WISE Design of Indoor Wireless Systems: Practical Computation and Optimization
IEEE Computational Science & Engineering
Impact of radio irregularity on wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Models and solutions for radio irregularity in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
ATPC: adaptive transmission power control for wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Improving wireless simulation through noise modeling
Proceedings of the 6th international conference on Information processing in sensor networks
An analysis of unreliability and asymmetry in low-power wireless links
ACM Transactions on Sensor Networks (TOSN)
Evaluation of impact factors on RSS accuracy for localization and tracking applications
Proceedings of the 5th ACM international workshop on Mobility management and wireless access
Exploring in-situ sensing irregularity in wireless sensor networks
Proceedings of the 5th international conference on Embedded networked sensor systems
Assessment of urban-scale wireless networks with a small number of measurements
Proceedings of the 14th ACM international conference on Mobile computing and networking
Propagation measurements and models for wireless communications channels
IEEE Communications Magazine
Context-aware wireless sensor networks for assisted living and residential monitoring
IEEE Network: The Magazine of Global Internetworking
A Pragmatic Approach to Wireless Channel Simulation in Built Environments
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
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The robust operation of many sensor network applications depends on deploying relays to ensure wireless coverage. Radio mapping aims to predict network coverage based on a small number of link measurements. This problem is particularly challenging in complex indoor environments where walls significantly affect radio signal propagation. Nevertheless, we show that it is feasible to accurately predict coverage through a two-step process: a propagation model is used to predict signal strength at a recipient node, which is then mapped to a coverage prediction. Through an in-depth empirical study, we show that complex models do not necessarily produce accurate estimates of signal strength: there is an important tradeoff between model accuracy and the number of parameters that must be estimated from limited training data. We find that the best performance is achieved by a family of models which classify walls based on their attenuation into a small number of classes and develop an algorithm to perform this classification automatically. Based on these insights, we build a novel Radio Mapping Tool (RMT) for predicting radio converge in indoor environments. Experimental results demonstrate RMT's effectiveness in two buildings: RMT reduces the number of locations where coverage is erroneously predicted to exist by as much as 39% and 54% compared to the classic log-normal radio propagation model.