An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications
Abstract
:1. Introduction
1.1. Faults Pose a Serious Threat
- low-cost components,
- limited resources (especially energy), and
- the often harsh environmental conditions.
- an outlier detection in the sensor data,
- a dense deployment to identify deviations between the measurements reported by neighboring nodes, or
- simple node-level diagnostics such as a monitoring of the battery voltage.
1.2. Active Node-Level Reliability
1.3. Contribution, Methodology and Outline
- a literature review on recent sensor node platforms,
- a taxonomy for faults in WSNs,
- a practical evaluation of the fault indicator concept proposed in [4], and
- the presentation of our embedded testbench (ETB), a Raspberry Pi hardware add-on that enables the analysis and profiling of embedded systems like sensor nodes.
- an indoor deployment (i.e., normal operation in a controlled environment),
- an outdoor deployment (i.e., normal operation in an uncontrolled environment), and
- a lab setup running automated experiments with configurable environmental conditions such as the ambient temperature or the supply voltage, thus, forcing the sensor node in a form of impaired operation in a controlled environment.
2. Faults in Wireless Sensor Networks
2.1. Terminology
- system-level fault tolerance,
- network-level fault tolerance, and
- node-level fault tolerance.
2.2. Wireless Sensor Network Fault Taxonomy
2.2.1. Fault Origin
2.2.2. Fault Severity
2.2.3. Fault Type
2.2.4. Fault Persistence
2.2.5. Fault Level
2.2.6. Fault Manifestation
- the measurement of certain physical quantities (i.e., data sensing),
- the (pre)processing of the acquired data (i.e., data processing), and
- the forwarding of these data via the network (i.e., data communication).
2.3. Faults vs. Anomalies
“Unless ground truth is known or given by something with high confidence, the term fault can only refer to a deviation from the expected model of the phenomenon.”
2.4. Fault Detection in WSNs
- sensor data analysis (see Section 2.4.1),
- group detection (see Section 2.4.2), and
- local self-diagnosis (see Section 2.4.3).
2.4.1. Sensor Data Analysis
- (i)
- statistics-based,
- (ii)
- rule-based,
- (iii)
- time series analysis-based, or
- (iv)
- learning-based methods.
2.4.2. Group Detection
- (i)
- the sensor nodes are deployed densely (i.e., the difference in the measurements of two error-free sensor nodes is negligibly small),
- (ii)
- faults occur rarely and without systemic dependencies (i.e., the number of faulty nodes is much smaller than the number of non-faulty nodes), and
- (iii)
- faults significantly alter the sensor data (i.e., a faulty sensor reading significantly deviates from proper readings of its local neighbors).
2.4.3. Local Self-Diagnosis
3. Sensor Node Platforms
- (i)
- to build sensor nodes from scratch (custom nodes),
- (ii)
- to utilize a generic embedded platform (semi-custom nodes), or
- (iii)
- to use an available sensor node platform (commercial or academic nodes).
3.1. Basic Components
- (i)
- a set of sensors,
- (ii)
- a processing unit (optionally with external memory),
- (iii)
- a radio transceiver, and
- (iv)
- a power unit with a power source (i.e., a battery).
3.2. Related Sensor Node Platforms
- “WSN” OR “wireless sensor network” OR “sensor network” OR “sensor”
- “node” OR “mote” OR “board” OR “platform”
- “design” OR “development” OR “implementation” OR “concept”
- “reliability” OR “resilience” OR “fault tolerance” OR “fault diagnosis”
-
- refers to nodes using a DC/DC converter,
-
- denotes nodes using a linear regulator (e.g., low-dropout regulator (LDO)), and
-
- highlights nodes that have the battery directly connected to the core supply rail.
-
- means that all related information is publicly available,
-
- refers to nodes where only parts are available (mostly the software), and
-
- shows that no information has been made publicly available.
3.2.1. Energy-Efficient Sensor Nodes
- (i)
- the duration of the active and the sleep phases (i.e., duty-cycling) and
- (ii)
- the power consumption in both phases (i.e., energy-efficient hardware).
3.2.2. Self-Diagnostic Sensor Nodes
- the secondary MCU can be impaired by faults, too, and
- checking the primary MCU’s ALU only is insufficient to ensure reliability.
