Energy Efficient Sleep Scheduling in Sensor Networks for Multiple Target Tracking
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
A New Adaptive Prediction-Based Tracking Scheme for Wireless Sensor Networks
CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference
Distributed Computation of Likelihood Maps for Target Tracking
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Range queries for mobile objects in wireless sensor networks
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
An energy efficient target tracking scheme for distributed wireless sensor networks
ISWCS'09 Proceedings of the 6th international conference on Symposium on Wireless Communication Systems
Exploring group moving pattern for an energy-constrained object tracking sensor network
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Fault-tolerant prediction-based scheme for target tracking application
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Model-based object tracking in wireless sensor networks
Wireless Networks
A novel mobility management scheme for target tracking in cluster-based sensor networks
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
Similarity in (spatial, temporal and) spatio-temporal datasets
Proceedings of the 15th International Conference on Extending Database Technology
Location tracking for wireless sensor networks
NEW2AN'07 Proceedings of the 7th international conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking
An Interactive and Energy-efficient Node Localization Scheme for Wireless Sensor Networks
Wireless Personal Communications: An International Journal
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We study the problem of tracking moving objects using distributed Wireless Sensor Networks (WSNs) in which sensors are deployed randomly. Due to the uncertainty and unpredictability of real-world objects' motion, the tracking algorithm is needed to adapt to real-time changes of velocities and directions of a moving target. Moreover, the energy consumption of the tracking algorithm has to be considered because of the inherent limitations of wireless sensors. In this paper, we proposed an energy efficient tracking algorithm, called Predict-and-Mesh (PaM) that is well suited for pervasively monitoring various kinds of objects with random movement patterns. PaM is a distributed algorithm consisting of two prediction models: n-step prediction and collaborative prediction, and a predication failure recovery process called mesh. The simulation results show that the PaM algorithm is robust against diverse motion changes and has the excellent performance.