Adopting Smart Technology in Rural Regions: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Jessie Wu and Version 1 by Salvatore Flavio Pileggi.

Smart Technology is a quickly and constantly evolving concept; it has different applications that cover a wide range of areas, such as healthcare, education, business, agriculture, and manufacturing. An effective application of these technologies increases productivity and performance within complex systems. OuResear contribution explicitlychers targets Rural Regions and their Sustainable Development.

  • smart technology
  • sustainable development
  • rural regions
  • smart city

1. From Smart City to Rural Areas

Smart City is commonly defined as an integration of Smart Technology with city elements (people, information, and other technology) to promote sustainable development practices that address the urbanization growth challenges [175,176][1][2]. According to this approach, people, information, and technology must be integrated to define a smart ecosystem that enhances quality of life [177,178,179][3][4][5]. Initially, Smart City was oriented to sustainable urban development with the goal of integrating and optimizing the use of resources [180][6]. More recently, the Smart City model has been applied more broadly to improve different aspects of life [181][7].
The Smart City approach has been widely accepted as a successful model for Smart Technology adoption. This model is often considered a reference to be adapted to address different challenges [182][8]. It is probably the case for rural regions that need to enhance their sustainability and foster development [183][9]. Therefore, there is a potential for the Smart City model to be adapted in rural regions to address major sustainable development challenges [183,184,185,186][9][10][11][12].
As far as the authors know, there is no application-oriented model that explicitly targets rural regions. Although applications may be considered the same as for Smart City (e.g., [187][13]), the Smart City model cannot be directly applied to rural regions due to the significant differences and peculiarities. Hence, existing solutions are expected to be adapted and customized to face sustainability challenges, improve quality of life, foster the economy, support social welfare, and enhance the stability in rural communities for people and organizations [185,188,189,190][11][14][15][16].
The United Nations reports that over 45% of the world’s population still resides in rural areas, so rural development remains a compelling challenge as many macro indicators points out low performance in rural development. For instance, it is estimated that the 80% of the world’s poverty comes from the population who live in the rural regions [191][17]. Additionally, the United Nations explicitly addressed concerning trends looking at the main pillars [74][18].

2. Rural Areas: Application Domains

2.1. Smart Farming

Smart Farming can be defined as an integration of Smart Technology with agricultural equipment to manage and automate farming activities [192][19]. The recent development of Smart Technology has made considerable changes to traditional farm activities, which are usually time and effort intensive [193][20]. Smart Farming aims to mitigate human intervention by automating the agricultural processes [194][21]. The significance of Smart Farming has recently increased due to its important role in addressing the global challenges of sustainable food supply [195][22]. These challenges result from the increased global population and food prices caused by a decreasing number of workers in traditional farms [196][23]. The applications of Smart Farming can contribute to enhancing sustainable development [197,198,199][24][25][26]. Smart Farming can provide numerous advantages to the agricultural industry, including increased crop yield, time and labor savings, reduced costs, and improved crop quality and quantity [200,201,202,203,204][27][28][29][30][31]. In general terms, adopting Smart Farming can offer unique benefits in rural regions by promoting a sustainable development approach [205][32]. In this context, Smart Technology in rural agriculture can perform multiple tasks automatically instead of using traditional approaches, which are time and energy intensive [206,207,208][33][34][35]. Smart Farming impacts different tasks, such as irrigation, fertilization, temperature control, and harvesting [201,209,210,211,212][28][36][37][38][39]. Moreover, Smart Farming can involve cattle activities by monitoring the nutrition process and health status of the entire herd [213][40]. In addition, it has the potential to improve e-commerce in a rural context by exploring new markets and enhancing the ability to connect with potential buyers, monitor market patterns, receive real-time pricing data, and gain deeper insights into consumer preferences, enabling the farmers to customize their products accordingly [214,215,216,217,218][41][42][43][44][45]. As urbanization continues, more people move from rural areas to urban cities in search for better employment opportunities. This trend causes a decrease in the number of farmers [226][46]. That is likely to happen in more developed countries, where cities are the preferred places for majority of people. It also causes a decline in the number of farms and a reduction in the amount of land being used for agriculture, which might be converted to non-agricultural uses. The consequences of this action could be reflected in increases in food prices and additional challenges for supply chains [227][47].

2.2. Smart Healthcare

Smart Healthcare is defined as a process of integrating Smart Technology within healthcare to improve the efficiency and quality of services, including better medical solutions and the development of proactive steps, through patient data analysis [228][48].  Smart Healthcare usually relies on cutting-edge technology [229,230,231,232][49][50][51][52]. The role of this type of technology is oriented to supporting treatment, such as improving the interaction between patients and doctors, monitoring and forecasting the patient’s condition, illness prevention and diagnosis, medical decision-making, and minimization of human mistakes in complex operations [229,233,234,235,236][49][53][54][55][56]. Smart Healthcare is also expected to play a significant role in creating more sustainable systems by improving access to services (telehealth), optimizing healthcare resources and their efficiency, and promoting preventive healthcare [237,238,239][57][58][59]. There is concrete evidence of effectiveness documented in the literature for opportunities like smart wearable devices [83,191,192,193][17][19][20][60] and Cloud solutions for data storage [240,241][61][62]. Big Data and AI are advancing the capability of diagnosis and interpretation and, more holistically, to medical research development [242][63]. Moreover, recent studies show the contribution of AI and Robotics in critical surgery operations [243,244,245][64][65][66]. The Smart Healthcare model could play a key role in addressing and bridging the gap with cities, given the historical shortage of qualified structures and services [23,246,247,248][67][68][69][70]. For example, a solid implementation of remote and more integrated services defines an explicit mainstream for development [249][71]. Smart Technology can facilitate remote access to patients’ data, especially for older people who require periodic check-ups [250,251][72][73]. Additionally, it can contribute to healthcare by predicting health developments so that more proactive solutions can be devised [252,253][74][75]. As people move from rural regions to urban cities in increasing numbers, rural regions are experiencing a further crisis in terms of healthcare facilities, providers, and resources [261][76]. This is consolidating gaps, especially in more and less developed countries. It reflects a more general focus on populated areas and a consequent lack of attention to rural regions. On the other side, in the least developed countries, rural regions usually receive healthcare services that are comparable to those offered in urban areas [262,263][77][78].

2.3. Smart Energy

Smart Energy can be defined as the ability to adopt Smart Technology to optimize energy production, distribution, and consumption to create more sustainable and reliable energy systems for both the consumer and the environment [264][79].  Smart Technology in the energy sector has recently expanded to reduce the construction of power stations that normally cause an increase in pollution [267][80]. The application of Smart Energy is gradually becoming part of our daily life. For instance, smart meters can dynamically optimize energy consumption [268,269][81][82]; electrical appliances can avoid peak hours to reduce costs [270][83]; outdoor lights can be controlled automatically to adapt to actual needs [271][84]. Moreover, with the recent increase in global demand, Smart Technology is expected to contribute to sustainable energy systems [272][85], which should be efficient, affordable, and aligned with environmental challenges [273][86]. Smart Energy Systems are expected to push the diversification of energy production toward an optimized use of renewable energy systems [274,275,276,277][87][88][89][90] and contribute to a decrease in the number of power stations [278][91]. From a rural region perspective, the application of Smart Energy is expected to enhance sustainability to provide systems that are clean, reliable, and affordable. Large and open areas normally characterize the ability of rural regions to generate clean energy in a way that is friendly to the environment at a lower cost [279,280,281][92][93][94]. Solar and wind energy systems are potential applications for Smart Energy to supply rural communities that use smart inverters to optimize energy production [282,283,284,285][95][96][97][98]. The characteristics of rural regions make them suitable for generating renewable energy: firstly, they have a surplus of open spaces, which is often necessary for renewable energy resources. Secondly, there is less obstruction for the sun and wind, allowing for better performance. Thirdly, rural regions have lower population densities than urban areas, meaning fewer concerns about disrupting habitats exist. Fourthly, lower electricity demand makes it easier to meet the local energy needs with renewable technologies [286][99]. Another example is a smart microgrid, which is a small, self-contained energy system that can operate independently to easily provide minor communities with energy supply. Its primary focus is to provide reliable and sustainable energy access to isolated areas [287,288][100][101]. The migration trends still play a key role as, in general terms, cities are becoming larger and more dense with an intrinsic additional pressure on the urban energy networks. This is particularly evident in both more and less developed countries that are characterized by highly densely populated cities [296][102]. On the other side, the decreasing population in rural regions might cause difficulties for energy supply over scattered areas and cause rising costs. This scenario is different in the least developed countries, where rural regions are highly populated, and people live in big communities that are concentrated in a few places. Moreover, rural regions in the least developed countries can have more options for energy supplies, such as the common energy network, renewable energy, and traditional sources [297][103].

