Big datasets of optical-wireless cyber-physical systems for optimizing manufacturing services in the internet of things-enabled industry 4.0.

The Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service performance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical-Wireless Sensor Networks (OWSNs) for optimizing service systems' performance in the electronics manufacturing Industry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data delivery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0.

Specifications Table

Value of the Data

The data presented in the article provides a fundamental building block of the next-generation Internet of things-enabled optical-wireless communication architectures for big data gathering in the electronics manufacturing Industry 4.0.

The published data will guide scientists for low-cost and energy efficient integration of different types of cyber-physical systems with varying data capacity requirements, and operate them optimally within realistic network scenarios in the electronics manufacturing Industry 4.0.

The data presented in the article will serve as a guide for readers for closely monitoring the assembly and manufacturing processes in real-time to minimize the faulty products and to boost the production process with lesser human interventions in the electronics manufacturing Industry 4.0.

The published data can be used as a benchmark problem by researchers interested in artificial intelligence-based network analysis of different types of manufacturing systems in the manufacturing Industry 4.0.

Data Description

Internet of things is an emerging domain that promises ubiquitous connection of various devices to the Internet in several industrial applications like e-health, manufacturing, logistics, and utilities [1], [2], [3]. However, the accuracy of obtaining the big data from the IoT-enabled OWSNs is very challenging due to moving objects, obstacles, line-of-sight, and non-line-of-sight issues in an electronics manufacturing Industry 4.0 [4], [5], [6], [7], [8]. The offered dataset in this article provides essential information for real-time observations of the electronics manufacturing process in an electronics manufacturing Industry 4.0. The offered datasets guide the researchers about how to identify the faulty systems placed in various positions. Thus, it allows the system monitoring and control personnel to take appropriate actions for improving the quality and quantity of the product to meet customer demands. The data offered in this article were collected using wireless and optical sensors placed in different positions on different electronics manufacturing and assembly systems in an indoor industrial environment. In the deployed network, each node is responsible to observe the surroundings and collaborates with the neighboring node to forward the sensed information to the cobot. Unlike the traditional sink, the cobot is an intelligent device that can learn from human actions and perform actions on demand. Therefore, the deployed optical-sensor network requires less human intervention in the monitoring and control processes.

Fig. 1 describes the network model deployed in Industry 4.0. In Fig. 1, the colored circle shape icons indicate the different types of sensor nodes, e.g., proximity sensor, level sensor, motion sensor, position sensor, etc. In particular, the red-colored circle icon is equipped with both wireless and optical line of sight characteristics compared to the reset of sensors, which only can communicate wirelessly in the network. The dotted circle shows the communication range of a sensor node embedded in the manufacturing systems for fault monitoring purposes. The blue-colored box icons show optical sensors equipped with multiple led in different lines of directions. The solid arrows and light black color dotted lines show the wireless and optical communication, respectively. The computer-like icon is a cobot (sink), which is equipped with optical sensors to communicate with the rest of the deployed network. The cloud-like icon indicates the Internet with different types of networks. Consequently, the cobot is equipped with 5G communication technology to communicate with the Internet. Consequently, a remote user using Internet of Services (IoS) and IoT such as 5G bi-directional communication links can interact with the deployed network to directly configure, monitor, control, and configure the network.

Fig. 1

A view of the network model in an electronics manufacturing Industry 4.0 [3].

Table 1 describes the datasets related to the ratio of data delivery in OWSNs. It clearly shows that the data delivery ratio (DDR) of OWRP in the initial rounds between 1 and 1000 is high around 99.95% compared to 93.15% in CARP. However, the DDR value of OWRP is decreasing from 99.15%, 99.81%, 99.83%, 99.59 %, and to 99.25% when the round numbers are between 2000 and 5000 in the network. However, the datasets show that the value of DDR is decreasing rapidly from 93.14%, 93.46%, 92.73%, 93.06%, and to 91.68% in CARP compared to the OWRP scheme in the network. On the other hand, the DDR value of DCFBR is reducing up to 90.93%, 88.83%, 87.26%, 85.50%, 85.69%, and 84.60% between round numbers 100 and 1000, 1001 and 2000, 2001 and 3000, 3001 and 4000, 4001 and 5000, and 5001 and 5500, respectively, in the network. The average of obtained PDR big datasets graphically is shown in Fig. 2.

