Cognitively Inspired Algorithm for Imprecise Navigation

This paper presents an algorithm, namely the private navigation algorithm. The aim of this algorithm is to bridge the gap between high quality navigation services and low quality of location information or imprecise data. Generally, the imprecision is due to the poor positioning technology and the algorithms use to protect location privacy. The benefits of the algorithm are at least two-fold: Firstly, it provides an efficient instructions for navigation under imprecision and secondly it supports location privacy protection while using navigation services. In common navigation systems, the navigation instructions generated are based on geometry oriented representation, e.g., shortest path which is based on the distance travelled and normally involves many turns. In human wayfinding, the navigation instruction is considered efficient if the instruction can reduce the cognitive load during the wayfinding activities as well as can guide users to a destination. The algorithm applies the simplest path computations for generating simple navigation instructions due to its ability to minimize the complexity of communicating the instructions. The research examines the efficiency of the algorithm based on several performance measurers. The research also takes into account the wayfinding heuristics such as the initial orientation and agent’s behavior (passive or active), that possibly can improve agent’s navigation performance. The cognitively motivated simplest cardinal direction weighting function is introduced which reflects the complexity of communicating cardinal instructions. The results show that the private navigation algorithm was efficient when it is incorporated with wayfinding heuristic for imprecise navigation.