In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, in reality, due to a limited budget or numerous targets, mobile sensors may be not enough to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (MWTCSCLMS) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the MWTCSCLMS problem. Based on the WMCBA, the Steiner-tree-based algorithm (STBA) is proposed for the MWTCSCLMS problem. Simulation results demonstrate that the STBA provides better performance than the other methods.
In this paper, a set of @math mobile sensors @math is pre-deployed in a sensing field. We assume that each mobile sensor in @math has the same sensing range @math to sense targets. In addition, the data sink and each mobile sensor have the same transmission range @math to communicate with the other mobile sensors. While given a set of @math targets @math with known locations in the field, mobile sensors can be scheduled to move in any direction and stop anywhere @cite_29 to cover targets or connect with the data sink and the other mobile sensors. In reality, all of the targets in the field may not be covered due to the limited mobile sensors. Targets in the sensing field, therefore, must be weighted by their importance; that is, the more important a target, the higher the weight of the target. Hereafter, the weight of target @math is denoted by @math .
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Consider @math sensors placed randomly and independently with the uniform distribution in a unit square. The sensors have identical sensing range equal to @math , for some @math . We are interested in moving the sensors from their initial positions to new positions so as to ensure that the unit square is completely covered, i.e., every point in the squarei¾?is within the range of a sensor. If the @math -th sensor is displaced a distance @math , what is a displacement of minimum cost? As cost measure for the displacement of the team of sensors we consider thei¾? @math -total movement defined as the sum @math , for some constant @math . We assume that @math and @math are chosen so as to allow full coverage of the square and @math . The main contribution of the paper is to show the existence of a tradeoff between the square sensing radius and @math -total movement and can be summarized as follows:1.If the square sensing radius is equal to @math and @math is the square of a natural number we present an algorithm and show that in expectation the @math -total movement is in @math .2.If the square sensing radius is greater than @math and @math is natural number then we present an algorithm and show that in expectation the @math -total movement is in @math . Therefore this sharp decrease from @math to @math in the @math -total movement of the sensors to attain complete coverage of the square indicates the presence of an interesting threshold on the square sensing radius when it increases from @math to @math . In addition, we simulate our algorithms above and discuss the results of our simulations. Consider @math mobile sensors placed independently at random with the uniform distribution on a barrier represented as the unit line segment @math . The sensors have identical sensing radius, say @math . When a sensor is displaced on the line a distance equal to @math it consumes energy (in movement) which is proportional to some (fixed) power @math of the distance @math traveled. The energy consumption of a system of @math sensors thus displaced is defined as the sum of the energy consumptions for the displacement of the individual sensors. We focus on the problem of energy efficient displacement of the sensors so that in their final placement the sensor system ensures coverage of the barrier and the energy consumed for the displacement of the sensors to these final positions is minimized in expectation. In particular, we analyze the problem of displacing the sensors from their initial positions so as to attain coverage of the unit interval and derive trade-offs for this displacement as a function of the sensor range. We obtain several tight bounds in this setting thus generalizing several of the results of [10] to any power @math . We address the problem of placing a sensor network so as to minimize the uncertainty in estimating the position of targets. The novelty of our formulation is in the sensing model: we focus on stereo sensors where the measurements from two sensors must be combined for an estimation. We study two versions of this problem. In the first version, which we call the placement problem, we are given a workspace and an error threshold. The objective is to place a minimum number of cameras so that no matter where the target is located in the workspace, the uncertainty in localizing it is less than the threshold. For this problem, we present an approximation algorithm and prove that the deviation of its performance from the optimal value is bounded by a constant. In the second version, called the deployment problem, we study the problem of relocating a mobile sensor team to minimize the uncertainty in localizing possibly moving targets. We present a distributed, discrete-time algorithm which explicit addresses communication and motion constraints and show how to compute the optimal move within the time-step for a given target sensor-pair assignment. The utility of the algorithm is demonstrated with simulations One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.