Utilization of convex optimization for data fusion-driven sensor management in WSNs

Soltani, Mohammadreza; Hempel, Michael; Sharif, Hamid

doi:10.1109/iwcmc.2015.7289257

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to address these constraints mentioned above, we will use data fusion or aggregation in our picture forwarding method. Picture data fusion in MCP is different from numeric data fusion (e.g., an average value of temperature) [18, 19] or splicing pictures in wireless sensor network [20], and it usually relies on a data selection scheme [7, 9, 15]. In other words, based on some selection constraints, the WN will only forward valuable pictures selected from a picture set union of this WN and others’ picture collections.…”

Section: Introductionmentioning

confidence: 99%

CooperSense: A Cooperative and Selective Picture Forwarding Framework Based on Tree Fusion

Chen

Guo

2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Mobile crowd photographing has become a major crowd sensing paradigm, which allows people to use cameras on smart devices for local sensing. In MCP, pictures taken by different people in close proximity or time period can be highly similar and different MCP tasks have diverse constraints or needs to deal with such duplicate data. In order to save the network cost and improve the transmitting efficiency, pictures will be preselected by mobile clients and then uploaded to the server in an opportunistic manner. In this paper, CooperSense, a multitask MCP framework for cooperative and selective picture forwarding, was designed. Based on the sensing context of pictures and task constraints, CooperSense structures sequenced pictures into a hierarchical context tree. When two participants encounter, their mobile clients will just exchange their context trees and at the same time automatically accomplish forwarding high-quality pictures to each other via a tree fusion mechanism. Via virtual or real pruning and grafting, mobile clients learn which picture should be sent to the encounter and which one should be abandoned. Our experimental results indicate that the transmission and storage cost of CooperSense are much lower comparing with the traditional Epidemic Routing (ER) method, while their efficiency is almost the same.

show abstract