2025· International Journal of Emerging Trends in Multidisciplinary Research· 0 citations
TL;DR
A Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems is proposed, which enhances resource utilization, reduces energy consumption and response time, improves system reliability, and supports scalable urban operations.
Abstract
The rapid integration of Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, edge computing, and advanced communication technologies is transforming traditional urban infrastructure into intelligent smart cities. Conventional resource allocation methods struggle to manage dynamic urban environments, creating a need for adaptive and decentralized decision-making systems. This study proposes a Multi-Agent Reinforcement Learning (MARL) framework for intelligent resource allocation across transportation, energy, water, healthcare, emergency response, and communication systems. Each urban subsystem functions as an autonomous learning agent that optimizes local decisions while coordinating to improve overall city performance. The framework combines IoT sensing, edge intelligence, cloud analytics, and deep reinforcement learning to enable real-time, adaptive resource management. It enhances resource utilization, reduces energy consumption and response time, improves system reliability, and supports scalable urban operations. The proposed approach also provides a foundation for future smart city technologies, including digital twins, federated learning, autonomous edge intelligence, and 6G networks, promoting sustainable, resilient, and efficient urban development.
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
Intelligent warehousing uses artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), robotics to automate and enhance logistics. It helps in changing conventional storage into efficient data-enabled ecosystems. When we think of any warehouse operations, Dynamic demand, high product variety, and complex resource coordination requirements are considered as key factors. In such dynamic environments, Conventional rule-based and centralized optimization methods seem to have limited adaptability. This paper presents an intelligent warehousing framework that integrates IoT sensing with Multi-Agent Reinforcement Learning (MARL) to enable dynamic storage allocation and resource optimization. Real-time data from IoT devices is used to create environmental states that show the state of the inventory, where items are, and how materials are being handled in the warehouse. Multiple autonomous agents corresponding to warehouse entities collaboratively learn decision policies to optimize storage assignment and task allocation. The proposed approach is implemented and evaluated in a simulated warehouse environment. System performance is assessed by using operational metrics including order fulfillment time, storage utilization, travel distance, average queue length and throughput. Experimental results demonstrate that the proposed IoT-enabled MARL framework improves adaptability and operational efficiency compared to static and rule-based strategies, highlighting its potential for next-generation intelligent warehouse systems.
N. N, V. T· 2026 7th International Confe...· 0 citations
Rapid urbanization has intensified pressure on energy systems, transportation networks, water resources, waste-management infrastructure, public health services, and the urban environment. Conventional city-management models, which often rely on fragmented information and reactive decision-making, are increasingly inadequate to address these interconnected challenges. Artificial Intelligence (AI) and the Internet of Things (IoT) provide a technological foundation for a transition from conventional urban administration toward intelligent, adaptive, and sustainability-oriented city management. IoT infrastructures enable continuous sensing and communication across physical urban environments, whereas AI converts large volumes of heterogeneous sensor data into predictions, classifications, recommendations, and automated decisions. This paper examines the integrated role of AI and IoT in sustainable smart city development through a conceptual and interdisciplinary review of research on smart urban systems, data analytics, edge computing, intelligent transportation, energy management, environmental monitoring, waste management, water conservation, public safety, and urban governance. The paper proposes an AI–IoT Closed-Loop Sustainable Urban Intelligence Framework consisting of sensing, connectivity, edge/cloud processing, artificial intelligence, decision-making and actuation, and sustainability evaluation layers. The analysis demonstrates that the value of AI–IoT integration lies not simply in increasing technological sophistication but in enabling cities to minimize resource consumption, anticipate infrastructure failure, reduce emissions, improve service responsiveness, and make urban systems increasingly adaptive. At the same time, cybersecurity vulnerabilities, privacy risks, algorithmic bias, interoperability problems, digital inequality, high infrastructure costs, and the environmental footprint of computing can weaken sustainability outcomes. The study therefore argues for a human-centered, secure, interoperable, transparent, and sustainability-measured model of smart city development. Future smart cities should be evaluated not by the quantity of connected devices deployed but by measurable improvements in environmental quality, resource efficiency, social inclusion, resilience, and quality of urban life.
P. S., Shaik Rahamtula, S. J et al.· Stanzaleaf International Jou...· 0 citations
Rapid urbanization, increasing demand variability, and digitalisation of urban infrastructure require intelligent decision-support systems that can improve sustainability, resilience, and operational efficiency in smart city operations. Artificial intelligence (AI) and data-driven optimization offer strong potential for adaptive urban services, including dynamic pricing, demand response, resource allocation, and energy-aware management. However, many reinforcement learning applications still focus mainly on short-term performance while giving limited attention to transparency, fairness, stability, and accountability. This study proposes a trustworthy reinforcement learning framework for AI-driven urban decision-making, using sustainable dynamic pricing and resource optimization as mechanisms for adaptive and responsible decision-making. A custom reinforcement learning environment was developed using historical e-commerce transactional data as a methodological proxy to simulate interactions among demand, resource or inventory availability, service categories, price elasticity, and changing market conditions. Three reinforcement learning algorithms, namely Deep Q-Network, Proximal Policy Optimization, and Advantage Actor–Critic, were evaluated under comparable experimental conditions. Performance was assessed using profitability, decision stability, fairness-oriented pricing behavior, decision consistency, and interpretability. To improve transparency, trajectory-based policy audits and SHapley Additive exPlanations were applied to identify the main factors influencing pricing decisions. The results show that the Deep Q-Network agent achieved the most balanced performance, increasing total profit by 12.58% while recording no unethical price increases under low-demand conditions. Explainability analysis showed that stock or resource levels, demand shifts, and price elasticity were the strongest positive drivers of pricing actions, whereas inventory hoarding and unfavorable price increases reduced decision quality. The findings indicate that reinforcement learning can support sustainable and resilient urban decision-making when optimization objectives are combined with trustworthy AI principles. The proposed framework provides a practical basis for accountable AI-based decision-support systems in smart city operations, including demand-responsive services, resource optimization, sustainable dynamic pricing, and energy-aware management.
Žydrūnas Bautronis, Robertas Alzbutas· Sustainability· 0 citations
The need for sustainable urban management systems for transport, energy, and waste has grown as urbanization has increased. Traditional management systems do not dynamically adjust and manage systems in real time, leading to waste and negative environmental impacts. This paper proposes an AI optimization framework, using IoT and edge computing, combined with hybrid machine learning models, to enhance urban infrastructure management systems. This approach employs Long Short-Term Memory (LSTM) networks for time-series forecasting and Reinforcement Learning (RL) for real-time, dynamic, adaptive, iterative model-based decision-making. IoT edge computing also enhances rapid processing and control by reducing latency and enabling responses to changes in managed systems. Testing was conducted on a framework built on datasets from simulated smart city environments (50,000 records for urban traffic, energy consumption, and waste). The framework reduced traditional energy management systems by 25 % and traffic management by 30-33%, and waste management systems by 20%. AI has the potential to enhance the effective management systems and the efficiency of urban centers. The research determined that incorporating AI into smart systems makes urban management scalable, flexible, and data-driven. Furthermore, this framework helps integrate smart systems and cities to achieve SDGs and enhance urban dwellers' quality of life.
Lincy Roy, Abhishek Sharma· 2026 4th International Confe...· 0 citations
A Multi-Agent Autonomous Control Framework that integrates Multi-Agent Systems (MAS), Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), Internet of Things (IoT), Vehicle-to-Everything (V2X) communication, and edge-cloud computing is proposed.
Seshagiri N· International Journal of Mod...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
