ecoSense: Minimize Participants’ Total 3G Data Cost in Mobile Crowdsensing Using Opportunistic Relays

TLDR: Evaluation results show that ecoSense could reduce total 3G data cost by up to up to $ {\sim }50$ %, when compared to the direct-assignment method that assigns each participant to UnDP or PAYG directly according to the size of her sensed data.

Abstract: In mobile crowdsensing (MCS), one of the participants’ main concerns is the cost for 3G data usage, which affects their willingness to participate in a crowdsensing task. In this paper, we present the design and implementation of an MCS data uploading mechanism—ecoSense—to help reduce additional 3G data cost incurred by the whole crowd of sensing participants. By considering the two most common real-life 3G price plans—unlimited data plan (UnDP) and pay as you go (PAYG), ecoSense partitions all the users into two groups corresponding to these two price plans at the beginning of each month, with the objective of minimizing the total refunding budget for all participants. The partitioning is based on predicting users’ mobility patterns and sensed data size. The ecoSense mechanism is designed inspired by the observation that during the data uploading cycles, UnDP users could opportunistically relay PAYG users’ data to the crowdsensing server without extra 3G cost, provided the two types of users are able to “meet” on a common local cost-free network (e.g., Bluetooth or WiFi direct). We conduct our experiments using both the Massachusetts Institute of Technology reality mining and the Small World In Motion (SWIM) simulation data sets. Evaluation results show that ecoSense could reduce total 3G data cost by up to $ {\sim }50$ %, when compared to the direct-assignment method that assigns each participant to UnDP or PAYG directly according to the size of her sensed data.