Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework

Wu, Jinyan; Yang, Bo; Wang, Lin; Park, Jaesung

doi:10.1109/access.2021.3049532

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…The traffic of most MTDs is substantially different from human traffic accessing the Internet [11]. In most IoT use cases, the traffic is either generated periodically, or as a burst after the detection of events [12].…”

Section: Proposed Fwus Schemementioning

confidence: 99%

“…Energy efficiency is a key design requirement for IoT networks composed of MTDs that must operate for several years without battery recharging or replacement [11], [12]. Most of these MTDs have limited energy resources due to their small size, low cost and/or hard-to-reach locations [13]- [15], which poses unprecedented challenges on the radio access network [16].…”

Section: Introductionmentioning

confidence: 99%

“…Improving the efficiency of the standardized DRX mechanism for MTC has been the focus of much attention, e.g., [11], [29]- [32]. Authors in [29], [30] discuss the tuning of a given set of DRX parameters in LTE/LTE-A and analyze the impact on energy-efficiency, while an improvement to DRX based on the inactivity timer management is provided in [31].…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [29], [30] discuss the tuning of a given set of DRX parameters in LTE/LTE-A and analyze the impact on energy-efficiency, while an improvement to DRX based on the inactivity timer management is provided in [31]. Authors in [32] propose a DRX mechanism that exploits the radio resource control connection release and re-establishment to save significant energy in MTDs, while an artificial intelligence (AI)-based adaptive DRX is devised in [11]. However, since the DRX mechanism is not specifically designed for MTDs, more suitable mechanisms are required [33].…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Ruíz-Guirola

Rodríguez-López

Montejo‐Sánchez³

et al. 2022

IEEE Internet Things J.

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educing energy consumption is a pressing issueeducing energy consumption is a pressing issueR in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, stateof-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

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Section: Proposed Fwus Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Ruíz-Guirola

Rodríguez-López

Montejo‐Sánchez³

et al. 2022

IEEE Internet Things J.

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“…This prediction is subsequently employed in the adaptation of one DRX parameter to the traffic. In [41], the arrival time of the next packet for IoT devices is predicted at the edge leveraging an ML solution. This prediction is subsequently employed in configuration of DRX parameters for IoT devices.…”

Section: Data-driven Drx Parameters Configurationmentioning

confidence: 99%

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Azari¹,

Salehi²,

Papapetrou³

et al. 2021

Preprint

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There is a lack of research on the analysis of peruser traffic in cellular networks, for deriving and following traffic-aware network management. In fact, the legacy design approach, in which resource provisioning and operation control are performed based on the cell-aggregated traffic scenarios, are not so energy-and cost-efficient and need to be substituted with user-centric predictive analysis of mobile network traffic and proactive network resource management. Here, we shed light on this problem by designing traffic prediction tools that utilize standard machine learning (ML) tools, including long shortterm memory (LSTM) and autoregressive integrated moving average (ARIMA) on top of per-user data. We present an expansive empirical evaluation of the designed solutions over a real network traffic dataset. Within this analysis, the impact of different parameters, such as the time granularity, the length of future predictions, and feature selection are investigated. As a potential application of these solutions, we present an ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this end, we leverage the derived ML models for dynamic DRX parameter adaptation to user traffic. The performance evaluation results demonstrate the superiority of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. Furthermore, the results show that adaptation of DRX parameters by online prediction of future traffic provides much more energy-saving at low latency cost in comparison with the legacy cell-wide DRX parameter adaptation.

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Edge Computing Technology Enablers: A Systematic Lecture Study

et al. 2022

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Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework

Cited by 8 publications

References 43 publications

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long Short-Term Memory Prediction

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

Edge Computing Technology Enablers: A Systematic Lecture Study

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