STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Research challenges in wireless networks of biomedical sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Sensor deployment strategy for target detection
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Research issues in data stream association rule mining
ACM SIGMOD Record
Energy-efficient monitoring of extreme values in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Using Data Mining to Estimate Missing Sensor Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
An online spatio-temporal association rule mining framework for analyzing and estimating sensor data
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Using data mining to handle missing data in multi-hop sensor network applications
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
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In Mobile Sensor Network (MSN) applications, sensors move to increase the area of coverage and/or to compensate for the failure of other sensors. In such applications, loss or corruption of sensor data, known as the missing sensor data phenomenon, occurs due to various reasons, such as power outage, network interference, and sensor mobility. A desirable way to address this issue is to develop a technique that can effectively and efficiently estimate the values of the missing sensor data in order to provide timely response to queries that need to access the missing data. There exists work that aims at achieving such a goal for applications in static sensor networks (SSNs), but little research has been done for those in MSNs, which are more complex than SSNs due to the mobility of mobile sensors. In this paper, we propose a novel data mining based technique, called Data Estimation for Mobile Sensors (DEMS), to handle missing data in MSN applications. DEMS mines the spatial and temporal relationships among mobile sensors with the help of virtual static sensors. DEMS converts mobile sensor readings into virtual static sensor readings and applies the discovered relationships on virtual static sensor readings to estimate the values of the missing sensor data. We also present the experimental results using both real life and synthetic datasets to demonstrate the efficacy of DEMS in terms of data estimation accuracy.