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Title
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Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing
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Author
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Abstract
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| The combination of Mobile Crowdsensing (MCS) with Opportunistic Networking (OppNet) allows mobile users to share sensed data easily and conveniently without the use of fixed infrastructure. OppNet is based on intermittent connectivity among wireless mobile devices, in which mobile nodes may store, carry and forward messages (sensing information) by taking advantage of wireless ad hoc communication opportunities. A common approach for the diffusion of this sensing data in OppNet is the epidemic protocol, which carries out a fast data diffusion at the expense of increasing the usage of local buffers on mobile nodes and also the number of transmissions, thereby limiting scalability. A way to reduce this consumption of local resources is to set a message expiration time that forces the removal of old messages from local buffers. Since dropping messages too early may reduce the speed of information diffusion, we propose a dynamic expiration time setting to limit this effect. Moreover, we introduce an epidemic diffusion model for evaluating the impact of the expiration time. This model allows us to obtain optimal expiration times that achieve performances similar to those other approaches where no expiration is considered, with a significant reduction of local buffer and network usage. Furthermore, in our proposed model, the buffer utilisation remains steady with the number of nodes, whereas in other approaches it increases sharply. Finally, our approach is evaluated and validated in a mobile crowdsensing scenario, where students collect and broadcast information regarding a university campus, showing a significant reduction on buffer usage and nodes message transmissions, and therefore, decreasing battery consumption. |
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Language
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English
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Source (journal)
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Pervasive and mobile computing. - Place of publication unknown
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Publication
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Place of publication unknown
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2020
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ISSN
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1574-1192
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DOI
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10.1016/J.PMCJ.2020.101201
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Volume/pages
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67
(2020)
, p. 1-18
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Article Reference
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101201
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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