effSense: A Novel Mobile Crowd-Sensing Framework for Energy-Efficient and Cost-Effective Data Uploading

Energy consumption and mobile data cost are two key factors affecting users´ willingness to participate in mobile crowd-sensing tasks. While data-plan (DP) users are mostly concerned with energy consumption, non-data-plan (NDP) users are more sensitive to data cost. Traditional ways of data uploading in mobile crowdsensing tasks often go to two extremes: either in real time or completely offline after the whole task is over. In this paper, we propose effSense-an energy-efficient and cost-effective data uploading framework, which utilizes adaptive uploading schemes within fixed data uploading cycles. In each cycle, effSense empowers the participants with a distributed decision making scheme to choose the appropriate timing and network to upload data. effSense reduces data cost for NDP users by maximally offloading data to Bluetooth/WiFi gateways or DP users encountered; it reduces energy consumption for DP users by piggybacking data on a call or using more energy-efficient networks rather than initiating new 3G connections. By leveraging the predictability of users´ calls and mobility, effSense selects proper uploading strategies for both user types. Our evaluation with the MIT reality mining and Nodobo datasets shows that effSense can reduce 55%-65% energy consumption for DP users, and 48%-52% data cost for NDP users, respectively, compared to traditional uploading schemes.