A New Task Scheduling Approach for Energy Conservation in Internet of Things

Tian, Man-Wen; Yan, Shu‐Rong; Guo, Wei; Mohammadzadeh, Ardashir; Ghaderpour, Ebrahim

doi:10.3390/en16052394

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…Energy conservation is crucial in IoT networks, given the real-time processing demands and energy constraints of IoT devices. In this context, Tian M. et al [6] introduced a pivotal method known as decisive task scheduling for energy conservation (DTS-EC). This method classifies tasks based on their energy requirements, with the goal of efficiently allocating and queuing data for processing.…”

Section: The Task Scheduling and Resource Optimisation In Iot Networkmentioning

confidence: 99%

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

Almudayni,

Soh,

2024

Future Internet

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The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA’s efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics.

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Section: The Task Scheduling and Resource Optimisation In Iot Networkmentioning

confidence: 99%

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

Almudayni,

Soh,

2024

Future Internet

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“…These challenges must be addressed to ensure the successful implementation and operation of IoT projects, particularly in domains where data accuracy, real-time analysis, and efficient data transport are crucial. To optimize IoT and mobile edge computing (MEC) architectures, techniques such as feature selection and data fusion can be leveraged, while also considering strategies for energy conservation and energy harvesting [ 2 ]. These approaches aim to improve the quality of low-cost sensor data, enhancing the overall performance and reliability of IoT systems [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling

Navaz

Serhani

Kassabi

et al. 2023

Sensors

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Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the wrong course of action. In this article, a novel approach called federated data quality profiling (FDQP) is proposed to assess the quality of the data at the edge. FDQP is inspired by federated learning (FL) and serves as a condensed document or a guide for node data quality assurance. The FDQP formal model is developed to capture the quality dimensions specified in the data quality profile (DQP). The proposed approach uses federated feature selection to improve classifier precision and rank features based on criteria such as feature value, outlier percentage, and missing data percentage. Extensive experimentation using a fetal dataset split into different edge nodes and a set of scenarios were carefully chosen to evaluate the proposed FDQP model. The results of the experiments demonstrated that the proposed FDQP approach positively improved the DQ, and thus, impacted the accuracy of the federated patient similarity network (FPSN)-based machine learning models. The proposed data-quality-aware federated PSN architecture leveraging FDQP model with data collected from edge nodes can effectively improve the data quality and accuracy of the federated patient similarity network (FPSN)-based machine learning models. Our profiling algorithm used lightweight profile exchange instead of full data processing at the edge, which resulted in optimal data quality achievement, thus improving efficiency. Overall, FDQP is an effective method for assessing data quality in the edge computing environment, and we believe that the proposed approach can be applied to other scenarios beyond patient monitoring.

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“…To attack this problem, a digital twin-based dynamic scheduling approach is proposed with a knowledge support system and a simulation module. Paper [9] studies decisive task scheduling for energy conservation in IoT (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Evolutionary Algorithm for Scheduling Jobs of Charging Electric Vehicles in an Autonomous Charging Station

Różycki,

Waligóra

2023

Energies

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The paper considers an innovative model of autonomous charging stations where a program implementing a scheduling algorithm and a set of jobs being scheduled are driven by the same common power source. It is assumed that one of the well-known local search metaheuristics—an evolutionary algorithm—is used for the scheduling process. The algorithm is designed to search for a sequence of charging jobs resulting in a schedule of the minimum length. Since processors with variable processing speeds can be used for computations, this has interesting consequences both from a theoretical and practical point of view. It is shown in the paper that the problem of choosing the right processor speed under given constraints and an assumed scheduling criterion is a non-trivial one. We formulate a general problem of determining the computation speed of the evolutionary algorithm based on the proposed model of a computational task and the adopted problem of scheduling charging jobs. The novelty of the paper consists of two aspects: (i) proposing the new model of the autonomous charging station operating according to the basics of edge computing; and (ii) developing the methodology for dynamically changing the computational speed, taking into account power and energy constraints as well as the results of computations obtained in the current iteration of the algorithm. Some approaches for selecting the appropriate speed of computations are proposed and discussed. Conclusions and possible directions for future research are also given.

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A New Task Scheduling Approach for Energy Conservation in Internet of Things

Cited by 7 publications

References 26 publications

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

IMBA: IoT-Mist Bat-Inspired Algorithm for Optimising Resource Allocation in IoT Networks

Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling

Energy-Aware Evolutionary Algorithm for Scheduling Jobs of Charging Electric Vehicles in an Autonomous Charging Station

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