Smart Cities News and Research

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Smart Sensing and Predictive Analytics in Geotechnical Investigations

In their study published in the journal Smart Cities, researchers employed smart sensing and predictive analytics to address challenges in Japan's urban development and infrastructure resilience. Focusing on Setagaya, Tokyo, the research produced predictive models accurately determining critical bearing layer depths, crucial for government plans and construction risk assessments.

13 May 2024

Wireless Sensor Networks: Blockchain and Swarm Intelligence Approach

Researchers introduce BS-SCRM, a novel method combining blockchain and swarm intelligence for secure clustering routing in WSNs, addressing energy efficiency and security challenges. Simulation results demonstrate superior performance in network lifetime, energy consumption, and security compared to existing methods, offering promise for diverse applications from IoT to healthcare.

7 May 2024

Edge Computing LoRa Gateway for Real-Time Messaging

Researchers developed an edge-computing LoRaWAN gateway for real-time confirmed messaging, significantly reducing messaging time and network server resource usage. The gateway's innovative design and hardware components optimized performance and lowered costs, making it suitable for diverse IoT applications.

17 Apr 2024

Deep Learning and Bayesian Regularization for Urban Planning

Researchers from South Korea and China present a pioneering approach in Scientific Reports, showcasing how deep learning techniques, coupled with Bayesian regularization and graphical analysis, revolutionize urban planning and smart city development. By integrating advanced computational methods, their study offers insights into traffic prediction, urban infrastructure optimization, data privacy, and safety and security, paving the way for more efficient, sustainable, and livable urban environments.

8 Mar 2024

Hierarchical Federated Learning for Smart City AIoT Systems

Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.

29 Feb 2024

Optimizing Content Caching in Vehicular Networks: A Gale–Shapley Approach

Researchers proposed a novel content caching scheme for fog nodes in 5G-enabled vehicular networks, utilizing a modified Gale–Shapley algorithm to optimize content placement based on the preferences of content providers and vehicles. Their approach effectively reduced content downloading time, improved quality of service, and ensured fair and efficient content placement by considering factors such as content popularity, vehicle connectivity, and channel quality.

21 Feb 2024

Urban Park Management Through Social Media and Deep Learning

Researchers propose a groundbreaking framework utilizing social media data and deep learning techniques to assess urban park management effectively. By analyzing visitor comments on seven parks in Wuhan City, the study evaluated various management aspects and identified improvement suggestions, demonstrating the potential of this approach to enhance park service quality and management efficiency. The framework's dynamic visualization capabilities and scalability make it a valuable tool for improving public spaces and contributing to the development of smart cities, with opportunities for expansion to other urban areas and data sources in future research.

20 Feb 2024

Real-time Water Quality Monitoring and Prediction System using IoT and Cloud Computing

Researchers developed a comprehensive system leveraging IoT and cloud computing to monitor and predict drinking water quality in real-time. The system integrates sensors, microcontrollers, web servers, and machine learning models to collect, transmit, analyze, and predict water quality parameters. Machine learning algorithms, particularly decision trees, achieved high accuracy in predicting drinkability, demonstrating the system's potential to enhance water safety and contribute to achieving Sustainable Development Goals.

16 Feb 2024

Robust Path-Following Control for Autonomous Ground Vehicles Using SNFTSM

Researchers introduced a novel control strategy for autonomous ground vehicles (AGVs) utilizing a self-tuning nonsingular fast terminal sliding manifold (SNFTSM) to enhance convergence and tracking accuracy. Integrated with a high-gain disturbance observer (HGDO) and a super-twisting algorithm (STW), the approach effectively addressed uncertainties and reduced chattering in control signals, demonstrating superior performance compared to existing methods in numerical simulations.

16 Feb 2024

AIoT's Impact on Sustainable Development: A Bibliometric Analysis

Analyzing 9,182 documents from 1989 to 2022, this study unveils the burgeoning role of Artificial Intelligence of Things (AIoT) in realizing Sustainable Development Goals (SDGs). With a focus on interdisciplinary collaboration, global trends, and thematic evolution, it emphasizes the dynamic synergy between AI, IoT, and sustainability, guiding future endeavors in leveraging technology for global well-being.

