Dynamic Management of Cooperating Peers in Wireless Embedded Systems: An Approach Driven by Information Quality

Abstract

Data-intensive applications like remote sensing or peer-to-peer communications are increasingly prevalent on wireless-connected, pervasive systems platforms. Since resource limitations make it infeasible to deliver all data to all participants at all times, these applications' data streams must be managed at runtime. The idea explored in this paper is to continuously manage data streams in terms of the quality of the information received by end users. Methods are developed that dynamically optimize application-relevant metrics of information quality. These methods are realized in peer-to-peer data delivery middleware, by continuously adapting application-specific, source-resident data filters when delivering information to end users. Results are presented for portable devices operating across 802.11b-based wireless networks, using camera-captured images as sample sensor data and using filters that implement a variety of data downsampling methods, with consequent effects on information quality. Experimental evaluations show that (1) dynamic management of information quality is feasible and generates substantial overall quality gains compared to non-managed approaches, and (2) that the complexity of multi-sensor, multi-client quality management indicates the use of heuristic solution algorithms specific to certain system configurations.