Resource allocation model for cloud-fog-based smart grid DOI Creative Commons

Zajim Aljicevic,

Suad Kasapović,

Jasna Hivziefendić

et al.

Science and Technology for Energy Transition, Journal Year: 2023, Volume and Issue: 78, P. 28 - 28

Published: Jan. 1, 2023

This paper investigates the allocation model, flexibility, and scalability of fully distributed communication architectures for metering systems in smart grids. Smart infrastructure aggregates data from Meters (SMs) sends collected to fog or cloud centres be stored analysed. The system needs scalable reliable respond increased demand with minimal cost. problem is find optimal distribution application among devices, clouds. need support computing at marginal resources, which can hosted within building itself shared construction complex, has become important over recent years. resource model presented optimize cost resources communications relevance parts (the processing cost). helps connectivity on edge network. explains how calculation/analysis performed closer collection site complement analysis that would undertaken centre. Results a range typical scenarios are show effectiveness proposed method.

Language: Английский

Crack Detection in Concrete Structures Using Deep Learning DOI Open Access

Vaughn Peter Golding,

Zahra Gharineiat, Hafiz Suliman Munawar

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(13), P. 8117 - 8117

Published: July 2, 2022

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically maintain its reliability and structural health. Visual signs of cracks depressions indicate stress wear tear over time, leading failure/collapse if these are located at critical locations, in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long times rely on their empirical subjective knowledge. This lengthy process results delays that further compromise the infrastructure’s integrity. To address this limitation, study proposes a deep learning (DL)-based autonomous crack detection method using convolutional neural network (CNN) technique. improve CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training pretrained VGG16 architecture create different models. The chosen methods (grayscale, thresholding, edge detection) have been used image processing (IP) detection, but not DL. found grayscale models (F1 score 10 epochs: 99.331%, 20 99.549%) had similar 99.432%, 99.533%), with increasing greater rate more (grayscale: +2 TP, +11 TN images; RGB: +4 images). thresholding edge-detection reduced compared (20-epoch F1 −0.723%, −0.402%). suggests DL does colour. Hence, model has implications automated concrete infrastructures gathered information.

Language: Английский

Citations

70
A comprehensive review of advancements in green IoT for smart grids: Paving the path to sustainability DOI Creative Commons

P. Pandiyan,

S. Saravanan,

Raju Kannadasan

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 5504 - 5531

Published: May 22, 2024

Electricity consumption is increasing rapidly, and the limited availability of natural resources necessitates efficient energy usage. Predicting managing electricity costs challenging, leading to delays in pricing. Smart appliances Internet Things (IoT) networks offer a solution by enabling monitoring control from broadcaster side. Green IoT, also known as Things, emerges sustainable approach for communication, data management, device utilization. It leverages technologies such Wireless Sensor Networks (WSN), Cloud Computing (CC), Machine-to-Machine (M2M) Communication, Data Centres (DC), advanced metering infrastructure reduce promote environmentally friendly practices design, manufacturing, IoT optimizes processing through enhanced signal bandwidth, faster more communication. This comprehensive review explores advancements smart grids, paving path sustainability. covers energy-efficient communication protocols, intelligent renewable integration, demand response, predictive analytics, real-time monitoring. The importance edge computing fog allowing distributed intelligence emphasized. addresses challenges, opportunities presents successful case studies. Finally, concludes outlining future research avenues providing policy recommendations foster advancement IoT.

Language: Английский

Citations

12
Dynamic GNN-based multimodal anomaly detection for spatial crowdsourcing drone services DOI Creative Commons
Junaid Akram, Walayat Hussain, Rutvij H. Jhaveri

et al.

Digital Communications and Networks, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

1
Chained-Drones: Blockchain-based privacy-preserving framework for secure and intelligent service provisioning in Internet of Drone Things DOI
Junaid Akram, Muhammad Umair, Rutvij H. Jhaveri

et al.

Computers & Electrical Engineering, Journal Year: 2023, Volume and Issue: 110, P. 108772 - 108772

Published: June 15, 2023

Language: Английский

Citations

22
Edge Offloading in Smart Grid DOI Creative Commons
Gabriel Ioan Arcas, Tudor Cioara, Ionuț Anghel

et al.

Smart Cities, Journal Year: 2024, Volume and Issue: 7(1), P. 680 - 711

Published: Feb. 19, 2024

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications services to improve resilience responsiveness ensure closer real-time control. However, the large-scale integration Internet Things (IoT) devices has led generation significant amounts data at edge grid, posing challenges for traditional cloud-based smart-grid architectures meet stringent latency response time requirements emerging applications. In this paper, we delve into grid computational distribution architectures, including edge–fog–cloud models, orchestration, frameworks support design offloading across continuum. Key factors influencing process, such as network performance, Artificial Intelligence (AI) processes, requirements, application-specific factors, efficiency, are analyzed considering operational requirements. We conduct a comprehensive overview current research landscape decision-making regarding strategies from cloud fog or edge. focus is on metaheuristics identifying near-optimal solutions reinforcement learning adaptively optimizing process. A macro perspective determining when what offload in provided next-generation AI applications, offering an features trade-offs selecting between federated solutions. Finally, work contributes understanding grids, providing Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis cost–benefit strategies.