Table 1

Datasets for packet delivery ratio in OWSNs.

No. of rounds	Packet delivery ratio values
Protocols	OWRP	Avg. ≅ (%)	CARP	Avg. ≅ (%)	DCFBR	Avg. ≅ (%)
100	0.009995		0. 009315		0. 009028
200	0.009989		0. 009387		0. 009276
300	0. 009989		0. 009344		0. 009295
400	0. 009988		0. 009369		0. 009168
500	0. 009966	0. 009978	0. 009361	0. 009361	0. 009068	0. 009093
600	0. 009990		0. 009282		0. 009077
700	0. 009959		0. 009477		0. 009133
800	0. 009997		0. 009206		0. 009074
900	0. 009959		0. 009397		0. 008906
1000	0. 009948		0. 009472		0. 008903
1100	0. 009969		0. 008968		0. 008916
1200	0. 009897		0. 009096		0. 008995
1300	0. 009992		0. 009292		0. 008915
1400	0. 009950		0. 009248		0. 008994
1500	0. 009880	0. 009962	0. 009429	0. 009315	0. 008849	0. 008883
1600	0. 009998		0. 009365		0. 008878
1700	0. 009973		0. 009467		0. 008837
1800	0. 009997		0. 009191		0. 008893
1900	0. 009978		0. 009570		0. 008776
2000	0. 009987		0. 009519		0. 008776
2100	0. 009988		0. 009490		0. 008741
2200	0. 009991		0. 009247		0. 008774
2300	0. 009993		0. 009389		0. 008779
2400	0. 009993		0. 009358		0. 008797
2500	0. 009983	0. 009981	0. 009334	0. 009346	0. 008696	0. 008726
2600	0. 009998		0. 009487		0. 008781
2700	0. 009997		0. 009406		0. 008702
2800	0. 009981		0. 009310		0. 008697
2900	0. 009981		0. 009115		0. 008616
3000	0. 009995		0. 009325		0. 008674
3100	0. 009989		0. 009372		0. 008679
3200	0. 009989		0. 009399		0. 008699
3300	0. 009979		0. 009299		0. 008694
3400	0. 009987		0. 008979		0. 008685
3500	0. 009988	0. 009983	0. 009561	0. 009273	0. 008646	0. 008650
3600	0. 009983		0. 009490		0. 008699
3700	0. 009986		0. 009151		0. 008559
3800	0. 009978		0. 009349		0. 008548
3900	0. 009977		0. 008918		0. 008614
4000	0. 009975		0. 009218		0. 008679
4100	0. 009995		0. 009378		0. 008679
4200	0. 009946		0. 009495		0. 008659
4300	0. 009988		0. 009334		0. 008554
4400	0. 009989		0. 009283		0. 008582
4500	0. 009978	0. 009959	0. 009290	0. 009306	0. 008550	0. 008569
4600	0. 009916		0. 008912		0. 008552
4700	0. 009962		0. 009398		0. 008592
4800	0. 009957		0. 009268		0. 008537
4900	0. 009942		0. 009314		0. 008515
5000	0. 009921		0. 009397		0. 008464
5100	0. 009955		0. 009203		0. 008493
5200	0. 009912		0. 009282		0. 008472
5300	0. 009933	0. 009925	0. 009091	0. 009168	0. 008470	0. 008460
5400	0. 009901		0. 009335		0. 008433
5500	0. 009925		0. 008930		0. 008430

Fig. 2

Effect of number of rounds to data delivery

Table 2 describes the datasets related to the latency in the OWSNs. The obtained big datasets illustrate that the latency value (LV) of OWRP with node density between 1 and 100 is low around 30ms compared to 63ms in CARP. However, the latency value of OWRP is increasing around 48ms, 66ms, 85ms, 117ms, and 131ms when the numbers optical-wireless sensor nodes are between 110 and 550 in the network. The datasets show that the LV is increasing rapidly around 63ms, 98ms, 170ms, 235ms, 291ms, and 350ms in CARP compared to the OWRP scheme in the network. On the other hand, the LV of DCFBR is noticed around 62ms, 89ms, 137ms, 186, 267ms, 308ms with number of nodes between 10 and 100, 101 and 200, 201 and 300, 301 and 400, 401 and 500, and 501 and 550, respectively, in the network. The average of obtained LV big datasets graphically is shown in Fig. 3.

