Pub Date : 2025-11-11DOI: 10.1016/j.suscom.2025.101242
Ankita Sarkar , Mansi Jhamb
The expansion of Internet of Things (IoT) devices has revolutionized various industries, particularly healthcare, where the Internet of Medical Things (IoMT) enables real-time data collection, analysis, and secure transmission of sensitive patient information. However, these resource-constrained devices face significant security challenges, particularly with the advent of quantum computing. This work introduces an intelligent cryptographic framework tailored to address these challenges, integrating lightweight cryptographic primitives, chaotic systems, and quantum-resistant techniques. Performance evaluation using image metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) demonstrates the framework's effectiveness. The results indicate an average MSE of 5590.816, an MAE of 83.909, a PSNR of 8.044 dB, and an SSIM of 0.0224, showcasing strong encryption and minimal data distortion. Furthermore, this hybrid cryptographic system ensures diffusion, nonlinearity, randomness, and strong key dependency while demonstrating resistance to cryptanalytic and quantum attacks. The proposed framework is computationally competent, making it particularly well-suited for resource-constrained IoMT devices with a minimum energy consumption of 3.536 µJ.
{"title":"A novel ultra-low power post quantum approach using artificial intelligence based key generation for cyber physical system in Internet of things","authors":"Ankita Sarkar , Mansi Jhamb","doi":"10.1016/j.suscom.2025.101242","DOIUrl":"10.1016/j.suscom.2025.101242","url":null,"abstract":"<div><div>The expansion of Internet of Things (IoT) devices has revolutionized various industries, particularly healthcare, where the Internet of Medical Things (IoMT) enables real-time data collection, analysis, and secure transmission of sensitive patient information. However, these resource-constrained devices face significant security challenges, particularly with the advent of quantum computing. This work introduces an intelligent cryptographic framework tailored to address these challenges, integrating lightweight cryptographic primitives, chaotic systems, and quantum-resistant techniques. Performance evaluation using image metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) demonstrates the framework's effectiveness. The results indicate an average MSE of 5590.816, an MAE of 83.909, a PSNR of 8.044 dB, and an SSIM of 0.0224, showcasing strong encryption and minimal data distortion. Furthermore, this hybrid cryptographic system ensures diffusion, nonlinearity, randomness, and strong key dependency while demonstrating resistance to cryptanalytic and quantum attacks. The proposed framework is computationally competent, making it particularly well-suited for resource-constrained IoMT devices with a minimum energy consumption of 3.536 µJ.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101242"},"PeriodicalIF":5.7,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1016/j.suscom.2025.101250
Eatedal Alabdulkreem , Randa Allafi , Munya A. Arasi , P. Geetha , Faisal Mohammed Nafie , A.Sumaiya Begum , G. Nallasivan , S. Vivek
Urban air pollution remains one of the most pressing public health challenges, intensified by the rapid pace of urbanization and industrial development in modern cities. This research introduces a novel model that integrates the Internet of Things (IoT), blockchain, and edge computing to create a secure, real-time, and scalable air quality monitoring system tailored for urban environments. The core objective is to design a decentralized framework that ensures data integrity, minimizes latency, and automates responses to pollution events. Blockchain technology plays a crucial role by providing a transparent and tamper-proof ledger that preserves the historical record of air quality data while safeguarding its authenticity. Additionally, smart contracts embedded within the blockchain enable automated alerts whenever pollution levels exceed predefined safety thresholds allowing the system to respond instantly without human intervention. Through our experimental testing, we found our model provided an average data accuracy rating of more than 95 % and a data completeness level of more than 98 % with an input latency of less than 500 ms, and a power efficiency greater than 90 %, thus providing us with a more responsive and efficient system than existing cloud-based detection solutions. This research would provide an improved method to optimize the surveillance of urban environmental conditions, and assist with advancing additional public health protection confidently due to the scalable process featuring a customized model for a range of urban scenarios.
