Dibakar Saha

Self-organized area coverage in wireless sensor networks by limited node mobility

For wireless sensor networks, monitoring large inaccessible areas where deterministic node deployment is not possible, self-organized techniques are in demand to cover an area using optimal number of nodes. In this paper, given an initial random deployment of mobile sensor nodes, we propose a simple and novel technique for self-organized node movement to satisfy the coverage of the given region of interest using a least number of nodes, such that the maximum node displacement is minimized. We present a simple centralized algorithm and also a distributed version of it for node placement. Moreover, in case of a node failure, a distributed fault recovery algorithm is proposed to replace it locally utilizing the available free nodes. Analysis, simulation, and comparison studies show that the proposed algorithms with less neighborhood information result in significant improvement in terms of average and maximum displacement of a node, rounds of communication, and number of active nodes.

A Digital-Geometric Approach for Computing Area Coverage in Wireless Sensor Networks

Given a set of n sensor nodes distributed randomly over a 2-D plane, this paper addresses the problem of computing the area covered by the sensors assuming that each sensor covers a circular area of radius r. To make the computation simple, instead of considering real circles, a digital geometry based approach is followed here. A detailed study on intersection of digital circles reveals many interesting properties that lead to the development of a novel On logn centralized algorithm using simple arithmetic operations for computing the area covered by n uniform digital circles. Next, a distributed version of the same is proposed to select a subset of nodes to cover a given area. Comparison with earlier works by simulation shows that the proposed distributed algorithm improves the estimated area coverage significantly.

Fast Estimation of Coverage Area in a Pervasive Computing Environment

In many applications of pervasive computing and communication, it is often mandatory that a certain service area be fully covered by a given deployment of nodes or access points. Hence, a fast and accurate method of estimating the coverage area is needed. However, in a scenario with a limited computation and communication capability as in self-organized mobile networks, where the nodes are not static, computation-intensive algorithms are not suitable. In this paper, we have presented a simple algorithm for estimating the area covered by a set of nodes randomly deployed over a 2-D region. We assume that the nodes are identical and each of them covers a circular area. For fast estimation of the collective coverage of n such circles, we approximate each real circle by the tightest square that encloses it as well as by the largest square that is inscribed within it, and present an O(n logn) time algorithm for computation. We study the variation of the estimated area between these two bounds, for random deployment of nodes. In comparison with an accurate digital circle based method, the proposed algorithms estimate the area coverage with only 10% deviation, while reducing the complexity of area computation significantly. Moreover, for an over-deployed network, the estimation provides an almost exact measure of the covered area.

Self-Organized Node Placement for Area Coverage in Pervasive Computing Networks

In pervasive computing environments, it is often required to cover a certain service area by a given deployment of nodes or access points. In case of large inaccessible areas, often the node deployment is random. In this paper, given a random uniform node distribution over a 2-D region, we propose a simple distributed solution for self-organized node placement to satisfy coverage of the given region of interest using least number of active nodes. We assume that the nodes are identical and each of them covers a circular area. To ensure coverage we tessellate the area with regular hexagons, and attempt to place a node at each vertex and the center of each hexagon termed as target points. By the proposed distributed algorithm, unique nodes are selected to fill up the target points mutually exclusively with limited displacement. Analysis and simulation studies show that proposed algorithm with less neighborhood information and simpler computation solves the coverage problem using minimum number of active nodes, and with minimum displacement in 95 % cases. Also, the process terminates in constant number of rounds only.

Boundary detection and area estimation of an event region in wireless sensor networks using digital-circles

In wireless sensor networks with large number of sensor nodes, deployed to monitor a given region, it is often required to detect and localize a critical event, like forest fire, or chemical pollution etc., in real time. The challenge is to select a minimal set of affected nodes to report so that the desired accuracy of area estimation can be achieved keeping the latency low. So far, in the literature, it has been assumed that the area enclosed by the points of location of all affected nodes, necessarily defines the event area. But in reality, a sensor node senses not just a point, but a region, ideally a circular one, determined by its sensing radius. Considering this realistic model of sensing, in this paper, we follow a digital geometry based approach to identify a minimal set of boundary nodes to report, and hence to estimate the affected area, assuming that each sensor node senses a circular area approximated by a digital circle. Simulation studies show that even for a sparse network the proposed technique may estimate the area with high accuracy using simple in-node processing.

Irregular-Shaped Event Boundary Estimation in Wireless Sensor Networks

In a wireless sensor network (WSN), sensor nodes are deployed to monitor a region. When an event occurs, it is important to detect and estimate the boundary of the affected area and to gather the information to the sink node in real time. In case, all the affected nodes are allowed to send data, congestion may occur, increasing path delay, and also exhausting the energy of the nodes in forwarding a large number of packets. Hence, it is a challenging problem to select a subset of affected nodes, and allow them only to forward their data to define the event region boundary satisfying the precision requirement of the application. Given a random uniform node distribution over a 2-D region, in this paper, three simple localized methods, based on local convex hull, minimum enclosing rectangle, and the angle of arrival of signal, respectively, have been proposed to estimate the event boundary. Simulation studies show that the angular method performs significantly better in terms of area estimation accuracy and number of nodes reported, even for sparse networks.

Coverage area maximization by heterogeneous sensor nodes with minimum displacement in mobile networks

Given a random deployment of heterogeneous mobile nodes having different sensing ranges, this paper addresses the problem of covering a region using minimum number of nodes with minimum displacement. We propose an energy-efficient and light-weight self-organized distributed greedy heuristic to maximize area coverage such that the amount of computation, rounds of communication, and the distance traversed by a node, can be reduced utilizing minimal number of nodes. Extensive simulation studies on random deployment of nodes with sufficient node density over a 2-D area, show that our proposed technique results hole free area coverage with small number of nodes with minimum possible displacement, both in turn help to prolong the network lifetime.