A Bidding Protocol for Deploying Mobile Sensors
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Estimating Coverage Holes and Enhancing Coverage in Mixed Sensor Networks
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Movement-Assisted Sensor Deployment
IEEE Transactions on Mobile Computing
Trade-offs between mobility and density for coverage in wireless sensor networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
A n5/2 algorithm for maximum matchings in bipartite
SWAT '71 Proceedings of the 12th Annual Symposium on Switching and Automata Theory (swat 1971)
On Minimizing the Maximum Sensor Movement for Barrier Coverage of a Line Segment
ADHOC-NOW '09 Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks
Prediction-based energy map for wireless sensor networks
Ad Hoc Networks
IEEE Communications Magazine
Mesh-based sensor relocation for coverage maintenance in mobile sensor networks
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Error analysis of range-based localisation algorithms in wireless sensor networks
International Journal of Sensor Networks
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Network coverage is one of the most decisive factors for determining the efficiency of a WSN. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid WSN containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximise the minimum remaining energy of all moved sensors since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. When the target area is too big, we present a scalable area-based algorithm which returns a near optimal solution. Furthermore, we also present a lightweight-distributed scheduling strategy for mobile sensors in case of small sensor failures.