Supplementary material for the article "A Sensor Network Utilizing Consumer Wearables for Telerehabilitation of Post-acute COVID-19 Patients": (1) TERESA (TEleREhabilitation Self-training Assistant) back-end application API documentation and (2) anonymous details of the Wristband protocols used in the study.


In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink. We investigate the benefits of the UAV-IRS on the internet of things (IoT) networks that improve the freshness of collected data of the IoT devices via optimizing power, sub-carrier, and trajectory variables, as well as, the phase shift matrix elements.


Abstract—In the 2021 and later we know that the technology

will have key participation of to help us in all kind of tasks

mainly using internet connection, due the new normality.

Industry 4.0 has been one of the most relevant field. IoT as part

of it. This Systematic Literature Review (SLR) we will cover

the South America countries and their development status,

addressing the development categories and the Hardware that

has been cited on papers on the last 5 years.


The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.


The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established.


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 [1], 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.

Openmote b+

A Python script ( 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 [1].

Table 6

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 ACK):

  • Default: default configuration of the two parameters as per OpenWSN version REL-1.24.0.
  • High Reliability: the likelihood that a frame is delivered to destination is improved by increasing N_tries.
  • 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 [1]).

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 [1].

The power consumption model employed in the script was obtained by means of measurements performed on real OpenMote B devices (details are reported in [1].

Main Targets

We decided to publish this database with two main targets in mind:

  1. 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 [1]). 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.
  2. provide the post-analysis script and some notions about our experience on the measurement technique based on the ping tool. The ping command 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 [1].

Directory structure

The database has the following directory structure:

        |-- 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 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 default-101-16-24h_1.dat, high_reliability-101-24-24h.dat, low_latency-11-3-24h.dat, and 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_1.dat to 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.

Data analysis

A Python script named was specifically included in the database folder to obtain the results reported in Table 6 in [1]. In some conditions, results obtained by the script are slightly different if compared with those of Table 6 in [1], because some rounding operations were performed.

The script needs to be invoked with the following input arguments:

python3 <ping_file> <N_slots> <N_tries> <T_app>

where <ping_file> is a database file (for instance the default-101-16-24h_1.dat file), <N_slots> is the number of slots in a slotframe (101 for default-101-16-24h_1.dat), <N_tries> is the maximum number of allowed transmission attempts per frame (16 for default-101-16-24h_1.dat), and <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 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 script.


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.


The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.


1. The "main.m" in Matlab code is the entry of simulation.
2. The "csv2mat" is a CPP program to convert raw records ( of our gr-adsb into matlab manipulatable format. Matio library ( is required.
3. The Gnuradio flowgraph is also provided with the enhanced version of gr-adsb, in which you are supposed to replace the original one ( And, you can specify an output file path in the property panel of the ADS-B decoder submodule.
4. Related publication: Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices, IEEE IoTJ (accepted for publication on 21 August 2020), DOI: 10.1109/JIOT.2020.3018677


We created various types of network attacks in Internet of Things (IoT) environment for academic purpose. Two typical smart home devices -- SKT NUGU (NU 100) and EZVIZ Wi-Fi Camera (C2C Mini O Plus 1080P) -- were used. All devices, including some laptops or smart phones, were connected to the same wireless network. The dataset consists of 42 raw network packet files (pcap) at different time points.

* The packet files are captured by using monitor mode of wireless network adapter. The wireless headers are removed by Aircrack-ng.


The dataset consists of 42 raw network packet files (pcap) at different time points.

* The packet files are captured by using monitor mode of wireless network adapter. The wireless headers are removed by Aircrack-ng.

* All attacks except Mirai Botnet category are the packets captured while simulating attacks using tools such as Nmap. The case of the Mirai Botnet category, the attack packets were generated on a laptop and then manipulated to make it appear as if it originated from the IoT device.


<packet file description>

benign-dec.pcap: benign-only traffic

mitm-arpspoofing-n(1~6)-dec.pcap: traffic containing benign and MITM(arp spoofing)

dos-synflooding-n(1~6)-dec.pcap: traffic containing benign and DoS(SYN flooding) attack

scan-hostport-n(1~6)-dec.pcap: traffic containing benign and Scan(host & port scan) attack

scan-portos-n(1~6)-dec.pcap: traffic containing benign and Scan(port & os scan) attack

mirai-udpflooding-n(1~4)-dec.pcap: traffic containing benign and 3 most typical attacks(UDP/ACK/HTTP Flooding) of zombie pc compromised by mirai malware



mirai-hostbruteforce-n(1~5)-dec.pcap: traffic containing benign and initial phase of Mirai malware including host discovery and Telnet brute-force attack


Database for FMCW THz radars (HR workspace) and sample code for federated learning