2.4. Smart Education

Smart Education can be defined as the ability to adopt Smart Technology in the education system to enhance effective, suitable, and sustainable learning approaches through innovative methods for more efficient outcomes [298][104]. It focuses on integrating smart learning technology into the education system [299,300,301,302][105][106][107][108]. It can provide an accessible and lifelong learning system with contemporary lifestyles [303,304,305,306,307][109][110][111][112][113]. Smart Technology can generate, store, analyze, and visualize massive data from a given domain to provide the best learning content [308,309][114][115]. Smart Education is usually characterized by an innovative environment that integrates Smart Technology in its infrastructure to provide interaction in real-time to the learners [310,311][116][117]. It also known as a self-directed and self-motivated system that is often enriched by resources that can provide learners with many advantages, such as connection, openness, and independence [312][118]. Smart Education is an appealing system compared to traditional education because of the novelty in teaching styles. Smart Education usually includes unique learning methods, such as gamification, interactive and collaborative groups, tactile-auditory presentation, and simulations [313,314][119][120]. Smart Education systems are expected to contribute to sustainable education systems that should be more efficient and accessible, and providing contemporary teachers with a more sustainable teaching lifestyle and allow for learners to gain the necessary knowledge and skills [315,316,317,318,319][121][122][123][124][125]. From a rural region perspective, the application of Smart Education is expected to enhance sustainability to provide accessible, comfortable, and sustainable learning systems that can address the challenges of disparity with urban areas. This is especially true for students with limited access to educational resources or constraints to move rural [320,321][126][127]. From a practical perspective, Smart Education can provide an accessible and comfortable way of learning through content and resources accessed via e-portfolio platforms that can be connected to remotely with common devices [309[115][128][129][130][131][132][133][134],322,323,324,325,326,327,328], enhancing remote activity [309,329,330,331][115][135][136][137]. For instance, augmented reality technology (AR) has a specific potential impact on rural education settings by enhancing actual practices and avoiding expensive travel [332,333,334,335,336][138][139][140][141][142]. Education is essential for human life. In both more and less developed countries, most educational resources and infrastructures are in cities [346][143], while there is a tangible gap with rural areas. The most influential factor in more and less developed countries is the drain of qualified educators to urban areas [347][144]. In the least developed countries, there is a much less tangible gap as the few resources are normally available in rural regions [80][145], where the quality of education is comparable to that available in cities [348][146].

2.5. Smart Government

There is no unique and universally accepted definition for Smart Government, as it is still a relatively new and emerging domain. Still, it is often understood as adopting Smart Technology and innovative solutions to enhance the efficiency of government services, performance, and responsiveness [349][147].  The Smart Government approach focuses on integrating new, emerging technology into its systems to improve government services and make them more efficient, effective, and sustainable [351][148]. Smart Government aims to find innovative solutions that allow for easier methods of connecting with the public that are increasingly responsive in the shortest time possible [352,353,354][149][150][151]. By utilizing Smart Technology, Smart Government can understand the different needs of people and organizations through the analysis of vast volumes of data from their transactions [355][152]. This can increase the efficiency of transparency of services and help create policies that are relevant to real-life situations [356,357][153][154]. Smart Government is expected to contribute to sustainable development by ensuring the minimum level of quality of life through leverage of Smart Technology to create programs, policies, and services that promote sustainable practices. These practices can become a sociotechnical approach to sustainably address the gap challenges between government and other stakeholders [358,359,360][155][156][157]. From the rural region perspective, the application of Smart Technology is expected to enhance sustainability by leveraging Smart Technology and innovative solutions to address the connection challenges between rural areas and the government. Therefore, the adoption of Smart Government has a potential impact on rural regions through enhanced online services that may encourage development. Moreover, Smart Government is expected provide accessible ways for rural residents to engage with government decisions making [351,361,362,363][148][158][159][160]. Rural people can then challenge to the government to provide better solutions that look to improve the quality of life and increase the prosperity of their regions [364,365][161][162]. Considering the current level of urbanization, in both more and less developed countries, governments can manage and provide services to their citizens in a relatively easy way in major cities [372][163]. Rural regions require a smart approach to receive a comparable level of service [373][164]. Again, in the least developed countries, the gap between urban and rural areas is much less evident [374][165].

2.6. Smart SMEs

Small and Medium Enterprises (SMEs) are classified based on business capacity and number of employees [375][166]. Smart SME can be defined as the approach of a SME business to adopt Smart Technology into its business strategy to optimize the daily business operations, services, and production [376][167].  Smart SMEs focus on integrating Smart Technology into business systems to increase the efficiency of business performance [377,378,379][168][169][170]. This process can improve decision-making that is based on data analytics and help improve weakness of the business and anticipate coming risks [380,381,382,383][171][172][173][174]. The Smart SMEs approach could bring many benefits to traditional business. Smart Technology might address business challenges and optimize business performance, such as cost reduction. It could enhance the quality of services and products and better understand customer needs [384,385][175][176]. SMEs are shaping most segments of the global business sector and have a significant impact that directly influences the gross domestic product (GDP). This is particularly true in developing and less developed countries, according to the World Bank [224][177]. Therefore, the significance of SMEs is growing due to their role as major job creators that require low capital to start the businesses [386][178]. Smart SMEs are expected to contribute to business sustainability to remain competitive in the market, ensure long-term economic growth, and support national economic growth that can improve the society’s social welfare. Moreover, Smart SMEs can efficiently improve resources to maximize production with minimal waste [387,388][179][180]. From the rural region perspective, the application of Smart SMEs is expected to enhance business sustainability and economic growth for rural communities [389,390][181][182]. For instance, rural SMEs can adopt Smart Technology, such as Cloud Computing, to overcome the unaffordable costs of buying new business software, especially those with limited IT capability [98,391][183][184]. Moreover, Smart Technology can help rural SMEs manage their business resources effectively to automate different activities that require more workers. Smart SMEs can also provide stable job opportunities for skilled people and educated people that cause them to remain in rural areas rather than moving away [392,393,394][185][186][187]. In urban and rural areas, SME presents an intrinsic and significant gap [403][188]. This is very evident in both more and less developed countries, where SME performance and probability of success in major cities clearly overcomes that in a rural context. This occurs because SMEs can become more competitive in highly populated areas due to larger markets and number of potential customers, the ability to produce goods and services at lower costs, the availability of labor, and the infrastructure required to operate efficiently. SMEs are not likely to have the same opportunities in rural regions [404][189]. Because of the different characteristics of rural regions, SME businesses might find relatively more favorable conditions in this context in least developed countries [99,405][190][191].

2.7. Smart Manufacturing

Smart Manufacturing adopts Smart Technology to optimize manufacturing processes and, accordingly, increase efficiency [406][192]. Smart Manufacturing integrates the different resources to enable connection and collaboration, which results in increased productivity at a lower cost [407,408][193][194]. The Smart Manufacturing approach focuses on managing multi-manufacturing activities within the manufacturing ecosystem [409[195][196],410], which aims to automate operations, reduce costs, and increase productivity [411,412,413,414][197][198][199][200]. In general terms, Smart Manufacturing fosters a Sustainable Development approach by improving efficiency and driving product innovation [415][201]. That is relevant in rural regions where a smart approach is expected to contribute to innovative solutions that can overcome current challenges, such as the lack of skilled workers [416,417][202][203]. By enhancing industries’ performance in rural regions, Smart Manufacturing contributes to enhancing resilience and adaptivity to gain a competitive advantage [418,419][204][205]. Additionally, a tangible impact is expected on employment, given the current critical difficulty in attracting people. Smart Technology can play a significant role in automating processes that require or involve a large number of workers [420,421,422][206][207][208]. Similarly, Smart Manufacturing can holistically improve the socio-economic condition of communities by creating job opportunities for educated people in a more attractive context. It has a direct effect on improving stability and driving economic growth. Finally, Smart Manufacturing can provide training opportunities for rural residents who graduate from universities or schools and seek training opportunities in the local community [423,424][209][210]. In general terms, high population density is favorable to manufacturing [426][211], and the shortage in the labor market may negatively affect a business, particularly in more developed countries. Smart Manufacturing has an opportunity to become determinant in the rural regions of the least developed countries, where population density is high and labor resources are usually available [427][212]. Smart Manufacturing solutions are expected to be designed as a function of the population density and other available resources [428][213].

