Title: Data collection with probabilistic guarantees in opportunistic wireless networks

Authors: Meirui Ren; Jianzhong Li; Longjiang Guo; Zhipeng Cai

Addresses: School of Electronic Engineering, Heilongjiang University, Harbin 150080, China; School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China ' School of Electronic Engineering, Heilongjiang University, Harbin 150080, China; School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150010, China ' School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China ' Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

Abstract: Sensors can be embedded in many mobile devices/objects such as smart phones, vehicles and animals to collect data from the surrounding environment. These mobile devices can communicate with each other by wireless techniques to form opportunistic wireless networks (OWNs). OWNs have volatile network topology and loose connectivity. Data collection from mobile devices is very challenging. This paper proposes a data collection protocol named DCPG with probabilistic guarantees in OWNs. DCPG consists of four parts: broadcasting sink's position periodically, electing collectors by partitioning a network into grids, direct transmission from general nodes to collectors, and directional transmission from collectors to the sink. The theoretical analysis shows that DCPG can adjust the parameters to guarantee that the average data collection ratio is greater than the user-specified data collection ratio. The simulation results show that DCPG outperforms the existing best protocol SMITE on the aspects of data collection ratio and communication overhead.

Keywords: wireless sensor networks; data collection; opportunistic networks; loose connectivity; mobile smart device.

DOI: 10.1504/IJSNET.2017.084655

International Journal of Sensor Networks, 2017 Vol.24 No.2, pp.125 - 137

Received: 13 Mar 2015
Accepted: 08 May 2015

Published online: 19 Jun 2017 *

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