Pub Date : 2025-03-13DOI: 10.1016/j.suscom.2025.101115
Garima Verma
Managing a distributed environment with a shared resource pool in cloud computing requires efficient task scheduling across multiple Virtual Machines (VMs). The effectiveness of the load-balancing algorithm used largely influences the system's performance. However, traditional load-balancing methods often neglect critical factors such as cost and energy consumption, which are vital for both economic and environmental sustainability. To tackle these challenges, this study introduces a new approach, Cost-Energy Aware Spider Monkey Optimization (CE-SMO). This improved version of the Spider Monkey Optimization (SMO) algorithm incorporates cost and energy efficiency into the load-balancing process. CE-SMO seeks to enhance performance by considering economic aspects like computing, storage, data transfer costs, and energy consumption. The algorithm ensures balanced, cost-efficient, and eco-friendly resource allocation. Simulations demonstrate that CE-SMO outperforms existing methods in load balancing, reaction time, makespan, and resource utilization while addressing cost and energy efficiency concerns.
{"title":"Sustainable cost-energy aware load balancing in cloud environment using intelligent optimization","authors":"Garima Verma","doi":"10.1016/j.suscom.2025.101115","DOIUrl":"10.1016/j.suscom.2025.101115","url":null,"abstract":"<div><div>Managing a distributed environment with a shared resource pool in cloud computing requires efficient task scheduling across multiple Virtual Machines (VMs). The effectiveness of the load-balancing algorithm used largely influences the system's performance. However, traditional load-balancing methods often neglect critical factors such as cost and energy consumption, which are vital for both economic and environmental sustainability. To tackle these challenges, this study introduces a new approach, Cost-Energy Aware Spider Monkey Optimization (CE-SMO). This improved version of the Spider Monkey Optimization (SMO) algorithm incorporates cost and energy efficiency into the load-balancing process. CE-SMO seeks to enhance performance by considering economic aspects like computing, storage, data transfer costs, and energy consumption. The algorithm ensures balanced, cost-efficient, and eco-friendly resource allocation. Simulations demonstrate that CE-SMO outperforms existing methods in load balancing, reaction time, makespan, and resource utilization while addressing cost and energy efficiency concerns.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101115"},"PeriodicalIF":3.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644580","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 : 2025-03-11DOI: 10.1016/j.suscom.2025.101113
Pratibha Sharma , Hemraj Saini , Arvind Kalia
Fog computing is an important paradigm in the current scenario among many sensing application services based on the Internet of Things (IoT). A traditional IoT environment suffers from a significant latency where all the devices access data from the cloud. To overcome this problem, fog computing is introduced to reduce the latency. However, several security limitations associated with fog computing have not been addressed. This research proposed the Secure Data Query and Storage Processing (SURETY-fog) method, which overcomes the security limitation. The proposed work has different processes to enhance security and efficiency including IoT device and user registration based on the Naor Reingold generator and Prince algorithm, Authentication by using a Multi-Factor Authentication model, secure optimized fog node selection and secure sensed data storage by using Deer Hunting Optimization (DHO) algorithm, Deep Q learning based secure data storage with improved trust in fog, and Lightweight based secure data transmission in fog layer by using bliss signature. The simulation is conducted by using iFogSim and evaluating the performance based on the following metrics, response time, attack detection rate, resource utilization, number of queries processed, transmission latency, and processing latency.
