Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1575 2023-06-22 10:18:34 |
2 Format correct Meta information modification 1575 2023-06-25 09:31:18 | |
3 Format correct -1 word(s) 1574 2023-06-26 12:19:29 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Scandurra, G.; Arena, A.; Ciofi, C. Green Substrates for Flexible Electronics for IoT. Encyclopedia. Available online: https://encyclopedia.pub/entry/45966 (accessed on 17 June 2024).
Scandurra G, Arena A, Ciofi C. Green Substrates for Flexible Electronics for IoT. Encyclopedia. Available at: https://encyclopedia.pub/entry/45966. Accessed June 17, 2024.
Scandurra, Graziella, Antonella Arena, Carmine Ciofi. "Green Substrates for Flexible Electronics for IoT" Encyclopedia, https://encyclopedia.pub/entry/45966 (accessed June 17, 2024).
Scandurra, G., Arena, A., & Ciofi, C. (2023, June 22). Green Substrates for Flexible Electronics for IoT. In Encyclopedia. https://encyclopedia.pub/entry/45966
Scandurra, Graziella, et al. "Green Substrates for Flexible Electronics for IoT." Encyclopedia. Web. 22 June, 2023.
Green Substrates for Flexible Electronics for IoT
Edit

The Internet of Things (IoT) is gaining more and more popularity and it is establishing itself in all areas, from industry to everyday life. Given its pervasiveness and considering the problems that afflict today’s world, that must be carefully monitored and addressed to guarantee a future for the new generations, the sustainability of technological solutions must be a focal point in the activities of researchers in the field. Many of these solutions are based on flexible, printed or wearable electronics. The choice of materials therefore becomes fundamental, just as it is crucial to provide the necessary power supply in a green way.

flexible electronics IoT substrates nanopaper sustainability

1. Introduction

The term sustainability has now become commonly used, it is of great importance and is also used in different contexts. It was used for the first time in 1992, during the first UN Conference on the environment. The definition of sustainability that has been given is this: Condition of a development model capable of ensuring the satisfaction of the needs of the present generation without compromising the possibility of future generations to realize their own [1]. This definition is centered not only on the economy and society, but above all on ecology. Sustainability and sustainable development are linked to a new idea of well-being that takes into account people’s quality of life. Environmental sustainability requires responsibility in the use of resources. It is therefore a development model to which everyone can and must contribute, starting from the awareness that every action performed by each of us has a deep impact on the environment.
Based on these considerations, the world of electronics, which for decades has been increasingly pervasive in all sectors of life (industry, medical, automation, automotive, military, consumption), cannot fail to pay maximum attention to the issue of sustainability. The electronics as fuel of the Internet of Things technology is surely leading us in a new way of conducting our lives and cities [2], also allowing the optimization of the production processes of companies and industries and the management of services and infrastructures, limiting the consumption of resources and pollution. Management of public lighting [3][4][5][6][7], air quality [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and noise pollution monitoring [23][24][25][26][27][28][29], smart home [30][31][32][33][34][35][36][37][38][39][40][41][42][43], smart roads, smart cars, urban mobility and transport [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], food and agriculture [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83], smart factories [84][85][86][87][88][89][90][91] and medicine [92][93][94][95][96][97][98][99][100][101][102] are examples of the great potentialities of the IoT. However, the increase in connectivity inevitably translates into an increase in electronic devices and systems (sensors, data acquisition and processing systems, communication systems) and therefore the problem of respecting the environment, both in the production step and disposal of disused systems is, nowadays, of fundamental importance also in the field of the IoT industry. Thanks to the availability of eco-compatible materials, flexible electronics, which is a solution that is increasingly gaining space in many applications due to its portability, wearability and low cost, could be the right path towards an increasingly green IoT (Figure 1).
Figure 1. Flexible electronics is an important building block for the creation of a sustainable and interconnected world.

2. Green Substrates: Paper and “Nanopaper”

The choice of the substrate on which to make a flexible device is surely a key factor for sustainable IoT because the greater quantity of material that makes up the device is precisely the substrate [103].
With the advent of flexible electronics, the favored substrates on which to build devices have, for a long time, been plastic materials. However, discarded plastics degrade to form micro and nano-plastics that are hazardous to human beings and the environment. If one thinks of the implementation of flexible devices that are “green”, surely paper is the first material that comes to mind as a substrate to substitute plastic [104]. In fact, paper is widely and easily available, low-cost, recyclable and biodegradable. Table 1 shows a comparison between paper and the plastic materials mostly used as substrates, in terms of impacts on climate change and resource use [105]. In this regard, it may be useful to recall that studies conducted on these same indicators as regards the production of silicon, the fundamental semiconductor in the electronics industry, have highlighted a development in the wrong direction for the silicon industry, facing increasing climate related pressures [106].
Table 1. Comparison between paper and the most used plastic substrates, in terms of the impact on climate change and resource use.
Substrate Material Climate Change Impact
kg CO2 eq. */Sheet ***
Resource Use
kg Sb eq. **/Sheet ***
Paper 1.3 × 10−4 5.2 × 10−11
PET (polyethylene terephthalate) 1.5 × 10−3 1.8 × 10−10
PEI (polyetherimide) 1.3 × 10−2 2.0 × 10−9
PEEK (polyether ether ketone) 7.4 × 10−3 2.2 × 10−9
* Indicator of potential global warming due to emissions of greenhouse gases to the air. ** Indicator of the depletion of natural non-fossil resources. *** Sheet with 25 cm2 surface area, 125 mm thickness.