2.8. Smart Living

Smart Living is a generic concept that refers to the application of Smart Technology to improving lifestyle so that it is more convenient, efficient, and sustainable [429][214]. In general terms, Smart Living plays an important role in enhancing sustainable development [430][215]. It leverages technology for most life aspects to improve the quality of life and sustainability for social communities to make daily life more efficient and more accessible [431,432][216][217]. Because of its broad purpose, Smart Living includes different sub-categories/applications, which are briefly discussed in this section. Smart Homes refers to the application of Smart Technology to domestic environments to better address people’s needs [433,434][218][219]. For instance, it can involve lighting, heating, and ventilation, as well as energy and security management; automation and remote control are also typical functions [435][220]. These applications allow residents to control various appliances and devices in their home via smartphones or voice commands. For instance, doors are automatically unlocked using facial recognition, lights are managed through presence sensors, temperature is automatically adjusted to a comfortable level, etc. [436,437][221][222]. Smart Waste Management is the corresponding Smart Technology adoption to foster a sustainable approach in this area [438][223]. Smart Waste Management is a key and central concept for public health, people’s well-being, and the environment [439][224]. An example of Smart Waste Management applications include Smart Bins [440][225], which can optimize overhead costs. For instance, trucks can collect bins where the waste level is over 80%, and they can be directed via GPS to reach their destination using the shortest possible route to reduce consumption and costs [441][226]. Smart Safety Systems address general safety issues by incorporating sensor data and data analysis into safety operations to deal with and anticipate threats [442][227]. The most intuitive example is a smart approach to surveillance [443,444,445][228][229][230]. Smart Climate and Environment System refers to an advanced use of technology to address the challenges of climate change and environmental degradation by enhancing sustainable development for a livable future [446,447][231][232]. In the last decade, climate change and environmental degradation have hugely impacted agriculture, landscape, and natural resources [448,449][233][234]. Smart Technology has therefore gained more and more relevance to face sustainability challenges [432][217], for instance, given the enhanced capabilities in terms of monitoring and analysis [447,450][232][235]. Smart Living is evidently a critical concept also in the rural context, where it is expected to play a significant role in enhancing sustainable development and improving the people’s quality of life [18,190,451,452][16][236][237][238]. Smart Living is likely to be successfully implemented in the more and less developed countries, where the population density is high in urban areas [475][239]. More and less developed countries present a generic competitive advantage because of the availability of infrastructure and resources. In contrast, this is perceived to be much more challenging in rural regions of more and less developed countries due to the lack of infrastructure and ICT availability [476][240]. In the least developed countries, Smart Living could find opposite conditions [477][241], as mentioned in previous discussions.

