ESP-NOW Based Decentralized Low Cost Voice Communication Systems For Buildings

Hoang, Tu Ngoc; Van, Su-Tran; Nguyen, B.D.

doi:10.1109/isee2.2019.8921062

Cited by 18 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have shown that ESP-NOW exhibits lower latency and longer range compared to Bluetooth and wifi [45]. Additionally, unlike Bluetooth low energy, ESP-NOW does not limit the number of connected nodes, which justified its selection as the network protocol for peer-to-peer interconnection [46]. On the other hand, the logical interface between the ACU-MAIN and the supervisory center was established through wired communication with a local server, adopting the MQTT protocol over ethernet.…”

Section: Methodology For Implementing the Energy Monitoring Systemmentioning

confidence: 99%

A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits

et al. 2023

View full text Add to dashboard Cite

Energy demand forecasting is crucial for planning and optimizing the use of energy resources in building facilities. However, integrating digital solutions and learning techniques into legacy buildings presents significant challenges due to limited or outdated resources, hampering predictive analytics in these buildings and their circuits. To fill this gap, this article proposes an innovative demand forecasting strategy using an AIoT retrofit architecture based on the SmartLVGrid metamodel. This architecture allows remote monitoring of legacy building circuits, facilitating the collection, processing and storage of data in the cloud. We use several learning algorithms, including linear regression, support vector regressor, random forest regressor, XGBoost regressor, and long short-term memory (LSTM) neural network, to predict energy demand 15 min ahead, identifying potential overruns of contracted demand in accordance with Brazilian regulations. After Bayesian optimization, the LSTM neural network outperformed other models for most of the selected datasets and detected 32 out of 38 demand overruns on the test set. XGBoost and random forest followed closely, detecting 30 demand overruns. Overall, our cost-effective solution optimizes energy usage and efficiently mitigates potential demand exceedances in building installations. This is achieved through a step-by-step approach to upgrading existing aging facilities, which promotes energy efficiency and sustainability.

show abstract

Section: Methodology For Implementing the Energy Monitoring Systemmentioning

confidence: 99%

A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The maximum payload of ESP-NOW is up to 250 bytes between devices which is sufficient to encode the required amount of sensor data [10]. Unlike the WLAN system, the connection is persistent for paired devices using their MAC addresses, where the transmission will resume whenever the system is powered on [11]. Once the slave node receives the package from the paired master nodes, the data will be passed to the LoRa terminal, which is also set in transparency mode for broadcasting RF signals.…”

Section: System Software Designmentioning

confidence: 99%

A LoRaWAN Based Solar PV Condition Monitoring System

Qian

Thinesh

Ayyalusamy

et al. 2022

2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

View full text Add to dashboard Cite

This paper presents the design and performance of a low-cost, low-power remote condition monitoring (CM) solution for large-scale solar photovoltaic (PV) systems. The developed CM solution utilises ESP-NOW and Long Range Wide Area Network (LoRaWAN) wireless communication protocols for data exchanges. The developed solution operates in selfpower mode, with each IoT node capable of monitoring up to 16 channels of voltage, current, and temperature of the PV panels. The 2.4GHz ESP-NOW wireless communication protocol is used for data transfer between the IoT nodes in a many-toone configuration. The transceiver node stores messages from all IoT nodes and broadcasts them via the LoRaWAN network to the server. The developed hardware and software solutions were tested to measure the accuracy and reliability of the CM system. As a result, a fully-functioning low-cost solar PV CM system is developed, with an overall sensor measurement error of less than 3%, with an average Received Signal Strength Indicator (RSSI) of -105dBm in non-line-of-sight conditions.

show abstract

“…This means that if suddenly one of the boards loses power or resets, when it restarts, it will automatically connect to its peer to continue the communication. This protocol enables a low power consumption between multiple devices, being more power-efficient and faster to deploy when compared to Wi-Fi [10], supporting up to 20 nodes and being limited to 250 bytes packets.…”

Section: A Esp-nowmentioning

confidence: 99%

Autonomous Configuration of Communication Systems for IoT Smart Nodes Supported by Machine Learning

Glória

Sebastião

2021

IEEE Access

View full text Add to dashboard Cite

Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time.

show abstract

ESP-NOW Based Decentralized Low Cost Voice Communication Systems For Buildings

Cited by 18 publications

References 6 publications

A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits

A Demand Forecasting Strategy Based on a Retrofit Architecture for Remote Monitoring of Legacy Building Circuits

A LoRaWAN Based Solar PV Condition Monitoring System

Autonomous Configuration of Communication Systems for IoT Smart Nodes Supported by Machine Learning

Contact Info

Product

Resources

About