Pub Date : 2025-04-25DOI: 10.1016/j.compeleceng.2025.110366
Pramod TC
The communication messages exchanged between industrial plant devices contain sensitive data. Secure communication plays a vital role in securing the communications of industrial devices. A key management mechanism is necessary for automation systems to handle the crypto keys used for secure communication. This paper focuses on presenting a framework for evaluating key management schemes specific to SCADA (Supervisory Control and Data Acquisition) requirements. It also aims to present a detailed review of key management schemes proposed for SCADA systems. The review considered the symmetric, asymmetric, and hybrid schemes, quantum key distribution, and blockchain-based key management schemes of SCADA systems. The paper outlines the existing SCADA key management schemes highlighting the benefits, limitations, and future scope. The future directions give the research gaps of SCADA key management schemes and possible research scope in the key management domain for industrial system security.
{"title":"A taxonomy of key management schemes of SCADA systems","authors":"Pramod TC","doi":"10.1016/j.compeleceng.2025.110366","DOIUrl":"10.1016/j.compeleceng.2025.110366","url":null,"abstract":"<div><div>The communication messages exchanged between industrial plant devices contain sensitive data. Secure communication plays a vital role in securing the communications of industrial devices. A key management mechanism is necessary for automation systems to handle the crypto keys used for secure communication. This paper focuses on presenting a framework for evaluating key management schemes specific to SCADA (Supervisory Control and Data Acquisition) requirements. It also aims to present a detailed review of key management schemes proposed for SCADA systems. The review considered the symmetric, asymmetric, and hybrid schemes, quantum key distribution, and blockchain-based key management schemes of SCADA systems. The paper outlines the existing SCADA key management schemes highlighting the benefits, limitations, and future scope. The future directions give the research gaps of SCADA key management schemes and possible research scope in the key management domain for industrial system security.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110366"},"PeriodicalIF":4.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867931","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 : 2025-04-25DOI: 10.1016/j.compeleceng.2025.110362
Hakan Aydin, Burak Aydin, Sedat Gormus
The Internet of Things relies on robust networking protocols like the Routing Protocol for Low-Power and Lossy Networks (RPL) to enable efficient communication in resource-constrained environments, particularly in 6TiSCH networks combining IEEE 802.15.4e TSCH with IPv6. However, RPL remains vulnerable to control message attacks, such as DIS flooding and rank manipulation, which degrade network stability, increase energy consumption, and disrupt performance. To address these challenges, this study introduces HaKAN-6T, a hybrid security framework designed to enhance RPL-based 6TiSCH networks through integrated detection and mitigation mechanisms. HaKAN-6T combines the Constrained Join Protocol (CoJP) for secure device authentication with a Network Analyzer for real-time anomaly detection. CoJP ensures only authorized devices join the network, preventing unauthorized access, while the Network Analyzer monitors node behavior to identify attacks like DIS flooding, increased rank, and decreased rank attacks. The performance of HaKAN-6T is evaluated through extensive simulations in grid and random topologies with 30 and 40 nodes, measuring key metrics such as packet delivery ratio (PDR), end-to-end delay (E2ED), control packet overhead, average power consumption (APC), processing overhead, and average detection latency. The results demonstrate that HaKAN-6T significantly mitigates the impact of DIS flooding attacks, reducing control packet overhead by up to 28.5% and improving PDR from 82.7% to 88.45% in 30-node scenarios. Against increased rank attacks, it minimizes E2ED, reducing delays by 19.3% while maintaining a stable network topology. For decreased rank attacks, it prevents excessive topology changes and lowers APC by up to 10.5%, enhancing energy efficiency.
