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ENHANCING MICRO GRID SUSTAINABILITY: GENETIC-DRIVEN REPTILE SEARCH ALGORITHM BASED CARBON EMISSION AND COST REDUCTION STRATEGY Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-03-02 Jiahua Hu, Xiaozhe Yin, Xuehai Zhao, Zheng Zhou
Grid architecture is continuously changing in response to modern energy rules that ensure the integration of more renewable sources to lower the carbon footprint. The integration of non-dispatchable renewable energy sources (RESs) using low voltage grids is a possible alternative to a centralized method. Due to a recent trend, microgrids (MGs) are reducing their reliance on the primary grid by incorporating
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A fault-tolerant and energy-efficient design of RAM cell and PIM structure in quantum technology Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-28 Leila Dehbozorgi, Reza Akbari-Hasanjani, Reza Sabbaghi-Nadooshan
In this paper, a RAM cell in ternary QCA is proposed. Moreover, a 2×1 memory array and a TPIM (ternary processing in memory) structure are designed using the proposed ternary RAM cell. The evaluation of the design parameters shows that the proposed ternary SRAM and PIM structures are efficient in terms of cost, area, and fault tolerance while the volume of information-carrying is high because of the
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Mining of heterogeneous time series information for predicting chlorophyll accumulation in oceans Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-27 Atharva Ramgirkar, Vadiraj Rao, Janhavi Talhar, Tusar Kanti Mishra, Swathi Jamjala Narayanan, Shashank Mouli Satapathy, Boominathan Perumal
Harmful algal blooms cause environmental harm, financial losses, and disease epidemics. It is also known that the algal blooms cannot be eradicated; hence the best option is to foresee their growth and regulate it. Machine learning algorithms can be used to forecast their presence and further classify the threat that each concentration level presents. In this research work, the dataset collected from
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A port consolidation model for data center network infrastructure energy efficiency Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-23 Syed Muhammad Sheraz, Asad Arfeen, Umaima Haider
Technological advancements have increased the energy consumption of data center network infrastructure causing an increase in global carbon footprints. Consolidation of hardware is considered to be one of the acceptable techniques for reducing energy consumption in data centers. However, consolidation of hardware may result in scarce fault tolerance which consequently leads to performance degradation
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Pelican-FOPID Based DSTATCOM for real-time load compensation and harmonics mitigation in three-phase distribution system Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-20 A. Kasim Vali, P. Srinivasa Varma, Ch. Rami Reddy
Power quality (PQ) explores the issues resulting from current and voltage deviations. Non-sinusoidal currents were drawn from the electric grid by the nonlinear loads. These non-sinusoidal currents contain harmonics and reactive power that lower the system's overall power quality. Distribution Static Compensator (DSTATCOM) emerged as a promising compensator to provide solution for all PQ issues. Yet
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A multi-agent reinforcement learning-based method for server energy efficiency optimization combining DVFS and dynamic fan control Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-08 Wenjun Lin, Weiwei Lin, Jianpeng Lin, Haocheng Zhong, Jiangtao Wang, Ligang He
With the rapid development of the digital economy and intelligent industry, the energy consumption of data centers (DCs) has increased significantly. Various optimization methods are proposed to improve the energy efficiency of servers in DCs. However, existing solutions usually adopt model-based heuristics and best practices to select operations, which are not universally applicable. Moreover, existing
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Grid-connected desalination plant economic management powered by renewable resources utilizing Niching Chimp Optimization and hunger game search algorithms Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-02 Yuanshuo Guo, Yassine Bouteraa, Mohammad Khishe, Banar Fareed Ibrahim
This study presents a novel Hunger Game Search and Niching Chimp Optimization Algorithms (HGS-NChOA) for optimizing grid-connected desalination plants powered by renewable energy. The primary innovation of this study is the significant advantages provided by the HGS-NChOA method, particularly in terms of reducing the cost of freshwater production and mitigating greenhouse gas emissions. Key outcomes
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Energy-efficient offloading based on hybrid bio-inspired algorithm for edge–cloud integrated computation Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-01 Hongjian Li, Liangjie Liu, Xiaolin Duan, Hengyu Li, Peng Zheng, Libo Tang
Mobile Edge Computing (MEC) is deployed closer to User Equipment (UE) and has strong computing power. Not only it relieves the load pressure on the central cloud, but also effectively reduces the transmission delay caused by offloading computation from devices because it is closer to users. Therefore, we study edge computing task offloading based on edge–cloud collaboration scenarios to meet the requirement
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Smart traffic routing and service allocation strategy to reduce water consumption in data centers through power reduction Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-01 Sajjad Ghanbari, Ali Ghiasian
Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water
