GPSR: greedy perimeter stateless routing for wireless networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Geography-informed energy conservation for Ad Hoc routing
Proceedings of the 7th annual international conference on Mobile computing and networking
Geometric spanner for routing in mobile networks
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
An Incremental Self-Deployment Algorithm for Mobile Sensor Networks
Autonomous Robots
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Recursive Position Estimation in Sensor Networks
ICNP '01 Proceedings of the Ninth International Conference on Network Protocols
A Bidding Protocol for Deploying Mobile Sensors
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Differentiated surveillance for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Sensor deployment and target localization in distributed sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Co-Grid: an efficient coverage maintenance protocol for distributed sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Intelligent fluid infrastructure for embedded networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Power conservation and quality of surveillance in target tracking sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Stochastic event capture using mobile sensors subject to a quality metric
Proceedings of the 12th annual international conference on Mobile computing and networking
The self-protection problem in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
IEEE Communications Magazine
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Providing field coverage is a key task in many sensor network applications. With unevenly distributed static sensors, quality coverage with acceptable network lifetime is often difficult to achieve. Fortunately, recent advances on embedded and robotic systems make mobile sensors possible, and we suggest that a small set of mobile sensors can be leveraged toward a cost-effective solution for field coverage. There are, however, a series of fundamental questions to be answered in such a hybrid network of static and mobile sensors: (1) Given the expected coverage quality and system lifetime, how many mobile sensors should be deployed? (2) What are the necessary coverage contributions from each type of sensors? (3) What working and moving patterns should the sensors adopt to achieve the desired coverage contributions? In this article, we offer an analytical study on these problems, and the results lead to a practical system design. Specifically, we present an optimal algorithm for calculating the contributions from different types of sensors, which fully exploits the potentials of the mobile sensors and maximizes the network lifetime. We then present a random walk model for the mobile sensors. The model is distributed with very low control overhead. Its parameters can be fine-tuned to match the moving capability of different mobile sensors and the demands from a broad spectrum of applications. A node collaboration scheme is then introduced to further enhance the system performance. We demonstrate through analysis and simulation that, in our mobile assisted design, a small set of mobile sensors can effectively address the uneven distribution of the static sensors and significantly improve the coverage quality.