4. ASN(x)—An AVR-Based Sensor Node with Xbee Radio
- enables active node-level reliability by incorporating self-check capabilities,
- offers an energy-efficient operation especially suitable for monitoring applications,
- is versatile regarding its usage (i.e., modular expandability),
- is based on current components that are highly available on the market,
- is comparably cheap (about $50 per node including the radio), and
- is completely open-source published on Github under the MIT license.
- a processing unit (see Section 4.1),
- a sensing unit (see Section 4.2),
- a power unit (see Section 4.3), and
- a transceiver unit (see Section 4.4).
4.1. Processing Unit
4.2. Sensing Unit
4.3. Power Unit
4.4. Transceiver Unit
4.5. Node-Level Indicators
4.5.1. Node Temperature Monitor
4.5.2. Supply Voltage Monitor
4.5.3. Battery Voltage Monitor
4.5.4. Active Runtime Monitor
4.5.5. Reset Monitor
- bit 0: power-on reset,
- bit 1: external reset (via the reset pin),
- bit 2: brown-out reset (in case the brown-out detection is enabled), and
- bit 3: watchdog reset.
4.5.6. Software Incident Counter
4.5.7. ADC Self-Check
4.5.8. USART Self-Check
5. Evaluation Experiment Setup
- an indoor deployment consisting of six SNs,
- an outdoor deployment consisting of four SNs, and
- a lab experiment setup analyzing one dedicated SN controlled by our embedded testbench (ETB).
5.1. Indoor Deployment
5.2. Outdoor Deployment
5.3. Embedded Testbench (ETB)-Based Lab Experiments
6. Results
- reliability and
- energy efficiency.
- the fault indicators can indicate an impaired node operation.
- the fault indicators do not cause false alarms in case of rare but proper events.
- some types of faults were not detected by our current fault indicators.
6.1. Power Consumption
- 13.4 mA/44.22 mW (MCU idling; XBee enabled; diagnostics enabled),
- 12.2 mA/40.26 mW (MCU idling; XBee enabled; diagnostics disabled),
- 4.68 mA/15.44 mW (MCU idling; XBee disabled; diagnostics disabled).
- 36.7 A/121.11 W (MCU power-down; XBee disabled; diagnostics disabled)
6.2. Indicator Evaluation
- successfully detected faults (see Section 6.2.1),
- proper events that can be distinguished from faults (see Section 6.2.2), and
- faults that have not been indicated by our current fault indicators (see Section 6.2.3).
6.2.1. Fault Indication
6.2.2. Event Indication
6.2.3. Undetected Faults
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ADC | analog-to-digital converter |
ALU | arithmetic logic unit |
AIS | artificial immune system |
ASN(x) | AVR-based Sensor Node with Xbee radio |
AVR | Alf and Vegard’s RISC |
BLE | Bluetooth low energy |
CH | cluster head |
CMOS | complementary metal oxide semiconductor |
CPS | cyber-physical system |
CPU | central processing unit |
DFS | dynamic frequency scaling |
DSP | digital signal processor |
DUT | device under test |
DVS | dynamic voltage scaling |
EEPROM | electrically erasable programmable read-only memory |
ETB | embedded testbench |
FPGA | field-programmable gate array |
GPIO | general purpose input/output |
HCI | hot carrier injection |
I2C | inter-integrated circuit |
IEEE | Institute of Electrical and Electronics Engineers |
ISM | industrial, scientific and medical |
ISP | in-system programmer |
JTAG | Joint Test Action Group |
LDO | low-dropout regulator |
LED | light-emitting diode |
