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Bogena, H.R.;  Weuthen, A.;  Huisman, J.A. Wireless Sensor Network Technology. Encyclopedia. Available online: https://encyclopedia.pub/entry/39913 (accessed on 19 July 2024).
Bogena HR,  Weuthen A,  Huisman JA. Wireless Sensor Network Technology. Encyclopedia. Available at: https://encyclopedia.pub/entry/39913. Accessed July 19, 2024.
Bogena, Heye Reemt, Ansgar Weuthen, Johan Alexander Huisman. "Wireless Sensor Network Technology" Encyclopedia, https://encyclopedia.pub/entry/39913 (accessed July 19, 2024).
Bogena, H.R.,  Weuthen, A., & Huisman, J.A. (2023, January 09). Wireless Sensor Network Technology. In Encyclopedia. https://encyclopedia.pub/entry/39913
Bogena, Heye Reemt, et al. "Wireless Sensor Network Technology." Encyclopedia. Web. 09 January, 2023.
Wireless Sensor Network Technology
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Wireless sensor network technology enables distributed sensing through efficient data communication between a multitude of environmental sensors. WSN is still a relatively new area of research, but the communication technology used for low-cost, low-power wireless networks has advanced greatly in recent decades.

soil moisture sensing wireless communication technology field and catchment scale

1. ZigBee Wireless Sensor Network Technologies

Among the first proprietary standards for low-cost mesh networks used for WSNs was ZigBee [1]. ZigBee is a set of high-level communication protocols that utilize 2.4 GHz low-power radio modules based on the IEEE 802.15.4 LPWA standard [2][3]. More recently, the ZigBee technology has been further developed into ZigBeePro [4] and JenNet [5]. Each component of a ZigBee based WSN has a radio module to enable wireless communication. The ZigBee radio modules have several software interfaces that connect the hardware devices (physical layer and peripherals) to the user application. The user has the possibility to control the sensor network and manage the communication between the devices by means of the application support layer (APS) and the application programming interface (API). The routing of data within the network and data transmission is handled by the media access control layer (MAC). This is based on the IEEE 802.15.4 standard and is located on the physical layer (PHY). The PHY layer includes the transceiver as well as the sensors and the power source [2]. Finally, the user has the possibility to realize advanced functions (e.g., logging function, sensor driver, etc.) by developing a special user software that configures the sensor network.
One example of a ZigBee-based soil moisture WSN is SoilNet [6][7], which was developed at the Forschungszentrum Jülich using the proprietary license free protocol stack JenNet developed by Jennic Ltd., South Yorkshire, UK [5]. JenNet uses the unlicensed 2.4 GHz band and supports star, tree, and linear topologies. In the case of a tree topology, JenNet can support WSN with up to 250 nodes and in the case of a linear topology, even up to 1000 nodes. JenNet transmission distances are limited to less than 100 m in the case of underground WSN. That is why SoilNet uses a hybrid WSN method consisting of a mixture of underground terminals, each wired to multiple ground sensors and above-ground router devices. This allows significantly greater transmission ranges of up to several 100 m and enables SoilNet to cover whole catchment areas (Figure 1).
Figure 1. The hybrid wireless underground network topology of SoilNet exemplified for a virtual catchment area (adapted from Bogena et al. [6]).

