strategies, in essence, the prediction of renewable energy for solar energy cannot be completely accurate. Also, when the prediction model is inaccurate, it may bring upon serious conditions of network outage. Therefore, the combination of solar energy supplemental prediction and adaptive energy utilization is an effective method. However, there are many situations involving network energy utilization, and efficient energy utilization is related to network performance, which makes the comprehensive optimization of EHWSNs very complicated. Obviously, there is still room for further research on efficient energy utilization in EHWSNs. System Model In this paper, the network is deployed by Sensors 2018, 18, x FOR PEER REVIEW 9 of 42 However, there are many situations involving network energy utilization, and efficient energy utilization is related to network performance, which makes the comprehensive optimization of EHWSNs very complicated. Obviously, there is still room for further research on efficient energy utilization in EHWSNs. System Model In this paper, the network is deployed by sensor nodes in a circular area centered as a special node called sink with a radius , according to ref [1, [7] [8] [9] 59] . Not considering data loss or error, all the generated data packets are transmitted directly or by relay nodes to the sink through multi-hop routing. The transmission radius of the node is . The size of the data packet is set as bytes, and the success rate per hop, which is called the reliability of the receiving node in this paper, is set as . Energy Harvesting Node Model
However, this network and the networks that were considered in previous research differ in terms of energy usage: if the surrounding environment provides sufficient energy, the main focus is no longer saving energy, but following the so-called neutral energy use principle REF , namely, the objective is to make full use of the energy that is absorbed from the surroundings to maintain the performance of the WSN, rather than to save energy.
An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
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