Data transformation and query management in personal health sensor networks

Abstract

Sensor technology has been exploited in many application areas ranging from climate monitoring, to traffic management, and healthcare. The role of these sensors is to monitor human beings, the environment or instrumentation and provide continuous streams of information regarding their status or well being. In the case study presented in this work, the network is provided by football teams with sensors generating continuous heart rate values during a number of different sporting activities. In wireless networks such as these, the requirement is for methods of data management and transformation in order to present data in a format suited to high level queries. In effect, what is required is a traditional database-style query interface where domain experts can continue to probe for the answers required in more specialised environments. The challenge arises from the gap that emerges between the low level sensor output and the high level user requirements of the domain experts. This paper describes a process to close this gap by automatically harvesting the raw sensor data and providing semantic enrichment through the addition of context data.

LifeLogging: Personal Big Data

“…Typical of this kind of sensing is where a single integrated sensor platform is not available for monitoring participants, say combining heart rate or respiration rate with on-field location or movement data (speed, distance covered, etc.) but instead the challenges of time alignment, normalised sampling rates and handling missing or error-some data are addressed directly, see Roantree et al (2012).…”

FibLSS: A Scalable Label Storage Scheme for Dynamic XML Updates

“…In earlier work [20], we managed repositories with large numbers of sensor values where repeated transformations were required to calibrate data in order to make it usable. However, certain aspects of sensor networks require different labels to what was managed in this current paper.…”

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

“…Sensing device: We use the energy model of Crossbow's TelosB [26] research sensor device to validate our ideas. TelosB is an ultra-low power wireless sensor equipped with an 8 MHz MSP430 core, 1MB of external flash storage, and a 250kbps Chipcon (now Texas Instruments) CC2420 RF Transceiver that consumes 23mA in receive mode (Rx), 19.5mA in transmit mode (Tx), 7.8mA in active mode (MCU active) with the radio off and 5.1µA in sleep mode.…”

“…For instance, a typical MICA mote with a 2KB SRAM might need to exploit the 512KB on-chip flash memory, while Intel's iMote might easily store these results in the 64KB SRAM. There is a growing trend for more available local storage in sensor devices [26] and therefore local buffering of results is not a threat to our model.…”