QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing

Abstract

Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as “pay as per usage”. However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

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

“…This study extends our past works [18], [19] in order to minimize the data traffic workload and optimize the limited resources of cluster-heads (CHs) in the IoT-enabled AMI network. We propose an efficient hybrid data aggregation strategy employed at the aggregator-head (AH) such that the data aggregation operation of IMR application data is shifted from CH to AH in each cluster in the NAN topology.…”

“…In our recent research works [18], [19] we emphasized on AMI applications traffic handling with QoS provisioning in IoT-enabled Smart Grid network coupled with cloud computing. In [18], SMs (relay-nodes) are clustered using modified K-Means algorithm to extend the AMI network coverage such that to eliminate the DC hotspot problem and reduce additional cost required in the communication network topology.…”

“…In [18], SMs (relay-nodes) are clustered using modified K-Means algorithm to extend the AMI network coverage such that to eliminate the DC hotspot problem and reduce additional cost required in the communication network topology. Similarly, a hybrid queue scheduling (HQS) scheme is proposed in [19] for AMI applications traffic in Smart Grid network. Further, AMI traffic are classified and scheduled using the priority metrics of these traffics in order to lower the cost of cloud service and ensure QoS in the Smart Grid network.…”

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

“…In mMTC, it accommodates large numbers of IoT devices with its passive operation and minimal energy requirements, addressing scalability and device connectivity efficiently [9]. For most mMTC applications, the typical uplink data rate falls within the range of 1-100 kb/s (kb/s), serving the needs of Massive IoT implementations such as smart grids and agricultural monitoring [10], [11], [12], [13], [14]. On the other hand, low data rate URLLC applications such as tactile interaction can leverage ABC's capabilities to achieve their data rate requirements.…”

Deep Neural Networks for Enhanced Security: Detecting Metamorphic Malware in IoT Devices

“…The IoT ecosystem [3] has enabled devices (things) such as computing servers, people, digital machines, and networks which are used almost anywhere in the global world to reduce labor and cost via process automa-tion in companies and industries [4]. The scope of IoT applications have enabled the hospitals, businesses, residences, and organizations [5] to continuously adopt inter-connected IoT devices for increased connectivity and convenience. Conventional computing platforms employ hardware or sensors with a specific function.…”