Selected Energy Consumption Aspects of Sensor Data Transmission in Distributed Multi-Microcontroller Embedded Systems
Abstract
:1. Introduction
Motivation
- A general overview of selected current research papers related to wireless networks, especially Wireless Sensor Body Networks from the perspective of energy efficiency.
- An in-depth insight for currently available works related to data transmission and in particular the data organization-dependent factors of energy efficiency.
- A summary of possible software and hardware solutions related to minimizing energy consumption in these systems.
- A proposal of a prototype distributed telemedicine system made up of nodes with the possibility of an individual operational setting.
- A search and comparison of different methods of data preparation for transmission in order to achieve higher energy efficiency in this system.
- An investigation of the energy-saving aspect depending on the frequency of data transmission, data size, and the degree of processing before sending (from raw signal to semantic status description).
- A recognition of data states in the node using artificial intelligence algorithms (e.g., fall as a fact is recognized from acceleration sensors, instead of sending raw data to the central node—concentrator).
2. Related Work
2.1. Embedded Systems
2.2. Distributed Systems
2.3. WBANs
2.4. Bluetooth Low Energy Mesh Long Range Communication
2.5. Artificial Intelligence Implemented in Microcontrollers
- Remote intelligence systems (implemented outside the embedded system);
- ○
- At the “edge” of the local network;
- ○
- In the “cloud” (Google Cloud, Amazon AWS, IBM-Cloud, Microsoft Azure, Oracle AI Cloud.
- Systems with their own “large” computing power (implemented based on TPU—Google, VPU—Intel, GPU—Nvidia, ARM Cortex-A, Raspberry Pi, and STM32MP1).
- Systems with limited resources (with “small” microcontrollers) tailored for a tiny form factor and energy efficiency.
2.6. Power Supply and Energy Saving
- Use of energy-efficient components (e.g., very highly efficient inverters instead of linear regulators, “ideal” diodes, and rectifier bridges with MOSFET transistors);
- Use of appropriate electronic designs (e.g., switching off unnecessary peripherals and eliminating the so-called “pull-up” resistor problem);
- Use of an appropriate microcontroller (energy-saving microcontroller with energy-efficient peripherals and power saving capabilities—appropriate operating state);
- Choosing the right supply voltage—the needs of the microcontroller and the peripherals;
- Selection of appropriate batteries (their voltage characteristics, weight, capacity, energy density, etc.)
- Detecting user activity (need for service) and on-demand switching on;
- Use of a suitable energy-efficient communication protocol (e.g., BLE);
- Optimal use of the protocol and transfer of processed data instead of raw measurement values;
- Use of artificial intelligence for the analysis and optimization of power consumption and data transmission;
- Activation of tasks after a defined time or by events (not pooling);
- Bare-metal programming—without an operating system;
- Using library functions;
- Using optimal algorithms and data structures;
- Adjustments of optimization options in a high-level language compiler;
- Global variables and function calls—online and naked functions;
- Pausing the microcontroller;
- Operating mode of the microcontroller (with careful settings of wake-up conditions);
- Pausing individual microcontroller modules;
- Minimization of frequency of the microcontroller oscillator (minimizing internal switching loss and resulting heat dissipation);
- Transmission of relative instead of absolute data (i.e., only what has changed).
3. Prototype Distributed Telemedical System
3.1. Hardware Platform
- Microcontroller;
- BLE antenna;
- Battery (or accumulator);
- Power supply system (protection, DC/DC converter, connectors);
- Service interface;
- Bus connecting the microcontroller with peripherals (e.g., I2C);
- Measurement sensors (type and number selected for a given application);
- Other optional circuits (e.g., signaling, displaying information);
- EEPROM memory.
3.2. Software Layer
- Management software;
- Hub software;
- Node software.
- Operation (power) options: continuous, periodic, event-based;
- Supported requests (e.g., read on demand);
- Frequency of data sending from the node;
- Self-test procedure.
- Frequency of reading data from the sensor;
- Frequency of sending data from the sensor;
- Data accuracy and its range;
- Alarm levels;
- Self-test.
4. Results
- One data segment consists of 11 bytes;
- Transmission speed ranges from 10 to 100 kbps (128–1280 bps);
- Continuous;
- Periodic (with different periods for multiple sensor operation);
- Event-based.
- Continuous data stream;
- Periodic (with different periods and duty cycles);
- Event-based.
- Raw data (accurate sensor readings);
- Simplified data (e.g., with reduced resolution or sampling frequency);
- State labels;
- Alarms.
