Novel efficient deployment schemes for sensor coverage in mobile wireless sensor networks

Highlights

• The Voronoi blind–zone polygon is studied for finding coverage holes efficiently.

• Two schemes are proposed based on Voronoi blind–zone polygon and local operators.

• Latest metrics are used to evaluate the performance of proposed deployment schemes.

• The proposed two deployment schemes show effectiveness by the simulation results.

Abstract

In the study of improving the efficiency of mobile wireless sensor networks (MWSNs), an important issue is to maximize the sensor coverage in a given sensing field by proper deployment of sensors. In this paper, two novel sensor deployment schemes are proposed to address the coverage issue in MWSNs. The first scheme is blind-zone centroid-based scheme (BCBS) and the second one is disturbed centroid-based scheme (DCBS). The main ideas both in BCBS and DCBS are the proposed schemes to find the target locations for sensors to heal the coverage holes efficiently. The definition of Voronoi blind-zone polygon is given for introducing the proposed two schemes. In order to clearly illustrating the effect of Voronoi blind-zone polygon, three possible cases of it are studied in detail. In BCBS, Voronoi blind-zone polygon of each sensor is firstly determined by considering the positions with its neighbors. And then the centroid of the Voronoi blind-zone polygon is regarded as the target location of each sensor if the coverage can increase. In DCBS, sensors find coverage holes according to the centroid-based scheme at first in each round. They then move to the target locations under the local perturbation and local reconstruction operators. These two operators are designed by studying two forms of local convergence. Under the guideline of testbed-based multi-metric quality measurement of sensor deployment schemes, simulation results are sufficiently presented to demonstrate the effectiveness of the proposed two deployment schemes.

Introduction

Wireless sensor networks (WSNs) are composed of a set of distributed tiny low-power sensors that can sense or monitor physical or environmental conditions. Over the past decades, WSNs have been used in a wide range of applications, including target tracking and detection [1], [2], [3], disaster intervention [4], environmental monitoring [5], [6], [7], health monitoring [8], [9], and so on. The problems in WSNs, such as deployment [10], data collection [11], network routing [12], energy consumption [13], have arisen and been studied. However, deployment of sensors is a fundamental aspect in WSNs as it greatly influences the performance of network, which includes the coverage, connectivity, uniformity, etc [10]. In many situations, WSNs work in unknown, hostile, remote harsh field, toxic region or disaster area which makes it difficult or impossible for human to deploy the sensors. A possible way is to use mobile sensors which can move autonomously, but random deployment of mobile sensors at initial phase can not guarantee the required coverage. Therefore, how to design efficient deployment algorithms to move the sensors for maximizing the sensor coverage is one of the fundamental design issues in mobile wireless sensor networks (MWSNs) [14], [41] and is the problem to be addressed in this paper. Extensive research efforts have been conducted in the field of MWSNs to develop deployment algorithms for increasing the network coverage [15]. Among the literature, virtual force based and computational geometry based deployment approaches have been studied as the main approaches [10].

In the virtual force based deployment algorithms, the sensors are regarded as virtual particles subject to repulsive or attractive virtual forces among them. The sensors move according to the resultant forces on them when the distance between sensors is less than a threshold. Originated from the concept of potential field used in mobile robotic route [16], Howard et al. [17] studied a potential field-based deployment approach and proved the convergence property. In [18], a deployment optimization strategy based on Target Involved Virtual Force Algorithm (TIVFA) is proposed, where the configuration of WSN can be adjusted dynamically for improving coverage and detection probability. Kribi et al. improved Virtual Force Algorithm (VFA) in [19] from four aspects, which are coverage, connectivity, fault-tolerance and energy [20]. A distributed deployment strategy based on VFA is presented in [21]. In [22], VFA is worked with Delaunay triangulation by considering the adjacent relationship among sensors. In [23], a centroid-directed virtual force deployment strategy is designed which incorporates the coverage hole fixing ability with the spreading ability. The specific approach to obtain the weights of virtual force on the sensors by the centroid of Voronoi polygon is not given in [23] and relies on numerous attempts. A distributed deployment algorithm based on virtual forces for three dimensional sensing area is provided in [24].

Voronoi diagram and Delaunay triangulation are two famous computational geometry structures with a wide range of applications [25] and have been widely used for sensor deployment problem [23]. Wang et al. successfully proposed three deployment algorithms based on Voronoi diagram, which are VEC, VOR, and Minimax, to discover coverage holes [26]. However, the performance of VOR and Minimax are greatly affected by initial position of sensors; VEC may suffer performance decreasing with the increasing of the number of sensors [27]. In [28], Centroid and Dual-Centroid schemes are proposed by using Voronoi diagram and centroid to maximize the sensing coverage. But the location of neighbours is not consider in [28], the centroid of Voronoi polygon cannot be guaranteed to be a good location to improve coverage. An energy-efficient deployment algorithm based on Voronoi diagram is presented in [29]. Boukerche and Fei presented a deployment algorithm to detect coverage holes locally based on the localized Voronoi diagram construction [30]. Based on the distances of each sensor and the points inside its coordinate Voronoi polygon from the edges or vertices of the polygon, Mahboubi et al. proposed a set of distributed deployment algorithms for efficient field coverage [27], [31]. In [32], a secure Voronoi-based deployment algorithm is studied which can guarantee termination and is tolerant of an attack at low expense of performance. In order to find the shortest node movement path to heal the coverage holes, a Delaunay-based coordinate-free mechanism for full coverage is proposed in [33], where local shortest paths for healing holes can be found without knowing exact location of sensors. Sung and Yang proposed a distributed greedy deployment approach for improving the coverage of directional sensor networks [34]. For better trade-off between the coverage and the energy consumption, an multi-objective immune algorithm using the Voronoi diagram properties was proposed in MWSNs [35].

In this paper, to address the coverage problem in MWSNs, we design two novel efficient centroid-based deployment schemes for sensor coverage in MWSNs by the iterative procedures. As pointed out in [41], ensuring the connectivity of MWSNs during deployment is also crucial for deployment schemes. Therefore, in this paper the communication range of sensors is considered in forming the Voronoi diagarm to ensure the connectivity, which differs from that in [26], [27], [28], [31], [42]. The first scheme is blind-zone centroid-based scheme (BCBS). In BCBS, the candidate destination location of each sensor is the centroid of Voronoi blind-zone polygon instead of the centroid of Voronoi polygon. The Voronoi blind-zone polygon is helped to find the coverage hole by considering the coverage of neighbors to the related Voronoi polygon. The second scheme is disturbed centroid-based scheme (DCBS) with the proposed local perturbation and local reconstruction operators and is very effective to increase the coverage when the sensors are stagnated. A distinguishing characteristic of BCBS and DCBS that different from the deployment schemes [26], [27], [28] is that there are no manual parameters need to be set. The performance of BCBS and DCBS are evaluated using two sets of simulations and compared with four other deployment algorithms in terms of coverage, moving distance, deployment time, uniformity, and connectivity based on the simulation results.

The rest of this paper is organised as follows. Section 2 presents the problem formulation and performance metrics. Section 3 introduces the preliminaries concerning the Voronoi diagram, Voronoi polygon and its centroid, the basic idea of centroid-based deployment scheme, and ideal distribution of sensors. In Section 4, two proposed deployment schemes are described. Simulation results and concluding remarks are drawn in Sections 5 and 6, respectively.