References

  1. Schaffers, H.; Komninos, N.; Pallot, M. Smart Cities as Innovation Ecosystems Sustained by the Future Internet. 2012. Available online: https://www.urenio.org/wp-content/uploads/2012/04/2012-FIREBALL-White-Paper-Final.pdf (accessed on 10 April 2023).
  2. Barrionuevo, J.M.; Berrone, P.; Ricart, J.E. Smart cities, sustainable progress. IESE Insight 2012, 14, 50–57.
  3. Albino, V.; Berardi, U.; Dangelico, R.M. Smart cities: Definitions, dimensions, performance, and initiatives. J. Urban Technol. 2015, 22, 3–21.
  4. Nam, T.; Pardo, T.A. Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, College Park, MD, USA, 12–15 June 2011.
  5. Bakıcı, T.; Almirall, E.; Wareham, J. A smart city initiative: The case of Barcelona. J. Knowl. Econ. 2013, 4, 135–148.
  6. Hajduk, S. The concept of a smart city in urban management. Bus. Manag. Educ. 2016, 14, 34–49.
  7. Shrivastava, P.; Ivanaj, S.; Ivanaj, V. Strategic technological innovation for sustainable development. Int. J. Technol. Manag. 2016, 70, 76–107.
  8. Mutule, A.; Domingues, M.; Ulloa-Vásquez, F.; Carrizo, D.; García-Santander, L.; Dumitrescu, A.-M.; Issicaba, D.; Melo, L. Implementing smart city technologies to inspire change in consumer energy behaviour. Energies 2021, 14, 4310.
  9. Shcherbina, E.; Gorbenkova, E. Smart city technologies for sustainable rural development. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2018.
  10. Naldi, L.; Nilsson, P.; Westlund, H.; Wixe, S. What is smart rural development? J. Rural Stud. 2015, 40, 90–101.
  11. Prause, G.; Boevsky, I. Smart rural development. Agric. Econ. Manag. 2015, 60, 63–69.
  12. Rochman, G.P.; Chofyan, I.; Sakti, F. Understanding the smart society in rural development. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2020.
  13. Pop, M.D.; Proștean, O. Identification of significant metrics and indicators for smart mobility. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2019.
  14. Cowie, P.; Townsend, L.; Salemink, K. Smart rural futures: Will rural areas be left behind in the 4th industrial revolution? J. Rural Stud. 2020, 79, 169–176.
  15. Brewer, E.; Demmer, M.; Du, B.; Ho, M.; Kam, M.; Nedevschi, S.; Pal, J.; Patra, R.; Surana, S.; Fall, K. The case for technology in developing regions. Computer 2005, 38, 25–38.
  16. Zhang, X.; Zhang, Z. How do smart villages become a way to achieve sustainable development in rural areas? Smart village planning and practices in China. Sustainability 2020, 12, 10510.
  17. World Bank. Rural Poverty. 2023. Available online: https://shorturl.at/jxyW4 (accessed on 10 April 2023).
  18. United Nations. Three Pillars of Sustainable Development. 2023. Available online: https://shorturl.at/tF024 (accessed on 10 April 2023).
  19. Pivoto, D.; Waquil, P.D.; Talamini, E.; Finocchio, C.P.; Dalla Corte, V.F.; de Vargas Mores, G. Scientific development of smart farming technologies and their application in Brazil. Inf. Process. Agric. 2018, 5, 21–32.
  20. Abdullahi, H.S.; Mahieddine, F.; Sheriff, R.E. Technology impact on agricultural productivity: A review of precision agriculture using unmanned aerial vehicles. In Proceedings of the International Conference on Wireless and Satellite Systems, Bradford, UK, 6–7 July 2015; Springer: Cham, Switzerland, 2015.
  21. Grogan, A. Smart farming. Eng. Technol. 2012, 7, 38–40.
  22. Symeonaki, E.G.; Arvanitis, K.G.; Piromalis, D.D. Cloud computing for IoT applications in climate-smart agriculture: A review on the trends and challenges toward sustainability. In Proceedings of the Innovative Approaches and Applications for Sustainable Rural Development: 8th International Conference, HAICTA 2017, Chania, Crete, Greece, 21–24 September 2017; Selected Papers 8. Springer: Cham, Switzerland, 2019.
  23. Ciccarese, L.; Silli, V. The role of organic farming for food security: Local nexus with a global view. Future Food J. Food Agric. Soc. 2016, 4, 56–67.
  24. Musa, S.F.P.D.; Basir, K.H.; Luah, E. The role of smart farming in sustainable development. Int. J. Asian Bus. Inf. Manag. (IJABIM) 2022, 13, 1–12.
  25. Musa, S.F.P.D.; Basir, K.H. Smart farming: Towards a sustainable agri-food system. Br. Food J. 2021, 123, 3085–3099.
  26. Raja, L.; Vyas, S. The study of technological development in the field of smart farming. In Smart Farming Technologies for Sustainable Agricultural Development; IGI Global: Hershey, PA, USA, 2019; pp. 1–24.
  27. Moysiadis, V.; Sarigiannidis, P.; Vitsas, V.; Khelifi, A. Smart farming in Europe. Comput. Sci. Rev. 2021, 39, 100345.
  28. Dagar, R.; Som, S.; Khatri, S.K. Smart farming–IoT in agriculture. In Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 11–12 July 2018; IEEE: Piscataway, NJ, USA, 2018.
  29. Farooq, M.S.; Riaz, S.; Abid, A.; Abid, K.; Naeem, M.A. A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. IEEE Access 2019, 7, 156237–156271.
  30. Madushanki, A.R.; Halgamuge, M.N.; Wirasagoda, W.S.; Syed, A. Adoption of the Internet of Things (IoT) in agriculture and smart farming towards urban greening: A review. Int. J. Adv. Comput. Sci. Appl. 2019, 10, 11–28.
  31. Joshi, A.; Dandekar, I.; Hargude, N.; Shrotri, A.; Dandekar, A. Application of Internet of the Things (IOT) for the Water Conservation and Entrepreneurship in the Rural Area. In Proceedings of the 2019 IEEE Pune Section International Conference (PuneCon), Pune, India, 18–20 December 2019; IEEE: Piscataway, NJ, USA, 2019.
  32. Mohamed, E.S.; Belal, A.A.; Abd-Elmabod, S.K.; El-Shirbeny, M.A.; Gad, A.; Zahran, M.B. Smart farming for improving agricultural management. Egypt. J. Remote Sens. Space Sci. 2021, 24, 971–981.
  33. Dlodlo, N.; Kalezhi, J. The internet of things in agriculture for sustainable rural development. In Proceedings of the 2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Windhoek, Namibia, 17–20 May 2015; IEEE: Piscataway, NJ, USA, 2015.
  34. Doshi, J.; Patel, T.; Bharti, S.K. Smart Farming using IoT, a solution for optimally monitoring farming conditions. Procedia Comput. Sci. 2019, 160, 746–751.
  35. Lohchab, V.; Kumar, M.; Suryan, G.; Gautam, V.; Das, R.K. A review of iot based smart farm monitoring. In Proceedings of the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 20–21 April 2018; IEEE: Piscataway, NJ, USA, 2018.
  36. Fennell, S.; Kaur, P.; Jhunjhunwala, A.; Narayanan, D.; Loyola, C.; Bedi, J.; Singh, Y. Examining linkages between smart villages and smart cities: Learning from rural youth accessing the internet in India. Telecommun. Policy 2018, 42, 810–823.
  37. Ferguson, A.G. Rise of Big Data Policing. In Rise of Big Data Policin; New York University Press: New York, NY, USA, 2017.
  38. Verma, N.K.; Usman, A. Internet of Things (IoT): A relief for Indian farmers. In Proceedings of the 2016 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 13–16 October 2016; IEEE: Piscataway, NJ, USA, 2016.
  39. Virk, A.L.; Noor, M.A.; Fiaz, S.; Hussain, S.; Hussain, H.A.; Rehman, M.; Ahsan, M.; Ma, W. Smart farming: An overview. In Smart Village Technology; Springer: Cham, Switzerland, 2020; pp. 191–201.
  40. Gokul, V.; Tadepalli, S. Implementation of smart infrastructure and non-invasive wearable for real time tracking and early identification of diseases in cattle farming using IoT. In Proceedings of the 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), Palladam, India, 10–11 February 2017; IEEE: Piscataway, NJ, USA, 2017.
  41. Feng, Z. Rural E-commerce development under the background of big data. In Proceedings of the International Conference on Big Data Analytics for Cyber-Physical-Systems, Shanghai, China, 28–29 December 2020; Springer: Singapore, 2020.
  42. Peisker, A.; Dalai, S. Data analytics for rural development. Indian J. Sci. Technol. 2015, 8, 50–60.
  43. Patil, V.C.; Al-Gaadi, K.A.; Biradar, D.P.; Rangaswamy, M. Internet of things (Iot) and cloud computing for agriculture: An overview. In Proceedings of the Agro-Informatics and Precision Agriculture (AIPA 2012), Hyderabad, India, 1–3 August 2012; pp. 292–296.
  44. Sarker, M.N.I.; Wu, M.; Chanthamith, B.; Yusufzada, S.; Li, D.; Zhang, J. Big data driven smart agriculture: Pathway for sustainable development. In Proceedings of the 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, 25–28 May 2019; IEEE: Piscataway, NJ, USA, 2019.
  45. Wang, Z.; Xue, M.; Wang, Y.; Song, M.; Li, S.; Daziano, R.A.; Wang, B.; Ma, G.; Chen, K.; Li, X.; et al. Big data: New tend to sustainable consumption research. J. Clean. Prod. 2019, 236, 117499.
  46. Iheke, O.R.; Ihuoma, U. Effect of urbanization on agricultural production in Abia State. Int. J. Agric. Sci. Res. Technol. Ext. Educ. Syst. 2016, 5, 83–89.
  47. Satterthwaite, D.; McGranahan, G.; Tacoli, C. Urbanization and its implications for food and farming. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 2809–2820.
  48. Bhardwaj, V.; Joshi, R.; Gaur, A.M. IoT-based smart health monitoring system for COVID-19. SN Comput. Sci. 2022, 3, 137.
