Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor

data Data Descriptor Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor Pavel Masek 1,* , Martin Stusek 1,2 , Ekaterina Svertoka 1,3 , Jan Pospisil 1, Radim Burget 1, Elena Simona Lohan 2, Ion Marghescu 3, Jiri Hosek 1and Aleksandr Ometov 2   Citation: Masek, P.; Stusek, M.; Svertoka, E.; Pospisil, J.; Burget, R.; Lohan, E.S.; Marghescu, I.; Hosek, J.; Ometov, A. Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor. Data 2021,6, 62. https://doi.org/ 10.3390/data6060062 Academic Editor: Xinyue Ye Received: 31 March 2021 Accepted: 1 June 2021 Published: 10 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Telecommunications, Brno University of Technology, 616 00 Brno, Czech Republic; [email protected] (M.S.); [email protected] (E.S.); [email protected] (J.P.); [email protected].cz (R.B.); [email protected].cz (J.H.) 2Unit of Electrical Engineering, Tampere University, 33720 Tampere, Finland; [email protected] (E.S.L.); aleksandr[email protected] (A.O.) 3Department of Telecommunications, University Politehnica of Bucharest, 061071 Bucharest, Romania; [email protected] *Correspondence: [email protected] Abstract: This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance. Dataset: Available on GitHub: https://github.com/BUTResearch/MDPI_Data_Urban_LPWA_ Measurement (accessed on 30 May 2021). Dataset License: CC-BY 4.0 Keywords: industrial IoT; LPWAN; LoRaWAN; urban measurements; dataset 1. Introduction Today, the definition of a smart machine includes devices that can exchange data with other devices—i.e., Machine-to-Machine (M2M) approach or the cloud—execute a number of specific commands, and change the logic of work depending on external conditions, to cope with work that previously needed a person, i.e., Human-to-Machine (H2M) [ 1 , 2 ]. High functionality in a small size, which, in a few touches, satisfies a large number of human needs, from communication to health monitoring, makes smart devices a part of the Internet of Things (IoT, which also covers massive Machine-Type Communications (mMTC), one of the fastest-growing technologies of the last decade [ 3 – 6 ]. Experts agree that the smart device market will continue to grow in the near future due to their convenience, broad capabilities, and new “interesting features” [ 7 ]. Moreover, the number of connected smart devices has doubled over the past five years, from 13 to 26 billion devices worldwide, and is estimated to reach the 38.6 billion milestone by 2025 and 50 billion by 2030 [ 8 ]. The largest share of this portion of the IoT market belongs to smartphones (approximately Data 2021,6, 62. https://doi.org/10.3390/data6060062 https://www.mdpi.com/journal/data Data 2021,6, 62 2 of 20 19% [ 8 ]), with the next most significant niche being wearable devices, and the market of wearable devices that is estimated to reach 62.82 billion dollars by 2025 [9]. Smart devices, including wearable ones, vary greatly in required bandwidth, latency, reliability, size, etc. [ 10 ]. For example, en-body electronics and simpler monitoring devices possess low bandwidth as they are usually in “sleep” mode and “wake up” periodically to conduct predefined tasks (measurements, data transmission), while bandwidth-hungry devices spend more energy and in a faster way [ 11 ]. Moreover, implantable patches should have a small form-factor so that the usual communication options are not applicable, and wireless connectivity requires the use of very high frequencies in the THz range. Such significant differences between wearable device classes make it impossible to have a list of common requirements, and, consequently, single silver bullet communication solutions do not exist. Communication technologies for the IoT devices can be divided into three groups depending on the range: (i) short-range solutions that allow for connections at a distance below few tens of meters (e.g., Bluetooth Low Energy (BLE), ZigBee [ 12 ]); (ii) mediumrange, which can enable communication up to a few hundred meters (e.g., Wi-Fi solutions, DASH7, Ingenu); (iii) wide-range solutions, reaching few tens of kms in communication range (e.g., LoRaWAN, Narrowband IoT (NB-IoT), and Sigfox) [ 13 – 17 ]. Despite many smart devices, most sensor-oriented ones are low-power (i.e., 10+ years of battery life) and have low data rates (usually, it refers to rates less than 1 Mbps). Such devices that are not aimed at high bandwidth can be equipped with Low-Power Wide-Area Network (LPWAN) solutions such as LoRaWAN, NB-IoT, or Sigfox for high energy efficiency and reliable operation in an urban scenario. The question regarding which LPWAN technology will bring the most revenue in the next five years is still unsolved. To answer, it is necessary to consider many parameters such as functionality, availability, price, current demand, etc. On