Energy-Efficient Task Scheduling Based on Traffic Mapping in Heterogeneous Mobile-Edge Computing: A Green IoT Perspective

Wang, Chaowei; Yu, Xiaofei; Xu, Lexi; Wang, Weidong

doi:10.1109/tgcn.2022.3186314

Cited by 46 publications

(7 citation statements)

References 37 publications

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“…[58], [61], [62], [65], [67], [27], [74], [90], [96], [106] Application Architecture 11 [62], [65], [25], [74], [36], [37], [80], [90], [96], [103], [6] Technology Architecture 54 [108], [60], [55], [23], [62], [20], [24], [63], [65], [112], [109], [67], [69], [70], [71], [29], [111], [73], [32], [75], [77], [34], [35], [81], [4], [5], [82], [39], [40], [83], [41], <...…”

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

“…[24], [64], [109], [74], [37], [38], [85], [87], [90], [101] Industrial Process and Product Use (IPPU)…”

Section: Number Of Research Referencesmentioning

confidence: 99%

“…In the realm of electric vehicles, IoT-driven smart charging infrastructure promotes cleaner transportation by monitoring charging station availability and providing real-time status updates [24]. Overall, the integration of IoT technologies in green transportation creates a connected ecosystem, fostering sustainable and efficient mobility solutions for a more environmentally responsible future [109].…”

Section: ) Iot Integration In Building Management and Transportationmentioning

confidence: 99%

See 2 more Smart Citations

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Somantri,

Surendro

2024

IEEE Access

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Computer Science Architecture (CSA), encompassing data, application, technology, and business architecture, is a vital tool for addressing climate change challenges. It aims to reduce greenhouse gas emissions from sectors such as stationary energy, transportation, industry, product use, waste, and land use. CSA principles extend to advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), Machine Learning (ML), data centers, blockchain, multi-agent systems, sensors, and smart grids. This review explores CSA's role in mitigating greenhouse gas emissions for a sustainable environment. It analyzes implications, indicators, and methodologies for data, application, technology, and business architecture, highlighting their direct impact on creating an environmentally friendly technological landscape. The study also delves into emerging trends and suggestions for future research, contributing to the discourse on leveraging technology for a greener future.INDEX TERMS Greenhouse gas emissions, computer science architecture, sustainable environment, review.

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Section: ) Results Reporting On Rq2mentioning

confidence: 99%

“…[24], [64], [109], [74], [37], [38], [85], [87], [90], [101] Industrial Process and Product Use (IPPU)…”

Section: Number Of Research Referencesmentioning

confidence: 99%

Section: ) Iot Integration In Building Management and Transportationmentioning

confidence: 99%

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Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Somantri,

Surendro

2024

IEEE Access

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“…Nevertheless, DE applications in IoT edge task clustering and scheduling are less explored. The research in [35] applied a DE mechanism for energy-efficient task scheduling; nonetheless, the study does not handle the key multidimensional task characteristics in IoT edge computing.…”

Section: Related Workmentioning

confidence: 99%

An Evolutionary Algorithm for Task Clustering and Scheduling in IoT Edge Computing

Yousif,

Bashir,

Ali

2024

Mathematics

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The Internet of Things (IoT) edge is an emerging technology of sensors and devices that communicate real-time data to a network. IoT edge computing was introduced to handle the latency concerns related to cloud computing data management, as the data are processed closer to their point of origin. Clustering and scheduling tasks on IoT edge computing are considered a challenging problem due to the diverse nature of task and resource characteristics. Metaheuristics and optimization methods are widely used in IoT edge task clustering and scheduling. This paper introduced a new task clustering and scheduling mechanism using differential evolution optimization on IoT edge computing. The proposed mechanism aims to optimize task clustering and scheduling to find optimal execution times for submitted tasks. The proposed mechanism for task clustering is based on the degree of similarity of task characteristics. The proposed mechanisms use an evolutionary mechanism to distribute system tasks across suitable IoT edge resources. The clustering tasks process categorizes tasks with similar requirements and then maps them to appropriate resources. To evaluate the proposed differential evolution mechanism for IoT edge task clustering and scheduling, this study conducted several simulation experiments against two established mechanisms: the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). The simulation configuration was carefully created to mimic real-world IoT edge computing settings to ensure the proposed mechanism’s applicability and the simulation results’ relevance. In the heavyweight workload scenario, the proposed DE mechanism started with an execution time of 916.61 milliseconds, compared to FA’s 1092 milliseconds and PSO’s 1026.09 milliseconds. By the 50th iteration, the proposed DE mechanism had reduced its execution time significantly to around 821.27 milliseconds, whereas FA and PSO showed lesser improvements, with FA at approximately 1053.06 milliseconds and PSO stabilizing at 956.12 milliseconds. The simulation results revealed that the proposed differential evolution mechanism for edge task clustering and scheduling outperforms FA and PSO regarding system efficiency and stability, significantly reducing execution time and having minimal variation across simulation iterations.

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“…However, most target maneuvers exhibit coupling across different coordinates, particularly from the observer's coordinate system [10]. To overcome this issue, some studies have adopted dynamic models of target characteristics to represent the control quantity generated by target maneuvers as a random process, such as the twodimensional model [14], three-dimensional turning model [15][16][17], and flight dynamics model [18,19]. In [19], the authors considered the influence of aircraft attitude angles on target acceleration and utilized a 16-dimensional state extended Kalman filter, significantly improving the prediction accuracy of the target's state.…”

Section: Introductionmentioning

confidence: 99%

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Chen,

Liu,

et al. 2024

Electronics

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When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.

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Energy-Efficient Task Scheduling Based on Traffic Mapping in Heterogeneous Mobile-Edge Computing: A Green IoT Perspective

Cited by 46 publications

References 37 publications

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

An Evolutionary Algorithm for Task Clustering and Scheduling in IoT Edge Computing

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

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