An optimized Bidding-based coverage improvement algorithm for hybrid wireless sensor networks☆
Introduction
Recent advances in micro-electro-mechanical systems technology, wireless communications, and digital electronics lead to the development of low-cost, low-power, tiny, and multifunctional sensor nodes. Each sensor node is capable of the sense of surrounding, aggregates data, and forwards own collected data and neighboring nodes data to the base station, which is known as sink node for further processing [1]. In general, sensor nodes are placed in a hard-to-reach location and are equipped with a limited power source, and given that recharging the energy of these sensor nodes is impossible, costly, and inconceivable in most applications so that efficient management of energy consumption is an essential to prolong the WSN lifetime [2]. However, designing energy efficient algorithms and managing the activities of the sensor nodes become important for enhancing the network lifetime and improving network coverage. In addition, it should be pointed out that there are many problems and challenges in WSNs applications, including energy limitation, hardware bottlenecks, dynamic topology, scalability, security, reliability and the vulnerability and prone to failure [3].
It is proven that among sensor activities, idle and transceiver modes require more energy consumption than processing mode. Moreover, it should be considered that the amount of energy consumption depends on the layout of sensor nodes and their communication strategy. The clustering is a well-known technique that due to decreasing the number of transmission packets can reduce the energy consumption [4]. In addition, applying simple and low overhead software, middleware, and operating system alongside the QoS-aware traffic management techniques have an important role in reducing the energy consumption. Therefore, Tiny Operating System (TinyOS) due to its features is most suitable for applying in resource constraint systems such as wireless sensor networks [5].
One of the major issue in relation to the placement of sensor nodes in WSNs is the strategy of their deployment, which depends on the application of network [6]. In general, deployment strategy of sensor nodes can be classified into pre-determined and random schemes. In pre-determined or deterministic deployment, the position of each sensor node is pre-defined and this scheme is applicable to small and medium-sized networks and friendly environment. This type of deployment is a proper choice for applications that their sensor nodes are expensive and their operations are significantly affected by sensor nodes position [7]. Whereas, sensor nodes are accidentally deployed in an inaccessible large-scale area in random strategy so that may lead to lack of exhaustive coverage in some regions that are referred as coverage holes [8]. Coverage holes refer to the positions, which are not covered through any sensor nodes, and no reports will be received from them.
Generally, creation the coverage holes in the network leads to decreasing the packet delivery ratio and degrading connectivity that determines the importance of identifying and taking the appropriate decision to reduce these points even eliminating these holes as possible. Therefore, selected approaches to deal with this problem in order to detection of holes, calculating their span, and resolving them directly affect network performance. Hence, covering all points of the surveillance circumference is one of the main challenges of randomly distributed networks such as WSNs. In traditional approaches, deploying a high number of stationary sensor nodes is performed as a solution to improve the network coverage. In the initial assessment, it might seem that expanding the number of sensor nodes can enhance the network coverage. However, it should be noted that increasing the number of deployed sensor nodes might be no significant change in the coverage rate but it may have adverse consequences, including increasing the overlapping rate, energy consumption, and network cost [9].
In general, coverage rate is defined as the proportion of the covered area by the sensor nodes to the total network area. The coverage objectives of WSN can be classified into three types such as area coverage, target coverage and barrier coverage [10]. The major objective of sensor nodes in area coverage is to fulfill complete observation of the surveillance circumference. Whereas, in the target coverage, an attempt has made to guarantee the coverage of only specific points of the area. Barrier coverage is concerned with finding a penetration path across the sensor field with some desired properties to apply in special applications such as tracing a mobile target [11]. Eventually, considering the application of WSN at the initialization phase of sensor nodes deployment is necessary to choose the most appropriate objective of coverage that affects directly to the network performance [12].
In some algorithms, mobile nodes can be applied alongside the stationary nodes to improve the coverage rate and the resulting network is called Hybrid Wireless Sensor Networks (HWSN). These nodes are randomly deployed at initialization phase similar to the stationary nodes so that applying a proper replacement technique is a requirement to find an optimal position for the mobile nodes. However, it should be noted that only a portion of sensor nodes could able to move that this property is a constraint of HWSN. In addition, some issues are addressed in the HWSN as challenges such as finding appropriate interest points, motion management to achieve the minimum moving distance, selecting a worthy mobile node between candidate nodes, and adjusting the sensing radius to minimize the overlapping areas.
