On k-connectivity for a geometric random graph
Random Structures & Algorithms
Wireless integrated network sensors
Communications of the ACM
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A taxonomy of wireless micro-sensor network models
ACM SIGMOBILE Mobile Computing and Communications Review
A coverage-preserving node scheduling scheme for large wireless sensor networks
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
A directionality based location discovery scheme for wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Connected sensor cover: self-organization of sensor networks for efficient query execution
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Set k-cover algorithms for energy efficient monitoring in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies
IEEE Transactions on Mobile Computing
On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing)
Efficient and robust protocols for local detection and propagation in smart dust networks
Mobile Networks and Applications
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Random Coverage with Guaranteed Connectivity: Joint Scheduling for Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Towards optimal sleep scheduling in sensor networks for rare-event detection
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Energy balanced data propagation in wireless sensor networks
Wireless Networks
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
IEEE Transactions on Parallel and Distributed Systems
Review: From wireless sensor networks towards cyber physical systems
Pervasive and Mobile Computing
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Due to the application-specific nature of wireless sensor networks, the sensitivity to coverage and data reporting latency varies depending on the type of applications. In light of this, algorithms and protocols should be application-aware to achieve the optimum use of highly limited resources in sensors and hence to increase the overall network performance. This paper proposes a probabilistic constrained random sensor selection (CROSS) scheme for application-aware sensing coverage with a goal to maximize the network lifetime. The CROSS scheme randomly selects in each round (approximately) k data-reporting sensors which are sufficient for a user/application-specified desired sensing coverage (DSC) maintaining a minimum distance between any pair of the selected k sensors. We exploit the Poisson sampling technique to force the minimum distance. Consequently, the CROSS improves the spatial regularity of randomly selected k sensors and hence the fidelity of satisfying the DSC in each round, and the connectivity among the selected sensors increase. To this end, we also introduce an algorithm to compute the desired minimum distance to be forced between any pair of sensors. Finally, we present the probabilistic analytical model to measure the impact of the Poisson sampling technique on selecting k sensors, along with the optimality of the desired minimum distance computed by the proposed algorithm.