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Energy-efficiency optimization and the comparative performance analysis for Wireless Body Area Networks (WBANs) Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-02-01 Neha Arora, Sindhu Hak Gupta, Basant Kumar
This paper examines the energy efficiency of a non-cooperative and cooperative On-body Wireless Body Area Network (WBAN) and link reliability in a cooperative WBAN using IEEE 802.15.6 based CM3A channel model. The proposed energy optimization framework is based on Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) techniques. For the optimized code rate, obtained results illustrate
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PV parameters estimation using optimized deep neural networks Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-12 Ahmad Al-Subhi, Mohamed I. Mosaad, Tamer Ahmed Farrag
Estimating the parameters of a Photovoltaic (PV) cell is crucial, given the significant integration of the PV systems into electrical power systems. One of the primary challenges in the estimation of PV cell parameters is identifying a generalized method applicable to any PV system, irrespective of environmental variations and power ratings. This paper introduces a novel application of an optimized
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Improved Green Anaconda Optimization Algorithm-based Coverage Path Planning Mechanism for heterogeneous unmanned aerial vehicles Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-17 K. Karthik, C Balasubramanian
The advancement of artificial intelligence and autonomous control has resulted in the widespread use of unmanned aerial vehicles (UAVs) in a variety of large-scale practical applications like target tracking, disaster surveillance, and traffic monitoring. Heterogeneous UAVs outperform homogeneous UAVs in terms of energy consumption and performance. The use of several unmanned aerial vehicles (UAVs)
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Komodo Mlipir Algorithm-based optimal route determination mechanism for improving Quality of Service in Vehicular ad hoc network Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-15 R.K. Soundarayaa, C. Balasubramanian
In Vehicular ad hoc network (VANETs), Quality of Service (QoS)- aware protocols helps in handling the necessitated demand of delay sensitive applications for facilitating intelligent transportation. The fundamental challenge of VANETs lies in the process of establishing vehicle to infrastructure and vehicle-to-vehicle communication that are prone to link failure. Bio-inspired algorithms are identified
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Power System Monitoring for Electrical Disturbances in Wide Network Using Machine Learning Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-11 XinJihong Wei, Abdeljelil Chammam, Jianqin Feng, Abdullah Alshammari, Kian Tehranian, Nisreen Innab, Wejdan Deebani, Meshal Shutaywi
Due to infrastructure developments, wide disturbances have occurred in the power system. There is a need for intelligent monitoring systems across wide power networks for the stability and security of systems. A significant challenge in a comprehensive power monitoring system is identifying the noises in electrical measurements and oscillatory errors. In this research, the disturbances in the power
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Data center and load aggregator coordination towards electricity demand response Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-14 Yijia Zhang, Athanasios Tsiligkaridis, Ioannis Ch. Paschalidis, Ayse K. Coskun
In a demand response scenario, coordinating multiple data centers with an electricity load aggregator provides opportunities to minimize electricity cost and absorb the volatility in the grid that is caused by renewable generation. To enable optimal coordination, this paper introduces a joint data center and aggregator optimization framework that minimizes the cost of data centers while they participate
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Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-11 Krishna Kumar, Aman Kumar, Gaurav Saini, Mazin Abed Mohammed, Rachna Shah, Jan Nedoma, Radek Martinek, Seifedine Kadry
Silt is the leading cause of the erosion of the turbine's underwater components during hydropower generation. This erosion subsequently decreases the machine's efficiency. The present study aims to develop statistical correlations for predicting the efficiency of a hydropower plant based on the Kaplan turbine. Historical data from a Kaplan turbine-based hydropower plant was employed to create the model
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Spatio-temporal management of renewable energy consumption, carbon emissions, and cost in data centers Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-07 Donglin Chen, Yifan Ma, Lei Wang, Mengdi Yao
Under the background of "carbon neutrality ", data center enterprises are confronted with the challenges of high energy costs and the need to manage carbon emissions. Compared with traditional energy sources, renewable energy possesses the advantages of being low-carbon and cost-effective, making it an essential avenue for data centers to enhance their utilization of renewable energy. By employing
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Deep Learning with Blockchain Based Cyber Security Threat Intelligence and Situational Awareness System for Intrusion Alert Prediction Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2024-01-04 J S Shyam Mohan, M .Thirunavukkarasu, N .Kumaran, D. Thamaraiselvi
Network security situation assessment (NSSA) is imperative and active defense technology in the network security situation. By examining NSSA data, one can examine the threat of network security and examine the network attack phase and hence fully grasp the complete network security situation. With the quick design of 5 G, the cloud model and Internet of things (IoT), the network platform is increasingly
