The dynamic and stochastic knapsack problem with deadlines
Management Science
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Towards optimal sleep scheduling in sensor networks for rare-event detection
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
IEEE Pervasive Computing
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Techniques for Improving Opportunistic Sensor Networking Performance
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Determining transportation mode on mobile phones
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Ear-phone: an end-to-end participatory urban noise mapping system
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
By their apps you shall understand them: mining large-scale patterns of mobile phone usage
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Real-time trip information service for a large taxi fleet
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
AppJoy: personalized mobile application discovery
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
USENIX ATC'12 Proceedings of the 2012 USENIX conference on Annual Technical Conference
Understanding and prediction of mobile application usage for smart phones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A reliable and accurate indoor localization method using phone inertial sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations. To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs. We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.