{"title":"Innovative IoT and blockchain integration for real-time urban air quality monitoring and autonomous response system","authors":"Eatedal Alabdulkreem , Randa Allafi , Munya A. Arasi , P. Geetha , Faisal Mohammed Nafie , A.Sumaiya Begum , G. Nallasivan , S. Vivek","doi":"10.1016/j.suscom.2025.101250","DOIUrl":"10.1016/j.suscom.2025.101250","url":null,"abstract":"<div><div>Urban air pollution remains one of the most pressing public health challenges, intensified by the rapid pace of urbanization and industrial development in modern cities. This research introduces a novel model that integrates the Internet of Things (IoT), blockchain, and edge computing to create a secure, real-time, and scalable air quality monitoring system tailored for urban environments. The core objective is to design a decentralized framework that ensures data integrity, minimizes latency, and automates responses to pollution events. Blockchain technology plays a crucial role by providing a transparent and tamper-proof ledger that preserves the historical record of air quality data while safeguarding its authenticity. Additionally, smart contracts embedded within the blockchain enable automated alerts whenever pollution levels exceed predefined safety thresholds allowing the system to respond instantly without human intervention. Through our experimental testing, we found our model provided an average data accuracy rating of more than 95 % and a data completeness level of more than 98 % with an input latency of less than 500 ms, and a power efficiency greater than 90 %, thus providing us with a more responsive and efficient system than existing cloud-based detection solutions. This research would provide an improved method to optimize the surveillance of urban environmental conditions, and assist with advancing additional public health protection confidently due to the scalable process featuring a customized model for a range of urban scenarios.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101250"},"PeriodicalIF":5.7,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1016/j.suscom.2025.101252
Mohammed Shuaib, Shadab Alam
The concept of decentralized energy trading is transforming the multiple ways of trading renewable energy, and the conventional method that requires aggregators is hindering speed and reliability. Therefore, we have suggested a decentralized IoT-blockchain architecture with Convolutional Neural Networks (CNN)-based fraud detection and K-Means cluster to match the prosumers and consumer. Our framework succeeds in the transaction in 93.9 % of cases compared to traditional aggregator-based trading platforms, which are characterized by a centralized system and delays in transactions, and achieve a higher fraud detection rate of 98.5. Also, it also improves energy distribution efficiency by 24.3 % and network resilience by 17.6 % and hence peer-to-peer markets can be made viable and secured. CNN model is used to identify anomalies (in real-time) as the clustering (best trade paths) is used to find the best trade paths based on demand profiles. To ensure the responsiveness, scalability, and security of the system, the simulations of trading and blockchain implementation scenarios were carried out in the MATLAB Simulink and Hyperledger Fabric. The current work has provided a more favorable platform to the decentralized paradigm of energy exchange by providing an intelligent, a faster and a safer model as compared to the traditional systems that were centralized around aggregators.
{"title":"A secure and energy-efficient IoT-blockchain framework for decentralized renewable energy trading","authors":"Mohammed Shuaib, Shadab Alam","doi":"10.1016/j.suscom.2025.101252","DOIUrl":"10.1016/j.suscom.2025.101252","url":null,"abstract":"<div><div>The concept of decentralized energy trading is transforming the multiple ways of trading renewable energy, and the conventional method that requires aggregators is hindering speed and reliability. Therefore, we have suggested a decentralized IoT-blockchain architecture with Convolutional Neural Networks (CNN)-based fraud detection and K-Means cluster to match the prosumers and consumer. Our framework succeeds in the transaction in 93.9 % of cases compared to traditional aggregator-based trading platforms, which are characterized by a centralized system and delays in transactions, and achieve a higher fraud detection rate of 98.5. Also, it also improves energy distribution efficiency by 24.3 % and network resilience by 17.6 % and hence peer-to-peer markets can be made viable and secured. CNN model is used to identify anomalies (in real-time) as the clustering (best trade paths) is used to find the best trade paths based on demand profiles. To ensure the responsiveness, scalability, and security of the system, the simulations of trading and blockchain implementation scenarios were carried out in the MATLAB Simulink and Hyperledger Fabric. The current work has provided a more favorable platform to the decentralized paradigm of energy exchange by providing an intelligent, a faster and a safer model as compared to the traditional systems that were centralized around aggregators.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101252"},"PeriodicalIF":5.7,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-09DOI: 10.1016/j.suscom.2025.101251
Sridhar Patthi , M. Karthiga , Kumari Priyanka Sinha , Suresh Kumar Mandala , Peruri Venkata Anusha , L. Bhagyalakshmi , P. Sreelatha , Manjunathan Alagarsamy