Although it is very promising from an environmental point of view and several devices made on paper substrates have appeared in the last decade, the use of paper as a substrate is still limited, due to the high surface roughness and poor barrier properties against water and solvents [104]. However, if considering that in applications in the IoT field, and therefore in electronics, one of the main properties of the substrates is that of allowing optimization of the device performance in terms of conductivity, paper substrates have performances no lower than the plastic ones most used up to now. In [104] an interesting comparison is made between different paper substrates and PET substrates. The main results are summarized in Table 2.
Table 2. Comparison between conductivity of the printed layer on paper and PET substrates. The layer thickness used in the volume resistivity measurement was considered to be equal on every substrate. An ink transfer volume of 7 mL/m2 has been considered.
Printing Technique Substrate Material Sheet Resistance
mΩ/Square
Volume
Resistivity (Ω·cm)
Flexo-printing P1 * 177 ± 19 2.2 × 10−6
P2 ** 169 ± 16 1.6 × 10−6
PET *** 260 ± 23 2.1 × 10−6
Rotary
screen-printing
P1 45.3 ± 1.3 4.1 × 10−5
P2 39.4 ± 0.6 3.4 × 10−5
PET 52.3 ± 2.5 4.7 × 10−5
* Coated paper, Stora Enso NovaPress Silk, 80 g/m2. ** Coated paper, ultra-smooth top side for printed electronics, Arjo Wiggins PowerCoat HD, 95 g/m2. *** Melinex ST506 (DuPont Teijin Films, Chester, VA, USA).
Continuing with the comparison between paper and plastic substrates, wishing to evaluate the performances in terms of elasticity, in Table 3 researchers report Young’s modulus. Among the plastic materials researchers have considered PET, precisely because it is the most used, PEN (polyethylene naphthalate) which has performances in terms of elasticity superior to other plastic substrates, and PDMS (polydimethylsiloxane), a popular elastomer in the manufacture of stretchable devices [107]. Results in Table 3 show that paper substrates can offer elastic performances comparable to PDMS under proper coating conditions.
Table 3. Comparison of Young’s modulus of paper and plastic substrates [107].
Substrate Material Young’s Modulus [GPa]
Paper Up to 3.5 *
PET 2.8
PEN 3.0
PDMS Up to 3.7 **
* Depending on coating. ** Depending on different crosslinking density.
Obviously, if the goal of making flexible devices that are absolutely sustainable is to be achieved, the separation of electronic materials, conductive metallic inks in most cases, from the paper substrate at the end of life of the devices must be easily performed. To overcome these limitations, a few solutions based on coating approaches have been presented to improve paper substrate performances. As an example, the use of shellac, that is a cheap biopolymer, has been proposed in [108]. Shellac, employed as a coating surface for paper substrates, forms planarized, printable surfaces. At the end of the life of the device, shellac behaves as a sacrificial layer that can be removed by immersing the printed device in methanol, enabling the separation of the paper substrate. Nevertheless, coating procedures and other surface treatments are not effective for all electronics applications [109][110]. In the last period, “nanopaper”, that is, planar substrates made of cellulose nanomaterials (CNM), gained relevance [111][112][113][114][115][116][117][118]. CNM are nanosized particles with highly ordered cellulose chains aligned along the bundle axis, that exhibit interesting characteristics with respect to pulp fibers and wood particles, such as high mechanical properties, low thermal expansion, low density, and simplicity of treatment that allows the implementation of additional functionalities [119][120][121]. To focus on sustainability, it is also important to evaluate the end-of-life performance, that is to carry out a study on the biodegradability of materials. In [112], for example, a comparison between the biodegradation of CNM samples with respect to microcrystalline cellulose (MCC), and a commercial thermoplastic polyurethane (TPU) has been performed and the results are summarized in Table 4.
Table 4. Example of biodegradability test on cellulose based and plastic samples. The test duration was 127 days [112].
Sample * Status Biodegradation **
CNF 50%, HEC 50% Printed 74%
CNF 50%, HEC 50% Unprinted 78%
MCC Unprinted 94%
TPU Unprinted No degradation
* CNF: cellulose nanofibrils; HEC: hydroxyethyl cellulose; MCC: microcrystalline cellulose; TPU: thermoplastic polyurethane. ** The data are extrapolated from [112]. Biodegradation of samples was estimated firstly by employing the CO2 evolution method and, secondly, by visually evaluating samples disintegration in soil upon burial.
The results reported in [112] show that in the first 70 days of testing, the biodegradability rate of the CNF-HEC compounds is comparable to that of pure cellulose, while subsequently there is a slowdown. Although there is no doubt that the biodegradability of cellulose-based samples is far superior to that of plastic materials, it is certainly clear that, to further improve the state of the art, studies need to be conducted to understand how to optimize the performance of paper substrates without lowering the biodegradability performance too much compared to pure cellulose. The biodegradability of the printed substrate is slightly lower than that of the non-printed substrate, also highlighting the importance of working on the eco-sustainability of the conductive layers. Without any doubt, the “nanopaper” technology, that is a relatively low-cost technology [122][123][124] for substrate fabrication for IoT applications, is strategic to fuel a transition toward a sustainable and green IoT, also working on the use of optimized nanocellulose with other materials and hybrid structures [125][126][127][128][129].

