Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra

Abstract

Abstract. Space and time are two dominant factors in context-aware pervasive systems which determine whether an entity is related to the moving user or not. This paper specifically addresses the use of spatio-temporal relations for detecting spatiotemporally relevant contexts to the user. The main contribution of this work is that the proposed model is sensitive to the velocity and direction of the user and applies customized Multi Interval Algebra (MIA) with Voronoi Continuous Range Query (VCRQ) to introduce spatio-temporally relevant contexts according to their arrangement in space. In this implementation the Spatio-Temporal Relevancy Model for Context-Aware Systems (STRMCAS) helps the tourist to find his/her preferred areas that are spatio-temporally relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 30 iterations of the algorithm. The evaluation process demonstrated the efficiency of the model in real-world applications.

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

“…The objective is to optimize the collection path with the following constraints: (i) each cluster shall be visited only by one vehicle; (ii) when a customer is visited within a cluster, all other customers in that cluster must be visited before the vehicle leaves the cluster. The literature presents different clustering algorithms and routing solutions to find the shortest distance (Akhtar et al., 2017; Cosma et al., 2022; Neysani Samany, 2019; Neysani Samany et al., 2013b; Sariklis & Powell, 2000; Tavares et al., 2008) and minimum time (Mes et al., 2014; Neysani Samany et al., 2013a; Tavares et al., 2008). Another important goal of clustering is to balance the workload between vehicles (Gu et al., 2016; Hajibabai et al., 2014; Ouyang, 2007; Wang et al., 2022).…”