{"title":"SURETY-Fog: Secure Data Query and Storage Processing in Fog Driven IoT Environment","authors":"Pratibha Sharma , Hemraj Saini , Arvind Kalia","doi":"10.1016/j.suscom.2025.101113","DOIUrl":"10.1016/j.suscom.2025.101113","url":null,"abstract":"<div><div>Fog computing is an important paradigm in the current scenario among many sensing application services based on the Internet of Things (IoT). A traditional IoT environment suffers from a significant latency where all the devices access data from the cloud. To overcome this problem, fog computing is introduced to reduce the latency. However, several security limitations associated with fog computing have not been addressed. This research proposed the Secure Data Query and Storage Processing (SURETY-fog) method, which overcomes the security limitation. The proposed work has different processes to enhance security and efficiency including IoT device and user registration based on the Naor Reingold generator and Prince algorithm, Authentication by using a Multi-Factor Authentication model, secure optimized fog node selection and secure sensed data storage by using Deer Hunting Optimization (DHO) algorithm, Deep Q learning based secure data storage with improved trust in fog, and Lightweight based secure data transmission in fog layer by using bliss signature. The simulation is conducted by using iFogSim and evaluating the performance based on the following metrics, response time, attack detection rate, resource utilization, number of queries processed, transmission latency, and processing latency.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101113"},"PeriodicalIF":3.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621018","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 : 2025-03-10DOI: 10.1016/j.suscom.2025.101114
Yan Zhang , Ru Jing , Yuanjie Zou , Zaihui Cao
The study presents an advanced optimization framework for power allocation in contemporary Internet of Things (IoT) systems, integrating multiple-input multiple-output (MIMO) technologies with non-orthogonal multiple access (NOMA). A novel deep reinforcement learning (DRL) approach is developed, incorporating an improved African Bison Optimization (IABO) algorithm to enhance system efficiency. Unlike existing methods, which primarily focus on either minimizing energy consumption or reducing information age, the proposed framework jointly optimizes both metrics, ensuring a balanced and adaptive power distribution strategy. The optimization framework leverages a DRL-driven approach to dynamically allocate power while addressing interference management in IoT networks. The IABO introduces an adaptive mechanism that refines the trade-off between exploration and exploitation, ensuring enhanced convergence and system stability. A key novelty of this work is the integration of discrete and continuous action spaces within the DRL model, allowing for efficient resource allocation in real-time scenarios. Extensive simulations validate the superiority of the proposed approach over conventional algorithms, such as genetic algorithms and random allocation methods. The results indicate a significant reduction in both energy consumption and information age, demonstrating improved transmission efficiency and overall network performance.
{"title":"Optimizing power allocation in contemporary IoT systems: A deep reinforcement learning approach","authors":"Yan Zhang , Ru Jing , Yuanjie Zou , Zaihui Cao","doi":"10.1016/j.suscom.2025.101114","DOIUrl":"10.1016/j.suscom.2025.101114","url":null,"abstract":"<div><div>The study presents an advanced optimization framework for power allocation in contemporary Internet of Things (IoT) systems, integrating multiple-input multiple-output (MIMO) technologies with non-orthogonal multiple access (NOMA). A novel deep reinforcement learning (DRL) approach is developed, incorporating an improved African Bison Optimization (IABO) algorithm to enhance system efficiency. Unlike existing methods, which primarily focus on either minimizing energy consumption or reducing information age, the proposed framework jointly optimizes both metrics, ensuring a balanced and adaptive power distribution strategy. The optimization framework leverages a DRL-driven approach to dynamically allocate power while addressing interference management in IoT networks. The IABO introduces an adaptive mechanism that refines the trade-off between exploration and exploitation, ensuring enhanced convergence and system stability. A key novelty of this work is the integration of discrete and continuous action spaces within the DRL model, allowing for efficient resource allocation in real-time scenarios. Extensive simulations validate the superiority of the proposed approach over conventional algorithms, such as genetic algorithms and random allocation methods. The results indicate a significant reduction in both energy consumption and information age, demonstrating improved transmission efficiency and overall network performance.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101114"},"PeriodicalIF":3.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632174","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 : 2025-03-10DOI: 10.1016/j.suscom.2025.101110
Vijayakumar Gali , Nitin Gupta , Prashant Kumar Jamwal , Manoj Kumawat , B. Chitti Babu
This article presents a novel control strategy for optimizing real-time battery energy storage system (BESS) performance in microgrids. The primary objective is to enhance power-sharing and improve energy management under the uncertainty of renewable energy sources (RES) and load fluctuations. Microgrids with BESS have the potential to enhance the performance metrics of electricity systems, including resilience and sustainability. To achieve this, a rate limiter is employed instead of a conventional low-pass filter (LPF) to ensure seamless BESS discharge and improved power balance among the Microgrid sources. This approach mitigates the challenges associated with unintentional cut-off frequency selection and enhances system stability. However, determining the optimal rate limiter value is crucial, as it significantly impacts BESS reliability and efficiency. To address this, a Harris Hawks optimization (HHO) is proposed to track the reference current of the rate limiter precisely to overcome the problems associated with conventional controllers arising from uncertainties due to system nonlinearities. The proposed method is validated through MATLAB®/Simulink simulations and real-time implementation using a WAVECT® WUC 300 FPGA digital controller. The comparative analysis demonstrates that the HHO-based control strategy effectively reduces voltage overshoots below 5 V and settling time to 0.01 s during uncertainty RES scenarios. Experimental results further validate the superior performance of the proposed controller in dynamic energy management and power-sharing optimization.