References

  1. Weiss, E.B. United Nations Conference on Environment and Development. Int. Leg. Mater. 1992, 31, 814–817.
  2. Whaiduzzaman, M.; Barros, A.; Chanda, M.; Barman, S.; Sultana, T.; Rahman, M.S.; Roy, S.; Fidge, C. A Review of Emerging Technologies for IoT-Based Smart Cities. Sensors 2022, 22, 9271.
  3. Deepaisarn, S.; Yiwsiw, P.; Chaisawat, S.; Lerttomolsakul, T.; Cheewakriengkrai, L.; Tantiwattanapaibul, C.; Buaruk, S.; Sornlertlamvanich, V. Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics. Sensors 2023, 23, 1853.
  4. García-Castellano, M.; González-Romo, J.M.; Gómez-Galán, J.A.; García-Martín, J.P.; Torralba, A.; Pérez-Mira, V. ITERL: A Wireless Adaptive System for Efficient Road Lighting. Sensors 2019, 19, 5101.
  5. Abarro, C.C.; Caliwag, A.C.; Valverde, E.C.; Lim, W.; Maier, M. Implementation of IoT-Based Low-Delay Smart Streetlight Monitoring System. IEEE Internet Things J. 2022, 9, 18461.
  6. Liu, C.-H.; Hsiao, C.-Y.; Gu, J.-C.; Liu, K.-Y.; Yan, S.-F.; Chiu, C.H.; Ho, M.C. HCL Control Strategy for an Adaptive Roadway Lighting Distribution. Appl. Sci. 2021, 11, 9960.
  7. Ordaz-García, O.O.; Ortiz-López, M.; Quiles-Latorre, F.J.; Arceo-Olague, J.G.; Solís-Robles, R.; Bellido-Outeiriño, F.J. DALI Bridge FPGA-Based Implementation in a Wireless Sensor Node for IoT Street Lighting Applications. Electronics 2020, 9, 1803.
  8. Guerrero-Ulloa, G.; Andrango-Catota, A.; Abad-Alay, M.; Hornos, M.J.; Rodríguez-Domínguez, C. Development and Assessment of an Indoor Air Quality Control IoT-Based System. Electronics 2023, 12, 608.
  9. Kim, J.; Bang, J.; Choi, A.; Moon, H.J.; Sung, M. Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data. Sensors 2023, 23, 585.
  10. Rollo, F.; Bachechi, C.; Po, L. Anomaly Detection and Repairing for Improving Air Quality Monitoring. Sensors 2023, 23, 640.
  11. Zhu, Y.; Al-Ahmed, S.A.; Shakir, M.Z.; Olszewska, J.I. LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring. Electronics 2023, 12, 107.
  12. Hawchar, A.; Ould, S.; Bennett, N.S. Carbon Dioxide Monitoring inside an Australian Brewery Using an Internet-of-Things Sensor Network. Sensors 2022, 22, 9752.
  13. García, L.; Garcia-Sanchez, A.-J.; Asorey-Cacheda, R.; Garcia-Haro, J.; Zúñiga-Cañón, C.-L. Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments. Sensors 2022, 22, 9221.
  14. Starace, G.; Tiwari, A.; Colangelo, G.; Massaro, A. Advanced Data Systems for Energy Consumption Optimization and Air Quality Control in Smart Public Buildings Using a Versatile Open Source Approach. Electronics 2022, 11, 3904.
  15. Kharbouch, A.; Berouine, A.; Elkhoukhi, H.; Berrabah, S.; Bakhouya, M.; El Ouadghiri, D.; Gaber, J. Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation. Sensors 2022, 22, 7978.
  16. Yasin, A.; Delaney, J.; Cheng, C.-T.; Pang, T.Y. The Design and Implementation of an IoT Sensor-Based Indoor Air Quality Monitoring System Using Off-the-Shelf Devices. Appl. Sci. 2022, 12, 9450.
  17. Khan, M.A.; Kim, H.-c.; Park, H. Leveraging Machine Learning for Fault-Tolerant Air Pollutants Monitoring for a Smart City Design. Electronics 2022, 11, 3122.
  18. Alvear-Puertas, V.E.; Burbano-Prado, Y.A.; Rosero-Montalvo, P.D.; Tözün, P.; Marcillo, F.; Hernandez, W. Smart and Portable Air-Quality Monitoring IoT Low-Cost Devices in Ibarra City, Ecuador. Sensors 2022, 22, 7015.
  19. Pastor-Fernández, A.; Cerezo-Narváez, A.; Montero-Gutiérrez, P.; Ballesteros-Pérez, P.; Otero-Mateo, M. Use of Low-Cost Devices for the Control and Monitoring of CO2 Concentration in Existing Buildings after the COVID Era. Appl. Sci. 2022, 12, 3927.
  20. Montanaro, T.; Sergi, I.; Basile, M.; Mainetti, L.; Patrono, L. An IoT-Aware Solution to Support Governments in Air Pollution Monitoring Based on the Combination of Real-Time Data and Citizen Feedback. Sensors 2022, 22, 1000.
  21. Sridhar, K.; Radhakrishnan, P.; Swapna, G.; Kesavamoorthy, R.; Pallavi, L.; Thiagarajan, R. A modular IOT sensing platform using hybrid learning ability for air quality prediction. Meas. Sens. 2023, 25, 100609.
  22. Fadda, M.; Anedda, M.; Girau, R.; Pau, G.; Giusto, D.D. A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari. IEEE Internet Things J. 2023, 10, 2373.
