Distributed Deployment Strategies for Improved Coverage in a Network of Mobile Sensors with Prioritized Sensing Field Hamid Mahboubi, Student Member, IEEE, Jalal Habibi, Member, IEEE, Amir G. Aghdam, Senior Member, IEEE, and Kamran Sayrafian-Pour, Senior Member, IEEE Abstract—Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a priority function. The mul- tiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results. Index Terms—Coverage, distributed deployment algorithm, mobile sensors, prioritized sensing field, wireless sensor networks, I. I NTRODUCTION W IRELESS sensor networks have drawn much attention recently due to their various civilian and military applications. Recent advances in microelectromechanical sys- tems (MEMS) technology have made it possible to fabricate small energy-efficient mobile sensors. Some of the emerging applications of cooperative mobile sensor networks include health-monitoring systems, space exploration and environmen- tal assessment, to name only a few [1], [2], [3]. Over the past two decades, researchers in different disciplines of engineering and computer science have made significant contributions in this area by deriving accurate models for such systems and developing cost-effective deployment algorithms for different purposes [4], [5], [6]. In the design of a practical sensor deployment strategy, some important constraints need to be taken into account. Such constraints include limited sensing and communication ranges of sensors, limited energy of sensors, and limited information exchange between them [7], [8], [9]. Furthermore, the initial location of the sensors in the field is not known a priori in many practical applications [10]. The problem of network coverage by a group of mobile sensors following a prescribed H. Mahboubi, J. Habibi and A. G. Aghdam are with the Department of Electrical & Computer Engineering, Concordia University, 1455 de Maison- neuve Blvd. W., EV012.179, Montr´ eal, Qu´ ebec H3G 1M8 Canada. E-mail: {h mahbo, jalal, aghdam}@ece.concordia.ca K. Sayrafian-Pour is with the National Institute of Standards and Technol- ogy (NIST), 100 Bureau Drive, Stop 8920 Gaithersburg, MD 20899 USA. E-mail: {ksayrafian}@nist.gov Copyright c  2012 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. This work has been supported by the National Institute of Standards and Technology (NIST), under grant 70NANB8H8146. trajectory is investigated in [11]. A multi-objective optimal sensor deployment and power assignment algorithm is pro- posed in [12], where the problem is decomposed into a number of scalar single-objective subproblems which are to be solved simultaneously. In [8], [9], multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to partition the sensing field in a network of mobile sensors with nonidentical sensing ranges. Different deployment strategies are subsequently introduced to improve sensing coverage of such networks. Vector-based and Voronoi-based algorithms are proposed in [7] to move the sensors in the field in such a way that network coverage increases. In [13], basic protocols and virtual movement proto- cols are introduced for sensor deployment to increase network coverage. Gradient-descent coverage algorithms are presented in [14], which are distributed in the sense of the Delaunay graph. In [15], coordination algorithms are provided for sensor deployment and coverage, where a class of aggregate objective functions is also considered based on the geometry of the Voronoi partitions and proximity graphs. In most of the above-mentioned sensor deployment algo- rithms, it is assumed that the coverage priority for different points in the network is uniform (i.e., the importance of coverage of every point in the network is the same). While this is a realistic assumption in many real-world problems, sometimes certain areas in the network have higher priority as far as coverage is concerned, due, for example, to safety considerations. The sensor deployment problem in a non- uniform field is considered in [15], [16], [17]. However, due to the computational complexity of the corresponding techniques, they may not be suitable for the case when the processing capability of the sensors in the network is limited. To the best of the authors’ knowledge, there is no result in the literature so far concerning coverage of a prioritized field with nonidentical sensors. In this work, new distributed deployment strategies are introduced to increase coverage in a network of mobile sensors with a prescribed priority function for the sensing field. To this end, a priority function is assumed to be given which specifies the coverage priority of different points in the sens- ing area. The MW-Voronoi diagram is used to partition the sensing field [18], [19], [20]. This partitioning is then used to discover coverage holes in the network and relocate the sensors accordingly to minimize them, while taking into account the coverage priority of different points in the field. Three algorithms are developed: maximum weighted vertex (MWV), maximum weighted point (MWP), and maximum distance weight (MDW). The main idea behind the proposed algorithms