2. LoRa Wireless Sensor Network Technology

Due to the use of 2.4 GHz low-power radio modules, the range of wireless communication between ZigBee nodes is limited to a few kilometres. Therefore, more recently, the LoRa (Long Range) communication technology has been introduced for long-range, low-power, low-bit-rate wireless communication, enabling larger WSN coverage with power consumption similar to ZigBee by using chirp spread spectrum (CSS) modulation technology [8]. This modulation technique maintains the same low power characteristics as standard radio modulation but significantly increases the communication range because it is more robust to interference.
LoRa consists of the network protocol (LoRaWAN) and the associated hardware components, such as radio module and antennas and is optimized for battery-powered devices [8]. LoRaWAN uses star topologies with three different types of devices: end devices (also called LoRa nodes) that can host a set of environmental sensors, a LoRa gateway, and a LoRa network server [9]. The basic structure of a LoRaWAN wireless network is presented in Figure 2. The LoRa network server is the top of the network tree and stores information about the network, initiates the wireless links within the network, and can connect to a database server (Figure 2). The LoRa gateway acts as a relay station that passes data from the sensor devices to the LoRa server, where it can be processed by the LoRa application software. The LoRa end devices are the environmental sensors, which should have just enough functionality to communicate with the gateway. This allows the LoRa end devices to be asleep a significant amount of the time to save energy.
Figure 2. Principle of the LoRa network topology and its basic system architecture, as well as data communication types demonstrated using the example of the SoilNetLoRa wireless sensor network.
Since LoRa networks are specifically designed to be applied to larger areas [10], it is important for the planning and optimization of LoRa networks to know the coverage probabilities depending on the distance between the LoRa transmitting and receiving stations. For this reason, the radio technology of LoRa and the calculation of the potential radio link distance is discussed in more detail in the following. At the heart of LoRa is a proprietary chirp spread spectrum (CSS) modulation technique [11]. For binary chirp modulation, the data passes through a chirp modulator that maps each bit block to 1 of 2 waveforms. The chirped LoRa signal can be described by:
s(t)=√(2Es/Ts)cos[2πfct±π(u(tTs)w(tTs)2)(1)
where Es is the energy of s(t) in the symbol duration Ts, fc is the carrier frequency, and the parameters u and w are the peak-to-peak frequency deviation and the sweep width, respectively, both normalized by the symbol rate. LoRa supports variable data rates, enabling the trade-off between throughput, range, robustness, and power consumption while maintaining bandwidth. The LoRa server manages these aspects by regulating the bandwidth BW and the so-called spreading factor SF that determines the length of the chirp symbol. The time-on-air of a transmission increases exponentially with SF, extending the communication range between gateway and end devices. The LoRa protocol has six SFs (7–12, Table 1). The lower SFs provide higher data rates but shorter communication distance, while the higher SFs provide lower data rates but higher transmission stability. A signal failure in the uplink can occur at the gateway if the received signal-to-noise ratio (SNR) is below an SF-specific threshold value (qSF, Table 1).
Table 1. Receiver sensitivities for different spreading factors and the corresponding bit rates. The parameter qSF indicates the specific noise threshold for a given spreading factor. Values are taken from Georgiou and Raza [11].
Spreading Factor Sensitivity (dBm) qSF (dBm) Bit Rate (bits/s)
7 −123 −6 5469
8 −126 −9 3125
9 −129 −12 1758
10 −132 −15 977
11 −134.5 −17.5 537
12 −137 −20 293
The expected communication performance of the LoRa signal transmission technique can be estimated for a single end-device using the following considerations. Following the Friis’ transmission equation, the path loss g can be calculated as a function of distance between sender and receiver d:
g(d)=λ/((4πd)η)   (2)
where λ is the carrier frequency wave length, and η is the path loss exponent (η ≥ 2), usually taken to be equal to 2.7 in suburban environments. In addition, path loss due to shadowing effects can be approximated with noise variance assuming white Gaussian noise with zero mean:
σ²=174+NF+10log10(BW)   (3)
where NF is the receiver noise and assumed to have a value of 6 dB, and BW is the bandwidth of s(t), assumed to be 125 kHz in this case. Finally, the coverage probability CP, defined as the probability that the signal-to-noise ratio SNR is equal to or larger than the threshold value qSF, can be obtained as
CP=exp((σ²qSF)/(Pg(d)))   (4)
in which P is the transmitted power, here assumed to be 14 dBm (25.12 mW).
Figure 3 presents the coverage probability as a function of distance obtained with values of qSF from Table 1 and Equation (4). The following conclusions can be drawn from the calculations. In the case of a path loss exponent of 2.5, radio links can cover distances from 6.5 to 23 km, assuming that 90% coverage is sufficient for reliable transmission. When the path loss exponent increases to 2.7, the possible distance of the radio links decreases to 2.7 to 9 km at 90% coverage. This demonstrates the strong sensitivity of the communication distance to the path loss exponent and suggests that this value should be chosen with care. To check the appropriateness of the path loss exponent for a given area, the theoretical results should be compared with transmission performance measurements with mobile LoRa receiver and gateway devices at different distances.
Figure 3. Coverage probabilities for path loss exponents 2.5 (left) and 2.7 (right) using different spreading factors on a carrier frequency of 868.5 MHz and radio link distances (from 0–30 km). The dashed line depicts the 90% coverage probability.
Recently, a number of studies have developed and deployed low-cost LoRa-based WSNs for long-distance soil moisture monitoring to test their usability for smart agriculture [12][13]. In addition, Wang et al. [14] developed and successfully tested a LoRa-based WSN to investigate the control of soil moisture on greenhouse gas emissions in a wetland. The results of the WSN experiment by Rachmani and Zulkifli [13] showed that LoRa achieves maximum performance when the communication range is below 700 m, with average values of RSSI (received signal strength), SNR (signal-to-noise ratio) and PDR (packet–delivery ratio) of −120 dBm, 1 dB, and 40%, respectively, for the given conditions in a star fruit plantation. This indicates that local conditions, such as dense vegetation surrounding the LoRa transmitters, can have a strong negative impact on the expected radio link distances of LoRa-based WSNs.

