Evolutionary path planning of a data mule in wireless sensor network by using shortcuts

Abstract

Data collection problem of generating a path for a data mule (single or multiple mobile robots) to collect data from wireless sensor network (WSN) is usually a NP-hard problem. Thus, we formulate it as a Traveling Salesman Problem with Neighborhoods (TSPN) to obtain the possibly short path. TSPN is composed of determinations of the order of visiting sites and their precise locations. By taking advantage of the overlap of neighborhoods, we proposed a clustering-based genetic algorithm (CBGA) with an innovative way for initial population generation, called Balanced Standard Deviation Algorithm (BSDA). Then, effective shortcut schemes named Look-Ahead Locating Algorithm (LLA) and Advanced-LLA are applied on the TSPN route. By LLA, a smoother route is generated and the data mule can move while ignoring about 39% clusters. Extensive simulations are performed to evaluate the TSPN route in some aspects like LLA hits, LLA improvement, Rotation Degree of Data Mule (RDDM), Max Step and Ruggedness.