Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Introduction to Algorithms
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Topology control for wireless sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
The impact of spatial correlation on routing with compression in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
A Minimum Cost Heterogeneous Sensor Network with a Lifetime Constraint
IEEE Transactions on Mobile Computing
Low-coordination topologies for redundancy in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
An Ultra Low Power System Architecture for Sensor Network Applications
Proceedings of the 32nd annual international symposium on Computer Architecture
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Avoiding Energy Holes in Wireless Sensor Networks with Nonuniform Node Distribution
IEEE Transactions on Parallel and Distributed Systems
Accurate, fast fall detection using posture and context information
Proceedings of the 6th ACM conference on Embedded network sensor systems
Using mobile wireless sensors for in-situ tracking of debris flows
Proceedings of the 6th ACM conference on Embedded network sensor systems
Battery allocation for wireless sensor network lifetime maximization under cost constraints
Proceedings of the 2009 International Conference on Computer-Aided Design
Benefits of multiple battery levels for the lifetime of large wireless sensor networks
NETWORKING'05 Proceedings of the 4th IFIP-TC6 international conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Deployment strategy of WSN based on minimizing cost per unit area
Computer Communications
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We aimed to deploy wireless sensor networks with guaranteed lifetimes for outdoor monitoring projects. The provision of a guaranteed lifetime has rarely been studied in previous deployment problems. The use of battery packs as the power source for sensors is common in many applications involving outdoor wireless sensor networks (WSNs). Because unified battery power is unable to provide both efficient collection and balance workload, we address the deployment procedure by considering adjustable battery packs. The key issue is determining the minimum number of battery packs required to guarantee both the system efficiency and lifetime. We formulate a constrained multiple deployment problem with energy models of the battery energy budget and sensor operations. The optimal solution is obtained using integer linear programming. We derived a lower bound of the deployment cost in terms of the number of battery packs. Due to the high time complexity for solving the optimal solution, we also propose two heuristics with polynomial-time complexity: (1) a battery-aware routing algorithm that selects routing paths based on consideration of the battery usage, and (2) a refinement procedure to improve existing WSN deployments by adjusting traffic in order to reduce the cost. Theoretical analyses of the proposed algorithms revealed their time complexity. We performed extensive simulations to evaluate the proposed algorithms in terms of the deployment cost and residual energy. The results show that our algorithm generates deployments close to the lower bound.