  49. Tian, S.; Yang, W.; Le Grange, J.M.; Wang, P.; Huang, W.; Ye, Z. Smart healthcare: Making medical care more intelligent. Glob. Health J. 2019, 3, 62–65.
  50. Zeng, X.; Deng, H.-T.; Wen, D.-L.; Li, Y.-Y.; Xu, L.; Zhang, X.-S. Wearable Multi-Functional Sensing Technology for Healthcare Smart Detection. Micromachines 2022, 13, 254.
  51. Alshehri, F.; Muhammad, G. A comprehensive survey of the Internet of Things (IoT) and AI-based smart healthcare. IEEE Access 2020, 9, 3660–3678.
  52. Nasr, M.; Islam, M.; Shehata, S.; Karray, F.; Quintana, Y. Smart healthcare in the age of AI: Recent advances, challenges, and future prospects. IEEE Access 2021, 9, 145248–145270.
  53. Gong, F.F.; Sun, X.Z.; Lin, J.; Gu, X.D. Primary exploration in establishment of China’s intelligent medical treatment. Mod. Hosp. Manag. 2013, 11, 28–29.
  54. Solanas, A.; Casino, F.; Batista, E.; Rallo, R. Trends and challenges in smart healthcare research: A journey from data to wisdom. In Proceedings of the 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), Modena, Italy, 11–13 September 2017; IEEE: Piscataway, NJ, USA, 2017.
  55. Catarinucci, L.; De Donno, D.; Mainetti, L.; Palano, L.; Patrono, L.; Stefanizzi, M.L.; Tarricone, L. An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J. 2015, 2, 515–526.
  56. Martin, J.L.; Varilly, H.; Cohn, J.; Wightwick, G.R. Preface: Technologies for a smarter planet. IBM J. Res. Dev. 2010, 54, 1–2.
  57. Xue, X.; Zeng, Y.; Zhang, Y.; Lee, S.; Yan, Z. A Study on an Application System for the Sustainable Development of Smart Healthcare in China. IEEE Access 2021, 9, 111960–111974.
  58. Khan, F.A.; Asif, M.; Ahmad, A.; Alharbi, M.; Aljuaid, H. Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development. Sustain. Cities Soc. 2020, 55, 102018.
  59. Papa, A.; Mital, M.; Pisano, P.; Del Giudice, M. E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technol. Forecast. Soc. Chang. 2020, 153, 119226.
  60. Pateman, T. Rural and urban areas: Comparing lives using rural/urban classifications. Reg. Trends 2011, 43, 11–86.
  61. Camacho-Collados, M.; Liberatore, F. A decision support system for predictive police patrolling. Decis. Support Syst. 2015, 75, 25–37.
  62. Jain, A. Digitalisation: The era of transformation in India. Int. J. Res. Cult. Soc. 2018, 2, 252–259.
  63. Awan, M.J.; Bilal, M.H.; Yasin, A.; Nobanee, H.; Khan, N.S.; Zain, A.M. Detection of COVID-19 in chest X-ray images: A big data enabled deep learning approach. Int. J. Environ. Res. Public Health 2021, 18, 10147.
  64. Balaji, S.; Patil, M.; McGregor, C. A cloud based big data based online health analytics for rural nicus and picus in india: Opportunities and challenges. In Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, Greece, 22–24 June 2017; IEEE: Piscataway, NJ, USA, 2017.
  65. Smys, S.; Raj, J.S. Internet of things and big data analytics for health care with cloud computing. J. Inf. Technol. 2019, 1, 9–18.
  66. Ting, H.-W.; Chien, T.-Y.; Lai, K.R.; Pan, R.-H.; Wu, K.-H.; Chen, J.-M.; Chan, C.-L. Differences in spontaneous intracerebral hemorrhage cases between urban and rural regions of taiwan: Big data analytics of government open data. Int. J. Environ. Res. Public Health 2017, 14, 1548.
  67. Guo, J.; Li, B. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2018, 2, 174–181.
  68. Kuziemsky, C.; Maeder, A.J.; John, O.; Gogia, S.B.; Basu, A.; Meher, S.; Ito, M. Role of artificial intelligence within the telehealth domain. Yearb. Med. Inform. 2019, 28, 035–040.
  69. Haleem, A.; Javaid, M.; Khan, I.H. Current status and applications of Artificial Intelligence (AI) in medical field: An overview. Curr. Med. Res. Pract. 2019, 9, 231–237.
  70. Khairnar, V.D.; Saroj, A.; Yadav, P.; Shete, S.; Bhatt, N. Primary healthcare using artificial intelligence. In Proceedings of the International Conference on Innovative Computing and Communications, Ostrava, Czech Republic, 21–22 March 2019; Springer: Singapore, 2019.
  71. Rohokale, V.M.; Prasad, N.R.; Prasad, R. A cooperative Internet of Things (IoT) for rural healthcare monitoring and control. In Proceedings of the 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), Chennai, India, 28 February–3 March 2011; IEEE: Piscataway, NJ, USA, 2011.
  72. Lin, C.-W.; Syed-Abdul, S.; Clinciu, D.L.; Scholl, J.; Jin, X.; Lu, H.; Chen, S.S.; Iqbal, U.; Heineck, M.J.; Li, Y.-C. Empowering village doctors and enhancing rural healthcare using cloud computing in a rural area of mainland China. Comput. Methods Programs Biomed. 2014, 113, 585–592.
  73. Rajput, D.S.; Basha, S.M.; Xin, Q.; Gadekallu, T.R.; Kaluri, R.; Lakshmanna, K.; Maddikunta, P.K.R. Providing diagnosis on diabetes using cloud computing environment to the people living in rural areas of India. J. Ambient. Intell. Humaniz. Comput. 2022, 13, 2829–2840.
  74. Rahman, M.; Kashem, M.A.; Nayan, A.A.; Akter, M.; Rabbi, F.; Ahmed, M.; Asaduzzaman, M. Internet of things (IoT) based ECG system for rural health care. arXiv 2022, arXiv:2208.02226.
  75. Semwal, N.; Mukherjee, M.; Raj, C.; Arif, W. An IoT based smart e-health care system. J. Inf. Optim. Sci. 2019, 40, 1787–1800.
  76. Fejfar, D.; Guo, A.; Kelly, E.; Tidwell, J.B.; Ochieng, O.; Cronk, R. Healthcare provider satisfaction with environmental conditions in rural healthcare facilities of 14 low- and middle-income countries. Int. J. Hyg. Environ. Health 2021, 236, 113802.
  77. Strasser, R.; Kam, S.M.; Regalado, S.M. Rural health care access and policy in developing countries. Annu. Rev. Public Health 2016, 37, 395–412.
  78. Douthit, N.; Kiv, S.; Dwolatzky, T.; Biswas, S. Exposing some important barriers to health care access in the rural USA. Public Health 2015, 129, 611–620.
  79. Lund, H.; Østergaard, P.A.; Connolly, D.; Mathiesen, B.V. Smart energy and smart energy systems. Energy 2017, 137, 556–565.
  80. Shwetha, B.; Balasangameshwara, J. Demand-side management in smart electricity grids: A review. Int. J. Intell. Enterp. 2021, 8, 436–458.
  81. Hargreaves, T.; Nye, M.; Burgess, J. Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors. Energy Policy 2010, 38, 6111–6119.
  82. Hargreaves, T.; Nye, M.; Burgess, J. Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term. Energy Policy 2013, 52, 126–134.
  83. Goulden, M.; Bedwell, B.; Rennick-Egglestone, S.; Rodden, T.; Spence, A. Smart grids, smart users? The role of the user in demand side management. Energy Res. Soc. Sci. 2014, 2, 21–29.
  84. Shaikh, A.; Thapar, M.; Koli, D.; Rambade, H. IOT based smart electric pole. In Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 29–31 March 2018; IEEE: Piscataway, NJ, USA, 2018.
  85. Paramati, S.R.; Shahzad, U.; Doğan, B. The role of environmental technology for energy demand and energy efficiency: Evidence from OECD countries. Renew. Sustain. Energy Rev. 2021, 153, 111735.
  86. Shi, C.; Feng, X.; Jin, Z. Sustainable development of China’s smart energy industry based on artificial intelligence and low-carbon economy. Energy Sci. Eng. 2020, 10, 243–252.
  87. Khan, T.; Yu, M.; Waseem, M. Review on recent optimization strategies for hybrid renewable energy system with hydrogen technologies: State of the art, trends and future directions. Int. J. Hydrogen Energy 2022, 47, 25155–25201.
  88. Ellabban, O.; Abu-Rub, H.; Blaabjerg, F. Renewable energy resources: Current status, future prospects and their enabling technology. Renew. Sustain. Energy Rev. 2014, 39, 748–764.
  89. Erdinc, O.; Paterakis, N.G.; Catalão, J.P. Overview of insular power systems under increasing penetration of renewable energy sources: Opportunities and challenges. Renew. Sustain. Energy Rev. 2015, 52, 333–346.
  90. Dincer, I. Renewable energy and sustainable development: A crucial review. Renew. Sustain. Energy Rev. 2000, 4, 157–175.
  91. Mathiesen, B.V.; Lund, H.; Connolly, D.; Wenzel, H.; Østergaard, P.A.; Möller, B.; Nielsen, S.; Ridjan, I.; Karnøe, P.; Sperling, K.; et al. Smart Energy Systems for coherent 100% renewable energy and transport solutions. Appl. Energy 2015, 145, 139–154.
  92. Mbungu, N.T.; Naidoo, R.M.; Bansal, R.C.; Vahidinasab, V. Overview of the optimal smart energy coordination for microgrid applications. IEEE Access 2019, 7, 163063–163084.
  93. Kim, H.; Choi, H.; Kang, H.; An, J.; Yeom, S.; Hong, T. A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities. Renew. Sustain. Energy Rev. 2021, 140, 110755.
  94. Dincer, I.; Acar, C. Smart energy systems for a sustainable future. Appl. Energy 2017, 194, 225–235.
  95. Setiawan, A.A.; Purwanto, D.H.; Pamuji, D.S.; Huda, N. Development of a solar water pumping system in Karsts Rural Area Tepus, Gunungkidul through student community services. Energy Procedia 2014, 47, 7–14.
  96. Kaya, O.; Klepacka, A.M.; Florkowski, W.J. Achieving renewable energy, climate, and air quality policy goals: Rural residential investment in solar panel. J. Environ. Manag. 2019, 248, 109309.
  97. Alotaibi, D.M.; Akrami, M.; Dibaj, M.; Javadi, A.A. Smart energy solution for an optimised sustainable hospital in the green city of NEOM. Sustain. Energy Technol. Assess. 2019, 35, 32–40.