{"title":"Real-time performance enhancement of battery energy storage system in sustainable microgrids using Harris Hawks Optimization","authors":"Vijayakumar Gali , Nitin Gupta , Prashant Kumar Jamwal , Manoj Kumawat , B. Chitti Babu","doi":"10.1016/j.suscom.2025.101110","DOIUrl":"10.1016/j.suscom.2025.101110","url":null,"abstract":"<div><div>This article presents a novel control strategy for optimizing real-time battery energy storage system (BESS) performance in microgrids. The primary objective is to enhance power-sharing and improve energy management under the uncertainty of renewable energy sources (RES) and load fluctuations. Microgrids with BESS have the potential to enhance the performance metrics of electricity systems, including resilience and sustainability. To achieve this, a rate limiter is employed instead of a conventional low-pass filter (LPF) to ensure seamless BESS discharge and improved power balance among the Microgrid sources. This approach mitigates the challenges associated with unintentional cut-off frequency selection and enhances system stability. However, determining the optimal rate limiter value is crucial, as it significantly impacts BESS reliability and efficiency. To address this, a Harris Hawks optimization (HHO) is proposed to track the reference current of the rate limiter precisely to overcome the problems associated with conventional controllers arising from uncertainties due to system nonlinearities. The proposed method is validated through MATLAB®/Simulink simulations and real-time implementation using a WAVECT® WUC 300 FPGA digital controller. The comparative analysis demonstrates that the HHO-based control strategy effectively reduces voltage overshoots below 5 V and settling time to 0.01 s during uncertainty RES scenarios. Experimental results further validate the superior performance of the proposed controller in dynamic energy management and power-sharing optimization.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101110"},"PeriodicalIF":3.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621017","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 : 2025-03-08DOI: 10.1016/j.suscom.2025.101106
Georges Da Costa
Energy usage and its ecological impact is now a major concern in High Performance Computing (HPC). To optimize supercomputers efficiency, researchers rely on models, as accessing actual platform is complex and costly. Changing DVFS (Dynamic Voltage and Frequency Scaling) is the most studied method, but it impacts power, performance and energy in a complex way.
We propose to bridge the gap between the theoretical and the practical approaches. We propose a multi cluster, multi application model accurately describing from a theoretical point of view the power and performance of applications subject to DVFS. We show how to use it on a runtime system with a minimal overhead, using only a few hardware performance counters and RAPL (Running Average Power Limit).
We validate our models using an extensive dataset, obtained using 18 different clusters and running 9 benchmarks. We also show how such model can be used to optimize the energy-to-solution for HPC workload.
{"title":"Hardware and application aware performance, power and energy models for modern HPC servers with DVFS","authors":"Georges Da Costa","doi":"10.1016/j.suscom.2025.101106","DOIUrl":"10.1016/j.suscom.2025.101106","url":null,"abstract":"<div><div>Energy usage and its ecological impact is now a major concern in High Performance Computing (HPC). To optimize supercomputers efficiency, researchers rely on models, as accessing actual platform is complex and costly. Changing DVFS (Dynamic Voltage and Frequency Scaling) is the most studied method, but it impacts power, performance and energy in a complex way.</div><div>We propose to bridge the gap between the theoretical and the practical approaches. We propose a multi cluster, multi application model accurately describing from a theoretical point of view the power and performance of applications subject to DVFS. We show how to use it on a runtime system with a minimal overhead, using only a few hardware performance counters and RAPL (Running Average Power Limit).</div><div>We validate our models using an extensive dataset, obtained using 18 different clusters and running 9 benchmarks. We also show how such model can be used to optimize the energy-to-solution for HPC workload.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101106"},"PeriodicalIF":3.8,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To share data securely, existing works use a public key and a private key for transmission. The secured data sharing along with user authentication is necessary for mobile devices. So, proper authorization is needed to access data owners' credential information from cloud computing. Therefore, the paper presents a novel framework for secured data sharing and user authentication. The security-based authorization in data transfer is taken significantly. Initially, DO and DU register to TA and then hashcode is generated using 2C-Cubehash for the login. After login, the data is encrypted by PWCC and then uploaded by the owner into the mobile cloud. Later, with an end-user request, the possessor and permitter verify the user and give PPK and smart contract to the end-user. Using the two PPK, the client downloads the data. The successful transaction in the blockchain is monitored by the proprietor by constructing UUI-MT. The proposed model resulted in 3123 ms processing time, 2941 ms latency, and 98.03 % security level. With the proposed model, secured user-authenticated data sharing is achieved.
