Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems

Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems is called ERT-CORE. It defines specific input parameters, i.e., system's workload, average request processing time and availability. Defined parameters reflect system's state and react on its changes. Based on these parameters system reliability evaluation is performed.


these files uploaded mainly used for supporting the research results displayed in paper.


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: In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.


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.


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.


The data set contains three folders: 'MCs lists', 'd_c_MCs lists', 'd_c_MCs lists2', and an additional file arr_tib.csv.

The folder 'MCs lists' contains 7 files in the csv format. Each of these files contains the list of all minimal cuts for an appropriate multistate flow network (MFN) from figures 2-4. 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 and the folder 'd_c_MCs lists2' contains 315 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 'arr_tib.csv' contains experimental results of numerical experiments conducted on a computer with Intel(R) Core(TM) i7-8750H CPU and 16 GB of RAM. This file is a result of the file 'Params_all.csv' containing the raw data from the numerical experiments, where computational times are expressed in nanoseconds.

Column 1 contains the consecutive numbers of cases;Column 2 'i' - MFN ID; Column 3 'j' - the number of the variant of the greatest state vector;Column 4 'm' - the number of edges in a given MFN;Column 5 'p' - the number of all MCS of this MFN;Column 6 'W' - coordinates of the greatest SSV W;Column 7 'd' - given level d of multistate minimal cuts; Column 8 'c' - the total budget c;Column 9 'q1/p' - the ratio of the number of elements of set Q_1 defined by (19) in Lemma 8 in the related article to p;Column 10 'q2/p' - the ratio of the number of elements of set Q_2 defined by (20) in Lemma 8 in the related article to p; Column 11 '#(d,c)-MCs' - the number of all (d,c)-MCs; Column 12 'T_F_K' - expressed in seconds 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; Column 13 'T_K' - expressed in seconds the mean computational time of the main algorithm from the presented algorithm;Column 14 'R" - the ratio T_F_K/T_K;Column 15 'T_aux' - expressed in seconds the mean computational time of the introductory algorithm from the presented algorithm;Column 16 'T_K_t' - expressed in seconds the total mean computational time of the presented algorithm;Column 17 'R_p' - the ratio T_F_K/T_K_t

Computational times in columns 12, 13, 15, and 16 in file 'arr_tib.csv' are expressed in seconds and are results of the use of the microbenchmark procedure from the microbenchmark library implemented in R.




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.


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.


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.


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:


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


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.