Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0.

Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyber-physical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) requirements in the smart grid. In this context, this paper describes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assignment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid.

Specifications Table

Value of the Data

The data provided in this paper provides can be used for efficient monitoring and control of the power generation and distribution processes in the smart grid.

The data provided in this paper can be used for the integration of distributed power generation sources into the power transmission and distribution systems within realistic network scenarios.

It can also support reliable and dynamic data capacity requirements of different types of advanced cyber-physical systems equipped with sensors and devices to operate them optimally, either manual or automatic controls, and provide information about their operations to the utilities.

In case of faults, the designed scheme intelligently monitoring and identifies the faulty systems located in a remote position and notifies the user in real-time, so that appropriate actions can be taken to supply steady electricity to the customers.

Data Description

The dataset provided in this paper offers valuable information for efficient monitoring and control of the power generation and distribution processes in the smart grid. The advantage of these data is to provide intelligently monitoring and identifies the faulty systems located in the remote positions to notify the user in real-time so that appropriate actions can be taken to supply steady electricity to the customers. The data provided in this article were gathered using multichannel wireless sensor nodes located at remote locations in an outdoor power generation and distribution centers in the smart grid. In the smart grid, each node by following an event-driven or query-based information gathering model monitors the surrounding, collaborates with each other, and reports the sensed data to the sink. The user using IoS via IoT can directly monitor, control, and configure any deployed sensor node through the base station and the sink as shown in Fig. 1 [1].

Fig. 1

A view of the network model in the smart grid.

In Fig. 1, the black colored icons are the wireless sensor nodes. The unique number on the right side of each sensor node shows the identity in the network. The device equipped with dual antennas on the right side of the deployed network is the sink while the pole like icon is the BS. The orange-colored thick multiple lines generate the same inference level, such as systems, subsystems, and electric poles in the SG. The thin orange-colored lines on the left and right sides defined the network boundary. The blue-colored circular line shows the sink range for message transmission and reception in the network. The black line between the sink and the base station and the base station to the user shows the highly stable bi-directional communication links in the network. The cloud-like icon indicates the network is either a LAN, NAN, or WAN.

Table 1 and Table 2 present the data of the probability of channel detection and the probability of false alarms in the MWSNs. Fig. 2 portrays the trends of both probabilities of channel detection and false alarms in the MWSNs. Table 3 describes the data values of the probability of missed-detection in the MWSNs. Fig. 3 presents the trends of the probability of channel detection and the probability of missed-detection in the MWSNs. Table 4 describes the packet delivery ratio data values while the graph in Fig. 4 presents the trends of packet delivery ratio in the MWSNs. Table 5 describes the latency data values in the MWSNs. Fig. 5 presents the trends of latency in the MWSNs. Table 6 describes the packet error rate data values while the graph in Fig. 6 shows the trends of the packet error rate in the MWSNs. Finally, Table 7 shows the congestion management data values and Fig. 7 presents the trends of congestion management values in the MWSNs.

Table 1

The probability of channel detection values in MWSNs.

No. of rounds	Probability of channel detection values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
100	0.9250		0.8550		0.7880
200	0.9280		0.8680		0.7780
300	0.9190		0.8300		0.7630
400	0.9300		0.8390		0.7570
500	0.9190		0.8220		0.7480
600	0.9180		0.8310		0.7290
700	0.9240		0.8610		0.7250
800	0.9320		0.8990		0.7610
900	0.9350		0.8400		0.7390
1000	0.9330		0.8580		0.7470
1100	0.9290		0.8590		0.7710
1200	0.9190		0.8300		0.7390
1300	0.9390		0.8290		0.7710
1400	0.9190		0.8320		0.7480
1500	0.9180	93.6%	0.8510	85%	0.7290	76%
1600	0.9240		0.8610		0.7250
1700	0.9290		0.8490		0.7610
1800	0.9390		0.8500		0.7790
1900	0.9310		0.8480		0.7770
2000	0.9320		0.8690		0.7810
2100	0.9300		0.8300		0.7690
2200	0.9310		0.8490		0.7810
2300	0.9300		0.8500		0.7590
2400	0.9280		0.8580		0.7470
2500	0.9220		0.8720		0.7590
2600	0.9290		0.8790		0.7510
2700	0.9390		0.8600		0.7390
2800	0.9280		0.8580		0.7470
2900	0.9280		0.8680		0.7470
3000	0.9320		0.8420		0.7590

Table 2

The probability of missed-detection values in MWSNs.

