Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage

Abstract

We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

“…An area is k-coverage if each physical point in the area is covered by at least k (k ≥ 1) active sensor nodes. The term k-connectivity (k ≥ 1) means that there are at least k disjoint paths between any pair of nodes in the network [8,16].…”

“…We consider R T = 2 × R S , it is proven that if the communication range is at least twice the sensing range, it's a sufficient condition to ensure that a full coverage of a convex area implies connectivity among active nodes [17]. However, this assumption of omni-directional sensing ability does not hold true for some types of sensor nodes, such as cameras, ultrasonic sensors, etc., which have a directional sensing region [8]. Figure 3 shows that if the circular area of the sensing range is a square, the diameter of the circle becomes the diagonal of the square.…”

“…Figure 3 shows that if the circular area of the sensing range is a square, the diameter of the circle becomes the diagonal of the square. sensing ability does not hold true for some types of sensor nodes, such as cameras, ultrasonic sensors, etc., which have a directional sensing region [8]. Figure 3 shows that if the circular area of the sensing range is a square, the diameter of the circle becomes the diagonal of the square.…”

“…A wide variety of meta-heuristic methods have been applied to the placement problem, ranging from the genetic algorithm (GA) [23], evolution algorithm with specialized operators [24], particle sensing ability does not hold true for some types of sensor nodes, such as cameras, ultrasonic sensors, etc., which have a directional sensing region [8]. Figure 3 shows that if the circular area of the sensing range is a square, the diameter of the circle becomes the diagonal of the square.…”

“…The binary detection model which assumes that there is no uncertainty, and the probabilistic sensing model with stochastic detection [6,20]. A probabilistic coverage better complies with the performance of real sensors in the environment [8]. The binary sensing model is the simplest and most widely analyzed model of coverage provided by a sensor [21,22].…”