  23. Meng, Q.; Lu, P.; Zhu, S. A Smartphone-enabled IoT System for Vibration and Noise Monitoring of Rail Transit. IEEE Internet Things J. 2023, 10, 8907.
  24. Alashaikh, A.S.; Alhazemi, F.M. Efficient Mobile Crowdsourcing for Environmental Noise Monitoring. IEEE Access 2022, 10, 77251.
  25. Segura-Garcia, J.; Calero, J.M.A.; Pastor-Aparicio, A.; Marco-Alaez, R.; Felici-Castell, S.; Wang, Q. 5G IoT System for Real-Time Psycho-Acoustic Soundscape Monitoring in Smart Cities with Dynamic Computational Offloading to the Edge. IEEE Internet Things J. 2021, 8, 12467.
  26. Monti, L.; Vincenzi, M.; Mirri, S.; Pau, G.; Salomoni, P. RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning. Sensors 2020, 20, 5583.
  27. Zhang, X.; Zhao, M.; Dong, R. Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network. Appl. Sci. 2020, 10, 1144.
  28. Mydlarz, C.; Sharma, M.; Lockerman, Y.; Steers, B.; Silva, C.; Bello, J.P. The Life of a New York City Noise Sensor Network. Sensors 2019, 19, 1415.
  29. Segura Garcia, J.; Pérez Solano, J.J.; Cobos Serrano, M.; Navarro Camba, E.A.; Felici Castell, S.; Soriano Asensi, A.; Montes Suay, F. Spatial Statistical Analysis of Urban Noise Data from a WASN Gathered by an IoT System: Application to a Small City. Appl. Sci. 2016, 6, 380.
  30. Arisdakessian, S.; Wahab, O.A.; Mourad, A.; Otrok, H.; Guizani, M. A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions. IEEE Internet Things J. 2023, 10, 4059.
  31. Lorenzo, O.G.; Suárez-García, A.; Peña, D.G.; Fuente, M.G.; Granados-López, D. A Low-Cost Luxometer Benchmark for Solar Illuminance Measurement System Based on the Internet of Things. Sensors 2022, 22, 7107.
  32. Al-Begain, K.; Khan, M.; Alothman, B.; Joumaa, C.; Alrashed, E. A DDoS Detection and Prevention System for IoT Devices and Its Application to Smart Home Environment. Appl. Sci. 2022, 12, 11853.
  33. Jhuang, Y.-Y.; Yan, Y.-H.; Horng, G.-J. GDPR Personal Privacy Security Mechanism for Smart Home System. Electronics 2023, 12, 831.
  34. Perumal, T.; Ramanujam, E.; Suman, S.; Sharma, A.; Singhal, H. Internet of Things Centric-Based Multiactivity Recognition in Smart Home Environment. IEEE Internet Things J. 2023, 10, 1724.
  35. Condon, F.; Martínez, J.M.; Eltamaly, A.M.; Kim, Y.-C.; Ahmed, M.A. Design and Implementation of a Cloud-IoT-Based Home Energy Management System. Sensors 2023, 23, 176.
  36. Iliev, Y.; Ilieva, G. A Framework for Smart Home System with Voice Control Using NLP Methods. Electronics 2023, 12, 116.
  37. Xu, B.; Hussain, B.; Wang, Y.; Cheng, H.C.; Yue, C.P. Smart Home Control System Using VLC and Bluetooth Enabled AC Light Bulb for 3D Indoor Localization with Centimeter-Level Precision. Sensors 2022, 22, 8181.
  38. Chen, X.; Fu, Z.; Song, Z.; Yang, L.; Ndifon, A.M.; Su, Z.; Liu, Z.; Gao, S. An IoT and Wearables-Based Smart Home for ALS Patients. IEEE Internet Things J. 2022, 9, 20945.
  39. Barber, R.; Ortiz, F.J.; Garrido, S.; Calatrava-Nicolás, F.M.; Mora, A.; Prados, A.; Vera-Repullo, J.A.; Roca-González, J.; Méndez, I.; Mozos, Ó.M. A Multirobot System in an Assisted Home Environment to Support the Elderly in Their Daily Lives. Sensors 2022, 22, 7983.
  40. Philip, A.; Islam, S.N.; Phillips, N.; Anwar, A. Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home. Sensors 2022, 22, 7102.
  41. Nyangaresi, V.O.; Abduljabbar, Z.A.; Mutlaq, K.A.-A.; Ma, J.; Honi, D.G.; Aldarwish, A.J.Y.; Abduljaleel, I.Q. Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes. Appl. Sci. 2022, 12, 12688.
  42. Putrada, A.G.; Abdurohman, M.; Perdana, D.; Nuha, H.H. Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey. IEEE Access 2022, 10, 45137.
  43. Lee, C.-T.; Chen, L.-B.; Chu, H.-M.; Hsieh, C.-J. Design and Implementation of a Leader-Follower Smart Office Lighting Control System Based on IoT Technology. IEEE Access 2022, 10, 28066.
  44. Griva, A.I.; Boursianis, A.D.; Wan, S.; Sarigiannidis, P.; Psannis, K.E.; Karagiannidis, G.; Goudos, S.K. LoRa-Based IoT Network Assessment in Rural and Urban Scenarios. Sensors 2023, 23, 1695.
  45. Rai, S.C.; Nayak, S.P.; Acharya, B.; Gerogiannis, V.C.; Kanavos, A.; Panagiotakopoulos, T. ITSS: An Intelligent Traffic Signaling System Based on an IoT Infrastructure. Electronics 2023, 12, 1177.