Table 2

Datasets for latency in OWSNs.

No. of nodes	Latency values
Protocols	OWRP	Avg. ≅(ms)	CARP	Avg. ≅(ms)	DCFBR	Avg. ≅(ms)
10	0.001588		0.003957		0.003950
20	0. 001818		0. 004988		0. 004875
30	0. 002239		0. 005737		0. 005633
40	0. 002545		0. 006152		0. 006055
50	0. 002741	0.002955	0. 006512	0.006312	0. 006310	0.006160
60	0. 003209		0. 006783		0. 006585
70	0. 003483		0. 007119		0. 006911
80	0. 003767		0. 007390		0. 007050
90	0. 004072		0. 007408		0. 007098
100	0. 004084		0. 007519		0. 007211
110	0. 004289		0. 007680		0. 007320
120	0. 004356		0. 008290		0. 008001
130	0. 004467		0. 008850		0. 008222
140	0. 004483		0. 009190		0. 008560
150	0. 004593	0.004770	0. 009530	0.009844	0. 008840	0.008942
160	0. 004677		0. 009910		0. 009315
170	0. 004868		0.010190		0.009695
180	0. 005099		0. 011020		0. 009723
190	0. 005378		0. 011770		0. 009772
200	0. 005489		0. 012010		0. 009964
210	0. 005541		0. 012991		0. 010888
220	0. 005688		0. 014122		0. 010278
230	0. 005954		0. 015711		0. 012556
240	0. 006373		0. 016802		0. 012915
250	0. 006555	0.006582	0. 017677	0.016984	0. 013147	0.013673
260	0. 006792		0. 017934		0. 014155
270	0. 006879		0. 018381		0. 015394
280	0. 007169		0. 018593		0. 015587
290	0. 007378		0. 018788		0. 015872
300	0. 007489		0. 018842		0. 015941
310	0. 007546		0. 018990		0. 015800
320	0. 007758		0. 019820		0. 016327
330	0. 008169		0. 020719		0. 016729
340	0. 008273		0. 021508		0. 016908
350	0. 008451	0.008544	0. 023077	0.023486	0. 017071	0.018627
360	0. 008672		0. 025137		0. 018188
370	0. 008778		0. 026085		0. 019088
380	0. 009135		0. 026393		0. 020399
390	0. 009266		0. 026588		0. 022522
400	0. 009393		0. 026547		0. 023240
410	0. 011197		0. 026899		0. 023890
420	0. 011389		0. 027488		0. 024422
430	0. 011592		0. 028076		0. 025079
440	0. 010777		0. 028558		0. 025889
450	0. 010911	0.011703	0. 028975	0.029048	0. 026972	0.026696
460	0. 011975		0. 029483		0. 026480
470	0. 012078		0. 029689		0. 027688
480	0. 012138		0. 029798		0. 027703
490	0. 012389		0. 029968		0. 028900
500	0. 012584		0. 031549		0. 029940
510	0. 012611		0. 032011		0. 030011
520	0. 012757		0.033922		0.030901
530	0. 013135	0.013064	0. 035510	0.035049	0. 030980	0.030782
540	0. 013313		0. 036301		0. 031001
550	0. 013501		0. 037501		0. 031015

Fig. 3

Effect of node density to network delay

Table 3 shows the datasets related to congestion management in the OWSNs. The obtained big datasets illustrate that the congestion management value (CM) of OWRP with node density between 1 and 100 is high around 99.8% compared to 98.8% in CARP. However, the CM value of OWRP is decreasing around 99.5%, 98.6%, 98.7%, 97.4 %, and 97.1% when the numbers optical-wireless sensor nodes are between 110 and 550 in the network. On the other hand, the datasets show that the CM is decreasing rapidly around 96.2%, 91.2%, 87.5%, 86%, and 85.6% in CARP compared to the OWRP scheme in the network. On the other hand, the CM value of DCFBR is recorded around 98.3%, 95.6%, 92%, 86%, 82.3%, and 81.3% with nodes density between 1 and 550 in the network. The average of obtained CM big datasets graphically is shown in Fig. 4.

