A Quality-Aware and Energy-Efficient Context Management Framework for Ubiquitous Systems

Sensor-rich Context Management Frameworks (CMF) for Ubiquitous Systems should be able to continuously gather raw data from observed entities in order to characterize the current situation. However, the death of independent sensors and monitoring platform reduce the ability of CMF for detecting the current situation, which directly affects the availability of context-aware applications/services. This paper proposes a quality-aware data reduction approach to minimize the amount of sensed raw data sent to CMF, reducing the energy consumption and network traffic. The proposed approach, based on Adaptative Simple Linear Regression (ASLR), rebuilds the gathered raw data that was not intentionally sent to CMF by prediction. Quality requirements defined on gathered data (Quality of Context) are respected by the reduction approach, avoiding the loss of precision (QoCI precision) and timeliness (QoCI up-to-dateness). The proposed data reduction approach has been integrated into our Context Management Framework (CxtMF), which provides context information for two context-sensitive services: beehive and ECG monitoring services. Experimental results indicate that the proposed approach reduces the amount of packets sent over network to 3% for the ECG monitoring service, and 12.15% for the beehive monitoring service, respectively.