  46. Dzemydienė, D.; Burinskienė, A.; Čižiūnienė, K.; Miliauskas, A. Development of E-Service Provision System Architecture Based on IoT and WSNs for Monitoring and Management of Freight Intermodal Transportation. Sensors 2023, 23, 2831.
  47. Xu, H.; Berres, A.; Yoginath, S.B.; Sorensen, H.; Nugent, P.; Severino, J.; Tennille, S.A.; Moore, A.; Jones, W.; Sanyal, J. Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic. IEEE Trans. Intell. Transp. Syst. 2023, 24, 3145.
  48. Kumar, P.; Kumar, S.V.; Priya, L. Smart and Safety Traffic System for the Vehicles on the Road. In IOT with Smart Systems. Smart Innovation, Systems and Technologies; Choudrie, J., Mahalle, P., Perumal, T., Joshi, A., Eds.; Springer: Singapore, 2023; 312p.
  49. Chakravarty, P.D.; Pandya, J.D.; Dave, A.; Rathod, Y.; Iyer, S.S. Emergency Vehicle-Based Vehicle Detection Model. In Futuristic Trends for Sustainable Development and Sustainable Ecosystems; Ortiz-Rodriguez, F., Ed.; IGI Global: Hershey, PA, USA, 2022; pp. 137–146.
  50. Cao, J.; Zhang, J.; Liu, M.; Yin, S.; An, Y. Green Logistics of Vehicle Dispatch under Smart IoT. Sens. Mater. 2022, 34, 3317.
  51. Mejjaouli, S. Internet of Things based Decision Support System for Green Logistics. Sustainability 2022, 14, 14756.
  52. Raji, C.G.; Shamna, S.K.; Murshidha; Fathimathul, F.V.P.; Shiljiya, K.T. Emergency Vehicles Detection during Traffic Congestion. In Proceedings of the 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 28–30 April 2022; pp. 32–37.
  53. Phan, A.-C.; Trieu, T.-N.; Phan, T.-C. Driver drowsiness detection and smart alerting using deep learning and IoT. Internet Things 2023, 22, 100705.
  54. Kuo, Y.-H.; Leung, J.M.Y.; Yan, Y. Public transport for smart cities: Recent innovations and future challenges. Eur. J. Oper. Res. 2023, 306, 1001.
  55. Rosayyan, P.; Paul, J.; Subramaniam, S.; Ganesan, S.I. An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors. Meas. Sens. 2023, 26, 100697.
  56. Mohammed, K.; Abdelhafid, M.; Kamal, K.; Ismail, N.; Ilias, A. Intelligent driver monitoring system: An Internet of Things-based system for tracking and identifying the driving behavior. Comput. Stand. Interfaces 2023, 84, 103704.
  57. Hari Prasad, S.A.; Kumar, R. IoT cloud system for traffic monitoring and vehicular accidents prevention. AIP Conf. Proc. 2023, 2427, 020055.
  58. Saxena, A.K.; Tripathi, R.C.; Khan, G. Design of a smart public transport system based on IoT. AIP Conf. Proc. 2023, 2427, 020031.
  59. Alanazi, F. Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach. Sustainability 2023, 15, 3158.
  60. ElKashlan, M.; Elsayed, M.S.; Jurcut, A.D.; Azer, M. A Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs). Electronics 2023, 12, 1044.
  61. Liu, D.; Zhang, Y.; Wang, W.; Dev, K.; Khowaja, S.A. Flexible Data Integrity Checking with Original Data Recovery in IoT-Enabled Maritime Transportation Systems. IEEE Trans. Intell. Transp. Syst. 2023, 24, 2618.
  62. Rocha, D.; Teixeira, G.; Vieira, E.; Almeida, J.; Ferreira, J. A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity. Sensors 2023, 23, 1724.
  63. Ghani Khan, M.U.; Elhadef, M.; Mehmood, A. Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network. IEEE Access 2023, 11, 19259.
  64. Vitali, G.; Arru, M.; Magnanini, E. A Scalable Device for Undisturbed Measurement of Water and CO2 Fluxes through Natural Surfaces. Sensors 2023, 23, 2647.
  65. Zou, X.; Liu, W.; Huo, Z.; Wang, S.; Chen, Z.; Xin, C.; Bai, Y.; Liang, Z.; Gong, Y.; Qian, Y.; et al. Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things. Sensors 2023, 23, 2528.
  66. Saban, M.; Bekkour, M.; Amdaouch, I.; El Gueri, J.; Ait Ahmed, B.; Chaari, M.Z.; Ruiz-Alzola, J.; Rosado-Muñoz, A.; Aghzout, O. A Smart Agricultural System Based on PLC and a Cloud Computing Web Application Using LoRa and LoRaWan. Sensors 2023, 23, 2725.
  67. Senoo, E.E.K.; Akansah, E.; Mendonça, I.; Aritsugi, M. Monitoring and Control Framework for IoT, Implemented for Smart Agriculture. Sensors 2023, 23, 2714.
  68. Garg, G.; Gupta, S.; Mishra, P.; Vidyarthi, A.; Singh, A.; Ali, A. CROPCARE: An Intelligent Real-Time Sustainable IoT System for Crop Disease Detection Using Mobile Vision. IEEE Internet Things J. 2023, 10, 2840.