The integration of cutting-edge technologies like IoT, blockchain, artificial intelligence (AI) has transformed precision environmental management by making it safe, scalable and effective. The study tackles the urgent problem of a fragmented, inefficient, and wasteful environmental monitoring and management system in urban greening, and agriculture. Many of the existing monitoring and management systems lack interoperability, responsiveness in the field in real-time, responsiveness to data security, and reactivity to environmental conditions. To address these challenges, the work illustrates a unified and smart framework that applies IoT, blockchain, and AI to support autonomously altered, secure, and efficient management of ecosystems. The most significant goals are to provide more data security, decision automation, and maximizing resource utilization. Smart contracts are used to automate tasks like irrigation and regulating the temperature to deliver fast and accurate response. Scalability and versatility of the framework is illustrated in the application of the framework in various environments. Most significant results show significant enhancements in both efficiency and sustainability. The model reduced water and energy consumption by 30 % and vegetation health indices by 15 %. The blockchain integration guaranteed data integrity and zero tampering while AI-powered analytics decreased response times to less than one second. These findings reveal the model’s potential to revolutionize resource allocation in smart cities and agriculture. The major contribution of this work is establishing and verifying an integrated IoT-blockchain-AI framework, which provides not just a secure and real-time control of environmental monitoring and management, while demonstrating improved efficiency, sustainability, and scalability.
{"title":"Integration of Internet of Things blockchain and artificial intelligence for scalable and secure precision environmental management","authors":"Sridhar Patthi , M. Karthiga , Kumari Priyanka Sinha , Suresh Kumar Mandala , Peruri Venkata Anusha , L. Bhagyalakshmi , P. Sreelatha , Manjunathan Alagarsamy","doi":"10.1016/j.suscom.2025.101251","DOIUrl":"10.1016/j.suscom.2025.101251","url":null,"abstract":"<div><div>The integration of cutting-edge technologies like IoT, blockchain, artificial intelligence (AI) has transformed precision environmental management by making it safe, scalable and effective. The study tackles the urgent problem of a fragmented, inefficient, and wasteful environmental monitoring and management system in urban greening, and agriculture. Many of the existing monitoring and management systems lack interoperability, responsiveness in the field in real-time, responsiveness to data security, and reactivity to environmental conditions. To address these challenges, the work illustrates a unified and smart framework that applies IoT, blockchain, and AI to support autonomously altered, secure, and efficient management of ecosystems. The most significant goals are to provide more data security, decision automation, and maximizing resource utilization. Smart contracts are used to automate tasks like irrigation and regulating the temperature to deliver fast and accurate response. Scalability and versatility of the framework is illustrated in the application of the framework in various environments. Most significant results show significant enhancements in both efficiency and sustainability. The model reduced water and energy consumption by 30 % and vegetation health indices by 15 %. The blockchain integration guaranteed data integrity and zero tampering while AI-powered analytics decreased response times to less than one second. These findings reveal the model’s potential to revolutionize resource allocation in smart cities and agriculture. The major contribution of this work is establishing and verifying an integrated IoT-blockchain-AI framework, which provides not just a secure and real-time control of environmental monitoring and management, while demonstrating improved efficiency, sustainability, and scalability.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101251"},"PeriodicalIF":5.7,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.suscom.2025.101249
Ubaid ur Rehman
This systematic literature review (SLR) investigates the role of artificial intelligence (AI) in energy management systems (EMS) for smart cities, analyzing 85 studies from 2019 to 2025 using the PRISMA protocol and Biblioshiny tool for bibliometric analysis. The study uniquely identifies six thematic clusters IoT integration, renewable energy integration, energy forecasting, smart energy policies, AI optimization techniques, and blockchain-enabled systems revealing trends, gaps, and future directions. Key findings highlight AI’s transformative potential in energy optimization, demand response, and renewable integration, while pinpointing critical limitations such as scalability, computational complexity, and real-time adaptability. By proposing a novel six-step SLR methodology and actionable guidelines, this review bridges theoretical advancements with practical challenges, offering a roadmap for scalable, efficient, and resilient AI-driven EMS. This work provides researchers and practitioners with a comprehensive framework to advance sustainable urban energy systems, addressing gaps in scalability, ethical considerations, and real-world implementation.
