scheduling algorithms Dotaz Zobrazit nápovědu
Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.
- Klíčová slova
- Edge computing, Firefly algorithm, Genetic operator, Quasi-reflection-based learning, Swarm intelligence, Workflow scheduling,
- Publikační typ
- časopisecké články MeSH
In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.
- Klíčová slova
- Cardiovascular Disease, ECG sensors, fog computing, health monitoring system, internet of medical things, low-latency, scheduling algorithms, task scheduling,
- MeSH
- algoritmy * MeSH
- cloud computing * MeSH
- elektrokardiografie MeSH
- internet MeSH
- počítačová simulace MeSH
- Publikační typ
- časopisecké články MeSH
Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR). To address these challenges, a crow search algorithm is developed for effective load scheduling within the distribution system. The proposed method minimizes the total operating cost (TOC) and maximizes VR under varying loading conditions and distributed generation (DG) configurations. A case study in Tamil Nadu, India, is conducted using a microgrid composed of three distributed generation sources (DGs), modeled and simulated using the Electrical Transient Analyzer Program (ETAP) environment. The proposed approach is tested under three operational scenarios: grid-connected mode, islanded mode, and grid-connected mode with one DG outage. Results indicate that the crow search algorithm significantly optimizes load scheduling, leading to a substantial reduction in power loss and enhancement in voltage profiles across all scenarios. The islanded mode operation using the crow search algorithm demonstrates a remarkable reduction in TOC and maximizes voltage regulation compared to other modes. The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. The findings showcase the effectiveness of the crow search algorithm in microgrid power management and its potential for application in other real-time power distribution systems.
- Klíčová slova
- Crow search algorithm, Distributed generation, ETAP simulation, Load scheduling, Microgrid, Power management, Total operating cost, Voltage regulation,
- Publikační typ
- časopisecké články MeSH
This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.
- Klíčová slova
- Binary optimization, Engineering applications, Metaheuristic algorithms,
- Publikační typ
- časopisecké články MeSH
Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.
- Klíčová slova
- Adaptive security deep q-learning network, Deep reinforcement learning, Fiber-optics, Internet of things, Telemedicine applications,
- MeSH
- algoritmy MeSH
- deep learning * MeSH
- internet věcí * MeSH
- lidé MeSH
- technologie optických vláken MeSH
- telemedicína * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.
- Klíčová slova
- Energy management, Micro-grid (MG), Multi-objective optimization, Photovoltaic (PV), Renewable energy sources (RESs), Sine cosine algorithm, Wind turbine (WT),
- Publikační typ
- časopisecké články MeSH
In this paper, possibilities for network traffic protection in future hybrid passive optical networks are presented, and reasons for realizing and utilizing advanced network traffic protection schemes for various network traffic classes in these networks are analyzed. Next, principles of the Prediction-based Fair Wavelength and Bandwidth Allocation (PFWBA) algorithm are introduced in detail, focusing on the Prediction-based Fair Excessive Bandwidth Reallocation (PFEBR) algorithm with the Early Dynamic Bandwidth Allocation (E-DBA) mechanism and subsequent Dynamic Wavelength Allocation (DWA) scheme. For analyzing various wavelength allocation possibilities in Hybrid Passive Optical Networks (HPON) networks, a simulation program with the enhancement of the PFWBA algorithm is realized. Finally, a comparison of different methods of the wavelength allocation in conjunction with specific network traffic classes is executed for future HPON networks with considered protection schemes. Subsequently, three methods are presented from the viewpoint of HPON network traffic protection possibilities, including a new approach for the wavelength allocation based on network traffic protection assumptions.
Due to the technical advances of mass spectrometers, particularly increased scanning speed and higher MS/MS resolution, the use of data-independent acquisition mass spectrometry (DIA-MS) became more popular, which enables high reproducibility in both proteomic identification and quantification. The current DIA-MS methods normally cover a wide mass range, with the aim to target and identify as many peptides and proteins as possible and therefore frequently generate MS/MS spectra of high complexity. In this report, we assessed the performance and benefits of using small windows with, e.g., 5-m/z width across the peptide elution time. We further devised a new DIA method named RTwinDIA that schedules the small isolation windows in different retention time blocks, taking advantage of the fact that larger peptides are normally eluting later in reversed phase chromatography. We assessed the direct proteomic identification by using shotgun database searching tools such as MaxQuant and pFind, and also Spectronaut with an external comprehensive spectral library of human proteins. We conclude that algorithms like pFind have potential in directly analyzing DIA data acquired with small windows, and that the instrumental time and DIA cycle time, if prioritized to be spent on small windows rather than on covering a broad mass range by large windows, will improve the direct proteome coverage for new biological samples and increase the quantitative precision. These results further provide perspectives for the future convergence between DDA and DIA on faster MS analyzers.
- Klíčová slova
- Data-independent acquisition, Isolation windows, Maxquant, Spectronaut, pFind,
- MeSH
- chromatografie s reverzní fází MeSH
- hmotnostní spektrometrie metody MeSH
- lidé MeSH
- nádorové buněčné linie MeSH
- peptidy analýza MeSH
- proteiny analýza MeSH
- proteomika metody MeSH
- software MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- peptidy MeSH
- proteiny MeSH
The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.
Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.
