Sustainable Computing-Informatics & Systems最新文献

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. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s

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 responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

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, having enough real-time capable measurement instruments, and two-way low-latency communication. This study proposes a unique method for detecting faults in the smart grid via the use of data monitoring and classification using a fuzzy machine learning model. Here, enhanced smart sensor metering performed in the cloud at the network's edge has been used to track data from the smart grid. Fuzzy reinforcement encoder adversarial NN has then been used to categorise the tracked data. Experimental analysis is carried out in terms of scalability, reliability, accuracy, mean average precision, throughput. The potential use of the current grid can be increased, and fault frequency can be decreased, with better monitoring technologies and predictive techniques. Proposed technique attained accuracy of 93 %, throughput of 94 %, reliability of 81 %, mean average precision of 89 %, scalability of 92 %.

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 for cloud computing systems. This framework elucidates the methodology of resource management in the context of cloud job scheduling. Then, we study the impact of a Virtual Machine’s (VM’s) physical resources on the consistency with which cloud services are executed. After that, we developed a priority-based fair scheduling (PBFS) algorithm to schedule jobs so that they have access to the required resources at optimal times. The algorithm has been devised utilizing three key characteristics, namely CPU time, arrival time, and job length. For optimal scheduling of cloud jobs, we also devised a backfilling technique called Earliest Gap Shortest Job First (EG-SJF), which prioritizes filling in schedule gaps in a specific order. The simulation was carried out with the help of the CloudSim framework. Finally, we compare our proposed PBFS algorithm to LJF, FCFS, and MAX-MIN and find that it achieves better results in terms of overall delay, makespan time, and flow time.

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 optimization at the SaaS level, which does not consider compliance aspects. Generally, the reported consolidation work does not address license management in the cloud environment as a whole, particularly from a resource management perspective, and the vast majority of consolidation work focuses on resource optimization at the infrastructure level. Thus, we propose a software license consolidation scheme based on multi-objective reinforcement learning that enables efficient use of resources and optimizes energy consumption, resource wastage, and costs while ensuring compliance with the processor-based licensing model. The experimental results show that our solution outperforms the baseline approaches in different scenarios with homogeneous and heterogeneous resources under different data center scales.

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 for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoT-based smart charging system for scheduling V2G connections for hybrid electrical vehicles. It allows for more precise and effective control and greater efficiency by enabling the system to learn from its surroundings.

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 (IVN). The automotive Ethernet and intrusion detection systems (IDSs) are promising solutions to the security problem inside the IVN. The automotive Ethernet has higher bandwidth capacity at an economical cost and flexibility for expansion than the current solution. The IDSs can protect the IVN from attackers compromising the vehicle. They can be implemented based on machine learning to learn from the normal IVN traffic behavior. However, numerous IDSs that utilize machine learning face hardware limitations when training within the in-vehicle network. Thus, the models have to be trained outside the IVN and then imported into it. Moreover, the IVN messages are unlabeled whether they are normal or under attack. We propose a lightweight unsupervised IDS that enables training in the IVN with limited computation resources. Our IDS models have an impressive parameter reduction of up to 94% compared to existing models. This leads to a remarkable reduction in memory usage of up to 86%, training time slashed by up to 69%, and a remarkable drop in energy consumption by up to 68%. Despite the size reduction, the proposed models remain only slightly less accurate than current solutions by up to 2%.

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 of resources, high consumption of power and energy. Hence, considering these difficulties, a novel virtual machine (VM) selection approach is proposed to minimize the constraints while maintaining the SLA. First, based on the assumptions of VMs and physical machines (PMs), the overutilized hosts are detected using a static threshold approach, while underutilized hosts are identified based on the utilized resources. After load detection, the VMs that need to be migrated over other PMs are selected using the tweaked chimp optimization algorithm (TCOA). After selecting VMs without influencing the capacity of other VMs, the placement process is performed over other PMs using a power aware best fit decreasing approach. The proposed approach can greatly improve the QoS by selecting the optimal VMs that need to be migrated. Cloudsim is used as a simulation tool, and the results are compared with existing techniques in terms of migration time, energy consumption, SLA violation per host and so on to prove the superiority. The energy consumption of the proposed model is obtained to be 195.3 kWh, the overall SLA violation rate is attained to be 0.032%, and the migration time for 500 virtual machines is 8.72 s

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 patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research.

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 of supporting a wide range of half-precision formats, including both their signed and unsigned versions. Our design emphasizes high reconfigurability, allowing it to adapt to the required dynamic range and precision. Moreover, experimental results showed that the introduced unsigned versions of the half-precision floating-point formats resulted in improved circuit parameters and energy consumption.