This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

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MATLAB scripts to generate the Markov models of three-level and four-level ANPC legs and compute their mean time to failure from these models.

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Many real-world systems can be modeled by multistate flow networks (MFNs) and their reliability evaluation features in designing and control of these systems. Considering the cost constraint makes the problem of reliability evaluation of an MFN more realistic. For a given demand value $d$ and a given cost limit $c$, the reliability of an MFN at level $(d, c)$ is the probability of transmitting at least $d$ units from the source node to the sink node through the network within the cost of $c$.  This article addresses this so-called $(d, c)$-MC problem in terms of minimal cuts.

Instructions: 

The data set contains two folders: 'MCs lists' and 'd_c_MCs lists', and two additional files Params.csv and t_aux.csv.

The folder 'MCs lists' contains 7 files in the csv format. Each of these files the list of all minimal cuts for appropriate multistate flow network (MFN) from figures 2-6. The arcs in networks 1,2,3,5,6,7 are ordered firstly according to the order of their beginning nodes and secondly on the order of their ending nodes. Only in the network Id 4 from Fig. 3 the order of the arcs is another, and this order is visible in Fig. 3.

The folder 'd_c_MCs lists' contains 126 files in the csv format. Each of these files has the name of the form i_j_(d,c)-MCs, where i denotes the MFN ID, j - the number of the variant of the greatest state vector, d - demand level, and c- cost constraint. Each of these files contains all (d,c)-MCs determined for given MFN ID - i, and the greatest state vector of the number of the variant - j.

The file 'Params.csv' contains experimental results of numerical experiments conducted on a computer with Intel(R) Core(TM) i7-8750H CPU and 16 GB of RAM.The first column contains MFN ID; the second - the number of the variant of the greatest state vector;the third - demand level d; the fourth - the total budget c;the fifth - the number of elements of set Q_1 defined by (20) in Lemma 8 in the related article;the sixth - the number of elements of set Q_2 defined by (21) in Lemma 8 in the related article; the seventh - the number of all (d,c)-MCs; the eight - the mean computational time T_{FK} of the algorithm described in the article M. Forghani-elahabad, N. Kagan, Assessing Reliability of Multistate Flow Networks Under Cost Constraint in Terms of Minimal Cuts Int. J. Reliab. Qual. Saf. Eng.2019,26, 1950025; the ninth - the mean computational time T of the presented algorithm;the tenth - the ratio T_{FK}/T.

The file 't_aux' contains the means of the computational times of the introductory algorithm.

Computational times in columns 8 and 9 in file 'Params.csv' and in file 't_aux' are expressed in nanoseconds and are results of the use of the microbenchmark procedure from the microbenchmark library implemented in R.

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To protect the equipment in a building struck directly by lightning, surge protective devices (SPDs) are installed between the N-phase and ground lines at the power utility substation of a TT system. In this case, when operating the SPD, the wiring system of the low-voltage distribution line will be a TN system. Although the overvoltage between these lines at the power utility substation can be limited during SPD operation, an overvoltage between these lines is induced at each floor distribution board connected thereafter.

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This dataset was prepared to estimate the winding temperature of a BLDC motor for a variable load and speed profile. It contains two files. The first one is the measurement results for the motor without cooling, while the second one is the measurement results after installing an additional cooling fan on the shaft. The data included in the files are time stamp, winding temperature, casing temperature, speed, current, power loss, mean and standard deviation of the measured quantities for 14400 data records.

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Distribution test systems with 24, 54, 86, and 136 nodes, which can be used for distribution system reliability evaluation. All these Networks are medium-voltage (MV) distribution grids.

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This dataset was curated for outage management in electricity networks. It is based on a modification of the IEEE 24 bus reliability test system network. The modified network comprises 10 generators, 50 buses, and 40 load zones; supplying 270,000 customers. For illustrative purposes, the network is assumed to span the region between longitudes 13.2989°W and 12.9133°W and latitudes 8.1712°N and 8.4998°N. This region happens to be located in Western Sierra Leone and covers an area of approximately 681 square kilometres.

The dataset comprises:

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Acoustic measurement data from Multilayer Ceramic Capacitors (MLCCs). Contains preprocessed data from intact and damaged MLCCs for damage detection (classification) purposes.

Instructions: 

Contains acoustic measurement data from 180 multilayer ceramic capacitors (2220 case size, 22 uF, 24V), soldered onto two test circuit boards. The measurements were performed by placing a piezoelectric point contact sensor on top of each capacitor, and subjecting the MLCC to a voltage frequency sweep from 100 Hz to 2 MHz over a duration of 100 ms. The resulting acoustic waveforms have been denoised, bandpass filtered, and downsampled. Furthermore, instantaneous phase response was calculated for each MLCC.

The dataset contains measurements from both intact and mechanically damaged components for quality assurance purposes (classification task). The acoustic signature of each MLCC is represented by an eight-dimensional feature vectror in the file inputs.mat:

  1. Acoustic emission amplitude at the highest resonace peak
  2. Frequency of the highest resonance peak
  3. Amplitude of the second-highest resonance peak
  4. Frequency of the second-highest resonance peak
  5. Total phase shift during frequency sweep
  6. Median amplitude of 10 of the highest resonance peaks
  7. Median frequency of 10 of the highest resonance peaks
  8. Mean group delay ripple calculated from the phase response of each component

The labels (0=no damage; 1=damage) for each component are found in targets.mat. Note that the labelling process was done by cross-sectioning each component and inspecting the sample visually under a microscope. Therefore, the labels may not be completely accurate, as the signs of damage can be difficult to observe.

 

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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.

Instructions: 

Wireless Sensor Networks Dataset (TSCH a Compromise Between Reliability, Power Consumption, and Latency)

Abstract

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 (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 [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)

Database

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:

    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 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 TSCHStats.py 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 TSCHStats.py <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 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 TSCHStats.py 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.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".

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