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PSOGSA: A parallel implementation model for data clustering using new hybrid swarm intelligence and improved machine learning technique Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-26 Shruti Chaudhari, Anuradha Thakare, Ahmed M. Anter
With the digitization of the entire world and huge requirements of understanding unknown patterns from the data, clustering becomes an important research area. The quick and accurate division of large datasets with a range of properties or features becomes challenging. The parallel implementation of clustering algorithms must satisfy stringent computational requirements to handle large amounts of data
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Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-25 Ali Ala, Muhammet Deveci, Erfan Amani Bani, Amir Hossein Sadeghi
The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Two-stage stochastic programming with recourse that performs well for
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Towards rapid modeling and prototyping of indoor and outdoor monitoring applications Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-20 Alessandra Rizzardi, Sabrina Sicari, Alberto Coen-Porisini
Nowadays, the capability to remotely monitor indoor and outdoor environments would allow to reduce energy consumption and improve the overall management and users’ experience of network application systems. The most known solutions adopting remote control are related to domotics (e.g., smart homes and industry 4.0 applications). An important stimulus for the development of such smart approaches is
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Trilateration method based node localization and energy efficient routing using rsa for under water wireless sensor network Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-22 A. Shenbagharaman, B. Paramasivan
UWSN refers to a collection of numerous underwater wireless sensor-nodes dispersed throughout the marine environment. Proposed work develops node-localization and optimal relay node selection based routing approach for UWSN. Target nodes initially listen to the beacon for a certain amount of time whenever they are within range of an anchor node before retrieving an anchor-node with RSS data. Next,
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Improved Black Widow Optimization: An investigation into enhancing cloud task scheduling efficiency Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-16 Muhannad A. Abu-Hashem, Mohammad Shehab, Mohd Khaled Yousef Shambour, Mohammad Sh. Daoud, Laith Abualigah
The Black Widow Optimization (BWO) algorithm has garnered significant attention within the realm of metaheuristic algorithms due to its potential to address diverse problems across various domains. However, a noteworthy weakness of BWO is its utilization of a random selection technique, which can lead to reduced diversity, expedited convergence, and potential entrapment in local optima. This research
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IoT-digital twin-inspired smart irrigation approach for optimal water utilization Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-10 Ankush Manocha, Sandeep Kumar Sood, Munish Bhatia
Agriculture industry faces the challenge of increasing productivity by 50% from 2012 to 2050 while reducing water usage, given that it currently consumes 69% of the world’s freshwater. To achieve this goal, smart technologies such as Artificial Intelligence (AI), Digital Twins (DT), and Internet of Things (IoT) are being increasingly utilized. However, the use of DT in agriculture is still in its early
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Load balancing in cloud computing via intelligent PSO-based feedback controller Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-12-09 Shabina Ghafir, M. Afshar Alam, Farheen Siddiqui, Sameena Naaz
Load balancing effectively distributes network load and balances the load during the scheduling and allocation process. Hence various load balancing techniques in task scheduling and resource allocation along with VM migration has been presented previously but they have a heavy load on some VM and violate cloud service level agreement with a single point of failure. Therefore, a novel Intelligent PSO-based
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Machine learning compliance-aware dynamic software allocation for energy, cost and resource-efficient cloud environment Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-28 Leila Helali, Mohamed Nazih Omri
With the growing number of cloud services protected by licenses, compliance management and assurance is becoming critical need to support the development of trustworthy cloud systems. In these systems, the multiplication of services and the inefficient resource utilization incurred energy consumption and costs increase despite the consolidation initiatives underway. Few works deal with resource allocation
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Soft computing based smart grid fault detection using computerised data analysis with fuzzy machine learning model Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-30 Taifeng Chen, Chunbo Liu
Electrical grids are more dependable, secure, and significant smart grid (SG) technologies. For effective and dependable electricity distribution, new risks are raised by its high reliance on digital communication technologies. The best grid monitoring and control skills are essential for system reliability. Among other things, SG applications include three key challenges: managing big data volumes
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Priority based job scheduling technique that utilizes gaps to increase the efficiency of job distribution in cloud computing Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-30 Saydul Akbar Murad, Zafril Rizal M. Azmi, Abu Jafar Md. Muzahid, Md. Murad Hossain Sarker, M. Saef Ullah Miah, MD. Khairul Bashar Bhuiyan, Nick Rahimi, Anupam Kumar Bairagi
