A weighted combination similarity measure for mobility patterns in wireless networks
This work addresses the need for better similarity measures in clustering trajectory data for wireless network applications, though it appears incremental as it builds on existing spatial and temporal similarity concepts.
The authors tackled the problem of measuring similarity between mobility patterns in wireless networks, which existing Euclidean or spatial network-based measures fail to capture appropriately, by proposing a new weighted combination similarity measure that integrates spatial and temporal aspects using timestamps, and they mathematically proved its properties and demonstrated its effectiveness through a case study.
The similarity between trajectory patterns in clustering has played an important role in discovering movement behaviour of different groups of mobile objects. Several approaches have been proposed to measure the similarity between sequences in trajectory data. Most of these measures are based on Euclidean space or on spatial network and some of them have been concerned with temporal aspect or ordering types. However, they are not appropriate to characteristics of spatiotemporal mobility patterns in wireless networks. In this paper, we propose a new similarity measure for mobility patterns in cellular space of wireless network. The framework for constructing our measure is composed of two phases as follows. First, we present formal definitions to capture mathematically two spatial and temporal similarity measures for mobility patterns. And then, we define the total similarity measure by means of a weighted combination of these similarities. The truth of the partial and total similarity measures are proved in mathematics. Furthermore, instead of the time interval or ordering, our work makes use of the timestamp at which two mobility patterns share the same cell. A case study is also described to give a comparison of the combination measure with other ones.