  69. Elashmawy, R.; Uysal, I. Precision Agriculture Using Soil Sensor Driven Machine Learning for Smart Strawberry Production. Sensors 2023, 23, 2247.
  70. Fathy, C.; Ali, H.M. A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher. Sensors 2023, 23, 2091.
  71. Dutta, M.; Gupta, D.; Sahu, S.; Limkar, S.; Singh, P.; Mishra, A.; Kumar, M.; Mutlu, R. Evaluation of Growth Responses of Lettuce and Energy Efficiency of the Substrate and Smart Hydroponics Cropping System. Sensors 2023, 23, 1875.
  72. Bertocco, M.; Parrino, S.; Peruzzi, G.; Pozzebon, A. Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning. Sensors 2023, 23, 2033.
  73. Contreras-Castillo, J.; Guerrero-Ibañez, J.A.; Santana-Mancilla, P.C.; Anido-Rifón, L. SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring. Appl. Sci. 2023, 13, 1961.
  74. Postolache, S.; Sebastião, P.; Viegas, V.; Postolache, O.; Cercas, F. IoT-Based Systems for Soil Nutrients Assessment in Horticulture. Sensors 2023, 23, 403.
  75. Habib, S.; Alyahya, S.; Islam, M.; Alnajim, A.M.; Alabdulatif, A.; Alabdulatif, A. Design and Implementation: An IoT-Framework-Based Automated Wastewater Irrigation System. Electronics 2023, 12, 28.
  76. Azfar, S.; Nadeem, A.; Ahsan, K.; Mehmood, A.; Siddiqui, M.S.; Saeed, M.; Ashraf, M. An IoT-Based System for Efficient Detection of Cotton Pest. Appl. Sci. 2023, 13, 2921.
  77. Singh, R.; Singh, R.; Gehlot, A.; Akram, S.V.; Priyadarshi, N.; Twala, B. Horticulture 4.0: Adoption of Industry 4.0 Technologies in Horticulture for Meeting Sustainable Farming. Appl. Sci. 2022, 12, 12557.
  78. Bristow, N.; Rengaraj, S.; Chadwick, D.R.; Kettle, J.; Jones, D.L. Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture. Sensors 2022, 22, 9100.
  79. Shaikh, F.K.; Karim, S.; Zeadally, S.; Nebhen, J. Recent Trends in Internet-of-Things-Enabled Sensor Technologies for Smart Agriculture. IEEE Internet Things J. 2022, 9, 23583.
  80. Gamal, Y.; Soltan, A.; Said, L.A.; Madian, H.A.; Radwan, A.G. Smart Irrigation Systems: Overview. IEEE Access, 2023; Early Access.
  81. Nadeem, A.; Chatzichristodoulou, D.; Quddious, A.; Shoaib, N.; Vassiliou, L.; Vryonides, P.; Nikolaou, S. UHF IoT Humidity and Temperature Sensor for Smart Agriculture Applications Powered from an Energy Harvesting System. In Proceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, 24–26 November 2022; pp. 186–190.
  82. Kour, K.; Gupta, D.; Gupta, K.; Anand, D.; Elkamchouchi, D.H.; Pérez-Oleaga, C.M.; Ibrahim, M.; Goyal, N. Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation. Sensors 2022, 22, 8905.
  83. Arrubla-Hoyos, W.; Ojeda-Beltrán, A.; Solano-Barliza, A.; Rambauth-Ibarra, G.; Barrios-Ulloa, A.; Cama-Pinto, D.; Arrabal-Campos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. Precision Agriculture and Sensor Systems Applications in Colombia through 5G Networks. Sensors 2022, 22, 7295.
  84. Ryalat, M.; ElMoaqet, H.; AlFaouri, M. Design of a Smart Factory Based on Cyber-Physical Systems and Internet of Things towards Industry 4.0. Appl. Sci. 2023, 13, 2156.
  85. Haricha, K.; Khiat, A.; Issaoui, Y.; Bahnasse, A.; Ouajji, H. Recent technological progress to empower Smart Manufacturing: Review and Potential Guidelines. IEEE Access 2023.
  86. Chen, H.; Jeremiah, S.R.; Lee, C.; Park, J.H. A Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework for Smart Manufacturing in IIoT Environment. Appl. Sci. 2023, 13, 1440.
  87. Noor-A-Rahim, M.; John, J.; Firyaguna, F.; Sherazi, H.H.R.; Kushch, S.; Vijayan, A.; O’Connell, E.; Pesch, D.; O’Flynn, B.; O’Brien, W.; et al. Wireless Communications for Smart Manufacturing and Industrial IoT: Existing Technologies, 5G and Beyond. Sensors 2023, 23, 73.
  88. Hsu, C.-H.; Cheng, S.-J.; Chang, T.-J.; Huang, Y.-M.; Fung, C.-P.; Chen, S.-F. Low-Cost and High-Efficiency Electromechanical Integration for Smart Factories of IoT with CNN and FOPID Controller Design under the Impact of COVID-19. Appl. Sci. 2022, 12, 3231.
  89. Yu, W.; Liu, Y.; Dillon, T.; Rahayu, W.; Mostafa, F. An Integrated Framework for Health State Monitoring in a Smart Factory Employing IoT and Big Data Techniques. IEEE Internet Things J. 2022, 9, 2443.
  90. Kwak, K.-J.; Park, J.-M. A Study on Semantic-Based Autonomous Computing Technology for Highly Reliable Smart Factory in Industry 4.0. Appl. Sci. 2021, 11, 10121.