{"title":"HaKAN-6T: Hybrid algorithm for DIS attack detection and mitigation using CoJP in RPL-based 6TiSCH networks","authors":"Hakan Aydin, Burak Aydin, Sedat Gormus","doi":"10.1016/j.compeleceng.2025.110362","DOIUrl":"10.1016/j.compeleceng.2025.110362","url":null,"abstract":"<div><div>The Internet of Things relies on robust networking protocols like the Routing Protocol for Low-Power and Lossy Networks (RPL) to enable efficient communication in resource-constrained environments, particularly in 6TiSCH networks combining IEEE 802.15.4e TSCH with IPv6. However, RPL remains vulnerable to control message attacks, such as DIS flooding and rank manipulation, which degrade network stability, increase energy consumption, and disrupt performance. To address these challenges, this study introduces HaKAN-6T, a hybrid security framework designed to enhance RPL-based 6TiSCH networks through integrated detection and mitigation mechanisms. HaKAN-6T combines the Constrained Join Protocol (CoJP) for secure device authentication with a Network Analyzer for real-time anomaly detection. CoJP ensures only authorized devices join the network, preventing unauthorized access, while the Network Analyzer monitors node behavior to identify attacks like DIS flooding, increased rank, and decreased rank attacks. The performance of HaKAN-6T is evaluated through extensive simulations in grid and random topologies with 30 and 40 nodes, measuring key metrics such as packet delivery ratio (PDR), end-to-end delay (E2ED), control packet overhead, average power consumption (APC), processing overhead, and average detection latency. The results demonstrate that HaKAN-6T significantly mitigates the impact of DIS flooding attacks, reducing control packet overhead by up to 28.5% and improving PDR from 82.7% to 88.45% in 30-node scenarios. Against increased rank attacks, it minimizes E2ED, reducing delays by 19.3% while maintaining a stable network topology. For decreased rank attacks, it prevents excessive topology changes and lowers APC by up to 10.5%, enhancing energy efficiency.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110362"},"PeriodicalIF":4.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867933","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 : 2025-04-25DOI: 10.1016/j.compeleceng.2024.110050
Naser Fakhri Saray , Mansour Rafiee , Mohammad Salay Naderi , Ali Mosallanejad , Abdolreza Esmaeli
Active power buffer (APB) modules in single-phase quasi-Z-source rectifiers (qZSRs) can effectively decrease the double-line frequency power ripple. APB modules can eliminate bulky passive devices, potentially improving system power density and reliability. However, single-phase qZSR systems with AC-side active power buffers (AC-SAPBs) raise system costs, power losses, and control complexity. They require low-reliability aluminum electrolytic capacitors (Al-Caps) and large inductors on the power grid input side to absorb the second-frequency ripple. This study, for the first time, proposes the inclusion of DC-side active power buffer (DC-SAPB) in single-phase qZSRs. The proposed system not only offers the benefits of AC-SAPBs but also reduces the power loss and control complexity. Moreover, metallized polypropylene film capacitors (MPPF-Caps) or multilayer ceramic capacitors (MLCCaps), and small inductors can be used with higher reliability levels. The proposed topology can be employed in the integrated on-board charger of electric vehicles. A comparison between the DC-SAPB and AC-SAPB modules reveals that the former provides a higher performance and efficiency than those of the latter owing to its reduced weight and volume; hence, a large amount of energy can be stored in the battery. The simulation and experimental results verified the performance of the proposed system.