Language: Английский

Citations

8
An enhanced coati optimization algorithm for optimizing energy management in smart grids for home appliances DOI Creative Commons

S. Balavignesh,

C. Kumar,

Ramalingam Sripriya

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 3695 - 3720

Published: March 22, 2024

This research presents an innovative approach to energy management in smart homes, aiming efficiently regulate demands while ensuring customer loyalty. The focus is on addressing the limitations of existing demand-side (DSM) programs, which predominantly target residential sector. proposed solution introduces Adaptive Coati Optimization algorithm, optimizes device organization based Critical-Peak-Price and Real-Time-Price power payment systems. By strategically managing consumption, algorithm reduces electrical expenses peaks without compromising user convenience. study evaluates effectiveness across three operational periods (60 minutes, 12 24 minutes) align with varying needs. Overall, offers a promising for cost-efficient combining both financial benefits enhanced satisfaction. results indicate significant decrease tariffs rates, up 30%, leading 20% increase satisfaction 25% improvement cost utilization.

Language: Английский

Citations

8
Digital Twin-Driven Trust Management in Open RAN-Based Spatial Crowdsourcing Drone Services DOI
Junaid Akram, Ali Anaissi, Rajkumar Singh Rathore

et al.

IEEE Transactions on Green Communications and Networking, Journal Year: 2024, Volume and Issue: 8(3), P. 1061 - 1075

Published: May 21, 2024

We introduce "TMIoDT," a pioneering framework aimed at bolstering communication security in the Internet of Drone Things (IoDT) integrated with Open Radio Access Networks (Open RAN), specific focus on bushfire monitoring applications. Our novel contributions include seamless integration digital twin technology blockchain to establish robust trust management system IoDT context. This approach addresses critical vulnerabilities associated unsecured wireless networks IoDT, such as data integrity issues and susceptibility cyber threats. The TMIoDT encompasses mutual authentication mechanism secure interactions key exchanges among entities, including drones Unmanned Ground Vehicles (UGVs). Furthermore, it leverages for credible employs twins model UGV servers accurately, enhancing relationship modeling. An advanced Intrusion Detection System (IDS), utilizing Stacked Variational Autoencoder (SVA) Attention-based Bidirectional LSTM (ABL), is implemented anomaly detection, complemented by blockchain-based transaction writing scheme verification. comprehensive evaluation, ToN-IoT ICIDS-2017 network intrusion datasets, confirms TMIoDT's effectiveness significantly improving reliability IoDT.

Language: Английский

Citations

8
An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization DOI Creative Commons
Fahad Alsokhiry, Andres Annuk, Mohamed A. Mohamed

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(4), P. 1752 - 1752

Published: Feb. 4, 2023

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities can cause delays in optimal management tasks. Therefore, it is clear that a fast reliable architecture needed to make more efficient. This paper assesses using cloud computing (CC), fog computing, resource allocation problem. Technically, makes SG efficient if (CFC) are integrated. The integration (FC) with CC minimizes burden maximizes allocation. There three key features for proposed layer: awareness position, short latency, mobility. Moreover, CFC-driven framework manage among different agents. In order system efficient, FC allocates virtual machines (VMs) according load-balancing techniques. addition, present study proposes hybrid gray wolf differential evolution optimization algorithm (HGWDE) brings (GWO) improved (IDE) together. Simulation results conducted MATLAB verify efficiency suggested high transaction computational time. According results, response time HGWDE 54 ms, 82.1 81.6 ms faster than particle swarm (PSO), (DE), GWO. HGWDE's processing 53 81.2 80.6 PSO, DE, Although GWO bit HGWDE, difference not very significant.

Language: Английский

Citations

14
DroneSSL: Self-Supervised Multimodal Anomaly Detection in Internet of Drone Things DOI
Junaid Akram, Ali Anaissi, Wajdy Othman

et al.

IEEE Transactions on Consumer Electronics, Journal Year: 2024, Volume and Issue: 70(1), P. 4287 - 4298

Published: Feb. 1, 2024

In this study, we introduce a pioneering framework, DroneSSL, that integrates the concept of spatial crowdsourcing with TinyML to enhance anomaly detection in Internet Drone Things (IoDT). This innovative approach leverages drones and unmanned ground vehicles (UGVs) for expansive data collection environments are typically inaccessible or hazardous, such as during Australian bushfire incidents. By employing lightweight machine learning models alongside advanced communication technologies, DroneSSL transcends traditional spatial-temporal analysis methods. It efficiently processes multimodal from diverse Points-of-Interest (PoIs), significantly improving quality speed analysis. The framework's integration temporal feature extraction module Graph Neural Network (GNN) its adaptable, scalable GNN architecture tailor real-time operations resource-constrained IoDT environments. Achieving an 89.6% F1 score, marks substantial 4.9% improvement over existing approaches, highlighting effectiveness critical applications environmental surveillance emergency response. advancement not only showcases potential combining but also sets new standard efficient, detection, paving way future innovations IoT edge devices monitoring systems.

Language: Английский

Citations

6
Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing DOI Creative Commons
Saurabh Singhal, Senthil Athithan, Madani Abdu Alomar

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(7), P. 3488 - 3488

Published: March 27, 2023

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number automated systems has increased rapidly, which in turn necessitates the rise cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite challenges managing resources, longer response plus processing time, higher energy consumption, more people using Fog extends It adds that minimize traffic, increase security, speed up processes. fog help save by aggregating distributing submitted requests. paper discusses load-balancing approach Smart Grid Rock Hyrax Optimization (RHO) to optimize time consumption. proposed algorithm assigns tasks virtual machines for execution shuts off unused machines, reducing consumed machines. model is implemented on CloudAnalyst simulator, results demonstrate method better quicker with lower requirements compared both static dynamic algorithms. suggested reduces 26%, 15%, consumption 29%, cost 6%, delay 14%.

Language: Английский

Citations

11