{"title":"The future role of artificial intelligence in energy management systems for smart cities: A systematic literature review of trends, gaps, and future direction","authors":"Ubaid ur Rehman","doi":"10.1016/j.suscom.2025.101249","DOIUrl":"10.1016/j.suscom.2025.101249","url":null,"abstract":"<div><div>This systematic literature review (SLR) investigates the role of artificial intelligence (AI) in energy management systems (EMS) for smart cities, analyzing 85 studies from 2019 to 2025 using the PRISMA protocol and Biblioshiny tool for bibliometric analysis. The study uniquely identifies six thematic clusters IoT integration, renewable energy integration, energy forecasting, smart energy policies, AI optimization techniques, and blockchain-enabled systems revealing trends, gaps, and future directions. Key findings highlight AI’s transformative potential in energy optimization, demand response, and renewable integration, while pinpointing critical limitations such as scalability, computational complexity, and real-time adaptability. By proposing a novel six-step SLR methodology and actionable guidelines, this review bridges theoretical advancements with practical challenges, offering a roadmap for scalable, efficient, and resilient AI-driven EMS. This work provides researchers and practitioners with a comprehensive framework to advance sustainable urban energy systems, addressing gaps in scalability, ethical considerations, and real-world implementation.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101249"},"PeriodicalIF":5.7,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.suscom.2025.101247
Ali Ghaffari , Vesal Firoozi , Ali Maleki , Mohammad Sadegh Sirjani , Maedeh Abedini Bagha
As the proliferation of Internet of Things (IoT) devices continues unabated, the demand for efficient task scheduling mechanisms becomes increasingly critical. Task scheduling in the IoT is pivotal for optimizing resource utilization, minimizing latency, and enhancing the overall system’s performance. This research proposes a novel method called QTE-IoT, standing for a Q-learning-based task scheduling scheme to enhance energy consumption and QoS in IoT environments. QTE-IoT commences by categorizing tasks into three classes: time-sensitive tasks, security tasks, and normal tasks. This classification is achieved using a multi-layer perceptron artificial neural network. Subsequently, time-sensitive tasks are offloaded to the fog layer and scheduled using the proposed African Vulture Algorithm combined with Q-learning, which we designate as QAVA. Security tasks are offloaded to the private cloud, while normal tasks are offloaded to the public cloud. For task scheduling in private and public cloud environments, QTE-IoT employs a proposed enhanced version of Artificial Rabbits Optimization integrated with the Q-learning algorithm, known as QARO. Additionally, the QTE-IoT method incorporates a monitoring agent to oversee resource workload, thereby preventing congestion and delays. Simulation results on instances of the HCSP benchmark dataset demonstrate that QTE-IoT outperforms other state-of-the-art methods in various performance metrics. QTE-IoT achieves significant improvements compared to other methods and algorithms, including a 6–12 % reduction in energy consumption. Furthermore, QTE-IoT exhibits substantial improvements in load imbalance (42–79 %), response time (25–40 %), and deadline satisfaction (6–39 %) compared to existing approaches.
