Optimization of Key Devices Positions in Large-Scale RF Mesh Networks

Mezher, Ahmad Mohamad; Rajendran, Nisha; Rivera, Pedro Enrique Iturria; Santos, Carlos Lester Dueñas; Meng, Julian; Guerra, Eduardo Castillo

doi:10.1145/3345860.3361515

Cited by 3 publications

(1 citation statement)

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“…Studies on optimizing the location of UDAPs (universal data aggregation points) have been carried out in the literature. Consequently, in studies such as [11][12][13]15,26,27], solutions are proposed based on the problems of distance, network clustering, or latency. The constraints considered are the transmission latency and the number of hops on the link.…”

Section: Related Workmentioning

confidence: 99%

Optimal Placement of UDAP in Advanced Metering Infrastructure for Smart Metering of Electrical Energy Based on Graph Theory

Machado

Inga

2022

Electronics

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This paper presents an algorithm to optimize the deployment of hubs for smart energy metering based on the Internet of Things. A georeferenced scenario is proposed in which each user must connect to a concentrator, either directly or through another user, minimizing the resources required to achieve connectivity. Consequently, to carry out the optimization, the minimum spanning tree between devices is found, in which the maximum connection distance and the capacity of the hubs are limited. Additionally, this work seeks to achieve a scalable algorithm applicable to any georeferenced scenario to be simulated. The main contribution of this work is an IoT-based smart metering architecture that optimizes resources and adapts to a scenario that changes or integrates more users to the energy metering network without losing the connectivity of the initial users. As a result of the application of the algorithm, a scenario route map is generated. The scenario’s parameters include the number of hops in the network, the optimal number of concentrators and their geographical location, the average number of hops, and the total distance of the path, among others. In this project, a georeferenced urban scenario was considered in which residential areas coexist with intelligent buildings. The scenario has growth stages in which the algorithm is applied, and in each one, the optimal route map is generated.

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