Abstract
Mobility model plays a crucial role in performance evaluation of mobile wireless networks. However, the majority of existing mobility models either does not exhibit realistic movement characteristics or modeling methods are too complex. In this paper, inspired from the trend that users usually move between several popular areas in daily movement, a new mobility model based on Geographic Community (GCMM) is proposed. We concern with the topology construct of geographic environment and the destination selection scheme for user moving in GCMM. Simulation result shown that GCMM better depict the mobility patterns of human. DOI: http://dx.doi.org/10.5755/j01.eee.18.9.2815
Highlights
With the advances of computation and communications technology, various mobile wireless devices are becoming more popularity in people’s lives
Heterogeneous Human Walk (HHW) model, which, from the society networks theory point of view, analyze the real traces and construct human mobility patterns in literature [8]. These models mentioned above are more reality than Random Walk Mobility model (RWMM), by in-depth analyzing, it can be found that agenda driven mobility model (AMM) are close to real life scenarios, the modeling methods are too complex and there exist a large application limitation
We focus on humans clustering features by analyzing GPS trace data, study the clustering region and the node's density distribution characteristic in an observation area, propose Geographic Community-based Mobility Model (GCMM)
Summary
With the advances of computation and communications technology, various mobile wireless devices (e.g. mobile phones, PDAs, and laptops) are becoming more popularity in people’s lives. Heterogeneous Human Walk (HHW) model, which, from the society networks theory point of view, analyze the real traces and construct human mobility patterns in literature [8]. These models mentioned above are more reality than Random Walk Mobility model (RWMM), by in-depth analyzing, it can be found that AMM are close to real life scenarios, the modeling methods are too complex and there exist a large application limitation. HHW, similar to AMM, construct people motion patterns using changing different society roles It cannot exhibit the clustering features of human movement. We focus on humans clustering features by analyzing GPS trace data, study the clustering region and the node's density distribution characteristic in an observation area, propose Geographic Community-based Mobility Model (GCMM).
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