In the past, due to the limitations of battery power, the lifetimes of sensors used in wireless sensor networks heavily depended on the energy provided by the batteries, which made the stability of the whole network greatly uncertain. Various scholars have put forward a range of studies in attempts to solve the problem of limited battery power, including the use of effective routing protocols [1
], efforts to ensure efficient energy consumption [2
], and so on. However, the above research did not succeed in solving the problem in a fundamental way. Although the advances they proposed can effectively extend the lifespan of the network, the network will still become unusable in the end when the sensors run out of battery power. In addition, if the sensors in a network rely on solar power provided by the sun, the sensors and the overall efficiency of the network will be affected by various environmental factors. In fact, a number of factors can make a sensor unable to operate effectively for extended periods of time. In order to overcome the power constraints of sensing devices, various discussions relating to the concept of wireless power transmission have been started in recent years [3
], and the related studies have provided some solutions to the limited lifetimes of wireless sensor networks. Through chargers, electric power is transmitted to radio-frequency identification (RFID) tags [4
], sensing devices [5
], and terminal equipment. Due to the convenience and rapid development of wireless charging technology, it has attracted the attentions of both academic scholars and industrial professionals. According to the TechNavio market research analysis and forecast [6
], the annual growth rate of the global wireless charging market will be more than 33% in 2020—this shows that this field has great potential.
How to improve the survival time of wireless sensor networks using wireless power transmission technology is thus a topic that is currently widely discussed and studied [6
]. These new types of networks, which enable the use of wireless charging to prolong the lifespan of the network sensors, are called wireless rechargeable sensor networks (WRSNs). In order to make the sensors within the sensing region maintain stable power, the deployment of the wireless chargers becomes the primary consideration. Due to cost considerations, the number of chargers in the region should be minimized to the greatest extent possible. On the other hand, the amount of power supplied to a sensor has an important influence on the performance of the sensor. Therefore, effectively minimizing the number of wireless chargers used in the deployment while also maximizing the charging ability of the wireless chargers is the goal of this research.
Although many studies relating to the deployment of wireless chargers have been published in recent years, these studies did not take into account some important factors. For example, previous research has solved the sensor coverage problem without calculating charging power and without considering the existence of the sink node. To overcome these limitations, this study aims to investigate how to set up X directional chargers in a given area. Previous studies assume a uniform power consumption of the sensor nodes, but this is not the case in a realistic setting. The data sink node collects sensing data from all the sensor nodes, and sensor nodes nearby the sink need to facilitate more data forwarding than sensor nodes far away from the sink, so the power consumption of the nodes close to the sink is larger and the area near the sink is called the hot zone [8
]. The sensor nodes in the hot zone run out of battery power more quickly than sensor nodes outside the hot zone. When the sensor nodes in the hot zone run out of battery power, the sensor network fails to work properly and this phenomenon is the famous “energy hole problem” [9
]. No matter in wireless sensor networks (WSNs) or WRSNs, multi-hop transmission is employed to forward data back to the sink node and the appearance of the energy hole will interrupt the communications. Figure 1
shows the concept of the multi-hop transmission and demonstrates how the sensor nodes in the hot zone take the responsibility of forwarding the data to the sink node.
Wireless charger deployment problems have attracted substantial attention in recent years. We review a number of the relevant previous studies. He et al. [10
] focused on the omnidirectional wireless charger deployment problem and deployed charging devices in the sensing area of interest to provide enough charging power to maintain the operation of the network, but they explored the space with a more traditional triangle solution. Chiu et al. [11
] divided the sensing areas into grids and placed omnidirectional wireless chargers on various grid points. Their deployment also took into consideration the moving paths of mobile sensors. In a study by Liao [12
], the coverage area of the chargers was conical, and the sleep schedule of the chargers was considered in order to reduce the power consumption. Compared with the aforementioned studies of omnidirectional wireless chargers, studies by Horster and Lienhart [13
] considered the deployment of the directional chargers and defined the charging area of a directional charger as a sector with a given angle. In the Horster and Lienhart study [13
], triangles were used to represent the charging area of directional chargers, so an integer linear programming solution was proposed in which each grid point represents a possible location of the deployment. The Han et al. study [14
], meanwhile, minimized the number of deployed chargers while also considering the connections among chargers. The reviewed literature is organized in Table 1
below. Interestingly, these studies did not consider the unbalanced power consumption effect caused by forwarding data to the sink node. Such unbalanced power consumption can make the sensor nodes near the sink rapidly lose power.
In this section, the simulations are conducted to verify the performance of the proposed approach. We compare the proposed power balance aware deployment (PBAD) algorithm with the location merging design (LMD) proposed by Fu et al. [15
] and random deployment.
3.1. Description of Parameter Setting and Comparisons
We use the following parameters to set up the experiment: sensors are placed in a two-dimensional space measuring 100 m × 100 m. The number of sensors is 20 (X = 20), and the charging power model parameters of the chargers are α1 = 80, β1 = 40, d1 = 16, and θ1 = 90. The upper bound value of the average charge demand fup = 30 m and flow = 10 m. Wmax = 40 mW, σ = 1, μ = 4 mW, and γ = 28 mW.
The proposed PBAD method is compared with the random position and random orientation (RPRO) approach and the aforementioned LMD. The following data are the values resulting from the respective implementations of our method, the RPRO, and the LMD for 300 simulations. Because LMD uses omnidirectional chargers for the deployment, it also uses different parameters. Specifically, those parameters are α2 = 70, β2 = 50, d2 = 10, and θ2 = 360.
3.2. Experimental Results
We demonstrate the results using different numbers of chargers, different numbers of sensors, and different charging demands to show that the proposed PBAD algorithm achieves better coverage and has better charging power. The performance of the charging efficiency of each sensor is also given.
Satisfying all the charging demand for the deployment of the chargers is our main purpose. As can be seen from Figure 8
above, our method uses eight chargers to achieve this purpose. Compared with the RPRO and LMD approaches, the deployment generated by our approach is better. While the LMD approach only takes the power of charging into consideration, it does not consider the existence of the data sink node. Also, the charging power of an omnidirectional charger is poor, so the effect is poor. Without a doubt, the RPRO is the worst among the three algorithms.
In this study, the influence of the data sink node on sensors is considered. Many studies have failed to pay attention to this important factor. This paper takes into account the effect of the data sink node and proposes a power balance aware deployment (PBAD) approach that can reduce the number of the required chargers and still maintain good charging effects. This study considers directional chargers which has been rarely considered among previous studies. The deployment given by the proposed PBAD method can reduce the required number of directional chargers and maintain the charging efficiency. The simulation results show that our approach outperforms the LMD and RPRO approaches by a significant degree and thus further demonstrate the usability of our algorithm.
In a realistic charger deployment, there are still many additional factors that need to be considered, such as charger recharging. It is expected that in the future, more factors can be considered to further optimize the algorithm, so that the charging power can be further improved.