CoMon+: A Cooperative Context Monitoring System for Multi-Device Personal Sensing Environments

Continuous mobile sensing applications are emerging. Despite their usefulness, their real-world adoption has been slow. Many users are turned away by the drastic battery drain caused by continuous sensing and processing. In this paper, we propose CoMon+, a novel cooperative context monitoring system, which addresses the energy problem through opportunistic cooperation among nearby users. For effective cooperation, we develop a benefit-aware negotiation method to maximize the energy benefit of context sharing. CoMon+ employs heuristics to detect cooperators who are likely to remain in the vicinity for a long period of time, and the negotiation method automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Especially, CoMon+ improves the negotiation method proposed in our earlier work, CoMon [30], to exploit multiple processing plans enabled by various personal sensing devices; each plan can be alternatively used for cooperation, which in turn will maximize overall power saving. We implement a CoMon+ prototype and show that it provides significant benefit for mobile sensing applications, e.g., saving 27-71 percent of smartphone power consumption depending on cooperation cases. Also, our deployment study shows that CoMon+ saves an average 19.7 percent of battery under daily use of a prototype application compared to the case without CoMon+ running.

1 Introduction

Continuous mobile sensing applications have been increasingly emerging, for instance, trajectory logging [44], dust level monitor [25], interaction monitor [17], [31], group-aware ads and resource planning [41], and calorie monitor [33]. These applications provide useful services to mobile users while running in the background, not requiring any explicit user intervention. However, many users are still reluctant to run such applications; these applications incur significant energy consumption and take up computational resources, potentially disrupting other common uses of the smartphones.