Towards a data quality-aware framework for cloud-based sensor services
Lawson, Victor John
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As the Internet of Things (IoT) paradigm gains popularity, the next few years will likely witness ‘servitization’ of domain sensing functionalities. In this setting, cloud-based eco-systems will exist in which high quality data from large numbers of independently-managed wireless sensor networks are shared or even traded in real-time. Such an eco-system will necessarily have multiple stakeholders such as sensor data providers, domain applications that utilize sensor data (data consumers), and cloud infrastructure providers who may collaborate as well as compete. Information systems that implement sensors as a service incorporate several major challenges. One such challenge is in the design, development, implementation and management of energy efficient dynamic green information systems. A second major challenge is to provide data streams with high data quality (DQ) to the consumer. A third challenge is to reduce the sensor energy usage and maintain energy efficiency of the sensors while providing dynamically adjustable smart sensors. A fourth challenge is to monitor and adjust the inherent tradeoff between the data quality of the data stream and the energy consumption of the sensor. A final challenge is in creating cloud services that handle the issues associated with the variety of mobile sensor devices including high volume flow, device tracking positioning and data control problems. 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. To address this issue, a multi-tiered cloud-service architecture called TAU-FIVE was designed and implemented. The technical contributions of this framework include data quality and energy efficiency models based on 7 DQ attributes implemented in a 5 tiered distributed system. This equilibrium is likely to impact energy awareness in the IoT as batch device data streams are integrated with the variety of social and professional networks in the Internet of Everything.