{"title":"Secured user authentication and data sharing for mobile cloud computing using 2C-Cubehash and PWCC","authors":"Surendar Rama Sitaraman , Kalyan Gattupalli , Venkata Surya Bhavana Harish Gollavilli , Harikumar Nagarajan , Poovendran Alagarsundaram , Haris M. Khalid","doi":"10.1016/j.suscom.2025.101107","DOIUrl":"10.1016/j.suscom.2025.101107","url":null,"abstract":"<div><div>To share data securely, existing works use a public key and a private key for transmission. The secured data sharing along with user authentication is necessary for mobile devices. So, proper authorization is needed to access data owners' credential information from cloud computing. Therefore, the paper presents a novel framework for secured data sharing and user authentication. The security-based authorization in data transfer is taken significantly. Initially, DO and DU register to TA and then hashcode is generated using 2C-Cubehash for the login. After login, the data is encrypted by PWCC and then uploaded by the owner into the mobile cloud. Later, with an end-user request, the possessor and permitter verify the user and give PPK and smart contract to the end-user. Using the two PPK, the client downloads the data. The successful transaction in the blockchain is monitored by the proprietor by constructing UUI-MT. The proposed model resulted in 3123 ms processing time, 2941 ms latency, and 98.03 % security level. With the proposed model, secured user-authenticated data sharing is achieved.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101107"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621019","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}
A Free and open-source Ghost (FOG) computing is a decentralized computing infrastructure, that helps in processing the data efficiently to end-user. None of the existing works concentrated on authorization between source, destination, and intermediate server during the Internet of Things (IoT) sensor data transmission. Therefore, the paper presents the authentication of the servers using Cholesky-HAVAL for secure IoT sensor data transmission. Initially, the IoT sensor devices are registered and logged into the FOG server. Next, the sensor nodes are clustered using Bray Pearson K-Means (BP-KMeans) clustering method. Through the cluster head, the IoT data is sensed, and the attributes are extracted. The sensed data is then secured using Gauss Montgomery Curve Cryptography (GMCC). The secured data is stored in the Hadoop Distributed File System (HDFS) FOG server. Here, the data is mapped using BP-KMeans and then reduced using the Schwefel Group Search Optimization Algorithm (SGSOA). Meanwhile, Merkle Tree (MT) is created using Cholesky-HAVAL regarding the sensor data attributes, IoT sensor ID (Identification), and FOG server ID. Next, to retrieve the sensor data, the user registers and logs into the server. Then, the user gives a query request for accessing the data present in the cloud. The attributes are extracted from the query, and using SGSOA, the query is optimized. Finally, the hashcode verification is done regarding the attributes from sensed data and the query. The IoT data is thus retrieved for the verified hashcodes. Thus, the proposed work clustered the sensor nodes in 4578 ms and generated the hashcode in 1476 ms.
{"title":"FOG computing based energy efficient and secured iot data sharing using SGSOA and GMCC","authors":"Swapna Narla , Sreekar Peddi , Dharma Teja Valivarthi , Sai Sathish Kethu , Durai Rajesh Natarajan , Dede Kurniadi","doi":"10.1016/j.suscom.2025.101109","DOIUrl":"10.1016/j.suscom.2025.101109","url":null,"abstract":"<div><div>A Free and open-source Ghost (FOG) computing is a decentralized computing infrastructure, that helps in processing the data efficiently to end-user. None of the existing works concentrated on authorization between source, destination, and intermediate server during the Internet of Things (IoT) sensor data transmission. Therefore, the paper presents the authentication of the servers using Cholesky-HAVAL for secure IoT sensor data transmission. Initially, the IoT sensor devices are registered and logged into the FOG server. Next, the sensor nodes are clustered using Bray Pearson K-Means (BP-KMeans) clustering method. Through the cluster head, the IoT data is sensed, and the attributes are extracted. The sensed data is then secured using Gauss Montgomery Curve Cryptography (GMCC). The secured data is stored in the Hadoop Distributed File System (HDFS) FOG server. Here, the data is mapped using BP-KMeans and then reduced using the Schwefel Group Search Optimization Algorithm (SGSOA). Meanwhile, Merkle Tree (MT) is created using Cholesky-HAVAL regarding the sensor data attributes, IoT sensor ID (Identification), and FOG server ID. Next, to retrieve the sensor data, the user registers and logs into the server. Then, the user gives a query request for accessing the data present in the cloud. The attributes are extracted from the query, and using SGSOA, the query is optimized. Finally, the hashcode verification is done regarding the attributes from sensed data and the query. The IoT data is thus retrieved for the verified hashcodes. Thus, the proposed work clustered the sensor nodes in 4578 ms and generated the hashcode in 1476 ms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101109"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643835","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 : 2025-03-03DOI: 10.1016/j.suscom.2025.101108
Haitao Li , Mohammad Khishe , Francisco Hernando-Gallego , Diego Martín
