Analyzing and modeling mobility for infrastructure-less communication. (July 2015)

• Record Type: Journal Article
• Authors: Lin, Zhiting; Wu, XiuLong
• Is Part Of: Journal of network and computer applications. Volume 53(2015)
• Page Start: 156
• Page End: 163
• Publication Date: 2015-07
• Subjects: Mobility model -- Infrastructure-less communication -- Opportunistic networks; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2015.04.003
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Unlike infrastructure-based counterparts, services and applications of future infrastructure-less mobile networks highly depend on the mobility patterns of wireless device carriers. Analyzing and modeling their mobility is essential in precise performance evaluation of these opportunistic networks, where inter-contacts are taken advantage of as opportunities for message forwarding. Recently, the fact that human mobility is not entirely random has been proved. To this end, some sophisticated mobility models have emerged to try to capture observed aggregate mobility characteristics. However, characteristics in aggregate level are different from those in individual node level. We provided general analysis and found that distributions of rates and durations played important roles in determining the nature of the aggregate distribution. Aggregate distribution can be regarded as being formed by distributions of different nodes or distributions of different rates. In addition, an individual node usually experiences active or inactive periods alternatively. Using only cumulative distribution characteristics cannot appropriately describe the nodal mobility. So as to precisely analyze the nature of the device carriers׳ mobility in both levels and capture transitions between active and inactive periods, we proposed a flexible framework, where the sequences of inter-contact times were modeled as Semi-Markov Modulated Process. This framework provides flexibility to configure each node׳s inter-contact time distribution independently, thus capturing the heterogenous behavior at will. At the same time, it could obtain required aggregate distribution and variable activities.