Re-thinking: Design and Development of Mobility Aware Applications in Smart and Connected Communities

Recently, several problems concerning smart and connected communities (S&CC) have been studied. In many S&CC applications such as autonomous driving, mobile crowdsourcing and crowd sensing, the mobility of vehicles and pedestrians has large impact on their performance and reliability. Many research works have used mobility generators to simulate realistic mobility, and evaluated the performance and reliability of the proposed applications and protocols. Although such mobility generators might be useful for producing typical mobility patterns, those mobility patterns are just snapshots and they cover only some part in the possible mobility patterns. Since many S&CC applications are used as social systems, their reliability and efficiency are very important. In order to improve the reliability of such mobility aware applications and accurately evaluate their performance, we need to collect many mobility patterns via simulation and/or observation from the real world, analyze their mobility influence statistically and provide adequate design platforms. In this paper, we first show the fact that many research works adopt snapshot-based mobility analyses. Then we propose a technique to reproduce a large part of possible mobility patterns, and provide a design platform to analyze their features and develop high-reliable mobility aware applications for S&CC. Some experimental results are also given.