the one hand, the latest cellular technologies such as NB-IoT and LTE Cat-M seem to be more profitable due to their ease of use and superior performance. According to the Riot research [ 18 ], cellular LPWAN (i.e., NB-IoT and LTE Cat-M) will occupy the leading position among other LPWAN systems in the next three years. On the other hand, the growth of noncellular networks such as LoRaWAN and Sigfox should not be underestimated; Sigfox, for example, had second place after NB-IoT in 2018 [ 19 ]. Compared to cellular IoT solutions, noncellular LPWAN solutions have a lower data rate, lower power consumption, lower cost, and operate in unlicensed bands. However, some developers may consider their utilization for data collection from nonsmart wearables as well. Among the LPWAN communication technologies, LoRaWAN is one of the most promising solutions for IoT [ 20 ], especially in the area of smart cities [ 21 , 22 ], smart farming [ 23 , 24 ], asset tracking [ 25 ], environmental monitoring [ 26 ], and smart waste management [ 25 , 27 ]. According to [ 25 ], the Compound Annual Growth Rate (CAGR) of the LoRaWAN market is going to achieve 47.2% between 2019 and 2027. The expected growth of the market could be explained by key features of the technology, which meet the requirements of rapidly increasing IoT systems, i.e., low power consumption and extended communication range. LoRaWAN solution offers low-cost deployment, different device classes for different conditions, direct connection to the cloud, positioning, and feasibility for both outdoor and indoor environments due to better penetration capabilities compared, for example, to Wi-Fi or BLE [28,29]. Currently, not many datasets are found regarding the application of the LoRaWAN [ 30 – 33 ]. Authors in [ 30 ] presented the dataset collected from the underground (up to 50 cm in depth) LoRaWAN network in Australia during 10 months using 5 transmitters, 2 receivers, and containing information related to farms such as soil moisture, temperature, etc. The dataset in [ 31 ] comprises the information (packet header and parameters reported by base stations) collected over 2–4 months by manually deploying 9 gateways (GW) (5 outdoor with Line-of-Sight (LOS), 3 indoor with limited LOS, and 1 indoor with Non-Line-of-Sight (NLOS)) in London. Authors identify that this work could Data 2021,6, 62 3 of 20 be used for different purposes, such as investigating the changes in network parameters in the long term or testing the algorithms to validate their appropriateness for certain applications. A large fingerprinting dataset [ 32 ] was collected using postal cars in Antwerp over 3 months with 68 GWs scattered throughout the city. The dataset contains information reported by the GWs and the real positions where uplink packets were sent from. LoRaWAN is an in-demand technology that has carved its niche among communication solutions for the IoT. It seems to be most applicable when we do not need high throughput but need the system’s low cost and high reliability, especially for resourceconstrained devices. The technology is improving, and statistics are required to explore and expand its potential, though there are very few open-source datasets. Therefore, in this paper, we provide a LoRaWAN coverage dataset collected in Brno, Czech Republic. The motivation to prepare this dataset was pushed forward by encouraging researchers to work with LoRaWAN technology to identify its application prospects and benefits as a communication solution for smart devices. The rest of the paper is organized as follows. First, Section 2provides an overview of LoRaWAN technology. Section 3covers the data description. Further, Section 4describes the preparation phase of the measurements and outlines the measurement setup for both shortand long-term measurement campaigns. The next section visualizes the most important findings from both measurement campaigns. Finally, the last section provides the summary of the data descriptor. 2. LoRaWAN Technology Overview The selected LoRaWAN technology is arguably one of the most well-known representative of LPWAN technologies operating in the Industrial, Scientific, and Medical (ISM) (unlicensed) frequency band. The LoRaWAN network infrastructure consists of end devices (ED), GWs, and a network server (NS). Besides these elements, the network may contain a specialized network join (NJ) server to handle roaming between networks. The bare minimum LoRaWAN network consists of a single GW and the NS (which may be integrated into the GW). These two elements are typically connected through an IP-based interface. The functionality of the NS and the user application programming interfaces (APIs) might differ depending on the operator. In most cases, the transmission is initiated by an ED by using an Aloha-like channel access mechanism [ 34 ]. Therefore, the ED can initiate communication at any time, without violating the selected radio channel’s operational restriction. Significantly, LoRaWAN belongs to ISM band. The key parameters can be found in Table 1, making the operation free in contrast to, for example, cellular-based NB-IoT technology operating in the licensed LTE band. The utilization of unlicensed bands is regulated by the government (in the case of the Czech Republic, the Czech Telecommunication Office is the regulator) and therefore has some operational restrictions. The LoRaWAN provides two options for infrastructure deployment. End-users can use the infrastructure provided by communication operators by paying for their services, see Figure 1with the description of the blocks in Table 2. An alternative is the deployment of private LoRaWAN infrastructure. The LoRaWAN network protocol does not have a strict specification for the used modulation technique, but an arbitrary method can be used. However, the most widely used is the Long-Range (LoRa) modulation patented by Semtech [ 35 ]. This modulation is based on a spread spectrum technique called Chirp Spread Spectrum (CSS). The spreading of the spectrum is achieved by generating a chirp signal that continuously varies in frequency. The data are then modulated on top of the chirp, spreading a narrowband signal over a wider bandwidth (of at least 125 kHz). The resulting signal has a spectral signature of noise, which makes it harder to detect or jam [36], and is resilient to a narrowband noise. Data 2021,6, 62 4 of 20 Table 1. Key parameters of LoRaWAN technology. Parameters Property Coverage (MCL) 157 dB Technology PHY (Proprietary)/MAC (Open) Frequency spectrum Unlicensed/ISM Duty cycle limitation Yes (0.1% or 1% in EU) Transmission power restrictions Yes (16 dBm = 40 mW) Data rates in Downlink (DL) 0.25–50 kbps (50 kbps with FSK) Data rates in Uplink (UL) 0.25–50 kbps (50 kbps with FSK) Max. message size UL 243 B Max. message size DL 243 B Current consumption Sleep < 2 uA, Tx < 45 mA (14 dBm), Rx < 15 mA Module cost <8 USD Security Medium (AES-128) Furthermore, the chirp waveform frequency characteristics—i.e., the slope (directly affecting the symbol duration) of the LoRa modulation—can be modified by the spreading factor (SF) parameter ranging from 7 to 12. With slower frequency variation, each symbol is emitted with higher energy, thus enabling longer communication ranges at the cost of a lower data rate and longer on-air time. In general, the achievable bit rate varies from 250 (for SF12/125 kHz) to 21,900 bps (SF7/500 kHz), resulting in a maximum payload size from 11 (SF10—US region) up to 242 bytes (SF7—most of the regions). Due to the need for power efficiency, the EDs are commonly configured to use the lowest SF that allows reliable communication [ 37 ]. The LoRaWAN protocol also defines channel coding-forward error correction. The coding rate parameter value indicates the ratio between the original information’s bits and the redundant bits, which also adds another overhead. HTTPS TR069 Gateway Gateway Gateway GNMS Network Server Routing Server Database Billing Server Positioning Server Server for C Server for B Server for A MQTT RESTful RESTful MQTT Customer Specific API OAuth2 End Nodes Concentrators Routing Services App Servers Figure 1. The network architecture of the LoRaWAN network. The LoRaWAN ED may start its uplink transmission at any time, see Figure 2. An ED randomly selects one of up to 16 available frequency channels for its transmission (valid for the EU region). The SF and the transmit power used by the ED are either predefined or, specified (either by the ED itself or by the NS)—which is strongly recommended—based on the radio channel conditions between the ED and the nearest GW. The maximum payload for LoRaWAN uplink packets depends on the SF used and is at least 51 bytes (for the EU Data 2021,6, 62 5 of 20 region). The number of messages per day is not limited either, but the ED is required to obey local restrictions concerning the maximum duty cycle. Packet retransmission in LoRaWAN is optional and rarely used. Table 2. Description of LoRaWAN network architecture blocks. End Nodes AES Advanced Encryption Standard used for encrypted communication. Concentrators TR-069 Technical specification that defines an application protocol for remote management of network devices. MQTTS Lightweight messaging protocol MQTT (for M2M, IoT devices) with TLS (SSL) encryption support. HTTPS Hypertext transfer protocol (HTTP) with encryption protocol TLS (SSL), mostly used for web browsing. Routing Services Network server Implements LoRaWAN protocol, validates authenticity and integrity of devices, packet deduplication, organizes downlink transmission, and many others. Routing server Routes traffic based on the customer’s account and the selected server. Database Data storage for billing server. Server for A, B, and C Customer server that handles end-device join procedures, key storage, and management. Positioning server Provides position calculation (estimation) of end devices in the network. Billing server Handles the tracking of billable products and services. GNMS (Global network management system) Provides remote monitor and control functionality. Oauth2 Industry-standard protocol for authorization that provides API clients limited access to user data on a web server. App Servers MQTT Lightweight messaging protocol MQTT (for M2M and IoT devices). RESTful Architectural style for API (application program interface) that uses HTTPS requests to access and use data. Customer-Specific API Proprietary protocol implementation. Potential downlink Potential downlink RX1 RX2 Message uplink LoRaWAN class A device Programmed Delay Programmed Delay Time Lora Gateways 1 sec 2 sec Figure 2. Summary diagram of ED’s behavior for LoRaWAN. The downlink functionality of the LoRaWAN ED depends on its class. Each LoRaWAN ED operating in class A opens two Receive Windows (RWs) following its uplink transmission. The first RW (RW1) follows after the preagreed waiting time (typically 1 s) and is opened in the same frequency channel that the ED used for its uplink transmission. The second RW (RW2) follows the first window after 1 s and is opened in the preagreed Data 2021,6, 62 6 of 20 frequency channel (in the EU, typically in the 869.5 MHz channel). For SF used by the ED, RW1 depends on its SF for uplink, while RW2 is always opened with a predefined SF. An ED having only these RWs is classified as ED class A. The devices of class B are required to synchronize with the network and open periodic RWs at prespecified time slots. Finally, class C ED listens the entire time it is not transmitting or having RW1. In addition to the duty cycle restrictions, there is also a limit on transmission power, especially for resource-constrained devices. The power values are defined here as Effective Radiated Power (ERP) values—the power that must be given to a reference half-wave dipole antenna to get the same electrical field strength as the actual device at the same distance in the direction of the antenna gain. Another often-used definition is Effective Isotropic Radiated Power (EIRP)—the power that must be given to a reference isotropic antenna to get the same electrical field strength as the actual device at the same distance. The EIRP and ERP can be converted into each other using PEIRP =PERP + 2.15 dB, if the powers are expressed in dBm [ 38 ]. Maximum transmission power limits are given by the LoRaWAN regional specification and by each country’s local regulations, which must be verified in advance. According to the LoRaWAN specification, EIRP limits generally range between 12.15–30 dBm [39]. LoRaWAN Regional Parameters for EU There are two frequency plans available for the European continent: EU863–870 and EU433. Since the Czech CRA agency operates the ISM band 868 MHz network, only the first frequency plan will be discussed in more detail. Table 3shows the relationship between the data rate (DR), which is a numeric value representing the spreading factor, bandwidth, and coding rate settings in the specified region, and the bit rate. For EU863–870, a coding rate of 4/5 is used by default. The table shows that the lower the data rate, the lower the bit rate (but this extends the communication coverage). Table 3. EU863-870 Uplink data rate [39]. Data Rate (DR) Configuration Physical Bit Rate [b/s] 0 LoRa: SF12/125 kHz 250 1 LoRa: SF11/125 kHz 440 2 LoRa: SF10/125 kHz 980 3 LoRa: SF9/125 kHz 1760 4 LoRa: SF8/125 kHz 3125 5 LoRa: SF7/125 kHz 5470 6 LoRa: SF7/250 kHz 11,000 7 FSK: 50 kb/s 50,000 8–14 Reserved - Six SF values using 125 kHz-bandwidth (namely, SF7 to SF12) are specified. For these, the on-air data throughput varies from 250 bps up to 5.47 kbps depending on the selected SF. Additionally, there is also one channel with a bandwidth of 250 kHz and SF7 with a throughput of 11 kbps [ 40 , 41 ]. Moreover, the channels with higher bandwidth or high-speed Frequency Shift Keying (FSK) modulation may be defined. Each ED selects one of the sixteen available channels in frequency bands from 863 to 870 MHz. However, the condition is that each device (GWs and EDs) must also communicate on the default channels listed in Table 4. The ED must also comply with the duty cycle restriction and the maximum transmission power. The selected ISM frequency band of 868 MHz imposes the limitation of a 1% duty cycle with the maximum transmission power of 14 dBm (25 mW) [42]. Data 2021,6, 62 7 of 20 Table 4. EU863-870 default/mandatory channels [39]. Bandwidth [kHz] Channel Frequency [MHz] Bit Rate [kb/s] Duty Cycle 125 868.1, 868.3, 868.5 0.25 (DR0) to 5 (DR5) <1% 3. Data Description The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each GW receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters [43]: 1. cmd —Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs. 2. seqno —Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number. 3. EUI —Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface. 4. ts —Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970). 5. fcnt —Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages. 6. port —The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved. 7. freq —Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudorandomly selects from the range of available LoRaWAN channels on which it will transmit the message. 8. toa —Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size. 9. dr —Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate. 10. ack —Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic. 11. gws —Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW. •rssi —Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [44]. Data 2021,6, 62 8 of 20 •snr —Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level. •ts —Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970). •tmms —Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity. •time —UTC of the received message, with microsecond precision in the ISO 8601 format. •gweui —GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW. •lat —Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity. •lon —Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity. 12. bat —Battery status of the ED 8-bit integer value (0—external power supply, 255— battery status is unknown, 1–254—correspond to battery status 0–100%). 13. data —The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements. 14. device_Lat—Latitude of the measurement point gathered from the GPS. 15. device_Lon—Longitude of the measurement point gathered from the GPS. An example of one entry could be found in the following listing: 1[ 2{ 3 "cmd" :"gw" , 4 " seqno " :128753292, 5 "EUI " :"0018B2000000185A", 6 " ts " :1568474800203, 7 " fc n t " :1, 8 " port " :1, 9 " freq " :868500000, 10 " toa " :1318, 11 " dr " :"SF12 BW125 4/5 ", 12 " ack " :false , 13 "gws" :[ 14 { 15 " r s s i " :−86 , 16 " snr " :4.5, 17 " ts " :1568474800203, 18 "tmms" :1252510018170, 19 " time " :"2020−09−14T15 :26 :40 .170469000Z", 20 " gweui " :"B827CBFFFFDB1D51 ", 21 " l a t " :49 .200309, 22 " lon " :16 .60869630000002 23 }, 24 { 25 " r s s i " :−106, 26 " snr " :−0.2, 27 " ts " :1568474800209, 28 "tmms" :1170, 29 " time " :"2020−09−14T15 :26 :40 .170479170Z", 30 " gweui " :"024B08FFFF0500A9 ", 31 " l a t " :49 .1988101, 32 " lon " :16 .5795896 33 }, 34 <... > 35 ] 36 " bat " :204, 37 " data " :"8F180000100F" 38 }, 39 <... > 40 ] Data 2021,6, 62 9 of 20 The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately. 4. Measurement Details To perform the measurement campaign, we designed and constructed the LPWA device prototype capable of transmitting data using diverse LPWA communication technologies. In the case of this paper, we take into account the LoRaWAN technology, i.e., the data transmissions utilizing the license-exempt frequency band. This section contains information related to the aforementioned construction of the device and discusses the executed measurement campaigns. 4.1. Evaluation Preparation For the measurement campaign, a measurement prototype was developed and subsequently used for in-depth evaluation of energy consumption and communication capabilities of LoRaWAN—a prototype equipped with a Microchip RN2483 Low-Power Wide-Area LoRaWAN technology transceiver module; its main technical details are given in Table 5 while the actual module is depicted in Figure 3. Table 5. General specifications of the RN2483 Low-Power Wide-Area LoRaWAN technology transceiver module. Parameter Value Frequency Range 863.0 MHz to 870.0 MHz; 433.050 MHz to 434.790 MHz RF Output Power Adjustable up to +14 dBm high Efficiency PA Modulation Schemes FSK, GFSK, and LoRa Technology Modulation Data Rate 300 kbps with FSK modulation; 10,937 bps with LoRa Technology modulation Output Power Adjustable up to max. 10 dBm on 433 MHz band (limited to meet regulations); max. 14 dBm on the 868 MHz band Receiver Sensitivity Up to −146 dBm RN2483 LoRaWAN Module LoRaWAN Antenna NB-IoT Antenna SIMHolder LE51 Sigfox Module Sigfox Antenna WiFiModule DC-DCConverter SARAN 210 NB-IoT Module Figure 3. Evaluation board constructed for testing LPWA technologies. Before the measurements, the constructed prototype was tested in the temperature Vötsch VC3 7018 chamber as well as in the Electromagnetic Compatibility (EMC) anechoic chamber as they are both part of testing laboratories at Brno University of Technology, Data 2021,6, 62 16 of 20 −125 −120 −115 −110 −105 −100 −95 −90 −85 RSSI [dBm] 0 50 100 150 200 250 Relative frequency of samples [-] Figure 14. City center sensor RSSI samples relative frequency histogram. In the case of SNR, depicted in Figures 15 and 16, the situation is slightly different. As expected, the BUT sensor indicates much better SNR values. Notably, almost 600 samples fit between 5 and 10 dB for the BUT sensor. Further, the SNR values of most of the samples are over 0 dB. Conversely, for the city center module, not a single sample has SNR better than 0 dB. On top of that, the SNR fluctuation of the city center module is significantly higher. The SNR values are more evenly distributed over the whole range of SNR values (around 10 dB). It is an exciting finding, as even in the case of the city center sensor, there are RSSI samples comparable to the BUT module. However, the SNR for these occasions is still significantly worse. Hence, it appears that SNR is more dependent on radio conditions and the overall separation distance between ED and GW than RSSI. −20 −15 −10 −5 0 5 10 15 0 100 200 300 400 500 600 SNR [dB] Reletive frequency of samples [-] Figure 15. BUT sensor SNR samples relative frequency histogram. Data 2021,6, 62 17 of 20 −20 −18 −16 −14 −12 −10 −8 SNR [dB] 0 50 100 150 200 250 Reletive frequency of samples [-] Figure 16. City center sensor SNR samples relative frequency histogram. 6. Data Descriptor Summary This data descriptor paper provides the dataset descriptor of LoRaWAN technology measurements in the midsize city scenario of Brno, Czech Republic. The dataset covers 311 outdoor test locations in a varying landscape processed on 39 static gateway nodes. The dataset provides various measurement data ranging from communication to locationrelated information, providing the researchers with broad opportunities to analyze the deployment and improvement of the LoRaWAN operation for numerous IoT-use cases. In addition, we explore the long-term characteristics of the real-world LoRaWAN network based on a measurement campaign spanning over two months. Finally, we report on power consumption measurements conducted on an off-the-shelf LoRaWAN communication module using different SF settings and various message sizes. All mentioned datasets are publicly available at GitHub and will be extended after the collection of new, real-world measurement data. Based on the conducted city-scale measurement campaign, it can be stated that LoRaWAN technology represents a solid communication technology for delayand losstolerant application as it provides a cumulative packet delivery ratio of 83% during the whole experiment. Impressively, only 16 locations of 311 were not served, giving the overall outage probability of only 5.15%. Notably, the results suggest that the geographically closest GW does not always provide the best RSSI nor SNR. In reality, 40% of GWs with the best RSSI and 34% with the best SNR are not the closest ones. The long-term measurements show exciting results. Even during the two months, the RSSI significantly fluctuated within the range of almost 50 dB. In terms of SNR, fluctuation ranged around 25 dB. These results suggest that the use of conventional empirical propagation models may lead to significant inaccuracies of the predicted path loss values. Hence, a more precise dynamic channels model will be needed. The provided data set may serve as a good starting point for the development of such a model. Author Contributions: Conceptualization, P.M. and R.B.; methodology, P.M. and M.S.; software, M.S.; validation, J.P., E.S. and A.O.; formal analysis, A.O.; resources, E.S.L.; data curation, M.S. and E.S.; writing—original draft preparation, A.O.; writing—review and editing, E.S., J.P., P.M., E.S.L.; visualization, P.M.; supervision, A.O., J.H., I.M.; project administration, E.S.L.; funding acquisition, E.S.L. and A.O. All authors have read and agreed to the published version of the manuscript. Data 2021,6, 62 18 of 20 Funding: This work was supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreement No. 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints). Informed Consent Statement: Not applicable. Data Availability Statement: Dataset is available on GitHub: https://github.com/BUTResearch/ MDPI_Data_Urban_LPWA_Measurement (accessed on 30 May 2021). Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the study’s design; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Abbreviations The following abbreviations are used in this manuscript: ADR Adaptive Data Rate API Application Programming Interface AR Augmented Reality BLE Bluetooth Low Energy CRA Czechia national LoRaWAN provider CSS Chirp Spread Spectrum DL Downlink ED End Device EIRP Effective Isotropic Radiated Power EIRP Effective Isotropic Radiated Power ERP Effective Radiated Power FSK Frequency-Shift Keying GFSK Gaussian Frequency-Shift Keying GW Gateway H2M Human-to-Machine IEC International Electrotechnical Commission IEEE Institute of Electrical and Electronics Engineers IoT Internet of Things ISM Industrial, scientific, and medical LPWAN Low-Power Wide-Area Network LTE Long-Term Evolution M2M Machine-to-Machine MCL Maximum Coupling Loss mMTC massive Machine-Type Communications MR Mixed Reality NB-IoT Narrowband Internet of Things NFC Near-Field-Communication NS Network Server RF Radio Frequency RFID Radio-Frequency Identification RSSI Received Signal Strength Indicator RW Receive Window SF Spreading Factor SNR Signal-to-Noise Ratio UL Uplink VR Virtual Reality WiFi Wireless Fidelity Data 2021,6, 62 19 of 20 References 1. Silverio-Fernández, M.; Renukappa, S.; Suresh, S. What is a Smart Device?—A Conceptualisation within the Paradigm of the Internet of Things. Vis. Eng. 2018,6, 3. [CrossRef] 2. Stojkoska, B.L.R.; Trivodaliev, K.V. A Review of Internet of Things for Smart Home: Challenges and Solutions. J. Clean. Prod. 2017,140, 1454–1464. [CrossRef] 3. McKinsey and Company. Growing Opportunities in the Internet of Things. Available online: https://www.mckinsey.com/ industries/private-equity-and-principal-investors/our-insights/growing-opportunities-in-the-internet-of-things# (accessed on 30 May 2021 ). 4. Grand View Research. IoT Market Analysis By Component (Devices, Connectivity, IT Services, Platforms), By Application (Consumer Electronics, Retail, Manufacturing, Transportation, Healthcare) And Segment Forecasts To 2022. Available online: https://www.grandviewresearch.com/industry-analysis/iot-market (accessed on 30 May 2021). 5. Mozny, R.; Masek, P.; Stusek, M.; Mikulasek, M.; Ometov, A.; Hosek, J. Pitfalls of LPWA Power Consumption: Hands-On Design of Current Probe. In Proceedings of the 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), Online, 5–7 October 2020; pp. 13–19. 6. Ometov, A.; Daneshfar, N.; Hazmi, A.; Andreev, S.; Carpio, L.F.D.; Amin, P.; Torsner, J.; Koucheryavy, Y.; Valkama, M. SystemLevel Analysis of IEEE 802.11ah Technology for Unsaturated MTC Traffic. Int. J. Sens. Netw. 2018,26, 269–282. [CrossRef] 7. Solentive. Smart Devices—The Fastest Technology Adoption In History. Radio Versions. 2020. Available online: https: //www.solentive.com.au/smart-devices-the-fastest-technology-adoption-in-history/ (accessed on 30 May 2021). 8. HelpNetSecurity. Number of Connected Devices Reached 22 Billion, Where is the Revenue? 2019. Available online: https: //www.helpnetsecurity.com/2019/05/23/connected-devices-growth/ (accessed on 30 May 2021). 9. Top 10 Companies in Wearable Devices Market. Meticulous Research. 2019. Available online: https://meticulousblog.org/top10-companies-in-wearable-devices-market/ (accessed on 30 May 2021). 10. Ometov, A.; Shubina, V.; Klus, L.; Skibi´nska, J.; Saafi, S.; Pascacio, P.; Flueratoru, L.; Gaibor, D.Q.; Chukhno, N.; Chukhno, O.; et al. A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges. Comput. Netw. 2021 ,193, 108074. [CrossRef] 11. Ometov, A.; Kozyrev, D.; Rykov, V.; Andreev, S.; Gaidamaka, Y.; Koucheryavy, Y. Reliability-Centric Analysis of Offloaded Computation in Cooperative Wearable Applications. Wirel. Commun. Mob. Comput. 2017,2017, 9625687. [CrossRef] 12. Svertoka, E.; Rusu-Casandra, A.; Marghescu, I. State-of-the-Art of Industrial Wearables: A Systematic Review. In Proceedings of the 13th International Conference on Communications (COMM), Bucharest, Romania, 18–20 June 2020; pp. 411–415. 13. Wen, S.; Heidari, H.; Vilouras, A.; Dahiya, R. A Wearable Fabric-based RFID Skin Temperature Monitoring Patch. IEEE Sens. 2016, 1–3. [CrossRef] 14. Ghamari, M.; Arora, H.; Sherratt, R.S.; Harwin, W. Comparison of Low-Power Wireless Communication Technologies for Wearable Health-Monitoring Applications. In Proceedings of the International Conference on Computer, Communications, and Control Technology (I4CT), Kuching, Malaysia, 21–23 April 2015; pp. 1–6. [CrossRef] 15. Alliance, W. The Benefit of Wi-Fi Connectivity in Wearable Devices. Available online: https://www.wi-fi.org/ko/beacon/jaywhite/the-benefit-of-wi-fi-connectivity-in-wearable-devices (accessed on 30 May 2021). 16. Mozny, R.; Masek, P.; Stusek, M.; Zeman, K.; Ometov, A.; Hosek, J. On the Performance of Narrow-Band Internet of Things (NB-IoT) for Delay-Tolerant Services. In Proceedings of the 42nd International Conference on Telecommunications and Signal Processing (TSP), Budapest, Hungary, 1–3 July 2019; pp. 637–642. 17. Sun, H.; Zhang, Z.; Hu, R.Q.; Qian, Y. Wearable Communications in 5G: Challenges and Enabling Technologies. IEEE Veh. Technol. Mag. 2018,13, 100–109. [CrossRef] 18. LPWAN Revenue Forecast, 2017 to 2023. 2017. Available online: https://shop.maravedis-bwa.com/products/lpwan-revenueforecast-2017-to-2023 (accessed on 30 May 2021). 19. LoRa and Sigfox get Five-Year Boost with LTE-M and NB-IoT Failures. Enterprise IoT Insights. 2019. Available online: https://enterpriseiotinsights.com/20190606/channels/news/lora-and-sigfox-get-boost-with-lte-m-nb-iot-failures (accessed on 30 May 2021). 20. De Carvalho Silva, J.; Rodrigues, J.J.; Alberti, A.M.; Solic, P.; Aquino, A.L. LoRaWAN—A Low Power WAN Protocol for Internet of Things: A Review and Opportunities. In Proceedings of the 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), Split, Croatia, 12–14 July 2017; pp. 1–6. 21. Adelantado, F.; Vilajosana, X.; Tuset-Peiro, P.; Martinez, B.; Melia-Segui, J.; Watteyne, T. Understanding the Limits of LoRaWAN. IEEE Commun. Mag. 2017,55, 34–40. [CrossRef] 22. Pasolini, G.; Buratti, C.; Feltrin, L.; Zabini, F.; De Castro, C.; Verdone, R.; Andrisano, O. Smart City Pilot Projects Using LoRa and IEEE 802.15.4 Technologies. Sensors 2018,18, 1118. [CrossRef] [PubMed] 23. Miles, B.; Bourennane, E.B.; Boucherkha, S.; Chikhi, S. A Study of LoRaWAN Protocol Performance for IoT Applications in Smart Agriculture. Comput. Commun. 2020,164, 148–157. [CrossRef] 24. Codeluppi, G.; Cilfone, A.; Davoli, L.; Ferrari, G. LoRaFarM: A LoRaWAN-based Smart Farming Modular IoT Architecture. Sensors 2020,20, 2028. [CrossRef] [PubMed] 25. LoRaWAN Market Projected to Grow by 47 Percent. GPS World. 2020. Available online: https://www.gpsworld.com/lorawanmarket-projected-to-grow-by-47-percent/ (accessed on 30 May 2021). Data 2021,6, 62 20 of 20 26. Almeida, R.; Oliveira, R.; Sousa, D.; Luis, M.; Senna, C.; Sargento, S. A Multi-Technology Opportunistic Platform for Environmental Data Gathering on Smart Cities. In Proceedings of the IEEE Globecom Workshops (GC Wkshps), Singapore, 4–8 December 2017; pp. 1–7. 27. Baldo, D.; Mecocci, A.; Parrino, S.; Peruzzi, G.; Pozzebon, A. A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management. Sensors 2021,21, 2600. [CrossRef] [PubMed] 28. Islam, B.; Islam, M.T.; Kaur, J.; Nirjon, S. LoRain: Making a Case for LoRa in Indoor Localization. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan, 11–15 March 2019; pp. 423–426. 29. Ertürk, M.A.; Aydın, M.A.; Büyükakka¸slar, M.T.; Evirgen, H. A Survey on LoRaWAN Architecture, Protocol and Technologies. Future Internet 2019,11, 216. [CrossRef] 30. Cardell-Oliver, R.; Hübner, C.; Leopold, M.; Beringer, J. Dataset: LoRa Underground Farm Sensor Network. In Proceedings of the 2nd Workshop on Data Acquisition to Analysis, New York, NY, USA, 10 November 2019; pp. 26–28. 31. Bhatia, L.; Breza, M.; Marfievici, R.; McCann, J.A. Dataset: LoED: The LoRaWAN at the Edge Dataset. arXiv 2020 , arXiv:2010.14211. 32. Aernouts, M.; Berkvens, R.; Van Vlaenderen, K.; Weyn, M. Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas. Data 2018,3, 13. [CrossRef] 33. Mikhaylov, K.; Stusek, M.; Masek, P.; Fujdiak, R.; Mozny, R.; Andreev, S.; Hosek, J. On the Performance of Multi-Gateway LoRaWAN Deployments: An Experimental Study. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea, 25–28 May 2020; pp. 1–6. 34. Laya, A.; Kalalas, C.; Vazquez-Gallego, F.; Alonso, L.; Alonso-Zarate, J. Goodbye, ALOHA! IEEE Access 2016 ,4, 2029–2044. [CrossRef] 35. Augustin, A.; Yi, J.; Clausen, T.; Townsley, W.M. A Study of LoRa: Long Range & Low Power Networks for the Internet of Things. Sensors 2016,16, 1466. 36. Reynders, B.; Meert, W.; Pollin, S. Range and Coexistence Analysis of Long Range Unlicensed Communication. In Proceedings of the 23rd International Conference on Telecommunications (ICT), Thessaloniki, Greece, 16–18 May 2016; pp. 1–6. [CrossRef] 37. LoRa Alliance. LoRaWAN 1.1 Specification. 2017. Available online: https://lora-alliance.org/wp-content/uploads/2020/11/ lorawantm_specification_-v1.1.pdf (accessed on 30 May 2021). 38. Loy, M.; Karingattil, R.; Williams, L. ISM-Band and Short Range Device Regulatory Compliance Overview; Texas Instruments: Dallas, TX, USA, 2005; pp. 5–13. 39. LoRa Alliance. LoRaWAN Regional Parameters, 2020. Available online: https://lora-alliance.org/sites/default/files/2020-02/ rp_2-1.0.1.pdf (accessed on 30 May 2021). 40. Georgiou, O.; Raza, U. Low Power Wide Area Network Analysis: Can LoRa Scale? IEEE Wirel. Commun. Lett. 2017 ,6, 162–165. [CrossRef] 41. Rizzi, M.; Ferrari, P.; Flammini, A.; Sisinni, E. Evaluation of the IoT LoRaWAN Solution for Distributed Measurement Applications. IEEE Trans. Instrum. Meas. 2017,66, 3340–3349. [CrossRef] 42. ERC Recommendation 70-03. 2020. Available online: https://docdb.cept.org/download/25c41779-cd6e/Rec7003e.pdf (accessed on 30 May 2021). 43. LoRaWAN Network Server 6.x—Uplink Data Message. Available online: https://docs.loriot.io/display/LNS/Uplink+Data+ Message (accessed on 30 May 2021). 44. SX1301 Datasheet. 2017. Available online: https://sforce.co/3dPFtby (accessed on 30 May 2021). 45. Keysight Technologies. N6700 Modular Power System Family—Data Sheet. Available online: https://www.keysight.com/fi/ en/assets/7018-01522/data-sheets/5989-6319.pdf (accessed on 30 May 2021).