LoRaWAN | long-range wide-area network |
LPWAN | low-power wide-area network |
MCU | microcontroller unit |
MCUSR | MCU status register |
MIPS | million instructions per second |
MOSFET | metal-oxide-semiconductor field-effect transistor |
NB-IoT | narrowband Internet of Things |
NBTI | negative bias temperature instability |
NTC | negative temperature coefficient |
OS | operating system |
OTR | outdoor relay node |
OWI | one-wire interface |
PCB | printed circuit board |
RAM | random-access memory |
RF | radio frequency |
RISC | reduced instruction set computer |
RSSI | received signal strength indicator |
RTC | real-time clock |
SDM | sequential dependency model |
SK | sink node |
SN | sensor node |
SNR | signal-to-noise ratio |
SoC | system-on-a-chip |
SPI | serial peripheral interface |
SQL | structured query language |
SRAM | static random-access memory |
SVM | support-vector machine |
TDDB | time dependent dielectric breakdown |
THT | through-hole technology |
TWI | two-wire interface |
USART | universal synchronous/asynchronous receiver-transmitter |
ULP | ultra low power |
USB | universal serial bus |
WDT | watchdog timer |
WSN | wireless sensor network |
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Sensor Node | Year | MCU/SoC | Arch. [bit] | FCPU [MHz] | Flash [kB] | RAM [kB] | EEPROM [kB] | Radio Transceiver | Communication Standard | Vcore [V] | Vbat [V] | Active Mode [mW] | Power-Saving [W] | Voltage Regulation | Energy-Efficiency | Self-Diagnostic | Open Source | Available | Price [$] | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Commercial | UC Berkeley TelosB [73] | 2005 | MSP430F1611 | 16 | 8 | 1072 a | 10 | 16 | CC2420 | IEEE 802.15.4 (Zigbee) | 3.0 | 1.8–3.6 | 6.6 | 20.1 | | | | | | 99.00 |
ETH Zürich Btnode [74] | 2005 | ATmega128L | 8 | 8 | 128 | 64 b | 4 | ZV4002 + CC1000 | IEEE 802.15.1 v1.2 + 433/868 MHz | 3.3 | 0.5–4.4 | 39.6 | 9900 | | | | | | 215.00 | |
UC Berkeley IRIS [75] | 2007 | ATmega1281 | 8 | 7.37 | 640 c | 8 | 4 | AT RF230 | IEEE 802.15.4 (Zigbee) | 3.0 | 2.7–3.6 | 26.4 | 26.4 | | | | | | 115.00 | |
SHIMMER [76] | 2010 | MSP430F1611 | 16 | 8 | 48 | 10 | 16 | CC2420 + RN-41 | IEEE 802.15.4 + 802.15.1 v2 | 3.3 | 1.8–3.6 | 5.9 | 16.8 | | | | | | 269.00 | |
OpenMote CC2538 [77] | 2015 | CC2538SF53 g | 32 | 32 | 512 | 32 | – | CC2538 g | IEEE 802.15.4 (6TiSCH) | 3.3 | 2.0–3.6 | 42.9 | × | | | | | | × | |
Libelium Waspmote v15 [78] | 2016 | ATmega1281 | 8 | 14.75 | 128 | 8 | 4 | 15 modules available (e.g., Zigbee, LoRaWAN) | 3.0 | 3.3–4.2 | 56.1 | 99.0 | | | | | | 174.00 | ||
Zolertia RE-Mote [79] | 2016 | CC2538SF53 g | 32 | 32 | 512 | 32 | – | CC2538 g + CC1200 | IEEE 802.15.4 (Zigbee) | 3.3 | 3.3–16 | 66.0 | 4.3 | | | | | | 112.00 | |
WiSense WSN1120L [80] | 2019 | MSP430G2955 | 16 | 16 | 56 | 4 | 128 | CC1120 | sub-1 GHz narrowband | 3.0 | 1.8–3.6 | 56.1 | 56.1 | | | | | | 48.00 | |
OpenMote B [81] | 2019 | CC2538SF53 g | 32 | 32 | 512 | 32 | – | CC2538 j + AT86RF215 | IEEE 802.15.4/802.15.4g | 3.3 | 2.0–3.6 | 42.9 | 4.3 | | | | | | 125.00 | |
Academia | Kmote [82] | 2007 | MSP430F1611 | 16 | 8 | 8240 d | 10 | 16 | CC2420 | IEEE 802.15.4 (Zigbee) | 3.3 | 2.3–6.0 | 4.9 | 22.1 | | | | | | 37.85 |
Beasties [83] | 2008 | ATmega8L | 8 | 4 | 8 | 33 e | 0.5 | Radiometrix NiM2 | 433 MHz (proprietary) | 5.0 | 7.0–20 | 77.5 | 40000 | | | | | | 139.00 | |