  91. Hsu, T.-C.; Tsai, Y.-H.; Chang, D.-M. The Vision-Based Data Reader in IoT System for Smart Factory. Appl. Sci. 2022, 12, 6586.
  92. Abril-Jiménez, P.; Merino-Barbancho, B.; Fico, G.; Martín Guirado, J.C.; Vera-Muñoz, C.; Mallo, I.; Lombroni, I.; Cabrera Umpierrez, M.F.; Arredondo Waldmeyer, M.T. Evaluating IoT-Based Services to Support Patient Empowerment in Digital Home Hospitalization Services. Sensors 2023, 23, 1744.
  93. Ahmed, S.T.; Kumar, V.; Kim, J. AITel: eHealth Augmented Intelligence based Telemedicine Resource Recommendation Framework for IoT devices in Smart cities. IEEE Internet Things J. 2023.
  94. Le, N.T.; Thwe Chit, M.M.; Truong, T.L.; Siritantikorn, A.; Kongruttanachok, N.; Asdornwised, W.; Chaitusaney, S.; Benjapolakul, W. Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory. Sensors 2023, 23, 546.
  95. Chang, J.; Ong, H.; Wang, T.; Chen, H.-H. A Fully Automated Intelligent Medicine Dispensary System Based on AIoT. IEEE Internet Things J. 2022, 9, 23954.
  96. Rathee, G.; Saini, H.; Kerrache, C.A.; Herrera-Tapia, J. A Computational Framework for Cyber Threats in Medical IoT Systems. Electronics 2022, 11, 1705.
  97. Rybak, G.; Strzecha, K.; Krakós, M. A New Digital Platform for Collecting Measurement Data from the Novel Imaging Sensors in Urology. Sensors 2022, 22, 1539.
  98. Fan, L. Usage of Narrowband Internet of Things in Smart Medicine and Construction of Robotic Rehabilitation System. IEEE Access 2022, 10, 6246.
  99. Nasser, A.R.; Hasan, A.M.; Humaidi, A.J.; Alkhayyat, A.; Alzubaidi, L.; Fadhel, M.A.; Santamaría, J.; Duan, Y. IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes. Electronics 2021, 10, 2719.
  100. Wang, B.; Hu, X.; Zhang, J.; Xu, C.; Gao, Z. Intelligent Internet of Things in Mammography Screening Using Multicenter Transformation between Unified Capsules. IEEE Internet Things J. 2023, 10, 1536.
  101. Firouzi, F.; Jiang, S.; Chakrabarty, K.; Farahani, B.; Daneshmand, M.; Song, J.; Mankodiya, K. Fusion of IoT, AI, Edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a Case Study in Healthcare and Medicine. IEEE Internet Things J. 2023, 10, 3686.
  102. Kim, B.; Kim, S.; Lee, M.; Chang, H.; Park, E.; Han, T. Application of an Internet of Medical Things (IoMT) to Communications in a Hospital Environment. Appl. Sci. 2022, 12, 12042.
  103. Hakola, L.; Jansson, E. Sustainable substrate for printed electronics. In Printing for Fabrication 2019: Materials, Applications, and Process—Technical Program and Proceedings; The Society for Imaging Science and Technology, IS&T: Cambridge, MA, USA, 2019; pp. 132–137.
  104. Jansson, E.; Lyytikäinen, J.; Tanninen, P.; Eiroma, K.; Leminen, V.; Immonen, K.; Hakola, L. Suitability of Paper-Based Substrates for Printed Electronics. Materials 2022, 15, 957.
  105. Prenzel, T.M.; Gehring, F.; Fuhs, F.; Albrecht, S. Influence of design properties of printed electronics on their environmental profile. Matér. Tech. 2021, 109, 506.
  106. Gudrun, S.; Halvor, K.; Thordur, M. Greenhouse Gas Emissions from Silicon Production -Development of Carbon Footprint with Changing Energy Systems. In Proceedings of the Proceedings of the 16th International Ferro-Alloys Congress (INFACON XVI), Virtual, 12 September 2021.
  107. Khan, Y.; Thielens, A.; Muin, S.; Ting, J.; Baumbauer, C.; Arias, A.C. A New Frontier of Printed Electronics: Flexible Hybrid Electronics. Adv. Mater. 2020, 32, 1905279.
  108. Hussein, R.N.; Schlingman, K.; Noade, C.; Carmichael, R.S.; Carmichael, T.B. Shellac-paper composite as a green substrate for printed electronics. Flex. Print. Electron. 2022, 7, 045007.
  109. Agate, S.; Joyce, M.; Lucia, L.; Pal, L. Cellulose and nanocellulose-based flexible-hybrid printed electronics and conductive composites—A review. Carbohydr. Polym. 2018, 198, 249.
  110. Liyanage, S.; Acharya, S.; Parajuli, P.; Shamshina, J.L.; Abidi, N. Production and Surface Modification of Cellulose Bioproducts. Polymers 2021, 13, 3433.
  111. Koga, H.; Nogi, M. Flexible Paper Electronics. In Organic Electronics Materials and Devices; Ogawa, S., Ed.; Springer: Tokyo, Japan, 2015.
  112. Jaiswal, A.K.; Kumar, V.; Jansson, E.; Huttunen, O.-H.; Yamamoto, A.; Vikman, M.; Khakalo, A.; Hiltunen, J.; Behfar, M.H. Biodegradable Cellulose Nanocomposite Substrate for Recyclable Flexible Printed Electronics. Adv. Electron. Mater. 2023, 9, 2201094.