{"title":"QTE-IoT: Q-learning-based task scheduling scheme to enhance energy consumption and QoS in IoT environments","authors":"Ali Ghaffari , Vesal Firoozi , Ali Maleki , Mohammad Sadegh Sirjani , Maedeh Abedini Bagha","doi":"10.1016/j.suscom.2025.101247","DOIUrl":"10.1016/j.suscom.2025.101247","url":null,"abstract":"<div><div>As the proliferation of Internet of Things (IoT) devices continues unabated, the demand for efficient task scheduling mechanisms becomes increasingly critical. Task scheduling in the IoT is pivotal for optimizing resource utilization, minimizing latency, and enhancing the overall system’s performance. This research proposes a novel method called QTE-IoT, standing for a Q-learning-based task scheduling scheme to enhance energy consumption and QoS in IoT environments. QTE-IoT commences by categorizing tasks into three classes: time-sensitive tasks, security tasks, and normal tasks. This classification is achieved using a multi-layer perceptron artificial neural network. Subsequently, time-sensitive tasks are offloaded to the fog layer and scheduled using the proposed African Vulture Algorithm combined with Q-learning, which we designate as QAVA. Security tasks are offloaded to the private cloud, while normal tasks are offloaded to the public cloud. For task scheduling in private and public cloud environments, QTE-IoT employs a proposed enhanced version of Artificial Rabbits Optimization integrated with the Q-learning algorithm, known as QARO. Additionally, the QTE-IoT method incorporates a monitoring agent to oversee resource workload, thereby preventing congestion and delays. Simulation results on instances of the HCSP benchmark dataset demonstrate that QTE-IoT outperforms other state-of-the-art methods in various performance metrics. QTE-IoT achieves significant improvements compared to other methods and algorithms, including a 6–12 % reduction in energy consumption. Furthermore, QTE-IoT exhibits substantial improvements in load imbalance (42–79 %), response time (25–40 %), and deadline satisfaction (6–39 %) compared to existing approaches.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101247"},"PeriodicalIF":5.7,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-05DOI: 10.1016/j.suscom.2025.101248
Mohammed Shuaib
This research presents the federated deep-learning (DL) based cybersecurity platform of smart-grid automation with the focus on privacy, distributed intelligence and energy efficiency. The federated learning system allows grid-edge devices (such as substations and smart meters) to cooperate in training a threat-detection model without sharing raw data hence maintaining local confidentiality. The proposed structure is a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model, which runs locally to predict spatiotemporal threats and the synchronization of the model is done in a Federated Averaging (FedAvg) algorithm. The model achieves a Threat Detection Accuracy (TDA) of 97.2 per cent, and a False Alarm Rate of 3.6 per cent. Compared to centralized learning, communication overhead is reduced by 41 % and, hence, the control response latency is maintained. The importance of optimisation update intervals and pruning of edge models reduce energy consumption during training by 22 % of the original consumption. The resilience of the system to fake data injection and command-spoofing attacks is verified by simulation on the modified KDD 99 data set and real-grid situations in NS −3. The federated solution ensures scalable implementation of heterogeneous grid resources. In general, this study is a safe and energy-efficient approach towards the reduction of changing cyber threats within real-time smart-grid settings.
{"title":"Federated deep learning for secure and energy-efficient cyber threat mitigation in smart grid automation","authors":"Mohammed Shuaib","doi":"10.1016/j.suscom.2025.101248","DOIUrl":"10.1016/j.suscom.2025.101248","url":null,"abstract":"<div><div>This research presents the federated deep-learning (DL) based cybersecurity platform of smart-grid automation with the focus on privacy, distributed intelligence and energy efficiency. The federated learning system allows grid-edge devices (such as substations and smart meters) to cooperate in training a threat-detection model without sharing raw data hence maintaining local confidentiality. The proposed structure is a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model, which runs locally to predict spatiotemporal threats and the synchronization of the model is done in a Federated Averaging (FedAvg) algorithm. The model achieves a Threat Detection Accuracy (TDA) of 97.2 per cent, and a False Alarm Rate of 3.6 per cent. Compared to centralized learning, communication overhead is reduced by 41 % and, hence, the control response latency is maintained. The importance of optimisation update intervals and pruning of edge models reduce energy consumption during training by 22 % of the original consumption. The resilience of the system to fake data injection and command-spoofing attacks is verified by simulation on the modified KDD 99 data set and real-grid situations in NS −3. The federated solution ensures scalable implementation of heterogeneous grid resources. In general, this study is a safe and energy-efficient approach towards the reduction of changing cyber threats within real-time smart-grid settings.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101248"},"PeriodicalIF":5.7,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-05DOI: 10.1016/j.suscom.2025.101245
J. Wen , Sergey Zhiltsov , Rustem Shichiyakh , Samariddin Makhmudov , Muzaffar Shojonov , Anorgul I. Ashirova , Yuldoshev Jushkinbek Erkaboy ugli , M. Mohammadi
This paper proposes a multi-objective functions and stochastic modeling aimed at optimizing and managing energy within a microgrid. This microgrid includes electric vehicles (EVs), fuel cell, battery energy storage system, photovoltaic (PV) panels, and microturbine with demand response. The multi-objective functions are modeled considering minimizations of the emissions pollution and operation costs under different weather conditions. Additionally, the stochastic method is represented using an unscented transformation method to model the uncertainties in power prices, power demand, and solar irradiation, thereby ensuring reliable and effective energy scheduling amidst uncertainty. The proposed optimaztion approach is implemented by numerical modeling in some case studies without and with considering demand response, electric vehicle and stochastic modeling. The results show the optimal values of the emissions pollution and operation costs with the participation of the demand response and electric vehicle by comparative analysis with improved sine cosine optimizer than other optimaztion algorithms.
