This paper proposes a probabilistic sensor model for the optimization of sensor placement. Traditional schemes rely on simple sensor behaviours and environmental factors. The consequences of these oversimplifications are unrealistic simulation of sensor performance and, thus, suboptimal sensor placement. In this paper, we develop a novel probabilistic sensing model for sensors with line-of-sight based coverage (e.g. cameras) to tackle the sensor placement problem for these sensors. The probabilistic sensing model consists of membership functions for sensing range and sensing angle, which takes into consideration sensing capacity probability as well as critical environmental factors such as terrain topography. We then implement several optimization schemes for sensor placement optimization, including simulated annealing, L-BFGS, and CMA-ES.

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.

Abstract. Wireless Sensor Networks (WSN) have been studied intensively for various applications such as monitoring and surveillance. Sensor deployment is an essential part of WSN, because it affects both the cost and capability of the sensor network. However, most deployment schemes proposed so far have been based on over-simplified assumptions, where results may be far from optimal in practice. Our proposal aims at automating and optimizing sensor deployment based on realistic topographic information, and is thus different from previous work in two ways: 1) it takes into account the 3D nature of the environment ; 2) it allows the use of anisotropic sensors. Based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed approach shows good potential for tackling diverse problems in the WSN domain. Preliminary results are given for a mountainous area of North Carolina where coverage is maximized.

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Abstract-An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These two populations co-evolve to better classify the underlying dataset. Unlike other approaches that use fuzzification of continuous attributes of the dataset for discovering fuzzy classification rules, the system presented here fuzzifies the relational operators "greater than" and "less than" using evolutionary methods. For testing our system, the system is applied to the Iris dataset. Our experimental results show that our system outperforms previous evolutionary and non-evolutionary systems on accuracy of classification and derivation of interrelation between the attributes of the Iris dataset. The resulting fuzzy rules of the system can be directly used in knowledge-based systems.