  98. Akinyele, D.; Rayudu, R.; Nair, N. Global progress in photovoltaic technologies and the scenario of development of solar panel plant and module performance estimation—Application in Nigeria. Renew. Sustain. Energy Rev. 2015, 48, 112–139.
  99. Shamsuzzoha, A.; Grant, A.; Clarke, J. Implementation of renewable energy in Scottish rural area: A social study. Renew. Sustain. Energy Rev. 2012, 16, 185–191.
  100. Liu, G.; Li, Z.; Xue, Y.; Tomsovic, K. Microgrid Assisted Design for Remote Areas. Energies 2022, 15, 3725.
  101. Jain, M.; Gupta, S.; Masand, D.; Agnihotri, G.; Jain, S. Real-time implementation of islanded microgrid for remote areas. J. Control Sci. Eng. 2016, 2016, 5710950.
  102. Yang, Y.; Liu, J.; Lin, Y.; Li, Q. The impact of urbanization on China’s residential energy consumption. Struct. Chang. Econ. Dyn. 2019, 49, 170–182.
  103. Mohammed, Y.S.; Mokhtar, A.S.; Bashir, N.; Saidur, R. An overview of agricultural biomass for decentralized rural energy in Ghana. Renew. Sustain. Energy Rev. 2013, 20, 15–25.
  104. Gwak, D. The Meaning and Predict of Smart Learning. Smart Learning Korea Proceeding; Korean e-Learning Industry Association: Seoul, Republic of Korea, 2010.
  105. Zhu, Z.-T.; Yu, M.-H.; Riezebos, P. A research framework of smart education. Smart Learn. Environ. 2016, 3, 4.
  106. Jia, W.; Li, X.; Huang, W.; Wang, H.; Zheng, Y.; Wang, H.; Mao, Z.; Tang, T. Smart Education Under 5G+. In Proceedings of the 2021 11th International Conference on Information Technology in Medicine and Education (ITME), Wuyishan, China, 19–21 November 2021; IEEE: Piscataway, NJ, USA, 2021.
  107. Palanivel, K. Emerging technologies to smart education. Int. J. Comput. Trends Technol. 2020, 68, 5–16.
  108. Memos, V.A.; Minopoulos, G.; Stergiou, C.; Psannis, K.E.; Ishibashi, Y. A revolutionary interactive smart classroom (RISC) with the use of emerging technologies. In Proceedings of the 2020 2nd International Conference on Computer Communication and the Internet (ICCCI), Nagoya, Japan, 26–29 June 2020; IEEE: Piscataway, NJ, USA, 2020.
  109. Wals, A.E.J.; Benavot, A. Can we meet the sustainability challenges? The role of education and lifelong learning. Eur. J. Educ. 2017, 52, 404–413.
  110. Kędzierska, B.; Magenheim, J.; Kędzierska, A.; Fischbach, R. The application and impact of ICT in education for sustainable development. In Proceedings of the X World Conference on Computers in Education, Torun, Poland, 2–5 July 2013.
  111. Jirgensons, M.; Kapenieks, J. Blockchain and the future of digital learning credential assessment and management. J. Teach. Educ. Sustain. 2018, 20, 145–156.
  112. Anthonysamy, L.; Koo, A.C.; Hew, S.H. Self-regulated learning strategies in higher education: Fostering digital literacy for sustainable lifelong learning. Educ. Inf. Technol. 2020, 25, 2393–2414.
  113. Foster, J. Education as sustainability. Environ. Educ. Res. 2001, 7, 153–165.
  114. Simović, A. A Big Data smart library recommender system for an educational institution. Libr. Hi Tech 2018, 36, 498–523.
  115. Singh, H.; Miah, S.J. Smart education literature: A theoretical analysis. Educ. Inf. Technol. 2020, 25, 3299–3328.
  116. Lee, J.; Zo, H.; Lee, H. Smart learning adoption in employees and HRD managers. Br. J. Educ. Technol. 2014, 45, 1082–1096.
  117. Thomson, S. Building a Conversational Framework for e-Learning to Support the Future Implementation of Learning Technologies; Sheffield Hallam University: Sheffield, UK, 2015.
  118. Gros, B. The design of smart educational environments. Smart Learn. Environ. 2016, 3, 15.
  119. Bontchev, B.; Vassileva, D. Affect-based adaptation of an applied video game for educational purposes. Interact. Technol. Smart Educ. 2017, 14, 31–49.
  120. Wei, N.; Yang, F.; Muthu, B.; Shanthini, A. Human machine interaction-assisted smart educational system for rural children. Comput. Electr. Eng. 2022, 99, 107812.
  121. Nataliya, B.; Natalya, V.; Viacheslav, F.; Irina, K.; Alexandra, S.; Nadezhda, I. The Concept of Smart-Education for Sustainable Development. In Proceedings of the International Conference on the Theory and Practice of Personality Formation in Modern Society (ICTPPFMS 2018), Yurga, Russia, 20–22 September 2018; Atlantis Press: Amsterdam, The Netherlands, 2018.
  122. Mazutti, J.; Brandli, L.L.; Salvia, A.L.; Gomes, B.M.F.; Damke, L.I.; da Rocha, V.T.; Rabello, R.D.S. Smart and learning campus as living lab to foster education for sustainable development: An experience with air quality monitoring. Int. J. Sustain. High. Educ. 2020, 21, 1311–1330.
  123. Al-Bahi, A.M.; Soliman, A.Y. Sustainability SMART indicators of engineering education for sustainable development. In Proceedings of the 2018 IEEE Global Engineering Education Conference (EDUCON), Santa Cruz de Tenerife, Spain, 17–20 April 2018; IEEE: Piscataway, NJ, USA, 2018.
  124. Chi, Y.; Qin, Y.; Song, R.; Xu, H. Knowledge graph in smart education: A case study of entrepreneurship scientific publication management. Sustainability 2018, 10, 995.
  125. Chou, P.-N. Skill development and knowledge acquisition cultivated by maker education: Evidence from Arduino-based educational robotics. EURASIA J. Math. Sci. Technol. Educ. 2018, 14, em1600.
  126. Hlalele, D. Rural education in South Africa: Concepts and practices. Mediterr. J. Soc. Sci. 2014, 5, 462.
  127. Anand, R.; Saxena, S.; Saxena, S. E-learning and its Impact on Rural Areas. Int. J. Mod. Educ. Comput. Sci. 2012, 4, 46.
  128. Snoussi, T. Learning management system in education: Opportunities and challenges. Int. J. Innov. Technol. Explor. Eng. 2019, 8, 664–667.
  129. Kasim, N.N.M.; Khalid, F. Choosing the right learning management system (LMS) for the higher education institution context: A systematic review. Int. J. Emerg. Technol. Learn. 2016, 11, 55–61.
  130. Cerezo, R.; Sánchez-Santillán, M.; Paule-Ruiz, M.P.; Núñez, J.C. Students’ LMS interaction patterns and their relationship with achievement: A case study in higher education. Comput. Educ. 2016, 96, 42–54.
  131. Forouzesh, M.; Darvish, M. Characteristics of learning management system (LMS) and its role in education of electronics. In Proceedings of the The International Scientific Conference eLearning and Software for Education, Bucharest, Romania, 26–27 April 2012; “Carol I” National Defence University: Bucharest, Romania, 2012.
  132. Chen, C.K.; Almunawar, M.N. Cloud learning management system in higher education. In Opening Up Education for Inclusivity Across Digital Economies and Societies; IGI Global: Hershey, PA, USA, 2019; pp. 29–51.
  133. Kulshrestha, T.; Kant, A.R. Benefits of learning management system (LMS) in Indian education. Int. J. Comput. Sci. Eng. Technol. (IJCSET) 2013, 4, 1153–1164.
  134. Alyami, A.; Pileggi, S.F.; Hawryszkiewycz, I. The impact of new technologies on learning: A literature review on mobile collaborative learning. In Proceedings of the Pacific Asia Conference on Information Systems, Dubai, United Arab Emirates, 22–24 June 2020.
  135. Glukhov, V.; Vasetskaya, N. Improving the teaching quality with a smart-education system. In Proceedings of the 2017 IEEE VI Forum Strategic Partnership of Universities and Enterprises of Hi-Tech Branches (Science. Education. Innovations)(SPUE), St. Petersburg, Russia, 15–17 November 2017; IEEE: Piscataway, NJ, USA, 2017.
  136. Jeong, J.-S.; Kim, M.; Yoo, K.-H. A content oriented smart education system based on cloud computing. Int. J. Multimed. Ubiquitous Eng. 2013, 8, 313–328.
  137. Kobayashi, T.; Arai, K.; Sato, H.; Tanimoto, S.; Kanai, A. An application framework for smart education system based on mobile and cloud systems. IEICE Trans. Inf. Syst. 2017, 100, 2399–2410.
  138. Bower, M.; Howe, C.; McCredie, N.; Robinson, A.; Grover, D. Augmented Reality in education–cases, places and potentials. Educ. Media Int. 2014, 51, 1–15.
  139. Chen, P.; Liu, X.; Cheng, W.; Huang, R. A review of using Augmented Reality in Education from 2011 to 2016. In Innovations in Smart Learning; Springer: Singapore, 2017; pp. 13–18.
  140. Huang, K.-T.; Ball, C.; Francis, J.; Ratan, R.; Boumis, J.; Fordham, J. Augmented versus virtual reality in education: An exploratory study examining science knowledge retention when using augmented reality/virtual reality mobile applications. Cyberpsychology Behav. Soc. Netw. 2019, 22, 105–110.
  141. Lee, K. Augmented reality in education and training. TechTrends 2012, 56, 13–21.
  142. Kesim, M.; Ozarslan, Y. Augmented reality in education: Current technologies and the potential for education. Procedia-Soc. Behav. Sci. 2012, 47, 297–302.
  143. Glewwe, P.; Kremer, M. Schools, teachers, and education outcomes in developing countries. Handb. Econ. Educ. 2006, 2, 945–1017.
  144. Jimenez-Castellanos, O. Relationship between educational resources and school achievement: A mixed method intra-district analysis. Urban Rev. 2010, 42, 351–371.
  145. Du Plessis, P.; Mestry, R. Teachers for rural schools—A challenge for South Africa. S. Afr. J. Educ. 2019, 39, S1–S9.
