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Wireless sensor network technology enables distributed detection through efficient data communication between multiple environmental sensors. WSN is still a relatively new research area, but the communication technology used for low-cost, low-power wireless networks has advanced significantly in recent decades.

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MDPI contributors’ registered usernames will link to their SciProfiles page. To register with us, please see https:///register : Heye Reemt Bogena , Ansgar Weuthen , Johan Alexander Huisman

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. ZigBee is a set of high-level communication protocols that use 2.4 GHz low-power radio modules based on the IEEE 802.15.4 LPWA standard.

. Each ZigBee-based WSN component has a radio module to enable wireless communication. ZigBee radio modules have several software interfaces that connect hardware devices (physical and peripheral layers) to user applications. The user has the possibility to control the sensor network and manage the communication between devices through the application support layer (APS) and the application programming interface (API). Network data routing and data transmission are managed by the media access control (MAC) layer. It is based on the IEEE 802.15.4 standard and is located in the physical layer (PHY). The PHY layer includes transceivers as well as sensors and power sources

. Finally, users have the possibility to implement advanced functions (for example, logging functions, sensor drivers, etc.) by creating a special user software that configures the network of the sensor.

, developed at Forschungszentrum Jülich using the JenNet proprietary license free protocol stack developed by Jennic Ltd., South Yorkshire, UK

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. JenNet uses the license-free 2.4 GHz band and supports star, tree, and linear topologies. In case of tree topology, JenNet can support WSN up to 250 nodes and in case of linear topology, even up to 1000 nodes. The transmission distance of JenNet is limited to less than 100 m in the case of underground WSN. So SoilNet uses a hybrid WSN approach consisting of a mixture of underground terminals, each cable to several sensors on the ground and router devices above ground. This allows the transmission range to be greater up to several 100 m and allows SoilNet to cover the entire watershed area (Figure 1).

Figure 1. Hybrid wireless underground network topology of SoilNet shown for a virtual catchment area (adapted from Bogena et al.

Due to the use of a low-power 2.4 GHz radio module, the range of wireless communication between ZigBee nodes is up to several kilometers. Therefore, recently, LoRa (Long Range) communication technology has been introduced for long-distance, low-power, low-bit-rate wireless communication, in order to make WSN coverage larger with similar power consumption of ZigBee by using chirped spread spectrum (CSS). ). ) modulation technology

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. This modulation technique maintains the same low power characteristics as conventional radio modulation but significantly increases the range of communication because it is more resistant to interference.

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LoRa consists of a network protocol (LoRaWAN) and related hardware components, such as radio modules and antennas and is optimized for battery-powered devices.

. LoRaWAN uses a star topology and three different types of devices: end devices (also called LoRa nodes) that can host a set of environmental sensors, LoRa gateways, and LoRa network servers.

. The basic structure of the LoRaWAN wireless network is shown in Figure 2. The LoRa network server is at the top of the network tree and stores information about the network, initiates wireless links in the network, and can connect to the database server (Figure 2). ). The LoRa gateway acts as a relay station that sends data from the sensor device to the LoRa server, where it can be processed by the LoRa application software. The LoRa end device is an environmental sensor, which must have enough functionality to communicate with the gateway. This allows the LoRa end device to sleep for hours to save energy.

Figure 2. The principle of LoRa network topology and its basic system architecture, as well as the type of data communication shown using the SoilNetLoRa wireless sensor network example.

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, important for the planning and optimization of the LoRa network to determine the probability of coverage depending on the distance between LoRa transmitting and receiving stations. Accordingly, LoRa radio technology and potential radio link distance calculation are discussed in more detail below. At the heart of LoRa is a proprietary chirped spread spectrum (CSS) modulation technique.

. For binary chirp modulation, the data passes through a chirp modulator that maps each block of bits to 1 of 2 waveforms. The described LoRa signal can be described by:

S ( t ) =√( 2Es /Ts ) c o s [ 2 πfc t ± π (u (t Ts ) −w (t Ts ) 2 ) ] (1)

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Where Es is the energy s(t) of the symbol duration Ts, fc is the carrier frequency, and the parameters u and w are the peak-to-peak frequency deviation and sweep width, respectively, both gi -normalized to symbol rate. . LoRa supports variable data rates, allowing trade-offs between throughput, range, stability, and power consumption while maintaining bandwidth. The LoRa server manages these aspects by managing the bandwidth BW and the so-called spreading factor SF which determines the length of the chime symbols. Time-in-air transmission greatly increases the SF, which extends the communication range between the gateway and the end device. The LoRa protocol has six SFs (7-12, Table 1). Lower SF provides higher data rate but shorter communication distance, while higher SF provides lower data rate but higher transmission stability. An uplink signal failure can occur at the gateway when the received signal-to-noise ratio (SNR) is below the SF-specific threshold value (qSF, Table 1).

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Table 1. receiver sensitivity for different spreading factors and corresponding bit rates. The qSF parameter indicates a certain noise threshold for a given spread factor. The value is taken from Georgiou and Raza

The expected communication performance of the LoRa signal transmission technique can be estimated for an end device using these considerations. Following the Friis transmission equation, the path loss g can be calculated as a function of the distance between the sender and the receiver d:

Where λ is the wavelength of the carrier frequency, and η is the path loss exponent (η ≥ 2), usually considered equal to 2.7 in suburban environments. Furthermore, the path loss due to the shadowing effect can be estimated with the variance of the noise assuming Gaussian white noise with zero mean:

Where NF is the noise of the receiver and is assumed to have a value of 6 dB, and BW is the bandwidth s (t), assumed to be 125 kHz in this case. Finally, the CP coverage probability, defined as the probability that the signal-to-noise ratio SNR is equal to or greater than the qSF threshold value, can be obtained as

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Figure 3 shows the coverage probability as a function of distance obtained with the qSF value 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, the radio link can cover distances from 6.5 to 23 km, when 90% of coverage is sufficient for reliable transmission. If the path loss exponent increases to 2.7, the possible radio link distance decreases from 2.7 to 9 km at 90% coverage. It shows the strong sensitivity of the communication distance to the path loss exponent and shows that this value should be chosen with care. To check the appropriateness of the path loss exponent for an area, the theoretical results should be compared with measurements of the transmission performance of mobile LoRa receivers and gateway devices at different distances.

Figure 3. The probability of the signal for the path loss exponents of 2.5 (left) and 2.7 (right) using different spread factors of the carrier frequency of 868.5 MHz and the distance of the radio link (from 0-30 km). The dashed line represents a probability of 90% coverage.

Recently, many studies have developed and deployed a low-cost LoRa-based WSN for remote soil moisture monitoring to test its applicability for smart agriculture.

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Developed and successfully tested a LoRa-based WSN to investigate soil moisture control of greenhouse gas emissions in wetlands. Results of WSN experiments by Rachmani and Zulkifli

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Shows that LoRa achieves the highest performance when the communication range is less than 700 m, with average values ​​of RSSI (received signal strength), SNR (signal-to-noise ratio) and PDR (packet -delivery ratio) -120 dBm, 1 dB, and 40%, respectively, for the given situation in the star fruit plantation. This shows that local conditions, such as dense vegetation around LoRa transmitters, can have a strong negative effect on the expected radio link distance of LoRa-based WSNs.

The commercial narrowband Internet of Things (NB-IoT) is a cellular LPWAN (Low Power Wide Area Network) that has become increasingly important as an alternative to ad-hoc networks such as LoRa, especially due to lower transmission costs compared to broadband. mobile radio standards

. NB-IoT has the same advantages as LoRa

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