Research on Ubiquitous Power Internet of Things Architecture

Zhai, Zhihao; Jia, Lixin; Wang, Yi; Ma, Yuxin; Jing, Wanli; Zhang, Zhuxiang

doi:10.1109/ei247390.2019.9061975

Cited by 16 publications

(10 citation statements)

References 4 publications

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“…The architecture of IoV is composed of terminal layer, network layer, platform layer and application layer [12,13].…”

Section: Architecture Of Iovmentioning

confidence: 99%

Design of Network Security Protection System for Internet of Vehicles under Electric Internet of Things Background

Wang¹,

Jun²

2022

J. Phys.: Conf. Ser.

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At present, China’s Internet of Vehicles (IoV) industry is booming and constantly promoting the society to the intelligent era. However, the development of network technology not only brings the opportunity of technology promotion for the IoV, but also brings new network security risks. Therefore, it is urgent to further strengthen the construction of the protection system for the IoV. Based on the original architecture of the IoV, this paper first analyses the security risks of the terminal layer, the network layer, the platform layer and the application layer under the background of the electric internet of things. According to the new security threats faced by the four layers of the IoV, the network security protection system is constructed, which strictly follows the overall information security protection strategy of the State Grid Corporation of China and the new requirements of “network security level protection 2.0”. Deepening the construction of the intelligent defence system of the IoV would be significant for the development of coordinated network security and the IoV, and would continuously improve the protection ability of the IoV.

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“…The architecture of IoV is composed of terminal layer, network layer, platform layer and application layer [12,13].…”

Section: Architecture Of Iovmentioning

confidence: 99%

Design of Network Security Protection System for Internet of Vehicles under Electric Internet of Things Background

Wang¹,

Jun²

2022

J. Phys.: Conf. Ser.

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“…With the development of wireless communication technology, more and more terminal devices are able to access the network, leading to a sharp increase in the traffic of the wireless access network [1]. Taking the power Internet of things (PIOT) as an example, the massive data collected by a large number of sensors needs to be transmitted efficiently, which constantly puts forward higher requirements for the performance of wireless communications [2]. Typical applications like the video integrated monitoring of power distribution room, the unmanned aerial vehicle inspection and the substation robot inspection.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

Tan

Chen

Sun

et al. 2023

J. Phys.: Conf. Ser.

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With the rapid increase in the number of sensors in Power Internet of Things (PIOT), the data volume surges and efficient communications are needed. Precise estimation of delay spread is critical to improve the communication performance. In this paper, we propose a statistical learning (SL) based root mean square delay spread (RMSDS) estimation method, which consists of offline training and online estimation. During the offline training process, the instantaneous delay spread (IDS) for each data frame is firstly computed based on the known pilots. The acquired IDS is a random variable and its statistical parameter is related to the actual RMSDS, which is preset and known during offline training. Then, the functional relationship between the statistical parameter and the preset RMSDS is acquired through the SL algorithm. The acquired functional relationship can be used in the online estimation process to accurately estimate the RMSDS. Simulation results indicate that the proposed method can accurately estimate the RMSDS and greatly improve the bit error rate performance.

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“…Now, the power IOT can promote the efficiency of energy services and new energy consumption dramatically. In [4], an architecture for ubiquitous power IOT is proposed which brings great convenience to the collection, transmission, processing, and storage of data. In [5], the characteristics of power IOT, its application, and the needs of smart grid for communication coverage and data acquisition are proposed.…”

Section: Introductionmentioning

confidence: 99%

A Load Forecasting Algorithm for Power Internet of Things Using Training Data Dimension Expansion and Ensemble Learning Technique

Liu¹,

Rao²,

Wang³

et al. 2022

Journal of Sensors

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With the development of the power internet of things (IOT), load forecasting will play an important role the power system. It can optimize the power generation planning and improve the economical operation of power IOT. In this paper, a new loading forecasting algorithm for power IOT is proposed using training data dimension expansion and ensemble learning. In the offline phase, the obtained meteorological data and time information is normalized to remove the unit effect at first. Then, the Hampel filter is used to cope with the outliers of the meteorological data from sensors. Through the preprocessing, the fingerprint of the training data is constructed. Next, the matrix multiplication method is proposed to expand the dimension of training data fingerprint information. Finally, the ensemble learning combining multiple long short-term memory (LSTM) networks are proposed to obtain multiple power load forecasting models and corresponding weight coefficients. In the online phase, the obtained meteorological data and time information are preprocessed to form the input of and power load forecasting models. The final power load forecasting is obtained by linear weighted sum method with intermediate forecasting result. In the proposed algorithm, more features of training data can be obtained by the data dimension expansion. Moreover, the ensemble learning using LSTM can make fully use of the timing sequence of training and improve the generalization performance of offline training. Experiment results illustrate that the proposed algorithm has better forecasting performance than existing methods.

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Research on Ubiquitous Power Internet of Things Architecture

Cited by 16 publications

References 4 publications

Design of Network Security Protection System for Internet of Vehicles under Electric Internet of Things Background

Design of Network Security Protection System for Internet of Vehicles under Electric Internet of Things Background

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

A Load Forecasting Algorithm for Power Internet of Things Using Training Data Dimension Expansion and Ensemble Learning Technique

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