  146. Blanchett, W.J.; Klingner, J.K.; Harry, B. The intersection of race, culture, language, and disability: Implications for urban education. Urban Educ. 2009, 44, 389–409.
  147. Anthopoulos, L.G.; Reddick, C.G. Smart city and smart government: Synonymous or complementary? In Proceedings of the 25th International Conference Companion on World Wide Web, Montreal, QC, Canada, 11–15 April 2016.
  148. Kankanhalli, A.; Charalabidis, Y.; Mellouli, S. IoT and AI for smart government: A research agenda. Gov. Inf. Q. 2019, 36, 304–309.
  149. Kliksberg, B. Rebuilding the state for social development: Towards ‘smart government’. Int. Rev. Adm. Sci. 2000, 66, 241–257.
  150. Bhatti, Z.K.; Kusek, J.Z.; Verheijen, T. Logged on: Smart Government Solutions from South Asia; World Bank Publications: Washington, DC, USA, 2014.
  151. Velsberg, O.; Westergren, U.H.; Jonsson, K. Exploring smartness in public sector innovation—Creating smart public services with the Internet of Things. Eur. J. Inf. Syst. 2020, 29, 350–368.
  152. Mellouli, S.; Luna-Reyes, L.F.; Zhang, J. Smart government, citizen participation and open data. Inf. Polity 2014, 19, 1–4.
  153. Von Lucke, J. Smart government–the potential of intelligent networking in government and public administration. In Proceedings of the 2016 conference for e-democracy and open government (CeDEM), Krems, Austria, 18–20 May 2016; IEEE: Piscataway, NJ, USA, 2016.
  154. Pereira, G.V.; Parycek, P.; Falco, E.; Kleinhans, R. Smart governance in the context of smart cities: A literature review. Inf. Polity 2018, 23, 143–162.
  155. De Guimarães, J.C.F.; Severo, E.A.; Júnior, L.A.F.; Da Costa, W.P.L.B.; Salmoria, F.T. Governance and quality of life in smart cities: Towards sustainable development goals. J. Clean. Prod. 2020, 253, 119926.
  156. Giuliodori, A.; Berrone, P.; Ricart, J.E. Where smart meets sustainability: The role of Smart Governance in achieving the Sustainable Development Goals in cities. BRQ Bus. Res. Q. 2023, 26, 27–44.
  157. Tomor, Z.; Meijer, A.; Michels, A.; Geertman, S. Smart governance for sustainable cities: Findings from a systematic literature review. J. Urban Technol. 2019, 26, 3–27.
  158. Kulkarni, P.; Akhilesh, K.B. Big data analytics as an enabler in smart governance for the future smart cities. In Smart Technologies: Scope and Applications; Springer: Singapore, 2020; pp. 53–65.
  159. Chun, S.A.; Shulman, S.; Sandoval, R.; Hovy, E. Government 2.0: Making connections between citizens, data and government. Inf. Polity 2010, 15, 1–9.
  160. Choenni, S.; Netten, N.; Bargh, M.S.; Braak, S.V.D. Exploiting big data for smart government: Facing the challenges. In Handbook of Smart Cities; Springer: Cham, Switzerland, 2020; pp. 1–23.
  161. Gil-Garcia, J.R.; Helbig, N.; Ojo, A. Being smart: Emerging technologies and innovation in the public sector. Gov. Inf. Q. 2014, 31, I1–I8.
  162. Jiménez, C.E.; Falcone, F.; Solanas, A.; Puyosa, H.; Zoughbi, S.; González, F. Smart government: Opportunities and challenges in smart cities development. In Civil and Environmental Engineering: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, 2016; pp. 1454–1472.
  163. Gil-Garcia, J.R.; Pardo, T.A.; Aldama-Nalda, A. Smart cities and smart governments: Using information technologies to address urban challenges. In Proceedings of the 14th Annual International Conference on Digital Government Research, Quebec City, QC, Canada, 17–20 June 2013.
  164. Park, C.; Cha, J. A trend on smart village and implementation of smart village platform. Int. J. Adv. Smart Converg. 2019, 8, 177–183.
  165. Strover, S. Rural internet connectivity. Telecommun. Policy 2001, 25, 331–347.
  166. Keskġn, H.; Ġentürk, C.; Sungur, O.; Kġrġġ, H.M. The importance of SMEs in developing economies. In Proceedings of the 2nd International Symposium on Sustainable Development, Sarajevo, Bosnia and Herzegovina, 8–9 June 2010.
  167. Vervest, P.; Preiss, K.; Van Heck, E.; Pau, L.-F. The emergence of smart business networks. J. Inf. Technol. 2004, 19, 228–233.
  168. Armeanu, D.; Istudor, N.; Lache, L. The role of SMEs in assessing the contribution of entrepreneurship to GDP in the Romanian business environment. Amfiteatru Econ. J. 2015, 17, 195–211.
  169. Aris, N.M. SMEs: Building Blocks for Economic Growth; Department of National Statistics, Malaysia: Putrajaya, Malaysia, 2007.
  170. Neuhofer, B.; Buhalis, D.; Ladkin, A. Smart technologies for personalized experiences: A case study in the hospitality domain. Electron. Mark. 2015, 25, 243–254.
  171. Liu, W.; Shanthikumar, J.G.; Lee, P.T.-W.; Li, X.; Zhou, L. Special issue editorial: Smart supply chains and intelligent logistics services. Transp. Res. Part E Logist. Transp. Rev. 2021, 147, 102256.
  172. Sen, D.; Ozturk, M.; Vayvay, O. An overview of big data for growth in SMEs. Procedia-Soc. Behav. Sci. 2016, 235, 159–167.
  173. Vilarinho, S.; Lopes, I.; Sousa, S. Developing dashboards for SMEs to improve performance of productive equipment and processes. J. Ind. Inf. Integr. 2018, 12, 13–22.
  174. Yigitbasioglu, O.M.; Velcu, O. A review of dashboards in performance management: Implications for design and research. Int. J. Account. Inf. Syst. 2012, 13, 41–59.
  175. Tsai, W.-H.; Chou, W.-C. Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP. Expert Syst. Appl. 2009, 36, 1444–1458.
  176. Mohammadian, H.D.; Mohammadian, F.D.; Assante, D. IoT-education policies on national and international level regarding best practices in German SMEs. In Proceedings of the 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, 27–30 April 2020; IEEE: Piscataway, NJ, USA, 2020.
  177. Fanelli, R.M. Barriers to Adopting New Technologies within Rural Small and Medium Enterprises (SMEs). Soc. Sci. 2021, 10, 430.
  178. Kongolo, M. Job creation versus job shedding and the role of SMEs in economic development. Afr. J. Bus. Manag. 2010, 4, 2288.
  179. Matinaro, V.; Liu, Y.; Poesche, J. Extracting key factors for sustainable development of enterprises: Case study of SMEs in Taiwan. J. Clean. Prod. 2018, 209, 1152–1169.
  180. De Sousa Jabbour, A.B.L.; Ndubisi, N.O.; Seles, B.M.R.P. Sustainable development in Asian manufacturing SMEs: Progress and directions. Int. J. Prod. Econ. 2019, 225, 107567.
  181. AlMulhim, A.F. Smart supply chain and firm performance: The role of digital technologies. Bus. Process. Manag. J. 2021, 27, 1353–1372.
  182. Zairis, A.G. The effective use of digital technology by SMEs. In Research Anthology on Small Business Strategies for Success and Survival; IGI Global: Hershey, PA, USA, 2021; pp. 548–559.
  183. Thota, S.; Nag, A.; Divyasukhananda, S.; Goswami, P.; Aravindakshan, A.; Rodriguez, R.; Mukherjee, B.; Nandi, S. Computing for Rural Empowerment: Enabled by Last-Mile Telecommunications (Extended Version). IEEE Commun. Mag. 2016, 54, 102–109.
  184. Choubisa, K. Cloud computing & rural development. Int. J. Informat. Technol. Knowl. Manag. 2012, 6, 98–100.
  185. Jones, N.B.; Graham, C.M. Can the IoT help small businesses? Bull. Sci. Technol. Soc. 2018, 38, 3–12.
  186. Moeuf, A.; Pellerin, R.; Lamouri, S.; Tamayo, S.; Barbaray, R. The industrial management of SMEs in the era of Industry 4.0. Int. J. Prod. Res. 2018, 56, 1118–1136.
  187. Mohammadian, H.D. IoT-Education technologies as solutions towards SMEs’ educational challenges and I4. 0 readiness. In Proceedings of the 2020 IEEE Global Engineering Education Conference (EDUCON), Porto, Portugal, 27–30 April 2020; IEEE: Piscataway, NJ, USA, 2020.
  188. Lee, N.; Rodríguez-Pose, A. Original innovation, learnt innovation and cities: Evidence from UK SMEs. Urban Stud. 2013, 50, 1742–1759.
  189. Manzoor, F.; Wei, L.; Sahito, N. The role of SMEs in rural development: Access of SMEs to finance as a mediator. PLoS ONE 2021, 16, e0247598.
  190. Siemens, L. Challenges, responses and available resources: Success in rural small businesses. J. Small Bus. Entrep. 2010, 23, 65–80.
  191. Uvarova, I.; Vitola, A. Innovation challenges and opportunities in European Rural SMEs. Public Policy Adm. 2019, 18, 152–166.
  192. Phuyal, S.; Bista, D.; Bista, R. Challenges, opportunities and future directions of smart manufacturing: A state of art review. Sustain. Futur. 2020, 2, 100023.
  193. Lu, Y.; Ju, F. Smart manufacturing systems based on cyber-physical manufacturing services (CPMS). IFAC-PapersOnLine 2017, 50, 15883–15889.
  194. Davis, J.; Edgar, T.; Graybill, R.; Korambath, P.; Schott, B.; Swink, D.; Wang, J.; Wetzel, J. Smart manufacturing. Annu. Rev. Chem. Biomol. Eng. 2015, 6, 141–160.
  195. Tao, F.; Qi, Q.; Liu, A.; Kusiak, A. Data-driven smart manufacturing. J. Manuf. Syst. 2018, 48, 157–169.
  196. Nagorny, K.; Lima-Monteiro, P.; Barata, J.; Colombo, A.W. Big data analysis in smart manufacturing: A review. Int. J. Commun. Netw. Syst. Sci. 2017, 10, 31–58.
  197. Kang, H.S.; Lee, J.Y.; Choi, S.; Kim, H.; Park, J.H.; Son, J.Y.; Kim, B.H.; Noh, S.D. Smart manufacturing: Past research, present findings, and future directions. Int. J. Precis. Eng. Manuf.-Green Technol. 2016, 3, 111–128.