3. Narrowband Internet of Things (NB-IoT)

The commercial narrowband Internet of Things (NB-IoT) is a cellular LPWAN (Low Power Wide Area Network) that is becoming increasingly important as an alternative to ad-hoc networks such as LoRa, especially due to the significantly lower transmission costs compared with broadband mobile radio standards [15]. NB-IoT has the same advantages as LoRa (i.e., low data rate, low power consumption and low bandwidth) and avoids the need to maintain an ad-hoc sensor network infrastructure (e.g., gateways, network server, etc.) because of the high coverage provided by cellular networks. NB-IoT has been integrated into the LTE (long-term evolution) broadband radio standard by the 3rd Generation Partnership Project (3GPP), and commercial marketing has been underway since 2018 [16]. To reduce device costs and minimize battery consumption, NB-IoT is kept as simple as possible by omitting many of the features of LTE [16]. NB-IoT also uses the licensed LTE frequency bands, and the NB-IoT core network relies on the evolved packet system (EPS) to select the best path for control and payload packets for uplink and downlink data [17]. The cell access procedure of an NB-IoT device is also similar to that of LTE. Data are transmitted via a serving gateway to a network gateway and forwarded to the application server via radio bearers that use the existing air interface and backbone (i.e., the Internet’s background network) of LTE. The use of NB-IoT is limited to 4G/LTE base stations, and the coverage should typically not be less than 23 dB for proper functioning [17]. For this reason, NB-IoT is often less suitable for rural areas than LoRaWAN.
NB-IoT already has almost nationwide coverage in China as do several North American and European countries through the major mobile phone connection providers. For instance, Deutsche Telekom has signed its first NB-IoT roaming agreements with several European operator partners, offering roaming services in 18 countries in Europe [18]. Figure 4 shows that Deutsche Telekom alone currently offers NB-IoT coverage for approximately 90% of the areas in Germany. The areas with poor NB-IoT coverage are located mainly at high altitudes and in deep valleys. In addition to this general availability of NB-IoT, local obstacles such as buildings or trees can limit the wireless connection between an NB-IoT device and the base stations, especially if the radio antenna is close to the ground, which is often the case with soil moisture sensors. However, with the continuous densification of the NB-IoT network and the increasing number of providers, these problems are continually becoming increasingly less frequent.
Figure 4. European countries where NB-IoT services are offered (upper left), NB-IoT coverage for Germany provided by Deutsche Telekom (right), and a large-scale detailed map indicating local NB-IoT gaps in deep valleys in a rural Eifel region in Germany (lower left). The NB-IoT coverage maps are based on available interactive maps [18].
It has been reported that the communication delay of NB-IoT technology is generally greater than the delay of LoRa due to the complex air interface scheduling process, which also requires iterations [19]. This can have a negative effect on the maintenance intervals of IoT devices because a longer transmission time increases power consumption. On the other hand, it has been shown that NB-IoT provides better transmission performance than LoRaWAN in underwater and underground environments [20].
The development of sensor devices with communication capabilities, such as NB-IoT, has been recognized as an essential component of smart agriculture [21]. Recently, several NB-IoT-based smart water management platforms for irrigation have been proposed and implemented [22][23][24][25]. With the increasing further expansion of the worldwide spatial coverage of NB-IoT, it is anticipated that smart agriculture will also become a feasible cost-effective technical solution for smaller farms [26].
NB-IoT networks can also support public participation of non-scientists in scientific projects, e.g., through community-based data collection with the help of large numbers of volunteers. For example, the ongoing CurieuzeNeuzen project [27] involves up to 5000 citizen scientists, who installed a NB-IoT soil moisture sensor in their garden, schoolyard, park, or private property. Using NB-IoT, the measurement data from these sensors are transmitted to a database at the University of Antwerp, making the data available to the involved (citizen) scientists in real time.

References

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