A growing number of services, accessible and usable by individuals and businesses on a pay-as-you-go basis, are being made available via cloud computing platforms. The business services paradigm in cloud computing encounters several quality of service (QoS) challenges, such as flow time, makespan time, reliability, and delay. To overcome these obstacles, we first designed a resource management framework
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A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-30 P. Pabitha, K. Nivitha, C. Gunavathi, B. Panjavarnam
Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is
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A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-30 Marlin S, Sundarsingh Jebaseelan
For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling
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Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-28 R. Manivannan
Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system. In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy
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Sustainable and lightweight domain-based intrusion detection system for in-vehicle network Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-25 Edy Kristianto, Po-Ching Lin, Ren-Hung Hwang
Intelligent transportation systems are designed to enhance and optimize the traffic flow, safety of urban mobility, and improve energy efficiency. While advanced vehicles are equipped with new features, such as communication technologies for the exchange of safety messages, the communication interfaces of the vehicles increase the attack surfaces for attackers to exploit, even into the in-vehicle network
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Tweaked optimization based quality aware VM selection method for effectual placement strategy Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-23 Rubaya Khatun, Md Ashifuddin Mondal
Cloud computing has become a standard and promising distributed computing framework for the provision of on-demand computing resources and pay-per-use concepts. Operations of these computing resources result in maximum power consumption, enraged cost and high Co2 emission to the environment. The major difficulties faced when accessing cloud data center are SLA violations, increased time, less utilization
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ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-20 Mohammad Masdari, Shahab S. Band, Sultan Noman Qasem, Biju Theruvil Sayed, Hao-Ting Pai
Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving
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An efficient multi-format low-precision floating-point multiplier Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-18 Hadis Ahmadpour Kermani, Azadeh Alsadat Emrani Zarandi
Low-precision computing has emerged as a promising technology to enhance performance in modern applications like deep neural network training and scientific computing. However, most existing circuits and systems are tailored to a single type of half-precision format, such as FP16 or BFloat16. In light of this limitation, this paper introduces the design of a multi-format floating-point multiplier capable
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A framework for real-time vehicle counting and velocity estimation using deep learning Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-07 Wei-Chun Chen, Ming-Jay Deng, Ping-Yu Liu, Chun-Chi Lai, Yu-Hao Lin
To better control traffic and promote environmental sustainability, this study proposed a framework to monitor vehicle number and velocity at real time. First, You Only Look Once-v4 (Yolo-v4) algorithm based on deep learning can greatly improve the accuracy of object detection in an image, and trackers, including Sort and Deepsort, resolved the identity switch problem to track efficiently the multiple
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ETNAS: An energy consumption task-driven neural architecture search Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-11-10 Dong Dong, Hongxu Jiang, Xuekai Wei, Yanfei Song, Xu Zhuang, Jason Wang
Neural Architecture Search (NAS) is crucial in the field of sustainable computing as it facilitates the development of highly efficient and effective neural networks. However, it cannot automate the deployment of neural networks to accommodate specific hardware resources and task requirements. This paper introduces ETNAS, which is a hardware-aware multi-objective optimal neural network architecture
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Eagle arithmetic optimization algorithm for renewable energy-based load frequency stabilization of power systems Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-23 Ligang Tang, Tong Kong, Nisreen Innab
Power systems' efficient management and planning are crucial in renewable energy-based systems. As the global electricity demand continues to rise, there is a growing need for alternative energy sources such as solar, wind, and hydropower. Consequently, numerous research studies have focused on maintaining load balancing within the renewable energy system and improving the forecasting of renewable
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Soil moisture simulation of rice using optimized Support Vector Machine for sustainable agricultural applications Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-13 Parijata Majumdar, Sanjoy Mitra, Diptendu Bhattacharya
The growth and development of rice crops primarily depend on appropriate soil water balance for which soil moisture is the key determinant. Soil moisture is a crucial parameter in the hydrological cycle, which has a vital role in optimal water management for sustainable agricultural growth as it has a significant impact on hydrological, ecological, and climatic processes. Thus, accurate estimation
