Matrix analysis
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Design requirements for technologies that encourage physical activity
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones
Proceedings of the 5th international conference on Mobile systems, applications and services
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
The Rise of People-Centric Sensing
IEEE Internet Computing
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 6th ACM conference on Embedded network sensor systems
Integrating sensor presence into virtual worlds using mobile phones
Proceedings of the 6th ACM conference on Embedded network sensor systems
BeTelGeuse: A Platform for Gathering and Processing Situational Data
IEEE Pervasive Computing
Proceedings of the 7th international conference on Mobile systems, applications, and services
A framework of energy efficient mobile sensing for automatic user state recognition
Proceedings of the 7th international conference on Mobile systems, applications, and services
EnTracked: energy-efficient robust position tracking for mobile devices
Proceedings of the 7th international conference on Mobile systems, applications, and services
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Opportunistic collaboration in participatory sensing environments
Proceedings of the fifth ACM international workshop on Mobility in the evolving internet architecture
Tapping into the Vibe of the city using VibN, a continuous sensing application for smartphones
Proceedings of 1st international symposium on From digital footprints to social and community intelligence
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
effSense: energy-efficient and cost-effective data uploading in mobile crowdsensing
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Continuous sensing applications (e.g., mobile social networking applications) are appearing on new sensor-enabled mobile phones such as the Apple iPhone, Nokia and Android phones. These applications present significant challenges to the phone's operations given the phone's limited computational and energy resources and the need for applications to share real-time continuous sensed data with back-end servers. System designers have to deal with a trade-off between data accuracy (i.e., application fidelity) and energy constraints in the design of uploading strategies between phones and back-end servers. In this paper, we present the design, implementation and evaluation of several techniques to optimize the information uploading process for continuous sensing on mobile phones. We analyze the cases of continuous and intermittent connectivity imposed by low-duty cycle design considerations or poor wireless network coverage in order to drive down energy consumption and extend the lifetime of the phone. We also show how location prediction can be integrated into this forecasting framework. We present the implementation and the experimental evaluation of these uploading techniques based on measurements from the deployment of a continuous sensing application on 20 Nokia N95 phones used by 20 people for a period of 2 weeks. Our results show that we can make significant energy savings while limiting the impact on the application fidelity, making continuous sensing a viable application for mobile phones. For example, we show that it is possible to achieve an accuracy of 80% with respect to ground-truth data while saving 60% of the traffic sent over-the-air.