Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Utility-based decision-making in wireless sensor networks
MobiHoc '00 Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing
Decentralizing Query Processing in Sensor Networks
MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
Structural damage detection and localization using NETSHM
Proceedings of the 5th international conference on Information processing in sensor networks
Application-Oriented Flow Control forWireless Sensor Networks
ICNS '07 Proceedings of the Third International Conference on Networking and Services
The impact of data aggregation on the performance of wireless sensor networks
Wireless Communications & Mobile Computing
Fundamental design issues for the future Internet
IEEE Journal on Selected Areas in Communications
Hi-index | 0.00 |
In recent years, improved wireless technologies have enabled the low-cost deployment of large numbers of sensors for a wide range of monitoring applications. Because of the computational resources (processing capability, storage capacity, etc.) collocated with each sensor in a wireless network, it is often possible to perform advanced data analysis tasks autonomously and in-network, eliminating the need for the post-processing of sensor data. With new parallel algorithms being developed for in-network computation, it has become necessary to create a framework in which all of a wireless network's scarce resources (CPU time, wireless bandwidth, storage capacity, battery power, etc.) can be best utilized in the midst of competing computational requirements. In this study, a market-based method is developed to autonomously distribute these scarce network resources across various computational tasks with competing objectives and/or resource demands. This method is experimentally validated on a network of wireless sensing prototypes, where it is shown to be capable of Pareto-optimally allocating scarce network resources. Then, it is applied to the real-world problem of rupture detection in shipboard chilled water systems.