On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
Protocols and Architectures for Wireless Sensor Networks
Protocols and Architectures for Wireless Sensor Networks
The DISCO network calculator: a toolbox for worst case analysis
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Modeling and Worst-Case Dimensioning of Cluster-Tree Wireless Sensor Networks
RTSS '06 Proceedings of the 27th IEEE International Real-Time Systems Symposium
Traffic Splitting with Network Calculus for Mesh Sensor Networks
FGCN '07 Proceedings of the Future Generation Communication and Networking - Volume 02
A Comprehensive Worst-Case Calculus for Wireless Sensor Networks with In-Network Processing
RTSS '07 Proceedings of the 28th IEEE International Real-Time Systems Symposium
Sensor network calculus – a framework for worst case analysis
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Application of network calculus to guaranteed service networks
IEEE Transactions on Information Theory
An analytical model of a hybrid network of static and mobile sensors
Proceedings of the 9th Asian Internet Engineering Conference
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Node deployment is a fundamental issue to be solved in Wireless Sensor Networks (WSNs). A proper node deployment scheme can reduce the complexity of problems in WSNs as, for example, routing, data fusion, communication, etc. Furthermore, it can extend the lifetime of WSNs by minimizing energy consumption. In this paper, we investigate random and deterministic node deployments for large-scale WSNs under the following performance metrics: coverage, energy consumption, and message transfer delay. We consider three competitors: a uniform random, a square grid, and a pattern-based Tri-Hexagon Tiling (THT) node deployment. A simple energy model is formulated to study energy consumption for each deployment strategy. Using basic geometry we propose a novel strategy for calculating the relative frequency of exactly k-covered points, which uses k-coverage maps, for both a square grid and THT. To model and consequently control the worst-case delay of a given WSN we build upon the so-called sensor network calculus (a recent methodology introduced in [7]). Finally, we analyze tradeoffs between these performance metrics for each deployment strategy to show which strategy is preferable under what factors, e.g., the number of nodes.