{"title":"Quasi-Z-source on-board charger based on reducing the size of the DC-Side active power buffer for electric vehicle applications","authors":"Naser Fakhri Saray , Mansour Rafiee , Mohammad Salay Naderi , Ali Mosallanejad , Abdolreza Esmaeli","doi":"10.1016/j.compeleceng.2024.110050","DOIUrl":"10.1016/j.compeleceng.2024.110050","url":null,"abstract":"<div><div>Active power buffer (APB) modules in single-phase quasi-Z-source rectifiers (qZSRs) can effectively decrease the double-line frequency power ripple. APB modules can eliminate bulky passive devices, potentially improving system power density and reliability. However, single-phase qZSR systems with AC-side active power buffers (AC-SAPBs) raise system costs, power losses, and control complexity. They require low-reliability aluminum electrolytic capacitors (Al-Caps) and large inductors on the power grid input side to absorb the second-frequency ripple. This study, for the first time, proposes the inclusion of DC-side active power buffer (DC-SAPB) in single-phase qZSRs. The proposed system not only offers the benefits of AC-SAPBs but also reduces the power loss and control complexity. Moreover, metallized polypropylene film capacitors (MPPF-Caps) or multilayer ceramic capacitors (MLC<img>Caps), and small inductors can be used with higher reliability levels. The proposed topology can be employed in the integrated on-board charger of electric vehicles. A comparison between the DC-SAPB and AC-SAPB modules reveals that the former provides a higher performance and efficiency than those of the latter owing to its reduced weight and volume; hence, a large amount of energy can be stored in the battery. The simulation and experimental results verified the performance of the proposed system.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110050"},"PeriodicalIF":4.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867922","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 : 2025-04-24DOI: 10.1016/j.compeleceng.2025.110395
Yixue Li , Ruqi Zhou , Yang Zhou , Zhong Shuo Chen
With the development of the global maritime industry and the intensification of environmental challenges, machine learning technology has emerged as an innovative solution to the environmental sustainability issues in the maritime industry. This study comprehensively reviews the applications of machine learning in the field, with a focus on two key sectors: ships and ports. It delves into important topics such as ship energy consumption prediction, ship emission prediction, ship emission monitoring, port emission prediction, port air quality prediction, and so on. This review provides an in-depth analysis of the current research status, challenges, and future directions. The review finds that in terms of applications, research related to ships is relatively mature, while research related to ports is limited. In terms of algorithms, Random Forest, Artificial Neural Networks, and Gradient Boosting Machines are the most widely used. As the industry continues to grow, future research may focus on the integration of multi-source heterogeneous data, improvement of the interpretability and generalizability of machine learning models, and utilization of more advanced models and algorithms, which are expected to improve the development in the field and contribute to maritime environmental sustainability.
{"title":"Machine learning techniques in maritime environmental sustainability: A comprehensive review of the state of the art","authors":"Yixue Li , Ruqi Zhou , Yang Zhou , Zhong Shuo Chen","doi":"10.1016/j.compeleceng.2025.110395","DOIUrl":"10.1016/j.compeleceng.2025.110395","url":null,"abstract":"<div><div>With the development of the global maritime industry and the intensification of environmental challenges, machine learning technology has emerged as an innovative solution to the environmental sustainability issues in the maritime industry. This study comprehensively reviews the applications of machine learning in the field, with a focus on two key sectors: ships and ports. It delves into important topics such as ship energy consumption prediction, ship emission prediction, ship emission monitoring, port emission prediction, port air quality prediction, and so on. This review provides an in-depth analysis of the current research status, challenges, and future directions. The review finds that in terms of applications, research related to ships is relatively mature, while research related to ports is limited. In terms of algorithms, Random Forest, Artificial Neural Networks, and Gradient Boosting Machines are the most widely used. As the industry continues to grow, future research may focus on the integration of multi-source heterogeneous data, improvement of the interpretability and generalizability of machine learning models, and utilization of more advanced models and algorithms, which are expected to improve the development in the field and contribute to maritime environmental sustainability.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110395"},"PeriodicalIF":4.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868068","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}
The rapid expansion of Internet of Things (IoT) devices has led to a substantial increase in data that needs to be processed efficiently. However, IoT devices face numerous challenges like constrained computational power, limited storage capacity, and finite battery life that hinder their ability to process extensive data efficiently. To address these issues, IoT devices are using the advantages of cloud and fog computing to process large tasks. However, task scheduling in cloud fog is another challenge as it is an NP-hard problem. In this study, we have introduced Cost, Load, Energy and Makespan based Optimized task scheduler (CLEMO) that assigns tasks to different cloud and fog nodes considering the cost, load, energy usage, and makespan involved in processing the dependent tasks. The CLEMO aims to identify optimal solutions for task scheduling in cloud fog by harnessing the inherent capabilities of genetic algorithms. To assess the performance of CLEMO, we conducted various exhaustive experiments and compared the results with other state-of-the-art methods. The outcomes demonstrate that the CLEMO outperforms other methods in terms of cost, load distribution, energy efficiency, and makespan. That indicated that the proposed method can make IoT applications more cost-efficient, conserve energy effectively with better execution time, and optimally utilize available resources in a cloud-fog environment.
