Joint Bluetooth/Wifi Scanning Framework for Characterizing and Leveraging People Movement on University Campus

Collecting the real human movement has drawn significant attention from research community since a better understanding of human movement could provide new insights in network protocol design and network management for wireless networks. However, previous projects have only collected either location trace or the ad hoc contact trace. A comprehensive trace of real human movement, in which both the location information and ad hoc contacts are collected, has been still missing. This paper presents a novel framework called UIM, which collects both location information and ad hoc contacts of the human movement at the University of Illinois campus using Google Android phones. Each UIM experiment phone encompasses a Bluetooth scanner and a wifi scanner capturing both Bluetooth MAC addresses and wifi access point MAC addresses in proximity of the phone. Then, Bluetooth MAC addresses are used to infer contact information and the wifi MAC addresses are used to infer physical location of the phone. Using the contact and location information, we investigate first the sensitivity analysis on contact duration and inter-contact duration. Then, we characterize the regularity of people movement, visit duration of people at locations, and the popularity of locations. We also study the social graph formed by ad hoc traces and find that the graph exhibits a small-world network in structure. Finally, we present the Hybrid Epidemic data dissemination protocol, which uses both wifi access point and ad hoc contact to expedite the data forwarding. We evaluate Hybrid Epidemic protocol with our collected ad hoc and wifi traces and find that in comparison with Epidemic data dissemination protocol, the Hybrid Epidemic protocol improves data forwarding delay considerably.