A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid

Abstract

Emerging Internet of Things (IoT) technologies and applications have enabled the Smart Grid Utility control center to connect, monitor, control and exchange data between the smart appliances, smart meters (SMs), data concentrators (DCs) and control center server (CCS) over the Internet. In particular, DC receives different Advanced Metering Infrastructure (AMI) applications data from multiple SMs for processing, queuing, aggregation, and forwarding onward towards the CCS over the things networking. However, DCs are expensive component of the AMI network. Recently, SMs are used as relay-devices to accomplish a cost-effective AMI network infrastructure and avoid the DC placement and bottleneck problem. However, SMs are recourse constrained (limited CPU, RAM, storage, and network capacity) intelligent devices which faces numerous communication challenges during outage conditions and summer peak hours where bulk amount of data with different traffic rates and latency are exchanged with the Utility control center. Therefore, an efficient data aggregation at relay-devices is required to deal with high volume of data exchange rates in order to optimize the constrained-resources of the AMI network. In this article, we propose a hybrid data aggregation strategy implemented on an aggregator-head (AH) in the clustering topology which performs data aggregation on the Interval Meter Reading (IMR) application data. AH induction greatly reduce the workload of the cluster-heads (CHs), and efficiently utilizes the constrained-resource of AMI devices in a cost effective-manner. The proposed strategy is evaluated for different existing approaches using the CloudSim simulation tool. Experimental and simulation results are obtained and compared which show the effectiveness of the proposed strategy such that limited resources are optimized, CH workload is minimized, and QoS of AMI applications are maintained.