Movement activity estimation and forwarding effects for opportunistic networking based on urban mobility traces. Issue 3 (27th March 2012)

• Record Type: Journal Article
• Authors: Hummel, Karin Anna; Hess, Andrea; Gerla, Mario; Maggiorini, Dario; Palazzi, Claudio E.
• Is Part Of: Wireless communications and mobile computing. Volume 13:Issue 3(2013)
• Page Start: 343
• Page End: 360
• Publication Date: 2012-03-27
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1002/wcm.2216
• Languages: English
• ISSNs: 1530-8669

Abstract

<abstract abstract-type="main" id="wcm2216-abs-0001"> <title>ABSTRACT</title> <p id="wcm2216-para-0003">Opportunistic, mobility‐assisted, or encounter networking is a method based on <italic>ad hoc</italic> networking and introduced to disseminate data in a store‐and‐forward manner by means of spontaneously connecting mobile devices. Although, in many networked systems, mobility is treated as a challenge requiring additional management, in opportunistic networks, movement facilitates networking as it creates additional contacts between devices. These new networking opportunities can be exploited in addition to traditional wireless infrastructure networks or in absence of these networks. Hereby, algorithms for opportunistic data dissemination make use of information about social ties, regularities in movement, and the future path of mobile entities. The availability of this information is reasonable for areas such as campuses or conference venues, where social or professional ties are strong or when traveling by, for example, public transport lines or vehicles following a navigation system. Other movement activities of humans in larger areas often lack this information, and new techniques are required to derive similar useful movement information. By observing movement characteristics of network users such as average velocities or revisiting patterns, estimates about the likelihood of getting in contact with other devices can be estimated. Our approach goes one step further by introducing users' movement activities derived from movement patterns typical in, for example, tourist movement, shopping activities, or evening activities. Movement activities are notions summarizing a particular movement situation that is meaningful to users and can be used to further estimate user needs and user‐generated network traffic. In case movement patterns are uncertain or fragmentary, knowledge about activities may help to faster estimate average movement characteristics. The main objective of this paper is to detail the approach of relating activities to observed multivariate mobility characteristics on the basis of the Naïve Bayes classifier. The approach is applied to four typical urban movement use case activities including pedestrian and vehicular movement. Results are presented on the basis of two different experimental training sets consisting of GPS outdoor traces: first, a training set of emulated movement activities and, second, a training set consisting of labeled real‐world daily activities over one month tracked by volunteers. The results of the classification study confirm that movement can be characterized as proposed. By using mobility activities and corresponding distributions of movement characteristics, the impact of activities on opportunistic forwarding performance in terms of contact and inter‐contact time, forwarding distance and coverage of an area, and predictability of the future path of a moving device is investigated. Copyright © 2012 John Wiley & Sons, Ltd.</p> </abstract>