{"title":"CLEMO: Cost, load, energy, and makespan-based optimized scheduler for internet of things applications in cloud-fog environment","authors":"Amritesh Singh, Rohit Kumar Tiwari, Sushil Kumar Saroj","doi":"10.1016/j.compeleceng.2025.110377","DOIUrl":"10.1016/j.compeleceng.2025.110377","url":null,"abstract":"<div><div>The rapid expansion of Internet of Things (IoT) devices has led to a substantial increase in data that needs to be processed efficiently. However, IoT devices face numerous challenges like constrained computational power, limited storage capacity, and finite battery life that hinder their ability to process extensive data efficiently. To address these issues, IoT devices are using the advantages of cloud and fog computing to process large tasks. However, task scheduling in cloud fog is another challenge as it is an NP-hard problem. In this study, we have introduced Cost, Load, Energy and Makespan based Optimized task scheduler (CLEMO) that assigns tasks to different cloud and fog nodes considering the cost, load, energy usage, and makespan involved in processing the dependent tasks. The CLEMO aims to identify optimal solutions for task scheduling in cloud fog by harnessing the inherent capabilities of genetic algorithms. To assess the performance of CLEMO, we conducted various exhaustive experiments and compared the results with other state-of-the-art methods. The outcomes demonstrate that the CLEMO outperforms other methods in terms of cost, load distribution, energy efficiency, and makespan. That indicated that the proposed method can make IoT applications more cost-efficient, conserve energy effectively with better execution time, and optimally utilize available resources in a cloud-fog environment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110377"},"PeriodicalIF":4.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867921","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 : 2025-04-24DOI: 10.1016/j.compeleceng.2025.110321
Tianyu Yang , Canghong Shi , Minfeng Shao , Sani M. Abdullahi , Imran Mumtaz , Yong Liu , Ling Xiong
In most watermarking algorithms, the choice of parameters directly affects the performance of the algorithms, and they are not resistant to the jittering attacks or have insufficient capacity. To address the effect of parameters on performance, inadequate capacity, and jittering attacks of digital watermarking, we propose a high-capacity and adaptive watermarking based on Shamir secret sharing. By applying improved Shamir secret sharing encryption, one of the generated shares serves as the encrypted watermark. This enables the recovery of the original watermark by embedding only one-third of the total watermark information. Then, we embed and extract the encrypted watermark by calculating the ratio of the singular values of the front and back segments of each frame of the first-level Discrete Wavelet Transform (DWT) approximation coefficients. We also derived the optimal modification method in the embedding process using the signal-to-noise ratio (SNR) formula which further optimizes the performance of the proposed algorithm. Compared with state-of-the-art audio watermarking algorithms, the proposed algorithm is more robust and has higher capacity. The average value of the bit error rate (BER) is lower than 10% when the value of SNR is greater than 20 dB. For jittering attacks, the proposed scheme achieves an average BER of 2.34%. Additionally, the watermarking capacity reaches 128 bits per second (bps) and the watermarking scheme can efficiently defend against the jittering attack.
