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In this paper, we present a novel approach for tracking a dynamic phenomenon. One of the central issues in sensor networks is energy efficient target tracking, where the goal is to monitor the path of a moving target using a minimum subset of sensor nodes while meeting the specified quality of service (QoS). Unlike other tracking methods that are b...
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Citations
... This can lead to fast energy exhaustion of nodes, and hence shortening the lifetime of the network. Many researchers try to overcome the point coverage problem by designing a suitable sleep-scheduling (or simply scheduling) mechanism for nodes in such a way that in each period of time, only nodes which can sense the target points in that period are awakened [13][14][15][16][17][18][19][20]. In terms of dynamicity, this solution can deal with changes that occur in the topology of the network and position of target points using a dynamic scheduling mechanism. ...
The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as few sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
One way to prolong the lifetime of a wireless sensor network is to schedule the active times of sensor nodes, so that a node is active only when it is really needed. In the dynamic point coverage problem, which is to detect some moving target points in the area of the sensor network, a node is needed to be active only when a target point is in its sensing region. A node can be aware of such times using a predicting mechanism. In this paper, we propose a solution to the problem of dynamic point coverage using irregular cellular learning automata. In this method, learning automaton residing in each cell in cooperation with the learning automata residing in its neighboring cells predicts the existence of any target point in the vicinity of its corresponding node in the network. This prediction is then used to schedule the active times of that node. In order to show the performance of the proposed method, computer experimentations have been conducted. The results show that the proposed method outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
