Heurísticas para o problema de cobertura em redes de sensores sem fio hierárquicas com sorvedouro móvel

Abstract

Wireless Sensor Network (WSN) is a special kind of ad hoc networks composed of devices capable of processing, storing, sensing the environment, and transmitting data via wireless communication interface. The sensor nodes have several limitations, among them the capacity of energy because to the reduced size. For this reason, many searches have been done with a view to improving the energy consumption of sensor nodes. This work aims to address the Problem of Coverage, Clustering and Routing with Mobile Sink (PCAR-SM, in portuguese Problema de Cobertura, Agrupamento e Roteamento com Sorvedouro Móvel) in WSN with mobile sink consisting of: given a set of sensor nodes and a monitoring area, develop algorithms to find the best subset of sensor nodes to cover the monitoring area, group them in a smaller number of clusters and find the shortest route to mobile sink navigate. The PCAR-SM is a strategy used to reduce the energy consumption of sensor nodes, data collisions, interference and redundant data in networks with high concentration of sensor nodes per area. The purpose of this paper is to solve each problem separately and together, in order to evaluate the impact of each problem on the other. The Coverage Problem has been solved with two metaheuristics: an Genetic Algorithm (GA) and a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm. In the latter we used two representations of solution: (a) representation by sensor, where each element of the solution vector represents a sensor node that must be switched on or off; (b) representation by demand, where each element of the solution vector represents a demand point will indicate which sensor node cover it. The AG uses only the representation by demand. The computational results for Coverage Problem used the benchmark of Beasley s OR Library and it was possible seen that the GRASP with representation by demand achieved better results than the GA and the GRASP with representation by sensor when the optimization criterion is to minimize the total cost of each sensor node used in the solution. For Clustering Problem was created approach of virtual grids. In this approach, we divide the area into grids and clusters are formed by a set of adjacent grids (maximum 5 grids in group) forming a cross schematic. The aim of the problem is to minimize the number of clusters in the area. With this approach, we can model the Clustering Problem as a Set Cover Problem (SCP) without overlapping (an element does not belong to more than one set), which was treated by a greedy heuristic called Greedy Clustering Algorithm (GCA). The virtual grids proved to be a good solution because it is simple to identify a node which grid it belongs. Its simplicity also makes it a appropriate method for a distributed version. The Routing Problem of sink was modeled as the Travelling Salesman Problem (TSP), where the mobile sink part of a corner of the monitoring area, runs through the area visiting all clusters and returns to the starting point. For this, we propose two greedy approaches based on nearest neighbor, the Routing Greedy Algorithm - Center (RGA-C) and Routing Greedy Algorithm - Border (RGA-B). The route of the sink was also solved by a heuristic based on algorithm Centralized Spatial Partitioning (CSP). In CSP approach, the route is fixed and reminds the movement of a snake. The results show that fixed route produces a path with smaller size compared to the greedy heuristic for TSP. We analyze also the PCAR-SM, creating heuristic strategies. The union of the Clustering Problem and Routing Problem proved more beneficial in relation to the size of the sink s route. The union of Coverage Problem and Clustering Problem only proved beneficial when the communication radius was about 3,9 times greater than the sensing radius. Our results show that solve problems together allows some changes in the algorithms will lead to better results.