A Quality-Aware Multi-Level Data Aggregation Approach to Manage Smart Grid AMI Traffic.

The inclusion of various intelligent electronic devices such as smart meters for AMI is expected to result in intermittent or frequent communications network congestion if additional network infrastructure investments are not made. One approach to deal with such a data volume challenge in smart grids without additional investments to increase network capacity is to aggregate data streams within the network whenever network congestion happens, but this needs to be done carefully so as not to significantly impact the applications that need the data. This paper proposes a novel approach to manage AMI data traffic volume in multi-level data collection trees through data aggregation that estimates the expected network delays messages would suffer and dynamically determines an aggregation policy to be applied at forwarding nodes of the tree to reduce the network delays with which the electric utility can get the necessary information. The proposed algorithm is evaluated for different network congestion scenarios using the NS-3 simulator. The results illustrate that the algorithm is immensely effective in controlling the increase in network latencies as congestion levels increase in AMI networks. In addition, it does well in satisfying quality-of-service (QoS) requirements in terms of the data granularity required by smart grid applications. [ABSTRACT FROM AUTHOR]