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Strip running deviation monitoring and feedback real-time in smart factories based on improved YOLOv5 Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-13 Jun Luo, Gang Wang, Mingliang Zhou, Huayan Pu, Jun Luo
The strip running deviation in steel production can cause significant economic losses by forcing a shutdown of the whole steel production line. However, due to the fast running speed (100–140 m/min) of the strip, it a difficult problem to accurately judge online whether the strip running deviation or not and control its deviation during operation. In this paper, a fast and accurate model for detecting
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Hybrid approach for virtual machine allocation in cloud computing Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-13 B. Booba, X. Joshphin Jasaline Anitha, C. Mohan, Jeyalaksshmi S
In this manuscript, a Combined Approach of Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm and Adaptive β-Hill Climbing Algorithm for Virtual Machine Allocation in Cloud Computing (BA-VMA-CC) is proposed. Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm (GBRAMP) is used for Virtual Machine (VM) Migration process and Adaptive β-Hill Climbing Algorithm
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How can a hybrid quantum-inspired gravitational search algorithm decrease energy consumption in IoT-based software-defined networks? Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-05 Lian Tong, Lan Yang, Xin Zhao, Li Liu
The growth of Internet of Things (IoT) devices has prompted the growing use of software-defined networks (SDNs) in today's quickly changing technological environment. In SDN, execution and security of supporting applications and creating an adaptable network design allow the network to associate with applications legitimately. As a result, SDN promotes the growth of IoT-enabled devices, boosts network
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Deep learning-based energy inefficiency detection in the smart buildings Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-05 Jueru Huang, Dmitry D. Koroteev, Marina Rynkovskaya
The operation of the heating, ventilation, and air conditioning (HVAC) system is essential for the indoor thermal environment and is significant for energy consumers in commercial properties. Although earlier studies suggested that reinforcement learning controls could increase HVAC energy savings, they lacked sufficient details regarding end-to-end management. Recently, the focus on gathering and
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A systematic review on techniques and approaches to estimate mobile software energy consumption Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-10-04 Andreas Schuler, Gabriele Kotsis
Developing green and sustainable software has become a prominent topic in research over the last years. While approaches are being constantly researched and developed to estimate and in turn optimize the energy consumption of software applications, there is still a lack of knowledge amongst practitioners how to address energy consumption as an important non-functional quality aspect and in turn develop
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BFSF: A secure IoT based framework for smart farming using blockchain Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-09-14 Shashi Shreya, Kakali Chatterjee, Ashish Singh
Many smart applications, including smart cities, healthcare, smart agriculture, manufacturing, etc., have widely adopted the Internet of Things (IoT). Smart Farming (SF) integrates a set of technologies, such as cloud and edge computing and greenhouse concepts, to improve agricultural processes. Due to its capacity to generate fresh agricultural products at an incredible pace of development and output
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E-AVOA-TS: Enhanced African vultures optimization algorithm-based task scheduling strategy for fog–cloud computing Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-09-14 R. Ghafari, N. Mansouri
In fog computing, inefficient scheduling of user tasks causes more delays. Moreover, how to schedule tasks that need to be offloaded to fog nodes or cloud nodes has not been fully addressed. The task scheduling process needs to be optimized and efficient in order to address the issues of resource utilization, response time, and energy consumption. This paper proposes an Enhanced African Vultures Optimization
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Energy efficient data gathering using mobile sink in IoT for reliable irrigation Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-09-06 Vishnuvarthan Rajagopal, Bhanumathi Velusamy, Muralitharan Krishnan, Sakthivel Rathinasamy
Due to the increasing world population, demand for food products have created the need to modernize and intensify agricultural operations through precision agriculture. The Internet of Things offers a wide variety of solutions for precision agriculture, but implementing it in the agriculture field imposes challenges to hardware and data communication in the network. Importantly, the sensor nodes have
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Agrivoltaic system for energy-food production: A symbiotic approach on strategy, modelling, and optimization Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-25 Nimay Chandra Giri, Ramesh Chandra Mohanty, Rama Chandra Pradhan, S. Abdullah, Uttam Ghosh, Amrit Mukherjee
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Optimizing wireless sensor network lifetime through K-coverage maximization and memetic search Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-27 Nguyen Thi Hanh, Huynh Thi Thanh Binh, Nguyen Van Son, Nguyen Thi Trang, Phan Ngoc Lan
