Figure 5 - available via license: Creative Commons Attribution 4.0 International
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A solar panel and solar irradiance sensors are equipped on the roof of a building on the campus where conventional networks such as Ethernet and Wi-Fi are not connected. A LoRa node (the gray enclosure below the panel) collects and sends related data to a data server via LoRa communication.
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The fundamental properties of long-range (LoRa) performance have been revealed by previous research, but advanced issues remain unresolved. This paper tackles three technical challenges that are confronted when establishing a LoRa network on a smart energy campus testbed in Korea. First, the communication range of LoRa in a combined indoor and outd...
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Context 1
... nodes (energy devices, sensors, and actuators) are installed at locations where typical networks such as the Ethernet or Wi-Fi do not reach. For instance, Figure 5 depicts a solar panel and solar irradiance sensors installed on the roof of a building on the campus. A new installation of network cables, Wi-Fi routers, or other short-range wireless networks is often avoided because of high installation costs. ...
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Citations
... Tabla 4.2: LoRa Spreading Factors[69].Los SF más bajos significan chrips más rápidos y, por tanto, una mayor velocidad de transmisión de datos. Por cada aumento del SF, la velocidad de barrido de los chrips se reduce a la mitad, al igual que la velocidad de transmisión de los datos Figura 4.4: Comparación de Spreading Factors de LoRa: SF 7 a SF 12[70]. Tabla 4.3: Especificaciones técnicas de la tecnología LoRa[69]. ...
... The choice of an appropriate method of communication depends on its application and requirements. The way data are transmitted is also an important factor in communication, as Kim et al. [15] described. These authors offer a LoRa-based network with 3000 end devices. ...
Building modern Internet of Things (IoT) systems is associated with a number of challenges. One of the most significant among them is the need for wireless technology, which will serve to build connectivity between the individual components of this technology. In the larger cities of Bulgaria, measures to ensure low levels of harmful emissions, reduce noise levels, and ensure comfort in urban environments have been taken. LoRa technology shows more advantages in transmission distance and low energy consumption compared to other technologies. That is why this technology was chosen for the design of wireless sensor networks (WSN) for six cities in Bulgaria. These networks have the potential to be used in IoT configurations. Appropriate modules and devices for building WSN for cities in Bulgaria have been selected. It has been found that the greater number of nodes in the WSN leads to an increase in the average power consumed in the network. On the other hand, depending on the location of these nodes, the energy consumed may decrease. The performance of wireless sensor networks can be optimized by applying appropriate routing protocols, which are proposed in the available literature. The methodology for energy efficiency analysis of WSN can be used in the design of wireless sensor networks to determine the parameters of the environment, with the possibility of application in IoT.
... Each technology offers advantages and limitations, either based on operational constraints or technological and contractual requirements [13,27], which have to meet the standards for the data required by the applied detection algorithms. The expected performance and limitations of NB-IoT [28][29][30][31], Sigfox [32] and LoRaWAN [30,[33][34][35][36][37][38][39] have been analyzed and tested extensively. In addition to energy consumption, the communication technologies vary regarding transmission rates (e.g., 100-600 bps for Sigfox versus 0.24-37.5 kbps for LoRaWAN), hourly or daily data transmission limits due to contractual terms or fair access policies in shared networks and expected transmission success or data loss rates. ...
While low-power wide-area network (LPWAN) technologies have been studied extensively for a broad spectrum of smart city applications, their potential for water distribution system monitoring in high temporal resolution has not been studied in detail. However, due to their low power demand, these technologies offer new possibilities for operating pressure-monitoring devices for near real-time leak detection in water distribution systems (WDS). By combining long-distance wireless communication with low power consumption, LPWAN technologies promise long periods of maintenance-free device operation without having to rely on an external power source. This is of particular importance for pressure-based leak detection where optimal sensor positions are often located in the periphery of WDS without a suitable power source. To assess the potential of these technologies for replacing widely-used wireless communication technologies for leak detection, GPRS is compared with the LPWAN standards Narrowband IoT, long-range wide area network (LoRaWAN) and Sigfox. Based on sampling and transmission rates commonly applied in leak detection, the ability of these three technologies to replace GPRS is analyzed based on a self-developed low-power pressure-monitoring device and a simplified, linear energy-consumption model. The results indicate that even though some of the analyzed LPWAN technologies may suffer from contractual and technical limitations, all of them offer viable alternatives, meeting the requirements of leak detection in WDS. In accordance with existing research on data transmission with these technologies, the findings of this work show that even while retaining a compact design, which entails a limited battery capacity, pressure-monitoring devices can exceed runtimes of 5 years, as required for installation at water meters in Austria. Thus, LPWAN technologies have the potential to advance the wide application of near real-time, pressure-based leak detection in WDS, while simultaneously reducing the cost of device operation significantly.
