Cognitive transmission based on data priority classification in WSNs for Smart Grid

Smart Grid integrates digital processing, sensor technology, automatic control and communication to the traditional power grid to achieve more efficient electricity distribution and management. Applying wireless sensor networks (WSNs) to Smart Grid can greatly facilitate the real-time information exchange within the power management system, and enable fast adaptation of the system to environmental changes. However, there are many challenges that need to be addressed for applying WSNs to the Smart Grid. One critical issue is how to receive data at the controller´s node in a timely manner considering the typically time sensitive environment in Smart Grid and the limited battery power supply in WSNs. Based on data classification, this paper proposes a data transmission strategy in WSNs. After appropriate processing while collecting those large and complex data during aggregation, they are classified into different priority levels according to their various features. This classification process ensures the most important data which occupy a quite small percent of the total amount. The proposed cognitive transmission measure can guarantee a minimum delay for the most key data under the constraints of the electric-power-system environment and battery power supply. We offer simulation results to show the performance of the proposed cognitive transmission scheme.