Table 3

Datasets for congestion management in OWSNs.

No. of nodes	Congestion management values
Protocols	OWRP	Avg. ≅(%)	CARP	Avg. ≅(%)	DCFBR	Avg. ≅(%)
10	0.009999		0.009999		0.009901
20	0.009999		0.009970		0.009900
30	0.009998		0.009961		0.009903
40	0.009997		0.009852		0.009800
50	0.009897	0.009975	0.009850	0.009877	0.009840	0.009834
60	0.009895		0.009840		0.009833
70	0.009993		0.009833		0.009832
80	0.009990		0.009830		0.009812
90	0.009989		0.009820		0.009805
100	0.009989		0.009815		0.009709
110	0.009986		0.009780		0.009700
120	0.009979		0.009755		0.009702
130	0.009978		0.009701		0.009661
140	0.009973		0.009670		0.009630
150	0.009872	0.009946	0.009653	0.009617	0.009603	0.009560
160	0.009865		0.009620		0.009570
170	0.009959		0.009569		0.009529
180	0.009955		0.009546		0.009500
190	0.009949		0.009470		0.009401
200	0.009948		0.009405		0.009304
210	0.009941		0.009360		0.009302
220	0.009915		0.009305		0.009301
230	0.009880		0.009260		0.009290
240	0.009865		0.009210		0.009280
250	0.009848	0.009860	0.009203	0.009115	0.009263	0.009194
260	0.009848		0.009101		0.009251
270	0.009843		0.009000		0.009190
280	0.009841		0.008947		0.009021
290	0.009841		0.008915		0.009082
300	0.009841		0.008850		0.008955
310	0.009830		0.008844		0.008944
320	0.009915		0.008820		0.008828
330	0.009812		0.008812		0.008755
340	0.009807		0.008801		0.008700
350	0.009803	0.009866	0.008770	0.008750	0.008630	0.008590
360	0.009801		0.008755		0.008511
370	0.009795		0.008709		0.008480
380	0.009791		0.008677		0.008380
390	0.009791		0.008656		0.008366
400	0.009790		0.008651		0.008301
410	0.009760		0.008644		0.008300
420	0.009751		0.008630		0.008288
430	0.009744		0.008623		0.008253
440	0.009743		0.008611		0.008251
450	0.009743	0.009741	0.008601	0.008605	0.008241	0.008225
460	0.009741		0.008600		0.008200
470	0.009738		0.008589		0.008199
480	0.009733		0.008587		0.008187
490	0.009730		0.008581		0.008181
500	0.009730		0.008581		0.008150
510	0.009728		0.008570		0.008140
520	0.009722		0.008566		0.008136
530	0.009719	0.009719	0.008549	0.008555	0.008129	0.008129
540	0.009718		0.008545		0.008125
550	0.009710		0.008544		0.008114

Fig. 4

Effect of nodes density on congestion management

Table 4 shows the datasets related to throughput in the OWSNs. The obtained big datasets show that the throughput value (TP) of OWRP with node density between 1 and 100 is high around 99.2% compared to 91.2% in CARP. However, the TP value of OWRP is changing around 99.1%, 98.9%, 98.95%, 98.84 %, and 99.04% when the numbers optical-wireless sensor nodes are between 110 and 550 in the network. The big datasets show that the TP is decreasing rapidly around 91.4%, 90.3%, 90.3% and rising up to 91.7%, and 91.8% in the same round numbers in CARP compared to the OWRP scheme in the network. On the other hand, the TP value in DCFBR is noticed low around 87.8%, 87.5%, 87.4%, 87.4%, 87.1%, and 87.5% between round numbers 100 and 1000, 1001 and 2000, 2001 and 3000, 3001 and 4000, 4001 and 5000, and 5001 and 5500, respectively. The average of obtained TP big datasets graphically is shown in Fig. 5.

