Predicting Energy Consumption for UAV-Enabled MEC Using Machine Learning Algorithm

Alzaghir, Abbas; Abdellah, Ali R.; Koucheryav, Andrey

doi:10.1007/978-3-030-97777-1_25

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Modern computational techniques are an appropriate solution in this context, as they allow storage and computational resources to be moved closer to IoT systems, as represented by base stations (BSs). The edge computing used in UAVs [12], a fundamental level in the IoT framework, reduces the delay between IoT systems and the computational backend infrastructure (mini-cloud). The UAV level adjacent to the IoT system offers more minor delays; however, it offers fewer resources than the use of BSs and a mini-cloud.…”

Section: Introductionmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

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Section: Introductionmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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“…Temperature [16], humidity [17], wind speed [18], and precipitation interact [19] in the weather forecast. MIMO forecasting predicts energy consumption [20], resource availability [21], and production outputs [22] in resource management and optimization. Due to its complexity and real-world applications, the problem of MIMO time-series forecasting has gained attention in recent years [23][24][25] [26].…”

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confidence: 99%

Deep learning approaches for MIMO time-series analysis

Kurniawan,

Sulaiman,

Konate

et al. 2023

Int. J. Adv. Intell. Informatics

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This study presents a comparative analysis of various deep learning (DL) methods for multi-input and multi-output (MIMO) time-series forecasting of stock prices. The analysis is conducted on a dataset comprising the stock price of Bitcoin. The dataset consists of 2950 rows from December 2017 to December 2021. This study aims to evaluate the performance of multiple DL methods, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). The evaluation criteria for selecting the best-performing methods in this research are based on two performance metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These metrics were chosen for specific reasons related to assessing the accuracy and reliability of the forecasting models. MAPE is used to assess accuracy, while RMSE helps detect outliers in the system. Results show that the LSTM method achieves the best performance, outperforming other methods with an average MAPE value of 8.73% and Bi-LSTM has the best average RMSE value of 0.02216. The findings of this study have practical implications for time-series forecasting in the field of stock trading. The superior performance of LSTM highlights its potential as a reliable method for accurately predicting stock prices. The Bi-LSTM model's ability to detect outliers can aid in identifying abnormal stock market behavior. In summary, this research provides insights into the performance of various DL models of MIMO for stock price forecasting. The results contribute to the field of time-series forecasting and offer valuable guidance for decision-making in stock trading by identifying the most effective methods for predicting stock prices accurately and detecting unusual market behavior.

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Artificial Intelligence Driven 5G and Beyond Networks

Abdellah

Koucheryavy

2023

Inf. tehnol. telekommun.

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5G networks and beyond are expected to meet numerous service requirements in various aspects of our daily lives. At the same time, the functional complexity of 5G telecommunication networks increases by an order of magnitude compared to existing networks. 5G data rates are dramatically faster, connection density is higher, and latency is much lower, among other improvements. An efficient 5G network cannot be complete without incorporating artificial intelligence (AI) techniques. All this requires the use of new technologies, including artificial intelligence, to ensure the stable operation of telecommunication networks, methodology, system analysis, and key results. Scientific tasks for 5G communication networks are identified where the use of artificial intelligence, including machine and deep learning, seems appropriate. Practical Relevance. The results of the work may be useful in training in networks and telecommunication systems and in defining new scientific tasks for PhD students.

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Predicting Energy Consumption for UAV-Enabled MEC Using Machine Learning Algorithm

Cited by 4 publications

References 16 publications

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

Deep learning approaches for MIMO time-series analysis

Artificial Intelligence Driven 5G and Beyond Networks

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