Underwater Wireless Sensor Networks (UWSNs) encounter serious challenges due to dynamic topology, energy constraints, and high latency underwater communication. Existing methods for clustering and routing often fail to strike an optimal balance between data delivery reliability, energy efficiency, and latency reduction. This paper overcomes these shortcomings by developing a hybrid model that integrates the Hunger Games Search (HGS) and Marine Predator Algorithm (MPA) for improved clustering and routing in UWSNs. The MPA was chosen due to its stability in selecting the first sensors/cluster heads and creating the clusters, drawing inspiration from the foraging strategies of marine predators, which guides it extensively in the balance of exploration and exploitation. Simulations demonstrate that the proposed method achieves significantly better results than classical methods. In particular, the HGS-MPA framework consumes 26.6 % less energy than GWO-PSO, increasing network lifetime by 22.1 % (FINOD) and 15.8 % (HANOD). The packet delivery ratio is improved by 3.1 % against the following best-performing method, reaching 92.4 %. A statistical test performed with ANOVA showed that these improvements are statistically significant at P < 0.001. The results reinforce how the HGS-MPA framework would help improve energy efficiency, network lifetime, and communication reliability in UWSN systems.
{"title":"A hybrid bio-inspired approach for clustering and routing in UWSNs using MPA and HGS","authors":"Haitao Li , Mohammad Khishe , Francisco Hernando-Gallego , Diego Martín","doi":"10.1016/j.suscom.2025.101108","DOIUrl":"10.1016/j.suscom.2025.101108","url":null,"abstract":"<div><div>Underwater Wireless Sensor Networks (UWSNs) encounter serious challenges due to dynamic topology, energy constraints, and high latency underwater communication. Existing methods for clustering and routing often fail to strike an optimal balance between data delivery reliability, energy efficiency, and latency reduction. This paper overcomes these shortcomings by developing a hybrid model that integrates the Hunger Games Search (HGS) and Marine Predator Algorithm (MPA) for improved clustering and routing in UWSNs. The MPA was chosen due to its stability in selecting the first sensors/cluster heads and creating the clusters, drawing inspiration from the foraging strategies of marine predators, which guides it extensively in the balance of exploration and exploitation. Simulations demonstrate that the proposed method achieves significantly better results than classical methods. In particular, the HGS-MPA framework consumes 26.6 % less energy than GWO-PSO, increasing network lifetime by 22.1 % (FINOD) and 15.8 % (HANOD). The packet delivery ratio is improved by 3.1 % against the following best-performing method, reaching 92.4 %. A statistical test performed with ANOVA showed that these improvements are statistically significant at P < 0.001. The results reinforce how the HGS-MPA framework would help improve energy efficiency, network lifetime, and communication reliability in UWSN systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101108"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562935","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 : 2025-03-03DOI: 10.1016/j.suscom.2025.101104
Tanmay Das , Ranjit Roy , Kamal Krishna Mandal
The study of modern power systems is complex and multi-dimensional with diverse constraints that are generally considered one at a time for seeking the optimal results for a specific set of goals. However, for a more realistic approach to problem solving, multiple goals are tackled simultaneously, optimizing a multi-objective problem. In this study, a multi-objective (MO) Flexible AC transmission system (FACTS) device incorporated in an optimal reactive power dispatch (ORPD) problem that has been developed considering these distinct objectives concurrently to formulate a ‘MO-ORPD-ELD-Emission-FACTS’ problem—the hourly cost of energy lost in transmission (from ORPD), generation cost (from economic load dispatch), the operational cost of FACTS devices such as static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC), and emissions of fossil fuel pollutants. The implementation technique is the Arithmetic Optimization Algorithm (AOA), and it is being tested on the standard IEEE 30, IEEE 57, and IEEE 118 bus systems. The fuzzy-based mechanism of Pareto optimality has been employed to determine the Best Compromising Solution (BCS) out of the set of Pareto solutions of the multi-objective problem. A specific case of load uncertainty has also been carried out for the larger IEEE 118 bus system with ten different variations of load demands for the same multi-objective function. The aim was to study the significance of the achieved results under the different loading conditions and compare the voltage profiles. The solutions obtained by AOA were observed to be the best and followed an ideal nature of Pareto optimality compared to the others. The incorporation of FACTS has significantly reduced the cost of economic load dispatch (ELD), the cost of energy loss, and fuel emissions, and with a much healthier voltage profile for all three test bus systems. The power loss has been reduced from 3.1129 MW to 2.8469 MW for the IEEE 30 bus system, from 11.366 MW to 10.0656 MW for the IEEE 57 bus system, and from 73.2977 MW to 64.2368 MW for the IEEE 118 bus system as obtained by the AOA. The IEEE 118 bus system showed the optimal overall operational cost and emission at 77.4 % loading, with a better voltage profile with the incorporation of FACTS devices.