4.1. Current Parameters of the Node
4.2. Comparison of Raw Data Transmission with Transmission of Recognized States
4.3. Comparison of the Energy of the Two Transmission Types (Battery Life)
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Modes of Sending Data | Node Operating Modes | ||||
---|---|---|---|---|---|
Continuous | Periodic I | Periodic II | Periodic III | Event | |
Continuous | + | - | - | - | - |
Periodic I | + | + | ? | ? | - |
Periodic II | + | ? | + | ? | - |
Periodic III | + | ? | ? | + | - |
Event | + | ? | ? | ? | + |
Transmission Process State | Duration [µs] | Current [mA] |
---|---|---|
Pre-processing | 60 | 4.2 |
Crystal ramp-up | 400 | 1.6 |
Standby | 1072 | 0.5 |
Start radio | 130 | 3.3 |
Window widening | 36 | 6.4 |
Radio RX | 88 | 6.4 |
Radio switch | 140 | 3.7 |
Radio TX | 80 | 6.7 |
Post-processing | 350 | 2.1 |
Microcontroller State | Current [µA] |
---|---|
Normal operation of CPU | 6300 |
Sleep | 1 |
Transmission (for 2.356 µs) | 188 |
Node Operation Mode | Data Transmission Mode | Character of Data Transmitted | Data Stream [bit/s] |
---|---|---|---|
Continuous | Continuous | Raw data | 128 |
Simplified data | 64 | ||
States | 1 | ||
Alarms | 0 | ||
Periodic I | Raw data | 64 | |
Simplified data | 32 | ||
States | 1 | ||
Alarms | 0 | ||
Periodic II | Raw data | 32 | |
Simplified data | 16 | ||
States | 1 | ||
Alarms | 0 | ||
Periodic III | Raw data | 16 | |
Simplified data | 8 | ||
States | 1 | ||
Alarms | 0 | ||
Event-based | Raw data | 2 | |
Simplified data | 1 | ||
States | 1 | ||
Alarms | 0 | ||
Periodic I | Periodic I | Raw data | 64 |
Simplified data | 32 | ||
States | 1 | ||
Alarms | 0 | ||
Periodic II | Periodic II | Raw data | 32 |
Simplified data | 16 | ||
States | 1 | ||
Alarms | 0 | ||
Periodic III | Periodic III | Raw data | 16 |
Simplified data | 8 | ||
States | 1 | ||
Alarms | 0 | ||
Event-based | Event-based | Raw data | 2 |
Simplified data | 1 | ||
States | 1 | ||
Alarms | 0 |
Operation Mode of the Node | Mode of Data Transmission | Character of Transmitted Data | Average Current [µA] |
---|---|---|---|
Continuous | Continuous | Raw data | 6300.05669 |
Simplified data | 6300.02835 | ||
States | 6300.00044 | ||
Alarms | 6300.00000 | ||
Periodic I | Raw data | 6300.02835 | |
Simplified data | 6300.01417 | ||
States | 6300.00044 | ||
Alarms | 6300.00000 | ||
Periodic II | Raw data | 6300.01417 | |
Simplified data | 6300.00709 | ||
States | 6300.00044 | ||
Alarms | 6300.00000 | ||
Periodic III | Raw data | 6300.00709 | |
Simplified data | 6300.00354 | ||
States | 6300.00044 | ||
Alarms | 6300.00000 | ||
Event-based | Raw data | 6300.00089 | |
Simplified data | 6300.00044 | ||
States | 6300.00044 | ||
Alarms | 6300.00000 | ||
Periodic I | Periodic I | Raw data | 0001.02835 |
Simplified data | 0001.61888 | ||
States | 0001.63034 | ||
Alarms | 0001.00000 | ||
Periodic II | Periodic II | Raw data | 0001.01417 |
Simplified data | 0001.30944 | ||
States | 0001.63034 | ||
Alarms | 0001.00000 | ||
Periodic III | Periodic III | Raw data | 0001.00709 |
Simplified data | 0001.15472 | ||
States | 0001.63034 | ||
Alarms | 0001.00000 | ||
Event-based | Event-based | Raw data | 0001.00089 |
Simplified data | 0001.00674 | ||
States | 0001.63034 | ||
Alarms | 0001.00000 |
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Szymczyk, M.; Augustyniak, P. Selected Energy Consumption Aspects of Sensor Data Transmission in Distributed Multi-Microcontroller Embedded Systems. Electronics 2022, 11, 848. https://doi.org/10.3390/electronics11060848
Szymczyk M, Augustyniak P. Selected Energy Consumption Aspects of Sensor Data Transmission in Distributed Multi-Microcontroller Embedded Systems. Electronics. 2022; 11(6):848. https://doi.org/10.3390/electronics11060848
Chicago/Turabian StyleSzymczyk, Magdalena, and Piotr Augustyniak. 2022. "Selected Energy Consumption Aspects of Sensor Data Transmission in Distributed Multi-Microcontroller Embedded Systems" Electronics 11, no. 6: 848. https://doi.org/10.3390/electronics11060848