No. of rounds	Probability of missed-detection values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
100	0.3380		0.5280		0.9050
200	0.3290		0.5210		0.9040
300	0.3340		0.5180		0.9240
400	0.3990		0.5600		0.9110
500	0.3160		0.5710		0.9020
600	0.3150		0.5350		0.9080
700	0.3250		0.5800		0.8950
800	0.3340		0.5780		0.8970
900	3.2980		0.5670		0.9000
1000	0.3980		0.5600		0.9100
1100	0.3040		0.5480		0.9170
1200	0.3290		0.5670		0.9090
1300	0.3040		0.5480		0.9190
1400	0.3160		0.5490		0.9180
1500	0.2990	3.3%	0.5550	5.5%	0.9180	9%
1600	0.3280		0.5400		0.9080
1700	0.3440		0.5380		0.9000
1800	0.3190		0.5570		0.9110
1900	0.3110		0.5500		0.8910
2000	0.3240		0.5380		0.8990
2100	0.3290		0.5470		0.9050
2200	0.3340		0.5380		0.9090
2300	0.3390		0.5670		0.9950
2400	0.3280		0.5400		0.8900
2500	0.3290		0.5300		0.9000
2600	0.3340		0.5680		0.9090
2700	0.3390		0.5620		0.8970
2800	0.3380		0.5600		0.9020
2900	0.3310		0.5500		0.9100
3000	0.3300		0.5530		0.9140

Fig. 2

The probability of false alarms and probability of detection.

Table 3

The probability of false alarm values in MWSNs.

No. of rounds	Probability of false alarms values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
100	0.3110		0.9710		0.1470
200	0.2370		0.8610		0.1530
300	0.3360		0.8580		0.1670
400	0.3420		0.9930		0.1530
500	0.3350		0.8510		0.1770
600	0.3380		0.9430		0.1270
700	0.2430		0.8480		0.1380
800	0.2460		0.8890		0.1490
900	0.3390		0.9930		0.1850
1000	0.2370		0.8710		0.1540
1100	0.3460		0.7890		0.1470
1200	0.2390		0.7950		0.1350
1300	0.3460		0.8810		0.1490
1400	0.3350		0.7510		0.1610
1500	0.3380	3.1%	0.8460	9.5%	0.1760	15%
1600	0.2430		0.8480		0.1420
1700	0.3460		0.9860		0.1490
1800	0.2390		0.8910		0.1350
1900	0.3370		0.9740		0.1530
2000	0.3460		0.7890		0.1490
2100	0.3390		0.8950		0.1350
2200	0.3460		0.7850		0.1480
2300	0.3390		0.8950		0.1350
2400	0.2370		0.9740		0.1540
2500	0.3400		0.9690		0.1440
2600	0.3460		0.8830		0.1490
2700	0.3390		0.8950		0.1350
2800	0.3370		0.9740		0.1540
2900	0.2370		0.9740		0.1530
3000	0.2400		0.8610		0.8400

Fig. 3

The probability of missed-detection and probability of detection.

Table 4

The packet delivery ratio values in MWSNs.

No. of rounds	Packet delivery ratio values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
100	0.9830		0.8910		0.8630
200	0.9850		0.8910		0.8540
300	0.9900		0.8940		0.8560
400	0.9900		0.8860		0.8440
500	0.9910		0.8910		0.8460
600	0.9890		0.8980		0.8450
700	0.9970		0.8970		0.8490
800	0.9960		0.9200		0.8460
900	0.9950		0.9290		0.8530
1000	0.9930		0.8970		0.8570
1100	0.9960		0.9160		0.8560
1200	0.9890		0.9290		0.8520
1300	0.9970		0.8920		0.8450
1400	0.9920		0.8940		0.8540
1500	0.9930	99.5%	0.8920	92%	0.8560	86.7%
1600	0.9930		0.9000		0.8540
1700	0.9940		0.9060		0.8610
1800	0.9900		0.9090		0.8600
1900	0.9940		0.9130		0.8680
2000	0.9940		0.9110		0.8690
2100	0.9930		0.9090		0.8390
2200	0.9900		0.8900		0.8650
2300	0.9910		0.9280		0.8490
2400	0.9920		0.9250		0.8630
2500	0.9910		0.9030		0.8680
2600	0.9930		0.8900		0.8600
2700	0.9930		0.9000		0.8620
2800	0.9970		0.9210		0.8600
2900	0.9950		0.9210		0.8630
3000	0.9950		0.9220		0.8680

Fig. 4

The packet delivery ratio vs number of rounds between 1 and 3000.