INGA [84] | 2012 | ATmega1284P | 8 | 4 | 128 | 16 | 4 | AT86RF231 | IEEE 802.15.4 | 3.3 | × | 61.7 | × | | | | | | 120.00 | |
Storm [85] | 2014 | ATSAM4LC8C | 32 | 48 | 1536 f | 64 | – | AT86RF233 | IEEE 802.15.4 | 3.3 | 1.8–3.6 | 4.5 | 7.6 | | | | | | 50.00 | |
Raju and Pratap [86] | 2015 | MSP430F5438 | 16 | 25 | 256 | 16 | – | CC2520 | IEEE 802.15.4 (Zigbee) | 3.3 | 1.8–3.8 | × | × | | | | | | × | |
Zeni et al. [72] | 2015 | ATmega328P | 8 | 1 | 32 | 2 | 1 | nRF24L01+ | 2.4 GHz (proprietary) | 3.0 | 1.9–3.6 | 5.8 | 15 | | | | | | 12.00 | |
panStamp NRG3 [87] | 2016 | CC430F5137 | 16 | 20 | 32 | 4 | – | CC1101 | 433/868 MHz (proprietary) | 3.3 | 2.0–3.6 | 46.2 | 8.3 | | | | | | × | |
EARNPIPE h [88] | 2016 | AT91SAM3X8E | 32 | 84 | 512 | 100 | – | × | IEEE 802.15.1 i | 3.3 | 7.0–12 | × | × | | | | | | × | |
uLoRa [89] | 2017 | STM32L051K8T6 | 32 | 32 | 64 | 8 | 2 | DRF1272F | 868 MHz (incl. LoRa) | 3.3 | × | 34.7 | 1.2 | × | | | | | 12.00 | |
Rusu and Dobra [90] | 2017 | STM32L443RC | 32 | 80 | 256 | 64 | – | AT86RF212B | IEEE 802.15.4 (ISA100) | 3.3 | × | × | × | × | | | | | × | |
Hamilton [58,91] | 2017 | ATSAMR21 g | 32 | 48 | 256 | 32 | – | AT86RF233 g | IEEE 802.15.4 | 3.0 | × | 3.2 | 19.5 | | | | | | 25.00 | |
Hazelnut [92] | 2019 | ATtiny85 j | 8 | 1 | 8 | 0.5 | 0.5 | ESP8266 j | IEEE 802.11 b/g/n | 3.3 | × | 231 | 650 | | | | | | × | |
Raposo et al. [54] | 2019 | MSP430F5229 | 16 | 25 | 128 | 8 | – | Linear DC9003A-C | IEEE 802.15.4 (WirelessHART) | 3.3 | × | × | × | | | | | | × | |
Babusiak et al. [93] | 2019 | ATmega328P | 8 | 1 | 32 | 2 | 1 | nRF24L01+ | 2.4 GHz (proprietary) | 3.0 | 1.5–3.6 | 10.5 | 22.2 | | | | | | 11.00 | |
MEGAN [94] | 2020 | ATmega324PA | 8 | 8 | 32 | 2 | 1 | Digi Xbee S2 | IEEE 802.15.4 (Zigbee) | 3.3 | × | 26.2 | 33.3 | | | | | | 20.00 | |
ASN(x) | 2021 | ATmega1284P | 8 | 4 | 128 | 16 | 4 | Digi Xbee 3 | IEEE 802.15.4 (Zigbee) | 3.3 | 1.8–5.5 | 15.4 | 121.1 | | | | | | 50.00 | |
|
Indicator | Category | Section | |
---|---|---|---|
node temperature monitor | artificial generic | Section 4.5.1 | |
supply voltage monitor | inherent component-specific | Section 4.5.2 | |
battery voltage monitor | artificial generic | Section 4.5.3 | |
active runtime monitor | inherent component-specific | Section 4.5.4 | |
reset monitor | inherent component-specific | Section 4.5.5 | |
software incident counter | inherent common | Section 4.5.6 | |
ADC self-check | artificial generic | Section 4.5.7 | |
USART self-check | artificial component-specific | Section 4.5.8 |
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Widhalm, D.; Goeschka, K.M.; Kastner, W. An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications. Sensors 2021, 21, 7613. https://doi.org/10.3390/s21227613
Widhalm D, Goeschka KM, Kastner W. An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications. Sensors. 2021; 21(22):7613. https://doi.org/10.3390/s21227613
Chicago/Turabian StyleWidhalm, Dominik, Karl M. Goeschka, and Wolfgang Kastner. 2021. "An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications" Sensors 21, no. 22: 7613. https://doi.org/10.3390/s21227613
APA StyleWidhalm, D., Goeschka, K. M., & Kastner, W. (2021). An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications. Sensors, 21(22), 7613. https://doi.org/10.3390/s21227613