Robot routing in sparse wireless sensor networks with continuous ant colony optimization

Sparse wireless sensor networks are characterized by the distances the sensors are from each other. In this type of network, gathering data from all sensors in a point of interest might be a difficult task, and in many cases a mobile robot is used to travel along the sensors and collect data from them. In this case, we need to provide the robot with a route that minimizes the traveled distance and allows data collection from all sensors. This problem can be modeled as the classic Traveling Salesman Problem (TSP). However, when the sensors have an influence area bounded by a circle, for example, it is not necessary that the robot touches each sensor, but only a point inside the covered area. In this case, the problem can be modeled as a special case TSP with Neighborhoods (TSPN). This work presents a new approach based on continuous Ant Colony Optimization (ACO) and simple combinatorial technique for TSP in 0order to solve that special case of TSPN. The experiments performed indicate that significant improvements are obtained with the proposed heuristic when compared with other methods found in literature.