Table 4

Datasets for throughput in OWSNs.

No. of rounds	Throughput values
Protocols	OWRP	Avg. ≅(%)	CARP	Avg. ≅(%)	DCFBR	Avg. ≅(%)
100	0.009891		0.009189		0.008790
200	0.009875		0.009174		0.008787
300	0.009888		0.009166		0.008771
400	0.009871		0.009157		0.008772
500	0.009899	0.009918	0.009150	0.009150	0.008760	0.008768
600	0.009885		0.009148		0.008772
700	0.009990		0.009137		0.008760
800	0.009992		0.009133		0.008762
900	0.009991		0.009128		0.008750
1000	0.009900		0.009119		0.008754
1100	0.009901		0.009165		0.008760
1200	0.009989		0.009146		0.008765
1300	0.009968		0.009161		0.008722
1400	0.009966		0.009150		0.008745
1500	0.009889	0.009908	0.009140	0.009140	0.008767	0.008753
1600	0.009874		0.009158		0.008787
1700	0.009879		0.009137		0.008734
1800	0.009801		0.009116		0.008789
1900	0.009911		0.009120		0.008712
2000	0.009898		0.009112		0.008745
2100	0.009846		0.009062		0.008761
2200	0.009867		0.009035		0.008734
2300	0.009887		0.009014		0.008734
2400	0.009895		0.009036		0.008745
2500	0.009888	0.009887	0.009013	0.009026	0.008765	0.008742
2600	0.009998		0.009011		0.008777
2700	0.009848		0.009002		0.008701
2800	0.009878		0.009018		0.008741
2900	0.009876		0.009019		0.008711
3000	0.009889		0.009050		0.008753
3100	0.009870		0.009045		0.008722
3200	0.009910		0.009022		0.008724
3300	0.009911		0.009012		0.008756
3400	0.009924		0.009023		0.008767
3500	0.009803	0.009895	0.009045	0.009033	0.008776	0.008744
3600	0.009889		0.009005		0.008737
3700	0.009899		0.009069		0.008723
3800	0.009891		0.009022		0.008727
3900	0.009931		0.009056		0.008754
4000	0.009920		0.009031		0.008754
4100	0.009869		0.009181		0.008702
4200	0.009855		0.009156		0.008711
4300	0.009876		0.009111		0.008722
44000	0.009940		0.009180		0.008710
4500	0.009801	0.009884	0.009165	0.009168	0.008701	0.008712
4600	0.009841		0.009178		0.008702
4700	0.009901		0.009145		0.008701
4800	0.009977		0.009189		0.008711
4900	0.009887		0.009187		0.008743
5000	0.009888		0.009188		0.008715
5100	0.009878		0.009178		0.008765
5200	0.009920		0.009186		0.008737
5300	0.009911	0.009904	0.009197	0.009179	0.008726	0.008750
5400	0.00990		0.009165		0.008743
5500	0.009911		0.009170		0.008781

Fig. 5

Effect of number of rounds to throughput

Table 5 shows the datasets related to packet error rate in the OWSNs. The obtained big datasets show that the packet error rate value (PER) of OWRP with node density between 1 and 100 is low around 0.2% compared to 0.35% in CARP and 0.39% in DCFBR. The PER value of OWRP is changing around 0.33%, 0.38%, 0.46%, 0.59%, and 0.73% when the numbers optical-wireless sensor nodes are between 110 and 550 in the network. Similarly, the PER value of CARP is changing around 0.46%, 0.63%, 1.1%, 1.65%, and 2.3% when the numbers optical-wireless sensor nodes are between 110 and 550 in the network. Compared to all other schemes, the PER value of DCFBR is observed high around 0.61%, 0.98%, 1.5%, 2.63%, and 3.37% between 110 and 550 against the OWRP and CARP in the network. The average of obtained PER big datasets graphically is shown in Fig. 6.

