Data Quality and Energy Management Tradeoffs in Sensor Service Clouds

Cloud-based sensor data collection services are becoming an essential part of the Internet of Things (IoT). As the consumer demand grows for these services, the data quality (DQ) of the stream becomes an increasingly vital issue. Of particular interest is the inherent tradeoff between the DQ and the energy consumption of the sensor. Unfortunately, there has been very little research on the management of this tradeoff that allows data consumers to receive high quality data while simultaneously conserving energy. Our work seeks to explore this tradeoff in detail by combining DQ services for the data stream consumer with customizable energy efficient "EE" throttling algorithms for the data feed producers. These energy management services provide cost reduction rewards for consumers who would otherwise make poor DQ/EE decisions. Our primary contributions include cloud-based services for monitoring the tradeoff, an architecture that adjusts to DQ needs and a producer/consumer data stream best matching cloud service. We envision that our services architecture will reward energy efficiency decisions and profoundly affect consumer choices.