Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective

Air pollution forecasting based on wireless communications: review

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field.

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

The role of digital social innovations to address SDGs: A systematic review

The impact of the COVID-19 pandemic has increased the search for solutions to social problems associated with the Sustainable Development Goals (SDGs). Main actors are turning to Digital Social Innovations (DSIs), defined as collaborative innovations where enterprises, users and communities collaborate using digital technologies to promote solutions at scale and speed, connecting innovation, the social world and digital ecosystems to reach the 2030 Agenda. This study aims to identify how digital transformations and social innovations solve social problems and address SDGs. We conducted a systematic review based on a sample of 45 peer-reviewed articles published from 2010 to 2022, combining a bibliometric study and a content analysis focusing on opportunities and threats impacting these fields. We observed the spread and increasing use of technologies associated with all 17 SDGs, specially blockchain, IoT, artificial intelligence, and autonomous robots that are increasing their role and presence exponentially, completely changing the current way of doing things, offering a dramatic evolution in many different segments, such as health care, smart cities, agriculture, and the combat against poverty and inequalities. We identified many threats concerning ethics, especially with the increased use of public data, and concerns about the impacts on the labor force and the possible instability and impact it may cause in low skill/low pay jobs. We expect that our findings advance the concept of digital social innovations and the benefits of its adoption to promote social advancements.

Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice.

Personality traits and social loafing among employees working in teams at small and medium enterprises: A cultural perspective data from emerging economies

The main objective of this research is to examine the association between personality traits such as conscientiousness and neuroticism and social loafing behavior of employees with moderating impact of individualistic behaviors. A multi correlational survey was used to investigate and analyze this study. The survey sample consisted of 241 supervisors and subordinates who attended a survey at manufacturing firms operating in three South Asian countries e.g., Pakistan, Bangladesh, and India. In the data analysis, a reliability and validity test were conducted and then correlation and regression analyses were used to investigate the results. Statistically substantial differences on some study variables were perceived vis-à-vis cultural traits. Correlation and regression verdicts exhibited that there was a substantial negative association between conscientiousness and social loafing, and a positive association between neuroticism and social loafing. Furthermore, this relationship between variables was strengthened when an individual behavior was included as moderator.