Multi-Timescale Multi-Dimension Resource Allocation for NOMA-Edge Computing-Based Power IoT With Massive Connectivity

Yu, Haijun; Zhou, Zhenyu; Jia, Zehan; Zhao, Xiongwen; Zhang, Lei; Wang, Xiaoyan

doi:10.1109/tgcn.2021.3076582

Cited by 31 publications

(10 citation statements)

References 21 publications

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“…There are 20 IoT devices, 4 gateways, and 16 multiplexed channels. The simulation parameters are summarised in Table 2 [19,20,25]. The proposed algorithm is compared with two algorithms.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…We consider a multi-mode green IoT network for smart building, which consists of M gateways and N multi-mode IoT devices. As shown in Figure 1 A multi-timescale model is adopted [25] where gateway selection is optimised in large timescale, i.e. period, and channel selection is optimised in small timescale, i.e.…”

Section: System Modelmentioning

confidence: 99%

“…A multi‐timescale model is adopted [25] where gateway selection is optimised in large timescale, i.e. period, and channel selection is optimised in small timescale, i.e.…”

Section: System Modelmentioning

confidence: 99%

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Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

et al. 2022

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Multi‐mode green internet of things (IoT) network that integrates multiple communication media can well meet data transmission and processing demands of low‐carbon smart building. However, network resource management optimisation including joint optimisation of gateway and channel selection still faces technical challenges such as differentiated quality of service (QoS) demand guarantee, coupling between optimisation problems with different timescales, and adversary caused by multi‐device competition. To address these challenges, an adversarial learning‐based multi‐timescale network resource management algorithm for multi‐mode green IoT is proposed. Specifically, the minimisation problem of weighted difference between energy consumption and throughput under the long‐term queuing delay constraints is formulated to achieve differentiated QoS guarantee. Large‐timescale gateway selection is decoupled from small‐timescale channel selection by establishing matching preferences based on empirical performance, and optimised by using bilateral matching with quota. Finally, an exponential‐weight algorithm for exploration and exploitation (EXP3)‐based small‐timescale channel selection algorithm is proposed to achieve adversary awareness. Simulation results demonstrate that compared with asynchronous greedy matching algorithm and auction‐based many‐to‐many matching algorithm, the proposed algorithm performs superior in terms of energy consumption and throughput.

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“…There are 20 IoT devices, 4 gateways, and 16 multiplexed channels. The simulation parameters are summarised in Table 2 [19,20,25]. The proposed algorithm is compared with two algorithms.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

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Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

et al. 2022

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“…Since the DR during peak load periods and the real-time energy management of 5G BSs are optimization problems on different timescales, the participation of 5G BSs in the DR includes two stages, that is, DR planning and online energy optimization. As shown in Figure 2, we utilize a multitimescale model (Yu et al, 2021) to divide the peak load period into T time slots, that is, small timescale, with length τ, the set of which is defined as T {1, ..., t, ..., T}. Consecutive T 0 time slots are combined into a time interval, that is, large timescale, the set of which is defined as M {1, ..., m, ..., M}.…”

Section: System Architecturementioning

confidence: 99%

Optimal Dispatch of Multiple Photovoltaic Integrated 5G Base Stations for Active Distribution Network Demand Response

et al. 2022

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Multiple 5G base stations (BSs) equipped with distributed photovoltaic (PV) generation devices and energy storage (ES) units participate in active distribution network (ADN) demand response (DR), which is expected to be the best way to reduce the energy cost of 5G BSs and provide flexibility resources for the ADN. However, the standalone PV-integrated 5G BS has the characteristics of wide distribution, small volume, and large load fluctuations, which will bring strong uncertainty to the ADN by directly participating in the DR. Therefore, a system architecture for multiple PV-integrated 5G BSs to participate in the DR is proposed, where an energy aggregator is introduced to effectively aggregate the PV energy and ES resources of 5G BSs. Then, a two-stage optimal dispatch method is proposed. Specifically, in the large-timescale DR planning stage, an incentive mechanism for multiple PV-integrated 5G BSs to participate in the DR is constructed based on the contract theory, which ensures that multiple 5G BSs respond to and satisfy the peak-shaving demand of the ADN. In the small-timescale online energy optimization stage, based on the energy sharing mode among 5G BSs, a Lyapunov-based online energy optimization algorithm is proposed to optimize the shared energy flow between the internal layer and the interactive layer of 5G BSs, which further improves PV absorption and ensures operation stability of ES in the 5G BS. Simulation results show that the proposed two-stage optimal dispatch method can effectively encourage multiple 5G BSs to participate in DR and achieve the win–win effect of assisting the ADN peak-shaving and low-carbon economic operation of 5G BSs.

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“…A number of research works focused on resource allocation and computing offloading in MEC in recent years [22,[31][32][33][34][35]. In [31], the authors jointly optimized the offline mode, channel allocation, and device-to-device (D2D) pairing to minimize the computational pressure of largescale computing in MEC.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Resource Allocation and Task Offloading for NOMA-Enabled IoT Services in MEC

Xing

et al. 2022

Security and Communication Networks

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Integrating nonorthogonal multiple access (NOMA) and edge computing into the Internet of Things (IoT) for resource allocation and computing offloading can effectively reduce delay and energy consumption and improve spectrum efficiency. Computation tasks can be split into several independent subtasks and can be locally processed by IoT devices or offloaded to the MEC servers to process. The limited computing resources deteriorate the system performance. Thus, it is crucial to design the reasonable allocation strategies of computation resource and transmission power resource. In this paper, we jointly determine the CPU-cycle frequency allocation and transmission power allocation and formulate a stochastic optimization to minimize the energy consumption of IoT devices. Based on the Lyapunov optimization theory, we decompose the optimization problem into two deterministic subproblems to solve separately. One of them is obtained by seeking the first derivative, and the other is solved by using the best response idea after establishing the game model. Meanwhile, we propose a dynamic resource allocation and task offloading (DRATO) algorithm. Moreover, the simulation experiments show that the proposed algorithm effectively improves system performance and reduces energy consumption compared to three other benchmark methods.

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Multi-Timescale Multi-Dimension Resource Allocation for NOMA-Edge Computing-Based Power IoT With Massive Connectivity

Cited by 31 publications

References 21 publications

Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

Adversarial learning‐based multi‐timescale network resource management in multi‐mode green IoT network for smart building

Optimal Dispatch of Multiple Photovoltaic Integrated 5G Base Stations for Active Distribution Network Demand Response

Dynamic Resource Allocation and Task Offloading for NOMA-Enabled IoT Services in MEC

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