{"title":"An adaptive and large payload audio watermarking against jittering attacks","authors":"Tianyu Yang , Canghong Shi , Minfeng Shao , Sani M. Abdullahi , Imran Mumtaz , Yong Liu , Ling Xiong","doi":"10.1016/j.compeleceng.2025.110321","DOIUrl":"10.1016/j.compeleceng.2025.110321","url":null,"abstract":"<div><div>In most watermarking algorithms, the choice of parameters directly affects the performance of the algorithms, and they are not resistant to the jittering attacks or have insufficient capacity. To address the effect of parameters on performance, inadequate capacity, and jittering attacks of digital watermarking, we propose a high-capacity and adaptive watermarking based on Shamir secret sharing. By applying improved Shamir secret sharing encryption, one of the generated shares serves as the encrypted watermark. This enables the recovery of the original watermark by embedding only one-third of the total watermark information. Then, we embed and extract the encrypted watermark by calculating the ratio of the singular values of the front and back segments of each frame of the first-level Discrete Wavelet Transform (DWT) approximation coefficients. We also derived the optimal modification method in the embedding process using the signal-to-noise ratio (SNR) formula which further optimizes the performance of the proposed algorithm. Compared with state-of-the-art audio watermarking algorithms, the proposed algorithm is more robust and has higher capacity. The average value of the bit error rate (BER) is lower than 10% when the value of SNR is greater than 20 dB. For jittering attacks, the proposed scheme achieves an average BER of 2.34%. Additionally, the watermarking capacity reaches 128 bits per second (bps) and the watermarking scheme can efficiently defend against the jittering attack.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110321"},"PeriodicalIF":4.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867932","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 : 2025-04-23DOI: 10.1016/j.compeleceng.2025.110392
Asim Iqbal , Yasir Muhammad , Saeed Ehsan Awan , Bakht Muhammad Khan , Muhammad Asif Zahoor Raja
The protective system is an essential part of all power network subsystems, including the protection systems of generation, transmission, and distribution networks, in order to ensure the integrity of the power system components, such as generators, bus bars, transformers, and feeder lines thus, a combination of different types of protection relays is utilized in the protection system, i.e., the prevention of the overcurrent, line to ground, line to line, double line to ground, faults in the associated system. In the current study, the performance of legacy power system protection is enhanced by means of reducing the total time of operation, including the directional over current relay (DOCR) operating time and coordination time among primary and backup DOCRs, while keeping the coordination time of interval (CTI), pickup tap setting (PTS), and time dial setting (TDS) within acceptable limits, during the protection of standard power system. In order to reduce the fitness evaluation function in IEEE 3-bus, 8-bus, and 15-bus systems, a new approach called fractional particle swarm optimization gravitational search algorithm entropy metric (FPSOGSA-EM) is designed for determining the optimal settings of the CTI, PTS, and TDS. The FPSOGSA-EM incorporates the underlying theories of fractional derivatives inside the mathematical framework of canonical particle swarm optimization aided with gravitational search algorithm along with entropy metric to improve its convergence rate and avoid sub optimality. The yielded results from FPSOGSA-EM are compared to those from other cutting-edge counterpart algorithms such as modified particle swarm optimization, modified water cycle technique, modified electromagnetic field optimization, enhanced grey wolf optimization, seeker algorithm, teaching learning-based optimization, harmony search algorithm and FPSOGSA. By sharply reducing the period of operation of DOCRs in traditional IEEE 3-bus, 8-bus, and 15-bus test systems, the FPSOGSA-EM has outperformed these previously described methods. While the consistency, robustness, optimization brilliance, reliability and stability of the proposed scheme are ascertained by means of statistical interpretations such as minimum fitness evolution, cumulative distribution function (CDF), Boxplot representations, histograms plots and quantile–quantile plot demonstrations as a measure of center tendency and diversity.
