Adaptive learning-based delay-sensitive and secure edge-end collaboration for multi-mode low-carbon power IoT

Liao, Haijun; Jia, Zehan; Wang, Ruiqiuyu; Zhou, Zhenyu; Wang, Fei; Han, Dongsheng; Xu, Guowang; Wang, Zhenti; Yan, Qi

doi:10.23919/jcc.2022.07.024

Cited by 11 publications

(7 citation statements)

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“…In the simulation experiment, an edge-end collaborative multi-mode low-carbon PIoT network is established to test the ACE 2 against two other frontier algorithms. ACE2 lowers queuing latency by 57.96% and 61.81%, energy consumption by 14.96% and 18.66%, and secrecy capacity by 5.57% and 7.21% when compared to ACPA and WoLF-PHC [10,12,13]. The ACE 2 algorithm has an excellent performance in energy use, queuing latency, and secrecy capacity.…”

Section: Artificial Intelligence Algorithm In Heterogeneous Iotmentioning

confidence: 99%

“…With the development of artificial intelligence technology, intelligent algorithms can be deployed on the edge clouds or cloud to implement new smart IoT applications [8]. Here are two algorithms, Smart-Edge-CoCaCo and ACE 2 [9,10], which can improve the indicators of edge computing to meet the QoE.…”

Section: Artificial Intelligence Algorithm In Heterogeneous Iotmentioning

confidence: 99%

“…Due to the network congestion, the gap between these two modes is narrowed as the abscissa goes up.In the simulation experiment, an edge-end collaborative multi-mode low-carbon PIoT network is established to test the ACE 2 against two other frontier algorithms. ACE2 lowers queuing latency by 57.96% and 61.81%, energy consumption by 14.96% and 18.66%, and secrecy capacity by 5.57% and 7.21% when compared to ACPA and WoLF-PHC[10,12,13]. The ACE 2 algorithm has an excellent performance in energy use, queuing latency, and secrecy capacity.Compared with Smart-Edge-CoCaCo, ACE 2 not only pays attention to the delay but also considers energy consumption and secrecy.…”

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confidence: 99%

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The State of Art and Future Development of Internet of Things Warehousing Technology

Sun¹,

Yao²

2023

HSET

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The Internet of Things (IoT) has received a lot of attention from the government, academia, and enterprise enterprises for its amazing software prospects, and the IoT warehousing technology has received vast attention. In this paper, primarily based on the study of the concept, workflow, and system components of the IoT storage system, we propose how to optimize the present IoT storage system scheme. The evaluation focuses on two aspects: the records transmission science of the system, and the enchantment area of the system algorithm. This paper gives the improvement technique of logistics information transfer in the common warehouse management process and additionally proposes the use of edge computing most desirable for optimization. This system has the advantages of being fast, convenient, accurate, efficient, and rather automatic in access, monitoring, inventory, and picking.

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Section: Artificial Intelligence Algorithm In Heterogeneous Iotmentioning

confidence: 99%

Section: Artificial Intelligence Algorithm In Heterogeneous Iotmentioning

confidence: 99%

mentioning

confidence: 99%

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The State of Art and Future Development of Internet of Things Warehousing Technology

Sun¹,

Yao²

2023

HSET

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“…With the development of new power systems, massive intelligent devices, renewable energy devices, and other distributed devices are integrated to the distribution power IoT [3][4][5][6], resulting in an stringent requirement for TS. While distributed IoT devices can operate at the same time by receiving external high-precision time synchronization signals, such as satellite clock sources and ground clock sources [7,8], it is not economically feasible to equip all distributed IoT devices with expensive TS modules [9][10][11]. Deploying TS power IoT gateways can effectively reduce costs and improve TS accuracy.…”

Section: Introductionmentioning

confidence: 99%

RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

Lu,

Zhao,

et al. 2024

CMC

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Time synchronization (TS) is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things (IoT). Multi-clock source time synchronization (MTS) has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales, and the coupling of synchronization latency jitter and pulse phase difference. In this paper, the multi-timescale MTS model is conducted, and the reinforcement learning (RL) and analytic hierarchy process (AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference. Specifically, the multi-clock source selection is optimized based on Softmax in the large timescale, and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale. Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

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“…In order to improve data aggregation efficiency, the gateway aggregate information from nearby data sources with edge computing technology, avoiding the large transmission overhead incurred by information aggregation directly to the master station [6] . However, the data aggregation at gateway will result in an overwhelming amount of data being transmitted to the master station, which constrains the efficiency of data analysis and processing by the master station [7][8][9][10] . Data compression technology can effectively reduce the amount of data further uploaded to the master station through compression operations, providing more effective data information to the master station [11][12] .…”

Section: Introductionmentioning

confidence: 99%

PLC-based information aggregation and data compression for distributed PV access

Shi,

Li,

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

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With the expanding scale of photovoltaic (PV) power generation installed in the grid, the scattered communication terminals and the large-scale data communication led to the problem that the master station cannot aggregate information from multiple terminals and analyze data efficiently in real time. In this paper, a power line communication (PLC)-based efficient information aggregation and data compression architecture is constructed. On the basis, we propose genetic algorithm-based information aggregation algorithm to optimize PLC channel selection and reduce information aggregation delay. Moreover, we propose a multi-intersection approximation data estimation algorithm to support the master station for regulation and decision-making by improving data compression accuracy. Simulation results show that the proposed algorithm has superior performance in terms of information aggregation delay and data compression accuracy.

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Adaptive learning-based delay-sensitive and secure edge-end collaboration for multi-mode low-carbon power IoT

Cited by 11 publications

References 0 publications

The State of Art and Future Development of Internet of Things Warehousing Technology

The State of Art and Future Development of Internet of Things Warehousing Technology

RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

PLC-based information aggregation and data compression for distributed PV access

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