Dynamic deployment of randomly deployed mobile sensor nodes in the presence of obstacles
Introduction
Nowadays, Wireless Sensor Networks (WSNs) have attracted tremendous research interest due to its various applications from environment monitoring, battlefield surveillance, target tracking, wildfire detection, precision agriculture, smart homes and offices, industrial process monitoring and asset management [1]. A mobile sensor network is a collection of inexpensive, low-powered, small size, and multifunctional mobile sensor nodes. The effectiveness of WSNs mainly depends on the network coverage, lifetime and connectivity provided by the sensor deployment strategies such as deterministic and random deployment. Placing sensor nodes manually in predetermined positions on the basis of simple geometric structure (e.g., Hexagon, Square, Rhombus, and Triangular Lattice) is simple and optimal, but this deployment strategy is not suitable in many applications where the application environment is unknown, hostile or inhospitable. For these applications, sensor nodes are required to be deployed randomly by means of dispersing sensors from aircraft or artillery ordinance.
An efficient self-deployment algorithm is highly required to ensure optimal network coverage while maintaining connectivity for such randomly deployed sensors. Presently, virtual force-based self-deployment strategies are adopted to overcome the limitations exhibited by random deployment [2], [3], [4], [5], [6], [7], [8], [9], [10]. In this work, an efficient distributed self-deployment algorithm has been proposed for randomly deployed homogeneous as well as heterogeneous mobile sensor nodes. This algorithm is named as Obstacle Avoidance Virtual Force Algorithm (OAVFA). Experimental results carried out with our proposed algorithm not only maximizes coverage area but also ensures the connectivity between all sensor nodes in the presence of obstacles. A set of sensor nodes with identical speeds, communication ranges, and sensing ranges has been identified as homogeneous sensor nodes while heterogeneous sensor nodes differs only in the sensing ranges which are strictly different for various sensors. It has been assumed that the speeds and the communication ranges for heterogeneous sensor remain constant during the process.
The proposed algorithm is localized and executed at each sensor node. In this algorithm, each sensor node considers all attractive and repulsive virtual forces due to its neighboring sensor nodes, obstacles, and the sensing field boundary to determine its movements to enhance the network coverage while maintaining connectivity, prevent the sensor nodes from moving out of sensing field boundary, and avoid the obstacles. Here neighbor sensor nodes of ith sensor si means the sensor nodes that are within the communication range of si.
In the next section, a brief but latest literature surveys on sensor node deployment has been outlined. Section 3 provides a basic discussion about the network coverage and sensing model. Our proposed deployment algorithm, Obstacle Avoidance Virtual Force Algorithm (OAVFA) has been described in Section 4. In Section 5, simulation results are presented followed by conclusions in Section 6.
Section snippets
Related work
Sensor arrangement is an imperative issue for some essential objectives in WSNs like coverage, lifetime, and connectivity. For randomly deployed sensor networks, an efficient deployment algorithm is required to self-deploy the mobile sensor nodes to maximize coverage area, ensure the network connectivity and prolong the network lifetime. In [2], [3], an incremental and greedy algorithm is presented in which nodes are deployed one at a time. The objective is to maximize the coverage under the
Coverage and sensing model
Coverage is one of the key parameters to evaluate the performance of deployment algorithms [2], [3], [4], [5], [6], [7], [8], [9]. According to Poduri and Sukhatme [9], there are three categories of coverage: barrier coverage, target or point coverage, and area coverage. In barrier coverage, sensor nodes have to form a barrier to detect intruders. Target coverage refers to monitoring fixed number of targets in a Region of Interest (ROI). Area coverage means that every point within ROI must be
Obstacle Avoidance Virtual Force Algorithm (OAVFA)
The proposed OAVFA is based on the following assumptions. They are: (i) all the sensor nodes have locomotion capability and can move effectively to any direction and any distance within the sensing boundary, (ii) each sensor node has one unique ID, (iii) all sensors are equipped with localization system (i.e. GPS), (iv) every sensor node is able to acquire the relative position of the other sensor nodes within its communication range, (v) all the sensor nodes have circular sensing and
Simulation results
We have implemented the deployment algorithms in MATLAB environment to demonstrate their performance. In our simulation, the sensor nodes are initially deployed at random over a 100 m by 100 m squared sensing field and grid scan method is used for evaluation of network coverage. The sensing field is treated as 100 by 100 grids when we calculate the coverage. In this paper we assume that the maximum velocity of each mobile sensor node is 0.5 m/s. For simulation, we set the maximum distance that a
Conclusion
In this paper, we propose a localized self- deployment scheme called OAVFA for homogeneous as well as heterogeneous mobile sensor networks with random initial distribution. This algorithm works well in the scenarios of the random initial distribution of mobile sensor nodes to maximize the area coverage and minimize the moving energy requirement in the presence of obstacles while maintaining connectivity. To prevent the sensor nodes from moving out of sensing field boundary, we consider a
Mrutyunjay Rout received his B.E. degree in Electronics and Instrumentation Engineering from Berhampur University, Orissa, India, with first class honors in 2002 and the M.Tech. degree in Measurement and Instrumentation from Indian Institute of Technology Roorkee (IITR), Uttarakhand, India, in 2010 with a gold medal. He is currently pursuing his Ph.D. degree in the Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur (IITKGP), West Bengal,
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Mrutyunjay Rout received his B.E. degree in Electronics and Instrumentation Engineering from Berhampur University, Orissa, India, with first class honors in 2002 and the M.Tech. degree in Measurement and Instrumentation from Indian Institute of Technology Roorkee (IITR), Uttarakhand, India, in 2010 with a gold medal. He is currently pursuing his Ph.D. degree in the Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur (IITKGP), West Bengal, India. His current research interests include mobile sensor networks and smart sensors.
Rajarshi Roy received the B.E. degree in electronics and telecommunication from Jadavpur University, Kolkata, India, with first class honors in 1992 and the M.Sc. (Engg) degree in electrical communication engineering from the Indian institute of Science, Bangalore, Karnataka, India in 1995. He received the Ph.D. degree in electrical engineering with a specialization in networking from Polytechnic University, Brooklyn, New York, in 2001 (Now known as New York University Tandon School of Engineering). In July 2002, he joined with the Department of Electronics and Electrical Communication Engineering, IIT Kharagpur, India, as an assistant professor. He was promoted to associate professor rank in year 2010. Prior to this, he worked for the Performance Analysis Group of the Advanced Development Department of Comverse, Wakefield, Massachusetts, as a performance analyst software engineer and as an engineer for Lucent Technologies India Development Center in Bangalore, India. He also worked in the Applied Statistics Unit of the Indian Statistical Institute (Fall 2001), Kolkata, India, as a visiting scientist, as an academic visitor for Comm. lab, Helsinki University of Technology, Espoo, Helsinki, Finland (Fall 2004), as a summer intern in Bell labs, Holmdel, NJ (Summer 1997) and as an adjunct teacher in Polytechnic Brooklyn (Summer 2000). His current research interests include communication networks, network coding, cooperation in communication and control, network science, and control, optimization, resource allocation, and performance evaluation of networked complex systems.