Many wireless sensor networks (WSNs) not only need to cover targets within their sensing fields, but also operate for extended periods without requiring expensive recharging. This is a challenging task when planning deployments, as sensing and data processing consumes sensor energy. Redundant sensors can help extend the lifetime, but also require good placement and scheduling to be effective. This
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Multi-exit DNN inference acceleration for intelligent terminal with heterogeneous processors Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-22 Jinghui Zhang, Weilong Xin, Dingyang Lv, Jiawei Wang, Guangxing Cai, Fang Dong
Recently, there has been a burgeoning popularity in the deployment of deep learning vision applications upon terminal devices. However, as the number of layers in deep neural networks (DNNs) and structural complexity increase, although the performance of DNN in handling computer vision tasks has become powerful, model inference tasks on computation resource constrained intelligent terminal devices
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Efficient realization of quantum balanced ternary reversible multiplier building blocks: A great step towards sustainable computing Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-19 Ehsan Faghih, MohammadReza Taheri, Keivan Navi, Nader Bagherzadeh
Nowadays, quantum computing plays a significant role in reducing the execution time in complicated computations. There are different reversible algorithms for quantum computation and many quantum circuits theoretically designed until now. These designs are only built from reversible gates because reversibility is a necessary logic in quantum circuits. Additionally, ternary representation can allow
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Leaf classification on Flavia dataset: A detailed review Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-18 Syed Umaid Ahmed, Junaid Shuja, Muhammad Atif Tahir
For decades, vision scientists have contemplated the topic of plant species classification. As plants are of great importance to medicinal research, they are utilized in a wide range of medications. Plants are required in a variety of ways in order to save the species from extinction and provide an abundance of food through agriculture. Therefore,Botanists and computer scientists must conduct extensive
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Sample selecting method based on feature density for pest identification in smart agriculture Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-18 Xinfeng Li, Shuai Xiao, Zhuo Zhang
Now, deep learning technology has gradually matured and successfully applied in various fields, bringing great convenience to human life. However, people neglect the importance of early sample collection and data processing while continuously improving the quality of network models, which often leads to poor application effect of models in practical projects. At present, the Data-centric AI campaign
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Bi-objective energy-efficient scheduling in a seru production system considering reconfiguration of serus Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-02 Jie Lian, Wenjuan Li, Guoli Pu, Pengwei Zhang
Owing to movable workstations, light equipment and multi-skilled workers, serus can be constructed, modified and dismantled rapidly. Such an advantage enables serus to be reconfigured in response to frequent changes in product types. As different configurations lead to different processing time and energy consumption, an important issue is how to arrange a best configuration of seru with the tasks
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Operations of data centers with onsite renewables considering greenhouse gas emissions Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-08-02 José Luis Ruiz Duarte, Neng Fan
In a highly technology-dependent society, processing large amounts of data has become essential, resulting in a growing number of data centers. The global data center industry accounts for a significant proportion of the world’s energy consumption. A partial or total transition of the energy sources that power the operation of data centers to cleaner energy, particularly onsite renewable technologies
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Hierarchical localization algorithm for sustainable ocean health in large-scale underwater wireless sensor networks Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-07-31 Tanveer Ahmad, Xue Jun Li, Aswani Kumar Cherukuri, Ki-Il Kim
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A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-07-24 Qi Liu, Oscar Famous Darteh, Muhammad Bilal, Xianming Huang, Muhammad Attique, Xiaodong Liu, Amevi Acakpovi
The drive for smarter, greener, and more livable cities has led to research towards more effective solar energy forecasting techniques and their integration into traditional power systems. However, the availability of real-time data, data storage, and monitoring has become challenging. This research investigates a method based on Bi-directional LSTM (BDLSTM) neural network. BDLSTM takes into account
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A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-07-18
Offloading assists in overcoming the resource constraints of specific elements, making it one of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery capacities can use the edge to offload some of the operations, which can significantly reduce latency and lengthen battery lifetime. Due to their restricted battery capacity, deep learning (DL) techniques are more
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Energy management of smart homes over fog-based IoT architecture Sustain. Comput. Inform. Syst. (IF 4.5) Pub Date : 2023-07-20 Muhammad Umair, Muhammad Aamir Cheema, Bilal Afzal, Ghalib Shah
Existing research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy