Open dataset from Machine Learning Repository of Center for Machine Learning and Intelligent Systems at the University of California, Irvine.
This repository hosts the data and code for studying the UAV positioning problem under obstructive environment. This study focuses on the scenario of the low-altitude UAV to ground communication in a dense urban environment. There could be a lot of local structure, such as buildings and trees, that blocks the communication signal. As a result, the UAV should be optimized to smartly explore a good propagation condition to communicate with the user. At the same time, the UAV also needs to balance the communication link with the BS.
This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:
- DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3
- Spektrum (4): DX5e, DX6e, DX6i, JR X9303
- Futaba (1): T8FG
- Graupner (1): MC32
- HobbyKing (1): HK-T6A
- FlySky (1): FS-T6
- Turnigy (1): 9X
- Jeti Duplex (1): DC-16.
In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms.
The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.
The drone RC RF dataset was used in the following papers:
- M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
- M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
- E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.
Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.
- Experiment design: O. Ozdemir and M. Ezuma
- Data collection: M. Ezuma
- Scripts: F. Erden and C. K. Anjinappa
- Documentation: F. Erden
- Supervision, revision, and funding: I. Guvenc
This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A, and in part by NSF under CNS-1939334 (AERPAW, one of NSF's Platforms for Advanced Wireless Research (PAWR) projects).
Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations.
Wireless Sensor Networks Dataset (TSCH a Compromise Between Reliability, Power Consumption, and Latency)
Performance of Wireless Sensor Networks (WSN) based on IEEE 802.15.4 and Time Slotted Channel Hopping (TSCH) has been shown to be mostly predictable in typical real-world operating conditions. This is especially true for performance indicators like reliability, power consumption, and latency. This article provides and describes a database (i.e., a set of data acquired with real devices deployed in a real environment) about measurements on OpenMote B devices, implementing the 6TiSCH protocol, made in different experimental configurations. A post-analysis Python script for calculating the above performance indicators from values stored in the database is additional provided. The results obtained by applying the script to the included database were published in , which contains more details than those reported in this short presentation of the dataset. Data and software are useful for two main reasons: on the one hand the dataset can be further processed to obtain new performance indices, so as to support, e.g., new ideas about possible protocol modifications; on the other hand, they constitute a simple yet effective example of measurement technique (based on the ping tool and on the accompanying script), which can be customized at will and reused to analyze the performance of other real TSCH installations.
Introduction and Main Targets
The data contained in this database/dataset were obtained from a real installation of OpenMote B devices communicating by means of 6TiSCH, in turn based on the Time Slotted Channel Hopping (TSCH) protocol. The network was composed of two motes, namely, a root mote and a leaf mote. The dataset was obtained by issuing a sequence of ping commands from the PC to which the root mote is connected to the leaf mote. The transmission pattern of ping packets is periodic with a period equal to 120 s.
A Python script (TSCHStats.py) is provided along with the database to calculate statistical indices related to reliability, power consumption, and latency. In particular, they were used to obtain the results reported in the following table, and in Table 6 that was published in .
A number of relevant, typical experimental conditions were identified and analyzed, each one characterized by distinct values for protocol parameters
N_slot (width of the slotframe, expressed as a number of slots) and
N_tries (maximum number of allowed transmission attempts per data frame, the sender understands that the frame has not reached the destination if it does not receive the related
- Default: default configuration of the two parameters as per OpenWSN version
- High Reliability: the likelihood that a frame is delivered to destination is improved by increasing
- Low Latency transmission latency is decreased at the expense of reliability and power consumption, both of which suffer a worsening.
- Low Power Consumption: power consumption is decreased at the expense of latency, which increases.
In all experimental conditions an additional random interfering load, obtained by means of four Wi-Fi adapters, was injected on the wireless medium (details are reported in ).
For the first 4 experiments the duration was set to 1 day (i.e., 720 samples), while for the “Default (15-days)” condition the duration was 15 days (i.e., 10800 samples). The latter experiment also includes the samples of the “Default” (1 day) condition, which are reported at the beginning of the dataset.
The table (and the post-analysis script provided with the database) reports statistics related to:
- Latency: minimum latency, mean latency, standard deviation of latency, 99-percentile of latency, maximum latency, and theoretical worst-case latency, in this order.
- Reliability: packet loss ratio, frame error probability, and packet delivery probability, in this order.
- Power Consumption: rate of frame exchanges (including retries), rate at which idle listening occurs, power consumption, in this order.
Other details about statistical indices and how they can be modelled with a mathematical formulation can be found in .
The power consumption model employed in the script was obtained by means of measurements performed on real OpenMote B devices (details are reported in .
We decided to publish this database with two main targets in mind:
- Publicly provide the database about our measurement campaigns, from which new performance indices related to TSCH neworks can be evaluated (in addition to those reported in ). Having it available can be used as the starting point to identify weaknesses of the current protocol (for instance, when the database highlights long delays or severe losses) and develop new ideas for possible improvements.
