CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
Energy consumption in mobile phones: a measurement study and implications for network applications
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Ear-phone: an end-to-end participatory urban noise mapping system
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Energy-delay tradeoffs in smartphone applications
Proceedings of the 8th international conference on Mobile systems, applications, and services
Parallel connections and their effect on the battery consumption of a mobile phone
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Energy-efficient trajectory tracking for mobile devices
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Behavior-based adaptive call predictor
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Balancing energy, latency and accuracy for mobile sensor data classification
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Using On-the-Move Mining for Mobile Crowdsensing
MDM '12 Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
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Energy consumption and mobile data cost are two key factors affecting users' willingness to participate in crowdsensing tasks. While data-plan users are mostly concerned about the energy consumption, non-data-plan users are more sensitive to data transmission cost incurred. Traditional ways of data collection in mobile crowdsensing often go to two extremes: either uploading the sensed data online in real-time or fully offline after the whole sensing task is finished. In this paper, we propose effSense - a novel energy-efficient and cost-effective data uploading framework leveraging the delay-tolerant mechanisms. Specifically, effSense reduces the data cost of non-data-plan users by maximally offloading the data to Bluetooth/WiFi gateways or data-plan users encountered to relay the data to the server; it reduces energy consumption of data-plan users by uploading data in parallel with a call or using less-energy demand networks (e.g. Bluetooth). By leveraging the prediction of critical events such as user's future calls or encounters, effSense selects the optimal uploading scheme for both types of users. Our evaluation with MIT Reality Mining and Nodobo datasets show that effSense can save 55%~65% energy and 45%~50% data cost for the two types of users, respectively, compared with the traditional uploading schemes.