Predicting relative topological stability of mobile users in a P2P mobile cloud

• Published in: SN applied sciences Vol. 2; no. 11; p. 1827
• Main Authors: Pramanik, Pijush Kanti Dutta, Bandyopadhyay, Gautam, Choudhury, Prasenjit
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2020; Springer Nature B.V
• Subjects: Algorithms; Applied and Technical Physics; Availability; Chemistry/Food Science; Cloud computing; Cloud Computing and IoT driven solutions for Real-Time Applications; Earth Sciences; Electronic devices; Engineering; Engineering: Big Data; Environment; Geographical distribution; Geographical locations; Internet of Things; Materials Science; Mobile computing; Mobility; Research Article; Smartphones; Stability analysis; Logistic regression; P2P cloud; Mobility prediction; Relative mobility; Ad hoc mobile cloud; Evaluation metrics; Goodness of fit
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-020-03584-3

In a P2P mobile cloud (PMC), an ad-hoc cloud is formed using the closely adjacent smart mobile devices (SMD). Here, a resource-deficient SMD user seeks its neighbouring resource-rich SMDs’ help to carry out a resource-wanting job. To maintain reliability and QoS of the PMC, it is necessary to be assured of the availability of the SMDs before submitting a job request. If the job is distributed to multiple SMDs, instead of assessing the availability of a single SMD, the availability of a group of SMDs is required for a certain period of time. The objective of the paper is to find a stable group of SMD users to increase the QoS of PMC. We aim to propose a novel mobility prediction method to assess the probability of a group of SMD users to be relatively static with respect to each other. We propose an algorithm to estimate the relative stability of an SMD with respect to its neighbourhood over a period of time, irrespective of its geographical location. We assess the short-term relative stability between a group of SMD users as well as the long-term mobility pattern between them. For mobility calculation, we have used real user traces from the UCSD dataset. The estimated results are analyzed and validated using different metrics.