Improving neighbor detection for proximity-based mobile applications

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Type
Article: article from journal or magazin.
Collection
Publications
Title
Improving neighbor detection for proximity-based mobile applications
Journal
Proceedings of the 2013 IEEE 11th International Symposium on Network Computing and Applications. NCA'13.
Author(s)
Bostanipour B., Garbinato B.
Publication state
In Press
Peer-reviewed
Oui
Pages
177-182
Language
english
Notes
peer-reviewed IEEE Press.
Abstract
In this paper, we consider the problem of improving the detection of a device by another device in mobile ad hoc networks, given a maximum amount of time that they remain in proximity of each other. Our motivation lies in the emergence of a new trend of mobile applications known as proximity-based mobile applications which enable a user to communicate with other users in some defined range and for a certain amount of time. The highly dynamic nature of these applications makes neighbor detection time-constrained, i.e., even if a device remains in proximity for a limited amount of time, it should be detected with a high probability as a neighbor. To address this problem, we perform a realistic simulation-based study in mobile ad hoc networks and we consider three typical urban environments where proximity-based mobile applications are used, namely indoor with hard partitions, indoor with soft partitions and outdoor urban areas. In our study, a node periodically broadcasts a message in order be detected as a neighbor. Thus, we study the effect of parameters that we believe could influence the detection probability, i.e., the transmission power and the time interval between two consecutive broadcasts. More precisely, for each environment, we determine when a change in the value of each of these parameters could lead to an improvement of the neighbor detection and when it hurts. Our experiments show that there exists no unique combination of values of these parameters that maximizes the detection probability in all environments. Accordingly, for each environment, we present the combination that maximizes the detection probability in that environment.
Create date
16/03/2014 12:28
Last modification date
01/11/2019 11:41
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