{"title":"Entropy driven integrated fractional particle swarm optimization - gravitational search algorithm optimization expedition for optimal coordination of directional over current relays","authors":"Asim Iqbal , Yasir Muhammad , Saeed Ehsan Awan , Bakht Muhammad Khan , Muhammad Asif Zahoor Raja","doi":"10.1016/j.compeleceng.2025.110392","DOIUrl":"10.1016/j.compeleceng.2025.110392","url":null,"abstract":"<div><div>The protective system is an essential part of all power network subsystems, including the protection systems of generation, transmission, and distribution networks, in order to ensure the integrity of the power system components, such as generators, bus bars, transformers, and feeder lines thus, a combination of different types of protection relays is utilized in the protection system, i.e., the prevention of the overcurrent, line to ground, line to line, double line to ground, faults in the associated system. In the current study, the performance of legacy power system protection is enhanced by means of reducing the total time of operation, including the directional over current relay (DOCR) operating time and coordination time among primary and backup DOCRs, while keeping the coordination time of interval (CTI), pickup tap setting (PTS), and time dial setting (TDS) within acceptable limits, during the protection of standard power system. In order to reduce the fitness evaluation function in IEEE 3-bus, 8-bus, and 15-bus systems, a new approach called fractional particle swarm optimization gravitational search algorithm entropy metric (FPSOGSA-EM) is designed for determining the optimal settings of the CTI, PTS, and TDS. The FPSOGSA-EM incorporates the underlying theories of fractional derivatives inside the mathematical framework of canonical particle swarm optimization aided with gravitational search algorithm along with entropy metric to improve its convergence rate and avoid sub optimality. The yielded results from FPSOGSA-EM are compared to those from other cutting-edge counterpart algorithms such as modified particle swarm optimization, modified water cycle technique, modified electromagnetic field optimization, enhanced grey wolf optimization, seeker algorithm, teaching learning-based optimization, harmony search algorithm and FPSOGSA. By sharply reducing the period of operation of DOCRs in traditional IEEE 3-bus, 8-bus, and 15-bus test systems, the FPSOGSA-EM has outperformed these previously described methods. While the consistency, robustness, optimization brilliance, reliability and stability of the proposed scheme are ascertained by means of statistical interpretations such as minimum fitness evolution, cumulative distribution function (CDF), Boxplot representations, histograms plots and quantile–quantile plot demonstrations as a measure of center tendency and diversity.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110392"},"PeriodicalIF":4.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859000","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 : 2025-04-23DOI: 10.1016/j.compeleceng.2025.110322
Pritam Rani , Prity Rani , Rohit Kumar Sachan
Climate change is one of the most critical challenges, requiring innovative solutions to strengthen environmental resilience. In this paper, we explore the potential of Quantum Blockchain Technology (QBT) as a novel approach to addressing climate change and fostering environmental sustainability. QBT merges the principles of quantum computing with the decentralized and secure nature of blockchain technology, offering promising avenues for revolutionizing various sectors, including energy, transportation, agriculture, and waste management. By harnessing the power of quantum mechanics and the transparency of blockchain, QBT presents opportunities for optimizing resource utilization, reducing carbon emissions, and promoting ecosystem preservation. This paper employs a Systematic Literature Review (SLR) process, covering the period from 2017 to 2024, to provide an in-depth analysis of existing literature and case studies. Through this methodical approach, we elucidate the theoretical foundations, technological advancements, and potential applications of QBT in mitigating climate change and enhancing environmental resilience. Furthermore, We discuss the applications, challenges, risks, and ethical considerations related to the adoption of QBT, along with its future prospects to ensure responsible deployment. Overall, this paper underscores the transformative potential of QBT in navigating the future towards a more sustainable and resilient world amidst the challenges posed by climate change.
