Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

Awan, Nabeela; Khan, Salman; Rahmani, Mohammad Khalid Imam; Tahir, Muhammad; Alam, Nur; Alturki, Ryan; Ullah, Ihsan

doi:10.32604/cmc.2021.014386

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…In Awan et al [11], the paper presents a task scheduling solution by utilizing Improved Multi-Levels Queues based deadline (MMLQ-D) for multiprocessors schedule. The invented mechanism combined two important algorithms for managing tasks assignments in the queues for execution through MMLQs protocol.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Algorithm for Grid Computing Using Shortest Job First with Time Quantum

Yosuf¹,

Mokhtar²,

Saeed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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The grid computing is one of the strong initiatives and technologies that has been introduced in the last decade for improve the resources utilization, optimization and provide very high throughput computation for wide range of applications. To attain these goals an effective scheduling for grid systems is a vital issue to realize the intended performance. The processes scheduling could be executed in various methods and protocols that have been extensively address in the literature. This works utilized shortest process first (SPF) protocol which gives the shortest jobs the highest priorities. For longer jobs, it should have lower priorities and wait in the queue longer time. With too many smallest tasks reach the queue, the long tasks can't have an opportunity to be processed and the number of tasks continue growing in front of them, which is well-known issue called Starvations. In this work we introduce a new time quantum-based for shortest process first to avoid the starvation problem. The objective of the model is to optimize the utilization of resources by reducing end time of the job and enhancing performance of job scheduling. The model introduces fair treatment for all the processes. A simulation environment for the proposed model is developed and implemented. The results show that the quantum time-based solution has improved the starvation issues severely in terms of time delay, optimization of grid resources and fairness. The results show that SJF with time quantum outperform the SJF in terms of end time process, resources utilization, delay to transmission and Fair treatment for all the processes. But in term of waiting time Shortest Job First is better, SJF does not always minimize waiting time.

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Section: Related Workmentioning

confidence: 99%

Scheduling Algorithm for Grid Computing Using Shortest Job First with Time Quantum

Yosuf¹,

Mokhtar²,

Saeed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…The ML algorithms are also used to schedule the power between smart cities. The scheduling algorithms collect information like power demand, price, and power availability for allocating the power to the required place in an efficient way (Awan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Random forest based power sustainability and cost optimization in smart grid

Durairaj¹,

Wróblewski

Joseph³

et al. 2022

Production Engineering Archives

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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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“…A new paradigm of the Smart City model is correlated with quality of life, public security, emergency relief and other urban development resources [1]. Connectivity of aerial vehicles can only be made possible by using internet of things to reshape the concept of smart cities [2]. Due to aerial vehicles changing topological structure, measuring signal strength either indoor or open-air is a tough task.…”

Section: Introductionmentioning

confidence: 99%

Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

Khan¹,

Hassan²,

Fayaz³

et al. 2022

Computers, Materials &Amp; Continua

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Most interesting area is the growing demand of flying-IoT mergers with smart cities. However, aerial vehicles, especially unmanned aerial vehicles (UAVs), have limited capabilities for maintaining node energy efficiency. In order to communicate effectively, IoT is a key element for smart cities. While improving network performance, routing protocols can be deployed in flying-IoT to improve latency, packet drop rate, packet delivery, power utilization, and average-end-to-end delay. Furthermore, in literature, proposed techniques are very much complex which cannot be easily implemented in realworld applications. This issue leads to the development of lightweight energyefficient routing in flying-IoT networks. This paper addresses the energy conservation problem in flying-IoT. This paper presents a novel approach for the internet of flying vehicles using DSDV routing. ISH-DSDV gives the notion of bellman-ford algorithm consisting of routing updates, information broadcasting, and stale method. DSDV shows optimal results in comparison with other contemporary routing protocols. Nomadic mobility model is utilized in the scenario of flying networks to check the performance of routing protocols.

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Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

Cited by 14 publications

References 11 publications

Scheduling Algorithm for Grid Computing Using Shortest Job First with Time Quantum

Scheduling Algorithm for Grid Computing Using Shortest Job First with Time Quantum

Random forest based power sustainability and cost optimization in smart grid

Improved Sequencing Heuristic DSDV Protocol Using Nomadic Mobility Model for FANETS

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