C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Cyberguide: a mobile context-aware tour guide
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Policy optimization for dynamic power management
DAC '98 Proceedings of the 35th annual Design Automation Conference
Dynamic power management for portable systems
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
A Context-Aware Approach to Conserving Energy in Wireless Sensor Networks
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Power-Efficient Access-Point Selection for Indoor Location Estimation
IEEE Transactions on Knowledge and Data Engineering
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
ICESS '08 Proceedings of the 2008 International Conference on Embedded Software and Systems
A novel reliable and energy-saving forwarding technique for wireless sensor networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
EnTracked: energy-efficient robust position tracking for mobile devices
Proceedings of the 7th international conference on Mobile systems, applications, and services
Markov Model Based Disk Power Management for Data Intensive Workloads
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Less is more: energy-efficient mobile sensing with senseless
Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds
Simultaneous placement and scheduling of sensors
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Energy-efficient rate-adaptive GPS-based positioning for smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Improving energy efficiency of location sensing on smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Learning travel recommendations from user-generated GPS traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Policy optimization for dynamic power management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Energy efficient activity recognition based on low resolution accelerometer in smart phones
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
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With the prevalence of smart mobile devices with multiple sensors, the commercial application of intelligent context-aware services becomes more and more attractive. However, limited by the battery capacity, the energy efficiency of context-sensing is the bottleneck for the success of context-aware applications. Though several previous studies for energy-efficient context-sensing have been reported, none of them can be applied to multiple types of high-energy-consuming sensors. Moreover, applying machine learning technologies to energy-efficient context-sensing is underexplored too. In this article, we propose to leverage machine learning technologies for improving the energy efficiency of multiple high-energy-consuming context sensors by trading off the sensing accuracy. To be specific, we try to infer the status of high-energy-consuming sensors according to the outputs of software-based sensors and the physical sensors that are necessary to work all the time for supporting the basic functions of mobile devices. If the inference indicates the high-energy-consuming sensor is in a stable status, we avoid the unnecessary invocation and instead use the latest invoked value as the estimation. The experimental results on real datasets show that the energy efficiency of GPS sensing and audio-level sensing are significantly improved by the proposed approach while the sensing accuracy is over 90%.