IEEE Access (Jan 2022)

Edge Computing and Microservices Middleware for Home Energy Management Systems

  • Luiz C. B. C. Ferreira,
  • Andreza Da Rosa Borchardt,
  • Gustavo Dos Santos Cardoso,
  • Dimas Augusto Mendes Lemes,
  • Gabriel Rodrigues Dos Reis de Sousa,
  • Fernando Bauer Neto,
  • Eduardo Rodrigues de Lima,
  • Gustavo Fraidenraich,
  • Paulo Cardieri,
  • Luis Geraldo P. Meloni

DOI
https://doi.org/10.1109/ACCESS.2022.3214229
Journal volume & issue
Vol. 10
pp. 109663 – 109676

Abstract

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A middleware software can be seem as an abstraction layer between hardware and user applications, that facilitates the development and deployment of services in various scenarios, such as those found in Home Energy Management Systems (HEMS). There are several middleware proposals for HEMS, with most of them taking the cloud computing approach. This approach is unconcerned about computing resources but raises a dependency on external connections. This paper presents a middleware for energy management systems, based on the concept of edge computing for smart homes. The paper presents a reference model for the proposed architecture, considering specific requirements for this type of application. The proposed architecture employs the concept of microservices for data access and system configuration. The proposed middleware is designed to work with embedded systems under computational constraints, such as processing capability and storage, to reduce costs and allow its application closer to the user. The middleware is open and customizable to meet the developer’s needs. The proposed solution was implemented and tested in a university laboratory, as well as at the Eldorado Research Institute to confirm the effectiveness of the middleware. The proposal stands out from others found in the literature as it can be implemented using low cost hardware. In addition to using microservices concepts, the proposed middleware is a valuable option for applications that need an edge computing approach. A performance analysis was carried out, using low cost hardware with limited resources. The results show that the proposal can handle a significant number of devices, offering low latency and low error rate, and consuming few processing resources and memory.

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