{"title":"Quantum blockchain for a greener tomorrow: A survey of emerging applications","authors":"Pritam Rani , Prity Rani , Rohit Kumar Sachan","doi":"10.1016/j.compeleceng.2025.110322","DOIUrl":"10.1016/j.compeleceng.2025.110322","url":null,"abstract":"<div><div>Climate change is one of the most critical challenges, requiring innovative solutions to strengthen environmental resilience. In this paper, we explore the potential of Quantum Blockchain Technology (QBT) as a novel approach to addressing climate change and fostering environmental sustainability. QBT merges the principles of quantum computing with the decentralized and secure nature of blockchain technology, offering promising avenues for revolutionizing various sectors, including energy, transportation, agriculture, and waste management. By harnessing the power of quantum mechanics and the transparency of blockchain, QBT presents opportunities for optimizing resource utilization, reducing carbon emissions, and promoting ecosystem preservation. This paper employs a Systematic Literature Review (SLR) process, covering the period from 2017 to 2024, to provide an in-depth analysis of existing literature and case studies. Through this methodical approach, we elucidate the theoretical foundations, technological advancements, and potential applications of QBT in mitigating climate change and enhancing environmental resilience. Furthermore, We discuss the applications, challenges, risks, and ethical considerations related to the adoption of QBT, along with its future prospects to ensure responsible deployment. Overall, this paper underscores the transformative potential of QBT in navigating the future towards a more sustainable and resilient world amidst the challenges posed by climate change.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110322"},"PeriodicalIF":4.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859001","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 : 2025-04-22DOI: 10.1016/j.compeleceng.2025.110329
Fahad Alsokhiry
This paper prescribes and examines a sensorless Neural Network (NN) model for the precise estimation of essential climatic resources-irradiance and temperature-integral to optimizing renewable energy systems. Reliable data on these variables is crucial across multiple disciplines, especially in renewable energy, where it drives numerous technical and economic objectives. However, achieving exact, real-time estimation remains complex, hindered by the dynamic and variable nature of these variables. This work proposes an NN approach that estimates irradiance and temperature using only the maximum power point (MPP) outputs from a modern photovoltaic (PV) system, eliminating the need for direct sensor measurements. This approach not only offers high adaptability but also integrates seamlessly into existing PV infrastructure, enabling real-time, cost-less implementation. To rigorously validate the model, extensive experimental evaluations were conducted across multiple days, demonstrating its accuracy and resilience. The model achieved a Mean Absolute Error (MAE) of 0.87 and 2.728 for irradiance and temperature, respectively; and a Root Mean Square Error (RMSE) of 2.1127 and 9.1008. These metrics highlight the model's precision and reliability, establishing it as a powerful tool for enhancing the efficiency and intelligence of renewable energy systems. The findings offer significant contributions to renewable energy development, providing a robust, sensorless solution for real-time climatic resource estimation with broad interdisciplinary applications, ultimately empowering smarter and more sustainable energy systems.
本文介绍并研究了一种无传感器神经网络（NN）模型，用于精确估算与优化可再生能源系统相关的重要气候资源--辐照度和温度。有关这些变量的可靠数据在多个学科中都至关重要，尤其是在可再生能源领域，它推动着众多技术和经济目标的实现。然而，由于这些变量的动态性和可变性，实现精确、实时的估算仍然十分复杂。本研究提出了一种 NN 方法，该方法仅使用现代光伏（PV）系统的最大功率点（MPP）输出来估算辐照度和温度，无需直接进行传感器测量。这种方法不仅适应性强，还能无缝集成到现有的光伏基础设施中，实现实时、无成本的实施。为了严格验证该模型，我们在多天内进行了广泛的实验评估，证明了其准确性和弹性。该模型在辐照度和温度方面的平均绝对误差（MAE）分别为 0.87 和 2.728，均方根误差（RMSE）分别为 2.1127 和 9.1008。这些指标彰显了该模型的精确性和可靠性，使其成为提高可再生能源系统效率和智能的有力工具。这些发现为可再生能源的发展做出了重大贡献，为跨学科应用的实时气候资源估算提供了强大的无传感器解决方案，最终为更智能、更可持续的能源系统赋能。
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Robotic navigation is a complex task requiring accurate localization, environmental perception, path planning, and control of actuators. Traditional navigation systems rely on pre-built maps or map building techniques such as simultaneous localization and mapping (SLAM). However, these approaches unnecessarily map the entire environment, including all objects and obstacles, making them computationally intensive and slow, particularly on resource-constrained devices. While mapless navigation methods address some of these issues they are often too impulse-based, lacking reliance on planning. Recent advances in deep learning have provided solutions to many navigation paradigms. In particular, Monocular Depth Estimation (MDE) enables the use of a