  113. Liang, Y.; Wei, Z.; Wang, H.E.; Wang, R.; Zhang, X. Flexible freestanding conductive nanopaper based on PPy:PSS nanocellulose composite for supercapacitors with high performance. Sci. China Mater. 2023, 66, 964.
  114. Zhong, J.; Li, G.; Guo, R.; Ning, H.; Zhang, H.; Fang, Z.; Fu, X.; Wei, X.; Yao, R.; Peng, J. Bilayer Metal Oxide Channel Thin Film Transistor with Flat Interface Based on Smooth Transparent Nanopaper Substrate. IEEE Electron Device Lett. 2022, 43, 2113.
  115. Zhang, J.; Liu, D.; Shi, Q.; Yang, B.; Guo, P.; Fang, L.; Dai, S.; Xiong, L. Bioinspired organic optoelectronic synaptic transistors based on cellulose nanopaper and natural chlorophyll-a for neuromorphic systems. Npj Flex Electron. 2022, 6, 30.
  116. Liang, Y.; Wei, Z.; Wang, H.-E.; Flores, M.; Wang, R.; Zhang, X. Flexible and freestanding PANI: PSS/CNF nanopaper electrodes with enhanced electrochemical performance for supercapacitors. J. Power Sources 2022, 548, 232071.
  117. Cunha, I.; Ferreira, S.H.; Martins, J.; Fortunato, E.; Gaspar, D.; Martins, R.; Pereira, L. Foldable and Recyclable Iontronic Cellulose Nanopaper for Low-Power Paper. Electron. Adv. Sustain. Syst. 2022, 6, 2200177.
  118. Li, Z.; Zhou, J.; Zhong, J. Nanocellulose Paper for Flexible Electronic Substrate. In Emerging Nanotechnologies in Nanocellulose; Hu, L., Jiang, F., Chen, C., Eds.; NanoScience and Technology: Danville, CA, USA, 2023; 211p.
  119. Moon, R.J.; Schueneman, G.T.; Simonsen, J. Overview of Cellulose Nanomaterials, Their Capabilities and Applications. JOM 2016, 68, 2383.
  120. Varshney, S.; Mishra, N.; Gupta, M.K. Progress in nanocellulose and its polymer based composites: A review on processing, characterization, and applications. Polym. Compos. 2021, 42, 3660.
  121. Liu, W.; Liu, K.; Du, H.; Zheng, T.; Zhang, N.; Xu, T.; Pang, B.; Zhang, X.; Si, C. Cellulose Nanopaper: Fabrication, Functionalization, and Applications. Nano-Micro. Lett. 2022, 14, 104.
  122. Lizundia, E.; Delgado-Aguilar, M.; Mutjé, P.; Fernández, E.; Robles-Hernandez, B.; de la Fuente, M.R.; Vilas, J.L. Cu-coated cellulose nanopaper for green and low-cost electronics. Cellulose 2016, 23, 1997.
  123. Shi, C.; Wu, Z.; Xu, J.; Wu, Q.; Li, D.; Chen, G.; He, M.; Tian, J. Fabrication of transparent and superhydrophobic nanopaper via coating hybrid SiO2/MWCNTs composite. Carbohydr. Polym. 2019, 225, 115229.
  124. Seydibeyoğlu, M.Ö.; Dogru, A.; Wang, J.; Rencheck, M.; Han, Y.; Wang, L.; Seydibeyoğlu, E.A.; Zhao, X.; Ong, K.; Shatkin, J.A.; et al. Review on Hybrid Reinforced Polymer Matrix Composites with Nanocellulose, Nanomaterials, and Other Fibers. Polymers 2023, 15, 984.
  125. Faraco, T.A.; Fontes, M.d.L.; Paschoalin, R.T.; Claro, A.M.; Gonçalves, I.S.; Cavicchioli, M.; Farias, R.L.d.; Cremona, M.; Ribeiro, S.J.L.; Barud, H.d.S.; et al. Review of Bacterial Nanocellulose as Suitable Substrate for Conformable and Flexible Organic Light-Emitting Diodes. Polymers 2023, 15, 479.
  126. Jain, K.; Wang, Z.; Garma, L.D.; Engel, E.; Ciftci, G.C.; Fager, C.; Larsson, P.A.; Wågberg, L. 3D printable composites of modified cellulose fibers and conductive polymers and their use in wearable electronics. Appl. Mater. Today 2023, 30, 101703.
  127. Chen, Z.; Hu, Y.; Shi, G.; Zhuo, H.; Ali, M.A.; Jamróz, E.; Zhang, H.; Zhong, L.; Peng, X. Advanced Flexible Materials from Nanocellulose. Adv. Funct. Mater. 2023, 33, 2214245.
  128. Wang, X.; Li, X.; Wang, B.; Chen, J.; Zhang, L.; Zhang, K.; He, M.; Xue, Y.; Yang, G. Preparation of Salt-Induced Ultra-Stretchable Nanocellulose Composite Hydrogel for Self-Powered Sensors. Nanomaterials 2023, 13, 157.
  129. Duroc, Y. From Identification to Sensing: RFID Is One of the Key Technologies in the IoT Field. Sensors 2022, 22, 7523.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , ,
View Times: 558
Revisions: 3 times (View History)
Update Date: 26 Jun 2023
1000/1000
Video Production Service