... This metric measures the received signal sensitivity from LoRa the gateway side [21]. ...
... The boost converter was simulated using the maximum effective constant duty cycle to determine the minimum size supercapacitor required to provide 140.0 mW of power for a duration of 2.14 s. It was found that the optimum output capacitance was 200 µF and that the minimum capacitance of the supercapacitor was 2 F. Figure 10 shows the sensor node circuit containing the fully cross-coupled RF-DC converter, the step-up DC-DC converter, the MDOT LoRa module [42,43], and the Microchip MCP9700 temperature sensor. ...
... It was found that the optimum output capacitance was 200 μF and that the minimum capacitance of the supercapacitor was 2 . Figure 10 shows the sensor node circuit containing the fully crosscoupled RF-DC converter, the step-up DC-DC converter, the MDOT LoRa module [42,43], and the Microchip MCP9700 temperature sensor. ...
The large-scale deployment of sensor nodes in difficult-to-reach locations makes powering of sensor nodes via batteries impractical. Besides, battery-powered WSNs require the periodic replacement of batteries. Wireless, battery-less sensor nodes represent a less maintenance-intensive, more environmentally friendly and compact alternative to battery powered sensor nodes. Moreover, such nodes are powered through wireless energy harvesting. In this research, we propose a novel battery-less wireless sensor node which is powered by a dedicated 4 W EIRP 920 MHz radio frequency (RF) energy device. The system is designed to provide complete off-grid Internet of Things (IoT) applications. To this end we have designed a power base station which derives its power from solar PV panels to radiate the RF energy used to power the sensor node. We use a PIC32MX220F32 microcontroller to implement a CC-CV battery charging algorithm to control the step-down DC-DC converter which charges lithium-ion batteries that power the RF transmitter and amplifier, respectively. A 12 element Yagi antenna was designed and optimized using the FEKO electromagnetic software. We design a step-up converter to step the voltage output from a single stage fully cross-coupled RF-DC converter circuit up to 3.3 V. Finally, we use the power requirements of the sensor node to size the storage capacity of the capacitor of the energy harvesting circuit. The results obtained from the experiments performed showed that enough RF energy was harvested over a distance of 15 m to allow the sensor node complete one sense-transmit operation for a duration of 156 min. The Yagi antenna achieved a gain of 12.62 dBi and a return loss of −14.11 dB at 920 MHz, while the battery was correctly charged according to the CC-CV algorithm through the control of the DC-DC converter.
... The network server determines which data belong to which node and removes duplicated data, redirecting them to the Application Layer. Hence, the applications that reside on the top of the SDN architecture are used to collect and analyse data from nodes [54]. ...
Industry 4.0 and digital farming rely on modern communication and computation technologies such as the Internet of Things (IoT) to provide smart manufacturing and farming systems. Having in mind a scenario with a high number of heterogeneous connected devices, with varying technologies and characteristics, the deployment of Industry 4.0 and digital farming solutions faces innovative challenges in different domains (e.g., communications, security, quality of service). Concepts such as network slicing and Software-Defined Networking (SDN) provide the means for faster, simpler, scalable and flexible solutions in order to serve a wide range of applications with different Quality-of-Service (QoS) requirements. Hence, this paper proposes a lightweight slice-based QoS manager for non-3GPP IoT focusing on different use cases and their varying requirements and characteristics. Our focus in this work is on non-3GPP IoT unlicensed wireless technologies and not specifically the end-to-end network slice perspective as described in 5G standards. We implemented and evaluated different QoS models in distinct scenarios in a real experimental environment in order to illustrate the potential of the proposed solution.
... therefore, In research [4] the comparison of radiofrequency modules such as GPRS, Narrowband-IoT, SigFox, and LoRa was tested at a very long distance of 7800 km 2 , proving that Radio module capability is capable of reaching very long distances. In research [5] LoRa communication was built to make smart energy Campus Testbed [5][6][7]. So that an appropriate architecture is needed to handle complex sensor node connections in the internet of things (IoT) and how to build architecture in various systematical domains [8]. ...
... therefore, In research [4] the comparison of radiofrequency modules such as GPRS, Narrowband-IoT, SigFox, and LoRa was tested at a very long distance of 7800 km 2 , proving that Radio module capability is capable of reaching very long distances. In research [5] LoRa communication was built to make smart energy Campus Testbed [5][6][7]. So that an appropriate architecture is needed to handle complex sensor node connections in the internet of things (IoT) and how to build architecture in various systematical domains [8]. ...
