The rapid rate of urbanization seen across the world has resulted in multifaceted challenges on cultural and environmental frontiers, particularly in developing countries. Smart Sustainable City Development (SSCD) has proposed the need for “green and clean” nature-oriented solutions for tackling environmental and climatic predicaments in urban areas, and the 2030 Agenda for Sustainable Development has emphasized the holistic approach required to meet the SDGs.
Without much contention, smart cities have become an integral part of Agenda 2030 for achieving many SDGs. As cities are becoming increasingly smart, the influx of information due to the growing prevalence of IoT devices, big data analytics, blockchain, and social networks over fast and efficacious wireless networks has made decision-making hectic. To assist with this decision-making, Point of Interest (POI) recommendation has emerged as a tool to benefit location-centric utilities in the socio-technical transformation.
POI recommendation for assisting in next-location prediction and human trajectory extrapolation is tedious, yet imperative for smart cities. The outcomes of POI recommendation can be extended to traffic predictions, tour planning, health care, and business advancement to improve efficiency, well-being, and sustainability outcomes in future cities.
This Research Topic will focus on the efficient development of technologies for POI recommendation, thereby mitigating the bridge between the virtual and real worlds. We welcome original and multidisciplinary studies focusing on topics including, but not limited to, the following:
• IoT-based location trajectory data collection for different spatial zones;
• Geographical movement-specific data privacy and protection over social networks;
• Big data analytics for temporal and location-based contextual information for smart city development;
• Deep learning for sequential data extrapolation;
• POI recommendation based on Artificial Intelligence approaches;
• Location prediction and smart cities;
• Blockchain technologies for federated learning in the development of smart cities.
The rapid rate of urbanization seen across the world has resulted in multifaceted challenges on cultural and environmental frontiers, particularly in developing countries. Smart Sustainable City Development (SSCD) has proposed the need for “green and clean” nature-oriented solutions for tackling environmental and climatic predicaments in urban areas, and the 2030 Agenda for Sustainable Development has emphasized the holistic approach required to meet the SDGs.
Without much contention, smart cities have become an integral part of Agenda 2030 for achieving many SDGs. As cities are becoming increasingly smart, the influx of information due to the growing prevalence of IoT devices, big data analytics, blockchain, and social networks over fast and efficacious wireless networks has made decision-making hectic. To assist with this decision-making, Point of Interest (POI) recommendation has emerged as a tool to benefit location-centric utilities in the socio-technical transformation.
POI recommendation for assisting in next-location prediction and human trajectory extrapolation is tedious, yet imperative for smart cities. The outcomes of POI recommendation can be extended to traffic predictions, tour planning, health care, and business advancement to improve efficiency, well-being, and sustainability outcomes in future cities.
This Research Topic will focus on the efficient development of technologies for POI recommendation, thereby mitigating the bridge between the virtual and real worlds. We welcome original and multidisciplinary studies focusing on topics including, but not limited to, the following:
• IoT-based location trajectory data collection for different spatial zones;
• Geographical movement-specific data privacy and protection over social networks;
• Big data analytics for temporal and location-based contextual information for smart city development;
• Deep learning for sequential data extrapolation;
• POI recommendation based on Artificial Intelligence approaches;
• Location prediction and smart cities;
• Blockchain technologies for federated learning in the development of smart cities.