Table 5

The latency values in MWSNs.

No. of nodes	Latency values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
10	0.3000		0.3200		0.4900
20	0.4500		0.6800		0.5400
30	0.5700		0.8800		0.7100
40	0.6400		0.1400		0.8000
50	0.7500	77.5%	0.1600	201.8%	0.9900	140.7%
60	0.8700		0.1970		0.1120
70	0.9500		0.2560		0.1390
80	0.9900		0.2630		0.1050
90	1.0800		0.2890		0.1910
100	1.1500		0.3010		0.2100
110	0.1400		0.3180		0.2270
120	0.1800		0.3290		0.2410
130	0.1980		0.3450		0.2720
140	0.2100		0.3590		0.2980
150	0.2200	226.7%	0.3730	418.20%	0.3200	379.54%
160	0.2230		0.3810		0.3350
170	0.2260		0.4390		0.3490
180	0.2600		0.4620		0.3680
190	0.2900		0.4770		0.3810
200	0.3200		0.4910		0.3870
210	0.3240		0.4990		0.3990
220	0.3300		0.5420		0.4200
230	0.3410		0.5710		0.4620
240	0.3640		0.5800		0.4750
250	0.3800	398.7%	0.6077	543.6%	0.4990	479.32%
260	0.3970		0.6130		0.5340
270	0.4370		0.6380		0.5470
280	0.4630		0.6690		0.5630
290	0.4710		0.6888		0.5820
300	0.4800		0.6940		0.5980

Fig. 5

The network delay vs number of sensor nodes between 1 and 300.

Table 6

The packet error rate values in MWSNs.

No. of nodes	Packet error rate values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
10	0.0100		0.0500		0.0490
20	0.0900		0.4250		0.2480
30	0.1800		0.3180		0.0680
40	0.1600		0.5100		0.0470
50	0.0600	1.1%	0.3890	3.88%	0.0670	1.8%
60	0.1200		0.3870		0.2890
70	0.1500		0.3860		0.1990
80	0.1300		0.3850		0.3850
90	0.0940		0.4990		0.3710
100	0.0530		0.5300		0.0780
110	0.2280		0.6080		0.3470
120	0.2150		0.7690		0.3510
130	0.2170		0.8800		0.4220
140	0.1700		0.9020		0.5080
150	0.1850	1.89%	0.9310	9.3%	0.6890	6.8%
160	0.1600		0.9810		0.7990
170	0.1800		1.2900		0.8710
180	0.1700		0.9020		0.9400
190	0.1800		0.9310		0.9290
200	0.1900		1.1810		0.8980
210	0.2790		0.8999		0.5910
220	0.2590		0.9380		0.8700
230	0.3310		1.3030		0.8820
240	0.3440		1.3270		0.9750
250	0.1660	2.8%	1.3180	12.6%	0.9710	9.3%
260	0.2990		1.2991		0.7990
270	0.2870		1.3180		1.2170
280	0.2590		1.2990		1.1110
290	0.2790		1.4370		0.9830
300	0.2850		1.4390		0.9920

Fig. 6

The packet error rate vs number of nodes between 1 and 300.

Table 7

The congestion management values in MWSNs.

No. of nodes	Congestion management values
Protocols	CARP	Avg. (≅)	G-RPL	Avg. (≅)	EQSHC	Avg. (≅)
10	0.9950		0.9700		0.9900
20	0.9940		0.9650		0.9870
30	0.9910		0.9560		0.9850
40	0.9900		0.9480		0.9810
50	0.9850	98.07%	0.9450	94.45%	0.9780	97.06%
60	0.9830		0.9430		0.9750
70	0.9770		0.9350		0.9630
80	0.9700		0.9300		0.9600
90	0.9660		0.9290		0.9560
100	0.9560		0.9240		0.9310
110	0.9510		0.9200		0.9180
120	0.9460		0.9160		0.9060
130	0.9300		0.9090		0.8970
140	0.9300		0.8940		0.8850
150	0.9250	93.02%	0.8900	89.25%	0.8800	87.99%
160	0.9240		0.8860		0.8780
170	0.9260		0.8820		0.8760
180	0.9240		0.8800		0.8650
190	0.9220		0.8750		0.8530
200	0.9240		0.8730		0.8410
210	0.9230		0.8710		0.8360
220	0.9230		0.8720		0.8250
230	0.9230		0.8700		0.8200
240	0.9210		0.8660		0.8190
250	0.9230	92.20%	0.8560	84.59%	0.8110	81.66%
260	0.9240		0.8490		0.8030
270	0.9220		0.8300		0.7990
280	0.9210		0.8260		0.7880
290	0.9200		0.8190		0.8850
300	0.9202		0.8000		0.7800

Fig. 7

The congestion management vs node density between 1 and 300.