{"title":"Economic and environmental multi-objective functions modeling in storage systems-based hybrid energy microgrid with demand side management strategy","authors":"J. Wen , Sergey Zhiltsov , Rustem Shichiyakh , Samariddin Makhmudov , Muzaffar Shojonov , Anorgul I. Ashirova , Yuldoshev Jushkinbek Erkaboy ugli , M. Mohammadi","doi":"10.1016/j.suscom.2025.101245","DOIUrl":"10.1016/j.suscom.2025.101245","url":null,"abstract":"<div><div>This paper proposes a multi-objective functions and stochastic modeling aimed at optimizing and managing energy within a microgrid. This microgrid includes electric vehicles (EVs), fuel cell, battery energy storage system, photovoltaic (PV) panels, and microturbine with demand response. The multi-objective functions are modeled considering minimizations of the emissions pollution and operation costs under different weather conditions. Additionally, the stochastic method is represented using an unscented transformation method to model the uncertainties in power prices, power demand, and solar irradiation, thereby ensuring reliable and effective energy scheduling amidst uncertainty. The proposed optimaztion approach is implemented by numerical modeling in some case studies without and with considering demand response, electric vehicle and stochastic modeling. The results show the optimal values of the emissions pollution and operation costs with the participation of the demand response and electric vehicle by comparative analysis with improved sine cosine optimizer than other optimaztion algorithms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101245"},"PeriodicalIF":5.7,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1016/j.suscom.2025.101246
Nahideh Derakhshanfard , Hossein Heydari , Abbas Mirzaei , Ali Asghar Pour Haji Kazem
Intelligent agriculture, digital health, or smart cities are only a few out of multiple uses of the Internet of Things. The limited energy supply of the IoT nodes, specifically battery-run sensor nodes, prevents them from maintaining consistent work and hinders the network’s functioning. In this regard, a smart framework that utilizes the progressive machine learning models with multi-criteria decision-making should ensure higher energy efficiency in the IoT networks. While the existing researches have attempted to decrease energy levels in the IoT networks, most of them apply primitive concepts: clustering, routing, and node sleep, and do not use the most efficient machine learning algorithms for energy prediction. Indeed, several people have tried using machine learning algorithms, like decision trees, linear regression, and elementary ANN for energy prediction. However, most of these algorithms are efficient if they considered as individual ones, and people almost never combine energy prediction and node priority. As a result, we propose a complex system of several designed operations that result in increased energy efficiency. First, the data on energy consumption are gathered at regular intervals and preprocessed: normalized, denoised, and empty value-imputed. Then the LSTM model is used to find temporal patterns and predict the future changes. After node ranking, various dynamic strategies like routing, some of the nodes put to sleep, and traffic are optimized. As a result, the lifetime of the network increases by 35 % whereas the energy consumption decreases by 23 %.