- provide the post-analysis script and some notions about our experience on the measurement technique based on the
pingcommand is a very simple means to inspect real TSCH installations from a quantitative point of view as well. In particular, the script can be used to analyze the behavior of the network in terms of three relevant performance indicators t (latency, reliability, and power consumption)
Acquisition and experimental environment
The two OpenMote B devices used to acquire the experimental data were installed with the OpenWSN (version REL-1.24.0) operating system. Operating from the PC on which the root mote was connected, the following ping command was issued to repeatedly query the leaf mote (every 120 s):
ping6 -s 30 -i 120 -c 720 bbbb:0:0:0:12:4b00:18e0:ba23 where
bbbb:0:0:0:12:4b00:18e0:ba23 represents the IPv6 address of the leaf mote, 30 is the message payload (in bytes), 120 is the period (in seconds), and 720 is the number of collected samples.
Experiments were performed by purposely injecting an additional interfering load to the usual background activity found on the wireless channel. This was obtained by means of 4 Wi-Fi interferers, which generate traffic on Wi-Fi channels 1, 5, 9, and 13, respectively. Details on the characteristic of this additional traffic pattern are given in Section V in .
The database has the following directory structure:
TSCH_db |-- TSCHStats.py |-- default-101-16-24h_1.dat |-- high_reliability-101-24-24h.dat |-- low_latency-11-3-24h.dat |-- low_power_consumption-201-16-24h.dat |-- 101-16-15_days |-- default-101-16-24h_1.dat |-- default-101-16-24h_2.dat |-- … |-- default-101-16-24h_15.dat |-- default-101-16-15days.dat
In the main folder (i.e.,
TSCH_db), the file named
TSCHStats.py contains the script used to perform the post analysis on the data files included in the database, which are characterized by the extension .dat. The files
low_power_consumption-201-16-24h.dat represent the conditions Default, High Reliability, Low Latency, and Low Power Consumption, respectively. Each of them consists of 720 samples.
Inside the subdirectory named
101-16-15_days, the 15 files (from
default-101-16-24h_15.dat) refer to 15 consecutive days during which the Default condition was analyzed. Each one of these files contains 720 samples. They were merged in a single file named
default-101-16-15days.dat, which contains 10800 samples.
Every database (for instance the
default-101-16-24h_1.dat file) is a text file, where samples are encoded as follows:
38 bytes from bbbb::12:4b00:18e0:b97c: icmp_seq=2 ttl=64 time=1365 ms
38 bytes from bbbb::12:4b00:18e0:b97c: icmp_seq=2 ttl=64 time=3381 ms (DUP!)
The second case (identified with the string “DUP!”) refers to a transmission which caused a duplicate. In particular, after the loss of an acknowledge frame (ACK), the sender node retransmits the data packet, as it is unaware that the frame was correctly delivered to destination. This is due to the fact that the current implementation of OpenWSN does not use the IEEE 802.15.4 MAC layer sequence numbers to discard duplicated frames at the MAC layer.
A Python script named TSCHStats.py was specifically included in the database folder to obtain the results reported in Table 6 in . In some conditions, results obtained by the script are slightly different if compared with those of Table 6 in , because some rounding operations were performed.
The script needs to be invoked with the following input arguments:
python3 TSCHStats.py <ping_file> <N_slots> <N_tries> <T_app>
<ping_file> is a database file (for instance the
<N_slots> is the number of slots in a slotframe (
<N_tries> is the maximum number of allowed transmission attempts per frame (
<T_app> is the sending period of ping packets (
120000 in all the proposed databases, which corresponds to 120 s). Consequently, to execute the script on
default-101-16-24h_1.dat the following command has to be typed:
python3 TSCHStats.py default-101-16-24h_1.dat 101 16 120000
Application to other TSCH networks
Similar datasets can be acquired on 6TiSCH OpenMote B nodes (or other kinds of WSN devices implementing TSCH), in different experimental environments (e.g., by varying the position of motes, and hence the distance between them), using the very same ping command exploited to obtain the databases analyzed in this work (i.e.,
ping6 -s 30 -i 120 -c 720 bbbb:0:0:0:12:4b00:18e0:ba23). In this way, new datasets can be acquired in different operating conditions, for which the expected performance of the network can be analyzed using the
S. Scanzio et al., "Wireless Sensor Networks and TSCH: A Compromise Between Reliability, Power Consumption, and Latency," in IEEE Access, vol. 8, pp. 167042-167058, 2020, doi: 10.1109/ACCESS.2020.3022434.
The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. Most of the earlier works in this area are based on computer vision. However, mostly these works are limited in providing real time solution for the detection activities. Therefore, we are currently working toward the Internet of Things (IoT) based solution for the human posture recognition.
Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.
Original SJC value test data for papers.
Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from period 01/07/2020 to 03/11/2020.