... Where A is the RSSI value at a distance of d_0 or a distance of 1 meter. So to get an estimate of the distance (d) between nodes of the RSSI parameter and A, it can be seen in (5). The value of n is path loss exponent 2-5, each environment is different [28], for example, Free Space, n value is 2. ...
LoRa is a Radio Frequency module that can send packet data up to 3 km in FSPL.LoRa has 3 different Frequency Radios i.e, 915 MHz, 868 MHz, and 433 MHz. LoRa testing is based on different distances, BME280 provides Barometric Pressure, Temperature, and Humidity data. An analysis from the results of the Received Signal Strength to the distance (m) to the farthest point to prove and provide QoS data from LoRa 915 MHz. Sensor nodes are built using ADR and Automatic sleep mode algorithms. Communication systems between nodes are built dynamic sensor nodes in mesh networking. Monitoring signal transferring on the 915 MHz Frequency waveform is carried out using the Textronix Spectrum analyzer. Based on the BME280 Data packet transmission from the LoRa 915 MHz Transmitter to the Receiver Receiver at a distance of 100m is -84 dBm and at a distance of 500m is -107 dBm. The LoRa Internet Gateway has 2 types of settings, i.e, application, and gateway, this setting is to find out the location of the gateway with longitude and latitude. Furthermore, the gateway holds Sensor data from the End node, while the Application Server displays sensor data in the form of Graphics in realtime.
... therefore, In research [4] the comparison of radiofrequency modules such as GPRS, Narrowband-IoT, SigFox, and LoRa was tested at a very long distance of 7800 km 2 , proving that Radio module capability is capable of reaching very long distances. In research [5] LoRa communication was built to make smart energy Campus Testbed [5][6][7]. So that an appropriate architecture is needed to handle complex sensor node connections in the internet of things (IoT) and how to build architecture in various systematical domains [8]. ...
... therefore, In research [4] the comparison of radiofrequency modules such as GPRS, Narrowband-IoT, SigFox, and LoRa was tested at a very long distance of 7800 km 2 , proving that Radio module capability is capable of reaching very long distances. In research [5] LoRa communication was built to make smart energy Campus Testbed [5][6][7]. So that an appropriate architecture is needed to handle complex sensor node connections in the internet of things (IoT) and how to build architecture in various systematical domains [8]. ...
... Where A is the RSSI value at a distance of d_0 or a distance of 1 meter. So to get an estimate of the distance (d) between nodes of the RSSI parameter and A, it can be seen in (5). The value of n is path loss exponent 2-5, each environment is different [28], for example, Free Space, n value is 2. ...
LoRa is a Radio Frequency module that can send packet data up to 3 km in FSPL.LoRa has 3 different Frequency Radios i.e, 915 MHz, 868 MHz, and 433 MHz. LoRa testing is based on different distances, BME280 provides Barometric Pressure, Temperature, and Humidity data. An analysis from the results of the Received Signal Strength to the distance (m) to the farthest point to prove and provide QoS data from LoRa 915 MHz. Sensor nodes are built using ADR and Automatic sleep mode algorithms. Communication systems between nodes are built dynamic sensor nodes in mesh networking. Monitoring signal transferring on the 915 MHz Frequency waveform is carried out using the Textronix Spectrum analyzer. Based on the BME280 Data packet transmission from the LoRa 915 MHz Transmitter to the Receiver Receiver at a distance of 100m is -84 dBm and at a distance of 500m is -107 dBm. The LoRa Internet Gateway has 2 types of settings, i.e, application, and gateway, this setting is to find out the location of the gateway with longitude and latitude. Furthermore, the gateway holds Sensor data from the End node, while the Application Server displays sensor data in the form of Graphics in realtime.
... The LoRa nodes n i are configured following LoRa SX1272 model. This is to validate n i 's performance against practical experiments carried out in [40], [41]. The nodes are stationary and communicate with the GW following Class A LoRaWAN protocol, while the GW communicates back through a temporary receive window that opens following each transmission from n i [1]. ...
... In [40], [41], two different experimental projects using LoRaWAN were carried out to evaluate the channel condition impact on the PDR. Both were carried out in urban environments where obstacles are highly deployed between the nodes and the gateway. ...
... Since this work is inspired by a forest scenario and based on the results in [40], [41], only one fifth of the nodes are distributed within a range of d 0 from the GW. These nodes are assumed to have a good channel condition with a PDR more than 90%. ...
Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network's transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network density while maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.
LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellín, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R2 of 0.917.
