CTRA: A complex terrain region-avoidance charging algorithm in Smart World
Introduction
The “Smart World” is set to make intelligent response to human needs by using information and communication technology to sense, analyze and integrate key information of the core system of urban operation. The development of Smart World involves many different fields, such as smart city, smart industry, smart environment and smart grid. Information perception is the foundation of all these practical applications. WSNs have realized the functions of data acquisition, processing and transmission. However, energy constraint problems have restricted the development and application of WSNs (Lee and Zhang, 2017; Yu et al., 2013; Korbi and Zeadally, 2014; Wang et al., 2017; Yang et al., 2017). With the development of wireless charging technologies, wireless rechargeable sensor networks (WRSNs) (Zhong et al., 2012) have gradually replaced traditional WSNs with longer running time. WRSNs use mobile chargers (MCs), also called wireless charging vehicles (WCVs) (Zhong et al., 2017) or SenCars, to replenish energy for low-energy nodes and resolve energy constraints efficiently and stably. Research on node energy replenishment in WRSNs can be divided into two categories: determining a charging schedule for nodes, and charging-path planning (Han et al., 2017a) for WCVs. In an earlier study (Zhao et al., 2011), Zhao et al. first examined the charging path planning and charging spots localization problem by utilizing radiation energy transfer technology. In addition, in another study (Peng et al., 2010), the chargers which utilized electromagnetic radiation were applied in a WRSN and scheduled by a proper charging sequence. The proposed greedy algorithm aimed at maximizing the lifetimes of sensor nodes.
However, existing charging algorithms have failed to consider terrain fluctuation problem in the deployment location of nodes. In practical applications, for example, setting up power grids in remote mountain areas, the network area is not entirely flat but contains complex terrain such as hills, forests, and rivers. Multiple nodes are required to be deployed in these complex terrain areas. Chargers have to enter such complex terrain areas and, as a result, extra time and energy are consumed on the traveling path. Based on an analysis of existing charging algorithms and routing algorithms (Han et al., 2017b, 2018; Mehmood et al., 2016; Azizi, 2016; Othmen et al., 2017; Yu et al., 2017; Zhao et al., 2017), this paper considers the terrain of deployment location of nodes and proposes a complex terrain region-avoidance (CTRA) charging algorithm.
The CTRA algorithm is a regional charging algorithm that employs a complex terrain avoidance strategy to solve the node replenishment problem. The algorithm includes the following features: the network area is divided into two types of regions according to terrain complexity and nodes are classified according to where they are deployed; different routing strategies are designed to reduce energy consumption of nodes in complex terrain areas; the charging schemes for different classes of nodes are adopted to supply energy for more nodes in time. The effectiveness of the CTRA algorithm is verified through the simulation experiment.
The main contributions of this paper are as follows:
- 1.
A network area division scheme is devised based on terrain complexity, that is, the network area contains two terrains and the entire area is divided into several regions based on the two terrains. Nodes are classified into three classes based on their location.
- 2.
Different data routing protocols are designed for different classes of node.
- 3.
Three different charging schemes are proposed for the three classes of nodes, respectively. The priority is set for each charging scheme to determine the order of response for charging requests.
The rest of this paper is organized as follows. Section 2 describes current developments in different WRSN charging algorithms. Section 3 presents the network model and assumptions of this paper. Section 4 depicts the network partition and node classification scheme. Section 5 presents the routing strategies for different classes of nodes. Section 6 describes the three charging schemes and the selection scheme of charging priority. Simulation results and analysis are provided in Section 7. Section 8 contains the conclusions and prospects of future work.
Section snippets
Background
For years, many researchers have devoted efforts to optimize charging algorithms and prolong the lifetime of WSNs. Several studies have focused on optimizing the non-regional partition algorithm. Guo et al. (2014) considered a combination of charging and data collection and proposed a wireless energy replenishment and mobile data gathering algorithm to force the charger to complete the data collection process while charging nodes. Liang et al. considered the NP-hard problem of the traditional
Network model and assumptions
As shown in Fig. 1, a set of static nodes is deployed in a square field of area with a side length L. The network area covers a variety of different terrains. In this paper, these different terrains are classified into two categories: ordinary terrain, where the charger is convenient to move; complex terrain, where the charger is difficult to move, such as hilly area, forest and river. In order to achieve simplicity, the network is divided into identical square grids. To avoid coverage holes in
Network partition and node classification
The network region is partitioned into two types: complex terrain region and ordinary terrain region. As shown in Fig. 2, the three cloud areas represent the complex terrain region and the rest of area represents the ordinary terrain region. For ease of investigation and calculation, the size of the complex terrain region is measured by the number of grid elements that it covers. For example, the size of the three complex terrain regions in Fig. 2 are 6, 4, 6 respectively. Ordinary terrain
Routing strategies for different classes of nodes
The complex terrain area places significant limitations on charger movement. The proposed algorithm aims to reduce the frequency of the charger visiting complex terrain as much as possible. However, once the charger visits complex terrain less often, the total amount of power transported by the charger to the complex terrain area is lowered as a result. To ensure the survival of nodes in the complex terrain, the energy consumption of these nodes must be reduced to achieve a balanced energy
Charging schemes for the three classes of nodes
As mentioned in Section 5, the energy consumption rates of the three node classes differ substantially in ATCS. The second and third class nodes in the ordinary terrain area forward data generated in complex terrain area. Hence, they consume more energy than the first class nodes. The energy consumption of the first class nodes is greatly reduced because the communication task is diverted.
Fig. 4 depicts the number of dead nodes in the three types of nodes without charging. Nearly all
Simulation and performance evaluation
To evaluate the performance of the CTRA algorithm, MATLAB simulation is used to simulate and analyze the impact parameters and evaluation indices of the CTRA algorithm. The results are analyzed and compared with traditional algorithms which do not consider the influence of complex terrain region. The simulation parameters of this algorithm are listed in Table 1.
This algorithm focuses on reducing the effect of complex terrain in the charging process. Therefore, the charging efficiency of the
Conclusion and future work
WRSNs are widely used in Smart World to collect information from the interesting area without the limitation of node energy. Based on the different terrains of node deployment in practical applications, a complex terrain region-avoidance algorithm (CTRA) for regional charging is proposed in this paper. The feasibility and superiority of the charging algorithm are verified through theoretical and simulation experiments. However, the proposed algorithm is not without shortcomings. In the future,
Acknowledgment
The work is supported by the National Key Research and Development Program, No. YS2017YFGH001945 and the National Natural Science Foundation of China under Grant No. 61572172 and No. 61872124, the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1801264 and supported by Six Talent Peaks Project in Jiangsu Province, No. XYDXXJS-007.
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