LORAWAN Internet of Things Network Planning for Smart Metering Services

The way of generating and distributing energy throughout the electrical grid to all users is evolving. The concept of Smart Grid (SG) took place to enhance the management of the electrical grid infrastructure and its functionalities from the traditional system to an improved one. To measure the energy consumption of the users is one of these functionalities that, in some countries, has already evolved from a periodical manual consumption reading to a more frequent and automatic one, leading to the concept of Smart Metering (SM). Technology improvement could be applied to the SM systems to allow, on one hand, a more efficient way to collect the energy consumption data of each user, and, on the other hand, a better distribution of the available energy through the infrastructure. Widespread communication solutions based on existing telecommunication infrastructures instead of using ad-hoc ones can be exploited for this purpose. In this paper, we recall the basic elements and the evolution of the SM network architecture focusing on how it could further improve in the near future. We report the main technologies and protocols which can be exploited for the data exchange throughout the infrastructure and the pros and cons of each solution. Finally, we propose an innovative solution as a possible evolution of the SM system. This solution is based on a set of Internet of Things (IoT) communication technologies called Low Power Wide Area Network (LPWAN) which could be employed to improve the performance of the currently used technologies and provide additional functionalities. We also propose the employment of Unmanned Aerial Vehicles (UAVs) to periodically collect energy consumption data, with evident advantages especially if employed in rural and remote areas. We show some preliminary performance results which allow assessing the feasibility of the proposed approach.

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SigFox Network Planning for Smart Metering Based on Intemet of Things for Dense Urban Scenario

2020 8th International Conference on Information and Communication Technology (ICoICT) ◽

10.1109/icoict49345.2020.9166398 ◽

2020 ◽

Author(s):

Muhammad Imam Nashiruddin ◽

Alvin Yusri

Keyword(s):

Network Planning ◽

Smart Metering ◽

Urban Scenario

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LRP Model and Algorithm of Expressway Logistics Network Planning Based on Internet of Things

Mobile Information Systems ◽

10.1155/2021/5648330 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zheng Kou ◽

Man Zhang

Keyword(s):

Particle Swarm Optimization ◽

Internet Of Things ◽

Particle Swarm Optimization Algorithm ◽

Network Planning ◽

Particle Swarm ◽

Swarm Optimization ◽

Actual Distribution ◽

Logistics Network ◽

Internet Of Things Technology ◽

The Internet Of Things

With the continuous improvement of the expressway logistics network, the location-routing problems (LRP) have become the obstacle to be overcome in the development of related industries. Based on the needs of modernization, in the era of the Internet of Things, classic traffic path planning algorithms can no longer meet the increasingly diverse needs, and related research results are not ideal. To reduce logistics costs and meet customer needs, this paper studies transportation route planning models and algorithms based on Internet of Things technology and particle swarm optimization. Firstly, the LRP model of expressway logistics network planning analyzes the achievement of goals, lists the assumptions, and builds the LRP model of expressway logistics network planning based on the mathematical model of path planning. Then the model is optimized and solved based on the particle swarm optimization algorithm. In order to verify the effectiveness and feasibility of the algorithm, MATLAB is used to simulate the algorithm. Finally, the LRP particle swarm optimization model of highway logistics network planning is put into the actual distribution work of a logistics company to analyze the change of distribution cost and investigate the related satisfaction. Experimental data show that the improved particle swarm optimization algorithm in this paper begins to converge in the 100th generation, the shortest running time is 57s, and the value of the objective function fluctuates slightly around 880. This shows that the model algorithm in this paper has strong search ability and stability. In the simulation experiment, the optimal objective function value of the model is 1001 yuan, which can be used to formulate the optimal distribution scheme. In the actual distribution work, the total cost of distribution before and after the application of the model was 12176.99 yuan and 9978.4 yuan, the fuel consumption cost decreased by 2097.23 yuan, and the penalty cost decreased by 85%. In the satisfaction survey, the satisfaction of all people was 9 points or above, with an average score of 9.42 points. This shows that the LRP particle swarm optimization model of expressway logistics network planning based on the Internet of Things technology can effectively save distribution costs and improve satisfaction.

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Smart metering system data analytics platform using multicore edge computing

International Journal of Reconfigurable and Embedded Systems (IJRES) ◽

10.11591/ijres.v10.i1.pp11-17 ◽

2021 ◽

Vol 10 (1) ◽

pp. 11

Author(s):

Juan C. Olivares-Rojas ◽

Enrique Reyes-Archundia ◽

José A. Gutiérrez-Gnecchi ◽

Ismael Molina-Moreno ◽

Adriana C. Téllez-Anguiano ◽

...

Keyword(s):

Internet Of Things ◽

Smart Grid ◽

Data Processing ◽

Data Analytics ◽

Electricity Consumption ◽

Edge Computing ◽

Smart Meter ◽

Smart Metering ◽

Smart Meters ◽

System Data

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

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