Experimental Design, Materials and Methods

In this study, we consider a 550 kV outdoor grid station with an area of 1100 (length)  ×  700 (width) meters containing 300 wireless sensors in the network. The grid contains power generation and distribution systems and subsystem, and electric poles with numbers 160 and 120, respectively. The initial energy of each wireless sensor is set to 5J in the MWSNs. In the MWSNs, each wireless sensor is embedded with physical layer standard IEEE 802.11g with a maximum communication range up to 85 m and data rates up to 256kbps. The IEEE 802.11g standard offers a total number of 12 channels in the 2.4GHz band, in which three, 1, 6, 11, are non-overlapping channels.

Consequently, each sensor is embedded with multiple radios and a single interface, where each radio at a given time serves as a receiver or a transmitter for the distinct channel, i.e., half-duplex mode. The number of available channels on each sensor is equal to the number of radios in MWSNs. Each sensor is equipped with a control channel as a default channel that is always in the receiving mode and can transmit control messages to its neighbors on-demand in a specific deployed area in the network. The Quadrature phase-shift keying (QPSK) modulation technique was assumed and the value of data packet size was set to 43 bytes in the network [3], [4], [5]. During the network operations, each wireless sensor observes the grid events and stores data in its memory of the maximum size of 2Mb. In the packet transmission process, the maximum value of energy consumed for transmitting with high and low power was set to 0.97W and 0.82W, while the energy consumed upon receiving data is set to 0.05W in the network.

The values of ideal listening and sleeping power were set to 0.023 W and 3 ×  10−6W, respectively. Finally, 53 sets of simulations were performed to provide consistent results of the proposed scheme against the existing schemes in the network. The widely used simulation parameters and their values used in our study are given in Table 8 [6], [7], [8], [9], [10].

Table 8

Simulation parameters and values.

Simulation Model Parameters	Values
Wireless sensors	300
Physical layer standard	802.11g
Frequency	2.412GHz to 2.484GHz
Number of channels	12
Non-overlapping channels	1,6,11
Initial sensor node energy	5J
High transmission power	0.97W
Low transmission power	0.82W
Packet receiving power	0.05W
Ideal listening	0.023W
Sleeping power	3×10^−6W
Data aggregation	0.019W
Packet length	43bytes
Data transfer rate	256 kbps
Cache	2Mb
Maximum hop distance	85m
Maximum communication range of the sink	150m
Topology	Random
Antenna	Omni-directional
Path loss exponent for the line of sight and non-line-of-sight	2.4, 3.5
The noise floor for the line of sight and non-line-of-sight	–83, –91
Shadowing deviation for the line of sight and non-line-of-sight	3.12, 2.92
Systems, subsystems, and poles in the grid	160, 120
Area: 2D (length×width)	1100 × 700m
Simulation time	120 sec
Set of simulations	53

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Subject	Computer Networks and Communication, Engineering.
Specific subject area	MWSNs communication in the smart grid
Type of data	Tables and Graphs
How data were acquired	Data was captured using sensors in the 500kV outdoor power grid station
Data format	Raw and analysed sensor data in the smart grid
Description of data collection	The data were gathered using sensors in the smart grid environment containing various systems or subsystems and electric poles with values 160 and 120, respectively. In order to gather data in different scenarios, random topologies were considered within the smart grid environment. In the meanwhile, a static sink was deployed near the sensors to collect real-time data in the smart grid. The remote user can access and configure each sensor by connecting to the sink and the base station using wired or wireless intranet and internet communication technologies.
Parameters for data collection	The data were collected during the day using 300 sensors, each of them equipped with physical layer standard 802.11g, the frequency range between 2.412GHz and 2.484GHz with random topology in the power grid.
Data source location	City/Town/Region: Kayseri, Country: Turkey.
Related research article	The updated data is related to the research article presented in [1].
Data accessibility	Data is provided within this article and,Data Repository name: MendeleyDirect URL to data: https://dx.doi.org/10.17632/32d6r6r6zk.1