In this paper, in line with the purpose of reducing the mobility dependency of the network, an attempt has made to introduce a novel cost-effective algorithm that is called CADTA (Coverage enhancing Algorithm based on Delaunay Triangulation with Adjustable rages) to improve the coverage of HWSN. In the proposed algorithm, stationary sensor nodes are able to identify the coverage holes and sort them based on their extents in the descending order. Then, an attempt is made to keep a few sensor nodes in active mode as possible and adjust the sensing radius of the sensor nodes to reduce the overlapping areas and consequently reduce the energy consumption and enhance the coverage rate. In addition, an optimized bidding-based movement strategy is applied to cover the uncovered points via the mobile sensor nodes if coverage rate is less than the respective threshold after the radius adjustment. Eventually, to validate the proposed algorithm its efficiency is compared with counterpart algorithms in terms of the coverage rate, number of alive nodes, number of moved nodes, and average moving distance.
The rest of the paper is organized as follows. Section 2 overviews the related works. Section 3 clarifies and describes basic concepts of the CADTA algorithm as well as some relevant definitions and mathematical models. A detailed description of the CADTA algorithm is given in Section 4. In Section 5, the results of the simulations are reported and analyzed. Finally, Section 6 concludes the paper and unfolds a number of noteworthy directions for future research.
Section snippets
Related works
In general, coverage issue refers to ensuring that sensor nodes of WSN should completely cover occurred events of the monitoring area. In fact, coverage is generally defined as the degree of supervision, observation, and how well each point of the monitoring area is covered by the deployed sensor nodes [13].
Coverage problem in WSNs in the most common case is classified into three types including blanket coverage, barrier coverage, and sweep coverage. The objective of WSN in blanket coverage is
Definition and mathematical models
Before the explanation of the proposed algorithm, essential terms and assumptions are defined as follows:
Sensing range: Sensing range of a sensor node refers to a circular disk with radius Rs where the sensor node is located at the center of this disk. As illustrated in Fig. 3(a), the sensing range of node ‘A’ is considered as a circle with ‘A’ in its center that all occurred events can be identified through this node.
Neighborhood: Two sensor nodes ‘A’ and ‘B’ are considered as single-hop
The proposed CADTA algorithm
In general, identification of all occurred events in the monitoring area is necessary that only can be achieved through complete coverage. Applying the mobile nodes is a popular mechanism to achieve the mentioned goal. Obviously, mobile nodes consume more energy that resulting in higher network costs and shorter network lifetime. In this regards, in the proposed algorithm an endeavor has been made to cover the holes independent of the mobile sensor nodes only through adjusting the sensing
Simulation and evaluation results
In this section, the effectiveness of the proposed algorithm is evaluated through the carried out simulation in Matlab tool version 9.0.4 in terms of the coverage ratio, overlapping area, number of active nodes, and energy consumption in different situations. As illustrated in Fig. 9(a), in the network model, 40 sensor nodes including stationary and mobile are randomly deployed in a 50 m × 50 m region. In the simulated model, a stationary node is considered as the sink node in the center of the
Conclusion
In this paper, a new bidding-based distributed algorithm has been presented to improve the coverage rate and decrease the overlapping area of the hybrid wireless sensor networks. The objectives of the proposed CADTA algorithm are to maintain the balance between network coverage and the minimum number of the moved nodes; also, reduce energy consumption through decreasing the overlapping due to putting the ineffective nodes in the sleep mode. The objectives have been achieved by adjusting the
Ayda Vatankhah was born in Tabriz, Iran, in 1988. She received her B.Sc. in computer engineering from Islamic Azad University of Tabriz in 2010. She received her M.Sc. degree in architecture of computer from Islamic Azad University of Tabriz in 2014. Her research interests include computer networks and Wireless Sensor Networks.
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Ayda Vatankhah was born in Tabriz, Iran, in 1988. She received her B.Sc. in computer engineering from Islamic Azad University of Tabriz in 2010. She received her M.Sc. degree in architecture of computer from Islamic Azad University of Tabriz in 2014. Her research interests include computer networks and Wireless Sensor Networks.
Shahram Babaie received his B.Sc. in Computer Engineering from Sajad University in 2003 and the M.Sc. and Ph.D. degrees in Architecture of Computer from Islamic Azad University. Currently, he is an Assistant Professor in the Department of Computer Engineering at the Islamic Azad University of Tabriz. His research interests include computer networks, Ad-Hoc and Wireless Sensor Networks, and Information Security.
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Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. M. H. Rehmani.