Concept of Energy Efficient ESP32 Chip for Industrial Wireless Sensor Network

Gatial, Emil; Balogh, Zoltán; Hluchý, Ladislav

doi:10.1109/ines49302.2020.9147189

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…Similarly, only one paper was found that addressed the energy-efficient operation of an ESP32 [17]. This paper gives a best practice for using an ESP32 in an industrial wireless sensor network.…”

Section: Related Workmentioning

confidence: 99%

IoT on an ESP32: Optimization Methods Regarding Battery Life and Write Speed to an SD-Card

Broell¹,

Hanshans²,

Kimmerle³

2023

Edge Computing - Technology, Management and Integration

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The ESP32 is a popular microcontroller for IoT use cases. For many IoT applications (e.g., environmental sensors or wearables), a continuous power supply is either not possible or too cumbersome, requiring battery operation. However, the ESP32 has a relatively high power consumption. This chapter focuses on battery life optimization methods for this family of microcontrollers. For scenarios where data logging is relevant, methods for increasing communication speed in relation to power consumption are examined in detail. Measurements of seven different commercially available development boards were evaluated in terms of sleep modes, reduced CPU frequencies, and serial communications with the goal of better power efficiency. Therefore, the common scenario of data logging was compared with the performance and power consumption when communicating with different SD cards and CPU frequencies via the SPI and SD bus. Our test results showed that peripheral components (such as voltage regulators) have a large impact on the power consumption of the ESP32 microcontrollers, especially in sleep mode. For data logging, higher clock rates combined with high-quality SD cards and using the SD bus in 4-bit mode resulted in the lowest battery discharge.

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Section: Related Workmentioning

confidence: 99%

IoT on an ESP32: Optimization Methods Regarding Battery Life and Write Speed to an SD-Card

Broell¹,

Hanshans²,

Kimmerle³

2023

Edge Computing - Technology, Management and Integration

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“…Gatial et al 15 have suggested a design for a low‐cost WSN for the industrial sector that incorporates the low‐power ESP32 microcontroller. The majority of the focus was on the real‐time operating system (RTOS) of ESP32 devices and best practices for integrating many sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to literature review, numerous systems are designed for data transmission in IWSN. Limited network architecture was used, 13 use single sensor device, 15 energy awareness routing was not considered, 18 suggested method doesn't compared with the other existing methods, 19 and practical factors were not considered 20 are some of the issues in the existing techniques. The proposed uses region‐based grid formation method, hybrid local search algorithm, and machine learning techniques to overcome the above mentioned issues.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel energy‐efficient routing protocol for industrial WSN using hybrid COOT‐LS algorithm with LSTM‐based DOM prediction

Ravikumar,

Kumar

2023

Int J Communication

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SummaryIndustrial wireless sensor networks (IWSNs) are very important to improve and simplify the way to manage, monitor, and control industrial factories. IWSNs have many benefits, but because of their vulnerability to extremely complex and variable industrial contexts, they also limit some of the potential that is currently available and present difficulties on numerous fronts. The proposed work develops energy‐efficient routing technique to minimize the overall energy consumption. Heterogeneous mobile nodes are randomly deployed in the experimental region; then, region‐based grid formation is done in the proposed method. In each grid, the base node is selected utilizing the parameters like residual energy and distance of mobile nodes. Hybrid COOT‐HOA optimization is used in the proposed method for selecting the optimal base node in each grid. A deep learning‐based machine learning algorithm called long short‐term memory (LSTM) is utilized to predict the future direction of movement (DOM) of each mobile node in the grid. Estimation of signal parameters via rotational invariance technique (ESPRIT) is utilized to find an active zone from the sender node to direction of base node. Then, the sender node transmits the data to its nearest node in the active region. This proposed energy‐efficient routing algorithm is tested with several metrics which attains better performance like 94% packet delivery ratio, 7% packet loss, average residual energy of 9.5 J, and 3.4 Mbps throughput. Thus, the energy‐efficient routing protocol used in the proposed approach transfers the data in an energy‐efficient manner for IWSN.

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“…Three different hardware have been selected for implementing each mist computing node of the proposed solution: Processing Subsystem: An Espressif ESP32 microcontroller was selected since it provides a good tradeoff among embedded computing power, low cost and energy efficiency [ 89 ], as well as abundant General Purpose Inputs Outputs (GPIOs), built-in Wi-Fi and Bluetooth communications, and an I2C interface for communicating with the sensor interface. Thermal Imaging Sensor: A FLIR Lepton 3.5 was selected.…”

Section: Application Case: Cphs-based Thermal Imaging Sensors For a S...mentioning

confidence: 99%

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

Fraga‐Lamas

Barros

Lopes

et al. 2022

Sensors

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While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution (e.g., a good trade-off between cost, accuracy, resilience and computational efficiency) that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.

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Concept of Energy Efficient ESP32 Chip for Industrial Wireless Sensor Network

Cited by 13 publications

References 10 publications

IoT on an ESP32: Optimization Methods Regarding Battery Life and Write Speed to an SD-Card

IoT on an ESP32: Optimization Methods Regarding Battery Life and Write Speed to an SD-Card

A novel energy‐efficient routing protocol for industrial WSN using hybrid COOT‐LS algorithm with LSTM‐based DOM prediction

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

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