Hence this will ultimately decrease the probability of detection and sensing in the environment. With a deterministic method, sensors are Y27632 Inhibitors,Modulators,Libraries deployed according to a predefined constraint such as; predetermined priority-regions on a field are equipped with more sensors in order to maximize the QoC. However, when the number of sensors is limited there will be coverage holes. Although both methods have their own advantages and disadvantages, they both fail to provide solutions to the problem of determination of the location coordinates of a predefined number of sensors which maximizes the coverage within a predefined 3D terrain. This is a kind of an NP-hard Minimum Set Cover (MSC) problem where the decision space grows exponentially with wider terrains.
For example, within a map size of 1,024 �� 1,024 pixels, there are 220 possible Inhibitors,Modulators,Libraries sensor locations. With 128 sensors, there are [220 (220-1) (220-2)��.. (220-127)] possible sensor deployment schemes. Thus, the huge decision Inhibitors,Modulators,Libraries space necessitates a heuristic search algorithm.As a search algorithm, Inhibitors,Modulators,Libraries an elitist and a steady state genetic algorithm (GA) have been utilized to track the optimal placement schemes of sensors on a 3D region. The GA is an optimization technique which is based on an adaptive mechanism of biological systems [2]. Two widely used GA techniques are the Standard-GA (S-GA) [3] and the Steady State-GA (SS-GA) [4]. In S-GA, new offspring are born from the parents of an old population using the crossover and mutation operators (genetic operators) and these individuals become the new population.
The new population gets old when the whole new population is created and the algorithm iterates until a termination condition is achieved [5,6]. The SS-GA is different from the S-GA that there is only one new child inserted into the new Dacomitinib population at each generation. The performance of a GA is highly problem specific and depends on the utilized parameters. Therefore, modeling and determination of the parameters is crucial for finding an optimal solution for a problem. Hence, in this study various methods with a wide parameter range have been evaluated and we have come up to the solution that S-GA and SS-GA methods both give satisfactory results and SS-GA overwhelms the S-GA in terms of number of nevertheless iterations.In this paper, first two deployment strategies are investigated i.e., the random deployment method and the Delaunay triangulation method [7]. With these two strategies, optimal solutions could not be achieved, thus a genetic algorithm based deployment strategy has been developed, in which each sensor is moved to a new position which bears an attractive force to change the current position of a sensor within the area of interest.