Table 5

Datasets for packet error rate in OWSNs.

No. of nodes	Packet error rate values
Protocols	OWRP	Avg. ≅(%)	CARP	Avg. ≅(%)	DCFBR	Avg. ≅(%)
10	0.001100		0.001498		0.001992
20	0.001200		0.002588		0.003383
30	0.001350		0.003694		0.003966
40	0.001600		0.003789		0.004089
50	0.001700	0.001986	0.003894	0.003538	0.004124	0.003893
60	0.001900		0.003881		0.004255
70	0.002100		0.003987		0.004275
80	0.002600		0.003977		0.004289
90	0.003110		0.003981		0.004276
100	0.003200		0.004091		0.004283
110	0.003208		0.004223		0.004356
120	0.003251		0.004243		0.004754
130	0.003285		0.004345		0.005187
140	0.003291		0.004456		0.005579
150	0.003301	0.003306	0.004534	0.004604	0.006155	0.006145
160	0.003310		0.00459		0.006584
170	0.003312		0.004765		0.006745
180	0.003330		0.004878		0.006911
190	0.003380		0.004989		0.007391
200	0.003393		0.005012		0.007789
210	0.003458		0.005176		0.007886
220	0.003531		0.005287		0.008179
230	0.003616		0.005574		0.008278
240	0.003688		0.005867		0.008510
250	0.003756	0.003765	0.006278	0.006346	0.008983	0.009757
260	0.003790		0.006549		0.009491
270	0.003852		0.006769		0.008782
280	0.003859		0.006922		0.009979
290	0.003970		0.007323		0.012710
300	0.004127		0.007711		0.014768
310	0.004278		0.008067		0.014968
320	0.004331		0.008567		0.015124
330	0.004366		0.009078		0.015663
340	0.004488		0.009387		0.015967
350	0.004536	0.004629	0.009789	0.010923	0.016276	0.015060
360	0.004620		0.011265		0.016837
3700	0.004752		0.011456		0.0017223
380	0.004887		0.012487		0.017627
390	0.004946		0.013543		0.017954
400	0.005089		0.015634		0.018454
410	0.005182		0.011543		0.021479
420	0.005275		0.011932		0.022686
430	0.005388		0.012430		0.023588
440	0.005566		0.013511		0.024990
450	0.005725	0.005886	0.015600	0.016519	0.025691	0.026294
460	0.005908		0.017201		0.026685
470	0.006160		0.018456		0.027790
480	0.006367		0.019409		0.028703
490	0.006518		0.022510		0.029711
500	0.006767		0.022601		0.030612
510	0.006845		0.022704		0.031619
520	0.007056		0.022802		0.032830
530	0.007376	0.007311	0.023311	0.023172	0.033528	0.033742
540	0.007587		0.023751		0.034845
550	0.007689		0.023291		0.035887

Fig. 6

Effect of number of nodes to packet error rate

Experimental Design, Materials and Methods

In this work, a set of optical and wireless sensor nodes were statically embedded in different systems located in an area of 285 (length)  ×  110 (width) in the indoor electronics manufacturing industrial environment. The number of optical sensor nodes, compliant to IEEE 802.15.7 physical layer standard and operating on the wavelength from 7000nm to 300nm are set to 100. On the other hand, the wireless sensor nodes, compliant to physical layer standard IEEE 802.15.4 are set to 450. In the deployment, the nodes equipped with both wireless and optical communication technologies act like gateway head nodes and are responsible for gathering observed data from neighboring nodes and forward it to the cobot via optical communication technology. The energy of each wireless node is set to 15J with a communication range of up to 3 to 5m and data rates up to 256 kbps [9]. While the communication range of the optical sensors was set to 10m and data rates up to 1 Gbps. The data packet size of the wireless sensor nodes is set to 72 bytes and uses the Quadrature phase-shift keying (QPSK) modulation mechanism in the network [10]. The memory size of wireless and optical sensor nodes was set to 5Mb and 10Mb, respectively. In addition, the channel and energy consumption model used in this study is the same as discussed in [3,11]. The widely used parameters and values used in existing studies are given in Table 6.