{"title":"An intelligent framework for energy optimization in IoT networks using LSTM and multi-criteria decision making","authors":"Nahideh Derakhshanfard , Hossein Heydari , Abbas Mirzaei , Ali Asghar Pour Haji Kazem","doi":"10.1016/j.suscom.2025.101246","DOIUrl":"10.1016/j.suscom.2025.101246","url":null,"abstract":"<div><div>Intelligent agriculture, digital health, or smart cities are only a few out of multiple uses of the Internet of Things. The limited energy supply of the IoT nodes, specifically battery-run sensor nodes, prevents them from maintaining consistent work and hinders the network’s functioning. In this regard, a smart framework that utilizes the progressive machine learning models with multi-criteria decision-making should ensure higher energy efficiency in the IoT networks. While the existing researches have attempted to decrease energy levels in the IoT networks, most of them apply primitive concepts: clustering, routing, and node sleep, and do not use the most efficient machine learning algorithms for energy prediction. Indeed, several people have tried using machine learning algorithms, like decision trees, linear regression, and elementary ANN for energy prediction. However, most of these algorithms are efficient if they considered as individual ones, and people almost never combine energy prediction and node priority. As a result, we propose a complex system of several designed operations that result in increased energy efficiency. First, the data on energy consumption are gathered at regular intervals and preprocessed: normalized, denoised, and empty value-imputed. Then the LSTM model is used to find temporal patterns and predict the future changes. After node ranking, various dynamic strategies like routing, some of the nodes put to sleep, and traffic are optimized. As a result, the lifetime of the network increases by 35 % whereas the energy consumption decreases by 23 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101246"},"PeriodicalIF":5.7,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuel cell hybrid electric vehicles (FCHEVs) present a promising solution for reducing emissions, enhancing energy efficiency, and extending driving range compared to pure electric vehicles. To overcome the limitations of fuel cell technology, auxiliary energy storage systems are incorporated, resulting in a hybrid powertrain. Effective energy management systems (EMS) are critical for optimizing power distribution among these diverse energy sources. This study proposes a novel EMS approach that combines fuzzy logic control with particle swarm optimization (PSO). The PSO algorithm is employed to optimize the membership functions of the fuzzy logic controller, thereby improving its overall performance. The primary objective is to maximize fuel economy while maintaining the battery state of charge (SOC) at the desired level. The proposed methodology was implemented and tested under four distinct driving conditions. Comparative analysis with both the original EMS and a non-optimized fuzzy logic system demonstrated significant improvements in hydrogen consumption and battery SOC maintenance. Specifically, the optimized fuzzy EMS with triangular membership functions outperformed ADVISOR by 26.91 % and showed a 15.56 % improvement post-optimization. Similarly, the optimized fuzzy EMS with trapezoidal membership functions outperformed ADVISOR by 25.14 %, with a 5.9 % enhancement after optimizing the membership functions. These results highlight the effectiveness of the proposed method in enhancing system performance, achieving significant improvements in hydrogen consumption, and maintaining optimal battery SOC.
{"title":"Particle swarm optimization of fuzzy logic-based energy management system for enhanced efficiency in fuel cell hybrid electric vehicles","authors":"Abdesattar Mazouzi , Nadji Hadroug , Ahmed Hafaifa , Abdelhamid Iratni , Ilhami Colak","doi":"10.1016/j.suscom.2025.101239","DOIUrl":"10.1016/j.suscom.2025.101239","url":null,"abstract":"<div><div>Fuel cell hybrid electric vehicles (FCHEVs) present a promising solution for reducing emissions, enhancing energy efficiency, and extending driving range compared to pure electric vehicles. To overcome the limitations of fuel cell technology, auxiliary energy storage systems are incorporated, resulting in a hybrid powertrain. Effective energy management systems (EMS) are critical for optimizing power distribution among these diverse energy sources. This study proposes a novel EMS approach that combines fuzzy logic control with particle swarm optimization (PSO). The PSO algorithm is employed to optimize the membership functions of the fuzzy logic controller, thereby improving its overall performance. The primary objective is to maximize fuel economy while maintaining the battery state of charge (SOC) at the desired level. The proposed methodology was implemented and tested under four distinct driving conditions. Comparative analysis with both the original EMS and a non-optimized fuzzy logic system demonstrated significant improvements in hydrogen consumption and battery SOC maintenance. Specifically, the optimized fuzzy EMS with triangular membership functions outperformed ADVISOR by 26.91 % and showed a 15.56 % improvement post-optimization. Similarly, the optimized fuzzy EMS with trapezoidal membership functions outperformed ADVISOR by 25.14 %, with a 5.9 % enhancement after optimizing the membership functions. These results highlight the effectiveness of the proposed method in enhancing system performance, achieving significant improvements in hydrogen consumption, and maintaining optimal battery SOC.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101239"},"PeriodicalIF":5.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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