single camera for depth estimation, offering a cost-effective alternative to selective mapping. While these approaches effectively address navigation challenges, they still face issues related to scalability and computational efficiency. This paper proposes a novel hybrid approach to robot navigation that combines map-building techniques from classical visual odometry (VO) with maples techniques that uses deep learning-based MDE. The system employs an object detection model to identify target locations and estimate travel distances, while the MiDaS MDE model provides relative depth to detect the nearest obstacle and navigable gaps after image segmentation removes floor and ceiling areas, enhancing the robot's perception of free spaces. Wheel odometry (WO) and VO determine the robot's position and its metric distance from detected nearest obstacle. An instantaneous Grid map is then formed with robot’s position, navigable gap, nearest obstacle and the goal location. Path planning is conducted using a modified A-star (A*) algorithm, followed by path execution with a Proportional Integral Derivative (PID) controller. The system’s performance is evaluated at both the modular level and the final system level using various metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and inference time for depth estimation, and navigation success rate across different robot speeds for final navigation performance. Additionally, a Friedman statistical test is conducted to validate the results. Experimental results show that the proposed approach reduces memory and computational demands, enabling real-world navigation on low-resource hardware. To our knowledge, this is the first integration of MDE-based mapless navigation with VO-based map-building, presenting a novel direction for research.
{"title":"Hybrid robot navigation: Integrating monocular depth estimation and visual odometry for efficient navigation on low-resource hardware","authors":"Ankit Vashisht , Geeta Chhabra Gandhi , Sumit Kalra , Dinesh Kumar Saini","doi":"10.1016/j.compeleceng.2025.110375","DOIUrl":"10.1016/j.compeleceng.2025.110375","url":null,"abstract":"<div><div>Robotic navigation is a complex task requiring accurate localization, environmental perception, path planning, and control of actuators. Traditional navigation systems rely on pre-built maps or map building techniques such as simultaneous localization and mapping (SLAM). However, these approaches unnecessarily map the entire environment, including all objects and obstacles, making them computationally intensive and slow, particularly on resource-constrained devices. While mapless navigation methods address some of these issues they are often too impulse-based, lacking reliance on planning. Recent advances in deep learning have provided solutions to many navigation paradigms. In particular, Monocular Depth Estimation (MDE) enables the use of a single camera for depth estimation, offering a cost-effective alternative to selective mapping. While these approaches effectively address navigation challenges, they still face issues related to scalability and computational efficiency. This paper proposes a novel hybrid approach to robot navigation that combines map-building techniques from classical visual odometry (VO) with maples techniques that uses deep learning-based MDE. The system employs an object detection model to identify target locations and estimate travel distances, while the MiDaS MDE model provides relative depth to detect the nearest obstacle and navigable gaps after image segmentation removes floor and ceiling areas, enhancing the robot's perception of free spaces. Wheel odometry (WO) and VO determine the robot's position and its metric distance from detected nearest obstacle. An instantaneous Grid map is then formed with robot’s position, navigable gap, nearest obstacle and the goal location. Path planning is conducted using a modified A-star (A*) algorithm, followed by path execution with a Proportional Integral Derivative (PID) controller. The system’s performance is evaluated at both the modular level and the final system level using various metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and inference time for depth estimation, and navigation success rate across different robot speeds for final navigation performance. Additionally, a Friedman statistical test is conducted to validate the results. Experimental results show that the proposed approach reduces memory and computational demands, enabling real-world navigation on low-resource hardware. To our knowledge, this is the first integration of MDE-based mapless navigation with VO-based map-building, presenting a novel direction for research.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110375"},"PeriodicalIF":4.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851805","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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