A cluster-based approach for routing in dynamic networks
ACM SIGCOMM Computer Communication Review
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Location-aided routing (LAR) in mobile ad hoc networks
Wireless Networks
Data mining: concepts and techniques
Data mining: concepts and techniques
Habitat monitoring: application driver for wireless communications technology
SIGCOMM LA '01 Workshop on Data communication in Latin America and the Caribbean
A transmission control scheme for media access in sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Exploring group mobility for replica data allocation in a mobile environment
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Understanding the semantics of sensor data
ACM SIGMOD Record
Replica allocation for correlated data items in ad hoc sensor networks
ACM SIGMOD Record
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Prediction-based monitoring in sensor networks: taking lessons from MPEG
ACM SIGCOMM Computer Communication Review - Special issue on wireless extensions to the internet
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
An Energy-Efficient Approach for Real-Time Tracking of Moving Object in Multi-Level Sensor Networks
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
On Mining Moving Patterns for Object Tracking Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Efficient In-Network Moving Object Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
ITNG '07 Proceedings of the International Conference on Information Technology
Energy efficient strategies for object tracking in sensor networks: A data mining approach
Journal of Systems and Software
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Mining multilevel and location-aware service patterns in mobile web environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Anomaly detection in monitoring sensor data for preventive maintenance
Expert Systems with Applications: An International Journal
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
International Journal of Ad Hoc and Ubiquitous Computing
A predictive duty cycle adaptation framework using augmented sensing for wireless camera networks
ACM Transactions on Sensor Networks (TOSN)
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A number of studies have been written on sensor networks in the past few years due to their wide range of potential applications. Object tracking is an important topic in sensor networks; and the limited power of sensor nodes presents numerous challenges to researchers. Previous studies of energy conservation in sensor networks have considered object movement behavior to be random. However, in some applications, the movement behavior of an object is often based on certain underlying events instead of randomness completely. Moreover, few studies have considered the real-time issue in addition to the energy saving problem for object tracking in sensor networks. In this paper, we propose a novel strategy named multi-level object tracking strategy (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. In MLOT, we first conduct hierarchical clustering to form a hierarchical model of the sensor nodes. Second, the movement logs of the moving objects are analyzed by a data mining algorithm to obtain the movement patterns, which are then used to predict the next position of a moving object. We use the multi-level structure to represent the hierarchical relations among sensor nodes so as to achieve the goal of keeping track of moving objects in a real-time manner. Through experimental evaluation of various simulated conditions, the proposed method is shown to deliver excellent performance in terms of both energy efficiency and timeliness.