  198. Qu, Y.J.; Ming, X.G.; Liu, Z.W.; Zhang, X.Y.; Hou, Z.T. Smart manufacturing systems: State of the art and future trends. Int. J. Adv. Manuf. Technol. 2019, 103, 3751–3768.
  199. Patel, P.; Ali, M.I.; Sheth, A. From raw data to smart manufacturing: AI and semantic web of things for industry 4.0. IEEE Intell. Syst. 2018, 33, 79–86.
  200. Yang, H.; Kumara, S.; Bukkapatnam, S.T.; Tsung, F. The internet of things for smart manufacturing: A review. IISE Trans. 2019, 51, 1190–1216.
  201. Cioffi, R.; Travaglioni, M.; Piscitelli, G.; Petrillo, A.; Parmentola, A. Smart manufacturing systems and applied industrial technologies for a sustainable industry: A systematic literature review. Appl. Sci. 2020, 10, 2897.
  202. Abubakr, M.; Abbas, A.T.; Tomaz, I.; Soliman, M.S.; Luqman, M.; Hegab, H. Sustainable and smart manufacturing: An integrated approach. Sustainability 2020, 12, 2280.
  203. Del Giudice, M.; Scuotto, V.; Papa, A.; Tarba, S.Y.; Bresciani, S.; Warkentin, M. A self-tuning model for smart manufacturing SMEs: Effects on digital innovation. J. Prod. Innov. Manag. 2021, 38, 68–89.
  204. Warke, V.; Kumar, S.; Bongale, A.; Kotecha, K. Sustainable development of smart manufacturing driven by the digital twin framework: A statistical analysis. Sustainability 2021, 13, 10139.
  205. Davis, J.; Edgar, T.; Porter, J.; Bernaden, J.; Sarli, M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 2012, 47, 145–156.
  206. Shestakofsky, B. Working algorithms: Software automation and the future of work. Work. Occup. 2017, 44, 376–423.
  207. Nazareno, L.; Schiff, D.S. The impact of automation and artificial intelligence on worker well-being. Technol. Soc. 2021, 67, 101679.
  208. Leduc, S.; Liu, Z. Robots or Workers? A Macro Analysis of Automation and Labor Markets; Federal Reserve Bank of San Francisco: San Francisco, CA, USA, 2019.
  209. Chege, S.M.; Wang, D. Information technology innovation and its impact on job creation by SMEs in developing countries: An analysis of the literature review. Technol. Anal. Strat. Manag. 2019, 32, 256–271.
  210. Wuest, T.; Romero, D.; Khan, M.A.; Mittal, S. The triple bottom line of smart manufacturing technologies: An economic, environmental, and social perspective. In Handbook of Smart Technologies: An Economic and Social Perspective; Routledge: London, UK, 2022; pp. 310–330. Available online: https://www.taylorfrancis.com/chapters/edit/10.4324/9780429351921-20 (accessed on 10 April 2023).
  211. Arndt, C.; Davies, R.; Thurlow, J. Urbanization, Structural Transformation, and Rural-Urban Linkages in South Africa. South African Urbanisation Review, Cities Support Programme (CSP) of the National Treasury. 2018. Available online: https://sa-tied.wider.unu.edu/article/urbanization-structural-transformation-and-rural-urban-linkages-in-south-africa (accessed on 10 April 2023).
  212. Hao, P.; Tang, S. Floating or settling down: The effect of rural landholdings on the settlement intention of rural migrants in urban China. Environ. Plan. A Econ. Space 2015, 47, 1979–1999.
  213. Porru, S.; Missoa, F.E.; Pani, F.E.; Repetto, C. Smart mobility and public transport: Opportunities and challenges in rural and urban areas. J. Traffic Transp. Eng. (Engl. Ed.) 2020, 7, 88–97.
  214. Leelaarporn, P.; Wachiraphan, P.; Kaewlee, T.; Udsa, T.; Chaisaen, R.; Choksatchawathi, T.; Laosirirat, R.; Lakhan, P.; Natnithikarat, P.; Thanontip, K.; et al. Sensor-driven achieving of smart living: A review. IEEE Sens. J. 2021, 21, 10369–10391.
  215. Han, M.J.N.; Kim, M.J. A critical review of the smart city in relation to citizen adoption towards sustainable smart living. Habitat Int. 2021, 108, 102312.
  216. Weck, M.; Humala, I.; Tamminen, P.; Ferreira, F.A.F. Supporting sustainable development using multiple criteria decision aid: Towards an age-friendly smart living environment. In Multiple Criteria Decision Making for Sustainable Development: Pursuing Economic Growth, Environmental Protection and Social Cohesion; Springer: Cham, Switzerland, 2021; pp. 151–173.
  217. Varghese, C.; Pathak, D.; Varde, A.S. SeVa: A food donation app for smart living. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 27–30 January 2021; IEEE: Piscataway, NJ, USA, 2021.
  218. Chan, M.; Estève, D.; Escriba, C.; Campo, E. A review of smart homes—Present state and future challenges. Comput. Methods Programs Biomed. 2008, 91, 55–81.
  219. King, N. Smart Home—A Definition; Intertek Research and Testing Center: Banksmeadow, Australia, 2003; pp. 1–6.
  220. Paetz, A.-G.; Dütschke, E.; Fichtner, W. Smart homes as a means to sustainable energy consumption: A study of consumer perceptions. J. Consum. Policy 2011, 35, 23–41.
  221. Robles, R.J.; Kim, T.-H. Applications, systems and methods in smart home technology: A. Int. J. Adv. Sci. Technol. 2010, 15, 37–48.
  222. Malche, T.; Maheshwary, P. Internet of Things (IoT) for building smart home system. In Proceedings of the 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), Palladam, India, 10–11 February 2017; IEEE: Piscataway, NJ, USA, 2017.
  223. Fatimah, Y.A.; Govindan, K.; Murniningsih, R.; Setiawan, A. Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia. J. Clean. Prod. 2020, 269, 122263.
  224. Ghorpade-Aher, J.; Wadkar, A.; Kamble, J.; Bagade, U.; Pagare, V. Smart dustbin: An efficient garbage management approach for a healthy society. In Proceedings of the 2018 International Conference on Information, Communication, Engineering and Technology (ICICET), Pune, India, 29–31 August 2018; IEEE: Piscataway, NJ, USA, 2018.
  225. Folianto, F.; Low, Y.S.; Yeow, W.L. Smartbin: Smart waste management system. In Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 7–9 April 2015; IEEE: Piscataway, NJ, USA, 2015.
  226. Kariapper, R.; Pirapuraj, P.; Razeeth, M.S.; Nafrees, A.; Rameez, K. Smart garbage collection using GPS & Shortest path algorithm. In Proceedings of the 2019 IEEE Pune Section International Conference (PuneCon), Pune, India, 18–20 December 2019; IEEE: Piscataway, NJ, USA, 2019.
  227. Xu, R.; Nikouei, S.Y.; Nagothu, D.; Fitwi, A.; Chen, Y. Blendsps: A blockchain-enabled decentralized smart public safety system. Smart Cities 2020, 3, 928–951.
  228. Bistry, H.; Zhang, J. A cloud computing approach to complex robot vision tasks using smart camera systems. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 18–22 October 2010; IEEE: Piscataway, NJ, USA, 2010.
  229. Minnikhanov, R.; Dagaeva, M.; Anikin, I.; Bolshakov, T.; Makhmutova, A.; Mingulov, K. Detection of Traffic Anomalies for a Safety System of Smart City. 2020. Available online: https://ceur-ws.org/Vol-2667/paper74.pdf (accessed on 10 April 2023).
  230. Hampapur, A.; Brown, L.; Connell, J.; Pankanti, S.; Senior, A.; Tian, Y. Smart surveillance: Applications, technologies and implications. In Proceedings of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint, Singapore, 15–18 December 2003; IEEE: Piscataway, NJ, USA, 2003.
  231. Kılkış, Ş.; Krajačić, G.; Duić, N.; Rosen, M.A.; Al-Nimr, M.A. Accelerating mitigation of climate change with sustainable development of energy, water and environment systems. Energy Convers. Manag. 2021, 245, 114606.
  232. Ullo, S.L.; Sinha, G.R. Advances in smart environment monitoring systems using IoT and sensors. Sensors 2020, 20, 3113.
  233. Morton, J.F. The impact of climate change on smallholder and subsistence agriculture. Proc. Natl. Acad. Sci. USA 2007, 104, 19680–19685.
  234. Raleigh, C.; Urdal, H. Climate change, environmental degradation and armed conflict. Pol. Geogr. 2007, 26, 674–694.
  235. Govada, S.S.; Rodgers, T.; Cheng, L.; Chung, H. Smart environment for smart and sustainable Hong Kong. In Smart Environment for Smart Cities; Springer: Singapore, 2020; pp. 57–90.
  236. Adamowicz, M.; Zwolińska-Ligaj, M. The “Smart Village” as a way to achieve sustainable development in rural areas of Poland. Sustainability 2020, 12, 6503.
  237. Mishbah, M.; Purwandari, B.; Sensuse, D.I. Systematic review and meta-analysis of proposed smart village conceptual model: Objectives, strategies, dimensions, and foundations. In Proceedings of the 2018 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, 22–26 October 2018; IEEE: Piscataway, NJ, USA, 2018.
  238. Cvar, N.; Trilar, J.; Kos, A.; Volk, M.; Stojmenova Duh, E. The use of IoT technology in smart cities and smart villages: Similarities, differences, and future prospects. Sensors 2020, 20, 3897.
  239. Shami, M.R.; Rad, V.B.; Moinifar, M. The structural model of indicators for evaluating the quality of urban smart living. Technol. Forecast. Soc. Chang. 2021, 176, 121427.
  240. Srivatsa, P. Rural urban migration: Disturbing the equilibrium between smart cities and smart villages. FIIB Bus. Rev. 2015, 4, 3–10.
  241. Liu, Y.; Liu, Y.; Chen, Y.; Long, H. The process and driving forces of rural hollowing in China under rapid urbanization. J. Geogr. Sci. 2010, 20, 876–888.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback