TAU-FIVE: A Multi-tiered Architecture for Data Quality and Energy-Sustainability in Sensor Networks

Abstract

Current research on wireless sensor networks "WSNs" in the Internet of Things "IoT" has focused on performance, scalability and energy efficiency. Innovations in these areas have many challenges due to the increasing volume of smart device data streams in the internet of Everything "IoE". Data feeds from future IoE systems such as the internet of vehicles, smart homes and smart-cities will need real time consolidation. This merger of technologies will require innovative big data algorithms and architectures that authenticate the data streams. A primary concern is in dynamically quantifying the data quality "DQ" of the streams while constructing real-time metrics to assess the energy efficiency "EE" of these IoE devices. In order to define the relationship between sensor stream DQ and EE, we propose our multi-tiered cloud-service architecture TAU-FIVE. The technical contributions of our framework includes data quality and energy efficiency models based on 7 DQ attributes and multiple reprogrammable smart sensors that dynamically modify and regulate the DQ and EE of a WSN. Our research maintains that WSN's can balance sustainability with quality of service by creating real-time metrics that merge energy usage with data stream integrity. This equilibrium will impact energy awareness in the IoT as the multitude of batch device data streams are integrated with the variety of social and professional networks and evolve into the IoE.