On Recent Advances in Time/Utility Function Real-Time Scheduling and Resource Management
ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Approximating Aggregation Queries in Peer-to-Peer Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Adaptive-Size Reservoir Sampling over Data Streams
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
P2P '08 Proceedings of the 2008 Eighth International Conference on Peer-to-Peer Computing
Region Sampling: Continuous Adaptive Sampling on Sensor Networks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Quality aware query scheduling in wireless sensor networks
Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Rapid detection of rare geospatial events: earthquake warning applications
Proceedings of the 5th ACM international conference on Distributed event-based system
Data Clustering on a Network of Mobile Smartphones
SAINT '11 Proceedings of the 2011 IEEE/IPSJ International Symposium on Applications and the Internet
| Hi-index | 0.00 |
Over the recent years, the proliferation of mobile networking and the increasing capabilities of smartphone devices have led to the development of the "Community-based Participatory Sensing" approach, where users participate in data collection and sharing in a wide range of application areas such as entertainment, transportation and environmental monitoring. This paper develops a participatory sensing system that uses a sampling mechanism that aims to stimulate user participation in dynamic groups that provide services and get compensated for the services they provide. Users participate in the community by sensing and sharing streams of events. The system then uses a sampling mechanism to define a subset of events that preserves the characteristics of the stream data and provides the highest "information gain" to the system, given the budget and resource constraints. Our experimental results illustrate that our approach is practical, efficient and depicts good performance.