scholarly journals Research on Transmission and Offloading Scheme of MEC-IRS for Distribution Network Service

2021 ◽  
Vol 2087 (1) ◽  
pp. 012074
Author(s):  
Bingsen Xia ◽  
Yuanchun Tang
Keyword(s):  
Reinforcement Learning ◽  
Internet Of Things ◽  
Minimization Problem ◽  
Power Distribution ◽  
Learning Algorithm ◽  
Distribution Network ◽  
Access Point ◽  
Delay Minimization ◽  
Single Antenna ◽  
Computing Node

Abstract the paper introduces IRS to assist offloading, and the propagation Environment can be intelligently changed by changing the reflection unit of the IRS, This article proposes an IRS-assisted MEC power distribution Internet of Things system, and studies the gain effect of IRS in the MEC system. In this system, the single antenna equipment can choose to unload a small part of its computing task to the edge computing node of the distribution Internet of things through the multi antenna access point with the help of IRS. In this paper, the delay minimization problem of the whole system is established, the DNQ reinforcement learning algorithm is used to solve the problem, which can effectively change the coverage of smart substations.

scholarly journals Optimal Energy Beamforming to Minimize Transmit Power in a Multi-Antenna Wireless Powered Communication Network

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 509
Author(s):  
Iqra Hameed ◽  
Pham-Viet Tuan ◽  
Mario R. Camana ◽  
Insoo Koo
Keyword(s):  
Communication Network ◽  
Minimization Problem ◽  
Time Allocation ◽  
Access Point ◽  
Power Minimization ◽  
Transmit Power ◽  
Optimal Energy ◽  
Single Antenna ◽  
Information Signal ◽  
Energy Beamforming

In this paper, we study the transmit power minimization problem with optimal energy beamforming in a multi-antenna wireless powered communication network (WPCN). The considered network consists of one hybrid access point (H-AP) with multiple antennae and multiple users with a single antenna each. The H-AP broadcasts an energy signal on the downlink, using energy beamforming to enhance the efficiency of the transmit energy. In this paper, we jointly optimize the downlink time allocation for wireless energy transfer (WET), the uplink time allocation for each user to send a wireless information signal to the H-AP, the power allocation to each user on the uplink, and the downlink energy beamforming vectors while controlling the transmit power at the H-AP. It is challenging to solve this non-convex complex optimization problem because it is numerically intractable and involves high computational complexity. We exploit a sequential parametric convex approximation (SPCA)-based iterative method, and propose optimal and sub-optimal solutions for the transmit power minimization problem. All the proposed schemes are verified by numerical simulations. Through the simulation results, we present the performance of the proposed schemes based on the effect of the number of transmit antennae and the number of users in the proposed WPCN. Through the performance evaluation, we show that the SPCA-based joint optimization solution performance is superior to other solutions.

scholarly journals Energy Efficiency Oriented Access Point Selection for Cognitive Sensors in Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
ChunHua Ju ◽  
Qi Shao
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Distributed Learning ◽  
Access Point ◽  
Selection Problem ◽  
Potential Game ◽  
Point Selection ◽  
Selection For

This paper studies the distributed energy efficient access point (AP) selection for cognitive sensors in the Internet of Things (IoT). The energy consumption is critical for the wireless sensor network (WSN), and central control would cause extremely high complexity due to the dense and dynamic deployment of sensors in the IoT. The desired approach is the one with lower computation complexity and much more flexibility, and the global optimization is also expected. We solve the multisensors AP selection problem by using the game theory and distributed learning algorithm. First, we formulate an energy oriented AP selection problem and propose a game model which is proved to be an exact potential game. Second, we design a distributed learning algorithm to obtain the globally optimal solution to the problem in a distributed manner. Finally, simulation results verify the theoretic analysis and show that the proposed approach could achieve much higher energy efficiency.

DQN-Based Edge Computing Node Deployment Algorithm in Power Distribution Internet of Things

2021 ◽  
pp. 349-357
Author(s):  
Shen Guo ◽  
Peng Wang ◽  
Jichuan Zhang ◽  
Jiaying Lin ◽  
Chuanyu Tan ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Power Distribution ◽  
Edge Computing ◽  
Node Deployment ◽  
Computing Node

scholarly journals Optimal Placement of IoT-Based Fault Indicator to Shorten Outage Time in Integrated Cyber-Physical Medium-Voltage Distribution Network

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4928
Author(s):  
Jing Li ◽  
Jinrui Tang ◽  
Xinze Wang ◽  
Binyu Xiong ◽  
Shenjun Zhan ◽  
...  
Keyword(s):  
Communication Network ◽  
Internet Of Things ◽  
Power Distribution ◽  
Distribution Systems ◽  
Fault Location ◽  
Distribution Network ◽  
Operating Cost ◽  
Test System ◽  
Optimal Placement ◽  
Fault Indicator

Traditional fault indicators based on 3G and 4G cannot send out fault-generated information if the distribution lines are located in the system across remote mountainous or forest areas. Hence, power distribution systems in rural areas only rely on patrol to find faults currently, which wastes time and lacks efficiency. With the development of the Internet of things (IoT) technology, some studies have suggested combining the long-range (LoRa) and the narrowband Internet of Things (NB-IoT) technologies to increase the data transmission distance and reduce the self-built communication system operating cost. In this paper, we propose an optimal configuration scheme for novel intelligent IoT-based fault indicators. The proposed fault indicator combines LoRa and NB-IoT communication technologies with a long communication distance to achieve minimum power consumption and high-efficiency maintenance. Under this given cyber network and physical power distribution network, the whole fault location process depends on the fault indicator placement and the deployment of the communication network. The overall framework and the working principle of the fault indicators based on LoRa and NB-IoT are first illustrated to establish the optimization placement model of the proposed novel IoT-based fault indicator. Secondly, an optimization placement method has been proposed to obtain the optimal number of the acquisition and collection units of the fault indicators, as well as their locations. In the proposed method, the attenuation of the communication network and the power-supply reliability have been specially considered in the fault location process under the investment restrictions of the fault indicators. The effectiveness of the proposed method has been validated by the analysis results in an IEEE Roy Billinton Test System (IEEE-RBTS) typical system.

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