On Handling QoS Traffic in Wireless Sensor Networks
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 9 - Volume 9
Aggregator-centric QoS for body sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Body area networking standardization: present and future directions
Proceedings of the ICST 2nd international conference on Body area networks
CareNet: an integrated wireless sensor networking environment for remote healthcare
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Novel QoS scheduling and energy-saving MAC protocol for body sensor networks optimization
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
A study on proposed IEEE 802.15 WBAN MAC protocols
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Design considerations for the WISDM smart phone-based sensor mining architecture
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
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Streaming and analysis of time series body sensor data have been well investigated recently for various applications, especially on health data monitoring. However, existing strategies work independently from each other. Obviously, lacking appropriate information sharing, feedback, and interaction mechanisms, these strategies, even in combination, do not provide an efficient and effective solution for real time body sensor data collection, transmission, and analysis. In this work, we propose a data analysis driven framework with feedback for efficient streaming of body sensor data. The core idea of this framework is based on a data analysis algorithm specific variance threshold that identifies the data reliability requirement. Then, a reliability index is generated and sent from the data aggregator to sensors as a feedback to guide the streaming protocol. At the sensor side, a data importance ranking and grouping strategy is designed so that samples that affect data analysis most significantly are given higher priority for transmission.