{"title":"Modelling and optimization of a FACTS devices operated multi-objective optimal reactive power dispatch (ORPD) problem minimizing both operational cost and fuel emissions","authors":"Tanmay Das , Ranjit Roy , Kamal Krishna Mandal","doi":"10.1016/j.suscom.2025.101104","DOIUrl":"10.1016/j.suscom.2025.101104","url":null,"abstract":"<div><div>The study of modern power systems is complex and multi-dimensional with diverse constraints that are generally considered one at a time for seeking the optimal results for a specific set of goals. However, for a more realistic approach to problem solving, multiple goals are tackled simultaneously, optimizing a multi-objective problem. In this study, a multi-objective (MO) Flexible AC transmission system (FACTS) device incorporated in an optimal reactive power dispatch (ORPD) problem that has been developed considering these distinct objectives concurrently to formulate a ‘MO-ORPD-ELD-Emission-FACTS’ problem—the hourly cost of energy lost in transmission (from ORPD), generation cost (from economic load dispatch), the operational cost of FACTS devices such as static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC), and emissions of fossil fuel pollutants. The implementation technique is the Arithmetic Optimization Algorithm (AOA), and it is being tested on the standard IEEE 30, IEEE 57, and IEEE 118 bus systems. The fuzzy-based mechanism of Pareto optimality has been employed to determine the Best Compromising Solution (BCS) out of the set of Pareto solutions of the multi-objective problem. A specific case of load uncertainty has also been carried out for the larger IEEE 118 bus system with ten different variations of load demands for the same multi-objective function. The aim was to study the significance of the achieved results under the different loading conditions and compare the voltage profiles. The solutions obtained by AOA were observed to be the best and followed an ideal nature of Pareto optimality compared to the others. The incorporation of FACTS has significantly reduced the cost of economic load dispatch (ELD), the cost of energy loss, and fuel emissions, and with a much healthier voltage profile for all three test bus systems. The power loss has been reduced from 3.1129 MW to 2.8469 MW for the IEEE 30 bus system, from 11.366 MW to 10.0656 MW for the IEEE 57 bus system, and from 73.2977 MW to 64.2368 MW for the IEEE 118 bus system as obtained by the AOA. The IEEE 118 bus system showed the optimal overall operational cost and emission at 77.4 % loading, with a better voltage profile with the incorporation of FACTS devices.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101104"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620888","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 : 2025-03-01DOI: 10.1016/j.suscom.2025.101099
Degan Zhang , Xiaoyang Wang , Jie Zhang , Ting Zhang , Lu Chen , Hongtao Chen , E. Honglin , Member, IEEE
With the rapid development of the Internet of Vehicles (IoV), the computation and communication demands of vehicles are increasing. The traditional centralized computing model can no longer meet these demands. Consequently, task offloading techniques have become crucial for enhancing computational performance and reducing vehicle load in IoV. In this paper, we propose a new method of task offloading for IoV computing based on the sparrow search optimization strategy. Specifically, we address the multifactorial influences on task offloading. Firstly, we design an offloading model that integrates multiple optimization objectives, such as delay and energy consumption. Secondly, we develop a fitness function that balances delay and energy consumption to evaluate and select task offloading strategies. Additionally, we design a network access model to maintain network access stability. Finally, we conduct an iterative optimization search for the offloading strategy using an improved sparrow search optimization algorithm. Through extensive simulation experiments and real-world scenario tests, we validated the effectiveness and performance advantages of the proposed method. The experimental results demonstrate that our new IoV task offloading method, based on the improved sparrow search optimization algorithm, enhances computational performance while reducing vehicle load, showing great potential for applications in the field of IoV task offloading.
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