Deep Reinforcement Learning-Based Mobility-Aware Robust Proactive Resource Allocation in Heterogeneous Networks

Li, Jing; Zhang, Xing; Zhang, Jiaxin; Wu, Jie; Sun, Qi; Xie, Yuxuan

doi:10.1109/tccn.2019.2954396

Cited by 29 publications

(12 citation statements)

References 27 publications

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“…It is possible to divide the infrastructure into three. ( 1) is includes mobiles, smartphones, and tablet computers in daily life, as well as self-driving cars, wearables, and robotic devices [4,[34][35][36]. ese gadgets may provide many different uses and services, not just using more sophisticated hardware but also using the background system enabled with DL algorithms.…”

Section: System Architecturementioning

confidence: 99%

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Arif

Ajesh

Shamsudheen

et al. 2022

Security and Communication Networks

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The use of application media, gamming, entertainment, and healthcare engineering has expanded as a result of the rapid growth of mobile technologies. This technology overcomes the traditional computing methods in terms of communication delay and energy consumption, thereby providing high reliability and bandwidth for devices. In today’s world, mobile edge computing is improving in various forms so as to provide better output and there is no room for simple computing architecture for MEC. So, this paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks is done using the LSTM algorithm, the strategy for computation offloading of mobile devices is based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling helps to optimize the edge computing offloading model. Experiments show that our proposed architecture, which consists of an LSTM-based offloading technique and routing (LSTMOTR) algorithm, can efficiently decrease total task delay with growing data and subtasks, reduce energy consumption, and bring much security to the devices due to the firewall nature of LSTM.

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Section: System Architecturementioning

confidence: 99%

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Arif

Ajesh

Shamsudheen

et al. 2022

Security and Communication Networks

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“…Tang and others [31] proposed a novel deep-learning-based traffic load prediction algorithm to forecast future congestion in SDN--IoT networks in combination with a partial channel assignment algorithm to allocate channels to each link intelligently. The authors of [32] proposed a deep-reinforcement-learning approach to minimize prediction uncertainty for dynamic resource allocation. In [33], the authors used deep learning to optimize traffic and enable low-latency and reliable content caching for transmitting virtual reality content from unmanned aerial vehicles.…”

Section: Workmentioning

confidence: 99%

A supervised‐learning‐based spatial performance prediction framework for heterogeneous communication networks

et al. 2020

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In this paper, we propose a supervised‐learning‐based spatial performance prediction (SLPP) framework for next‐generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine‐learning ubiquitous for accurate data‐based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis‐based prediction approach has been proposed in this paper. Comparison results with different machine‐learning techniques on real‐world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.

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“…Using machine learning for mobility prediction, the literature [22] proposed a centralized routing scheme to minimize the overall vehicular service delay. Some other mobility-aware-based network protocols could be found in [23,24] for low-cost topology control, in [25] for location management, in [26] for clustering, and in [27] for resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Data Transmission in Mobility-Aware Wireless Networks

Zhang

Liang

et al. 2022

Wireless Communications and Mobile Computing

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Data transmission scheme is an effective mode to improve the energy-efficiency in packet delivery. In this paper, we investigate the energy-efficient data transmission over reliable links and unreliable links in mobility-aware wireless networks. The network topologies in mobility-aware wireless networks are changed from one time slot to another, and they could be described by a sequence of static graphs. We first model these network topologies in a time period as a virtual space-time graph. On the virtual space-time graph, energy-efficient data transmission problems over reliable links and unreliable links are defined. The aim of the two data transmission problems is to find a spatial-temporal path with the minimum energy cost. Next, we propose an Energy-Efficient Data Transmission algorithm over Reliable Links (EEDT-RL) to find the optimal space-time path. Based on EEDT-RL, we also develop a heuristic data transmission protocol over unreliable links (named EEDT-UL), in which the path reliability is taken into consideration. Simulation results show that our proposed algorithms perform well in terms of energy cost and transmission count compared with some existing algorithms.

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Deep Reinforcement Learning-Based Mobility-Aware Robust Proactive Resource Allocation in Heterogeneous Networks

Cited by 29 publications

References 27 publications

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

A supervised‐learning‐based spatial performance prediction framework for heterogeneous communication networks

Energy-Efficient Data Transmission in Mobility-Aware Wireless Networks

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