Table 6

Simulation parameters and values

Simulation Model Parameters	Values
Simulation tool	EstiNet 12 & MongoDB
Cobot (sink)	1
Wireless sensors	450
Optical sensors	100
Physical layer wireless standard	802.15.4
Physical layer optical standard	802.15.7
Wavelength for optical standard	7000nm to 300nm
Initial sensor node energy	15J
High transmission power	0.46W
Low transmission power	0.31W
Packet receiving power	0.05W
Idle listening	0.023W
Sleeping power	3×10^−6W
Data aggregation	0.019W
Packet length	72 bytes
Wireless data transfer rate	256 kbps
Optical data transfer rate	1Gbps
Wireless & optical node cache size	5Mb,10Mb
Maximum hop distance wireless sensor	3-5m
Maximum hop distance optical sensor	10m
Maximum communication range of the cobot	50m
Topology	Static
Wireless Antenna	Omni-directional
LED (Optical)	Line-of-sight
Path loss exponent for the LoS and non-LoS	1.4, 1.9
The noise floor for the LoS and non-LoS	-89, -97
Shadowing deviation for the LoS and non-LoS	1.12, 1.92
Area: 2D (length×width)	285 × 110m
Simulation time	300 sec
Set of simulations	60

Ethics Statement

We declare that the manuscript adheres to Ethics in publishing standards and the submitted dataset is the real data recorded in the experiment, and there is no act of stealing other people's data or modifying data.

CRediT Author Statement

Muhammad Faheem: Conceptualization, Methodology, Software, Simulation, Formal analysis, Writing – Original Draft, Project administration; Rizwan Aslam Butt: Methodology, Validation, Writing – review & editing.

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 Science: Computer Networks and Communications
Specific subject area	Optical-wireless communication in the electronics manufacturing Industry 4.0.
Type of data	Graphs and Tables
How the data were acquired	Data was captured using Internet of things-enabled optical-wireless sensor networks in the electronics manufacturing Industry 4.0.
Data format	Raw and analyzed optical-wireless sensors data in an electronics manufacturing Industry 4.0.
Description of data collection	The big data sets were collected by optical-wireless sensor networks deployed on different types of manufacturing and assembly systems in the electronics Industry 4.0. To collect the big data in a particular scenario, a static topology by taking into account the line-of-sight and the non-line-of-sight issues was considered in an indoor industrial environment.To gather real-time big data from the systems involved in the electronics manufacturing process a cobot, i.e., the static sink was deployed in a specific location in the plant. The remote user can access and configure both wireless and optical nodes by connecting to the cobot through the intranet or the internet communication technologies such as the 5G. Distinct from the existing sink, the cobot can intelligently monitor, learn and configure the entire deployed network by closely monitoring the human interventions. Thus, the cobot minimizes the user interventions in the whole big data gathering process in Industry 4.0.
Parameters for data collection	The data was gathered in day and night by employing wireless and optical sensors numbering 450 and 100, respectively. The wireless sensor nodes are equipped with physical layer standard IEEE 802.15.4 and frequency 2.4 GHz unlicensed industrial, scientific and medical (ISM) band. The optical nodes are equipped with physical layer standard IEEE 802.15.7 using light wavelengths from 7000 nm to 300 nm (LED technology), which varies based on the applications. In addition, the group leader nodes are equipped with both physical layer standards IEEE 802.15.4 and IEEE 802.15.7 for wireless and optical communication in the network.
Data source location	City/Town/Region: Kayseri/Kocasinan, Country: Turkey, Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: N38 °71′ and E35 °43′.
Data accessibility	Data repository name: MendeleyData identification number: DOI:10.17632/8kvdbhrgxt.3Direct URL t to data: https://data.mendeley.com/datasets/8kvdbhrgxt/3
Related research paper	M. Faheem, R. A. Butt, R. Ali, B. Raza, M. A. Ngadi, and V. C. Gungor, ``CBI4. 0: A Cross-layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0,'' Journal of Industrial Information Integration, p. 100236, 2021.https://doi.org/10.1016/j.jii.2021.100236