12 Feb 2024

MaTE: Macroscopic Traffic Estimator for Enhanced Traffic Flow and Travel Time Prediction

MaTE, a novel traffic estimator, utilizes macroscopic flow theory, logit-based stochastic traffic assignment, and neural networks to accurately predict traffic flow and travel time in areas with limited sensor coverage. Demonstrating superiority over existing models, MaTE offers interpretability and scalability, bridging the gap between data-driven and model-based methodologies in transportation planning.

7 Feb 2024

A Federated Learning Approach for Passenger Demand Forecasting in Smart Cities

Researchers present a groundbreaking Federated Learning (FL) model for passenger demand forecasting in Smart Cities, focusing on the context of Autonomous Taxis (ATs). The FL approach ensures data privacy by allowing ATs in different regions to collaboratively enhance their demand forecasting models without directly sharing sensitive passenger information. The proposed model outperforms traditional methods, showcasing superior accuracy while addressing privacy concerns in the era of smart and autonomous transportation systems.

1 Feb 2024

Securing the Seas: XAI-Infused Zero-Trust Defense

Korean researchers introduce a groundbreaking framework marrying Explainable AI (XAI) and Zero-Trust Architecture (ZTA) for robust cyberdefense in marine communication networks. Their deep neural network, Zero-Trust Network Intrusion Detection System (NIDS), not only exhibits remarkable accuracy in classifying cyber threats but also integrates XAI methodologies, SHAP and LIME, to provide interpretable insights. This innovative approach fosters transparency and collaboration between AI systems and human experts, promising enhanced cybersecurity in marine, and potentially other, critical infrastructures.

10 Jan 2024

AI-Driven Robotic Crop Farming for Sustainability

Researchers propose an AI-powered robotic crop farm, Agrorobotix, utilizing deep reinforcement learning (DRL) for enhanced urban agriculture. Tested in simulated conditions, Agrorobotix showcased a 16.3% increase in crop yield, 21.7% reduced water usage, and a 33% decline in chemical usage compared to conventional methods, highlighting its potential to transform urban farming, improve food security, and contribute to smart city development.

3 Jan 2024

AI Fortification: Safeguarding IoT Systems Through Comprehensive Algorithmic Approaches

This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.

5 Dec 2023

Optimizing V2V Communication with Deep Reinforcement Learning Beam Management

This paper presents a groundbreaking approach to tackle beam management challenges in vehicle-to-vehicle (V2V) communication. Leveraging a deep reinforcement learning (DRL) framework, specifically the Iterative Twin Delayed Deep Deterministic (ITD3) model with Gated Recurrent Unit (GRU), the study significantly improves spectral efficiency and reliability in intelligent connected vehicles, crucial for advancing smart cities and intelligent transportation systems.

26 Nov 2023

NFT-Based Authentication: Securing IoT Assets in Smart City Infrastructure

Researchers introduce a groundbreaking Non-Fungible Token (NFT)-based authentication structure for IoT assets in smart cities, enhancing security and autonomy. The architecture, leveraging blockchain and Externally Owned Addresses (EOA), provides a cost-effective and accessible solution by digitizing devices without the need for additional hardware upgrades.

17 Nov 2023

IoT-Integrated Drone Cybersecurity: Using DL and ML for Intrusion Detection

This paper explores the integration of IoT with drone technology to enhance data communication and security across various industries, including agriculture and smart cities. The study focuses on the use of machine learning and deep learning techniques to detect cyberattacks within drone networks and presents a comprehensive framework for intrusion detection.

2 Nov 2023

Leveraging Artificial Intelligence for Urban Sustainability and Mobility

A recent research publication explores the profound impact of artificial intelligence (AI) on urban sustainability and mobility. The study highlights the role of AI in supporting dynamic and personalized mobility solutions, sustainable urban mobility planning, and the development of intelligent transportation systems.

26 Oct 2023

RECAP: Elevating Audio Captioning with Retrieval-Augmented Models

Researchers from the University of Maryland introduce RECAP, a groundbreaking approach in audio captioning. RECAP leverages retrieval-augmented generation to enhance cross-domain generalization, excelling in describing complex audio environments, novel sound events, and compositional audios. This innovation promises a significant step forward in diverse applications, from smart cities to industrial monitoring, by addressing domain shift challenges in audio captioning.

22 Sep 2023