Market-based resource allocation for distributed data processing in wireless sensor networks
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 10, 2013
- Source ID
- 10.1145/2442116.2442134
Entities
People
- Andrew T. Zimmerman
- Frank T. Ferrese
- Jerome Lynch
Organizations
- Naval Surface Warfare Center
- Office of Naval Research
- University of Michigan