For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the Tra...
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This article delves into the issue of formation-containment control for multiple Euler-Lagrange systems with input saturation, where the leaders have unknown bounded control inputs. In this article, a distributed even...
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Due to the everchanging dynamics of traffic situation, managing real-time traffic congestion with great efficiency is exceedingly challenging. Deep Reinforcement Learning (DRL) in intelligent Transportation System (IT...
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Due to the everchanging dynamics of traffic situation, managing real-time traffic congestion with great efficiency is exceedingly challenging. Deep Reinforcement Learning (DRL) in intelligent Transportation System (ITS) under the concept of Edge computing is an approach that determines the optimal traffic signal strategy for dealing with traffic congestion. Optimizing traffic signal with a DRL agent involves transmitting state information collected by edge devices. However, network congestion, device malfunctions, and transmission delays often impede the transmission of information. Consequently, the decision-making capacity of the agent suffers from inadequate information, leading to decreased efficacy. To mitigate this issue, the study proposes two distinct masking methods on input states. A single DRL agent deals with these masked inputs from the environment through the Edge devices. In order to train the agent, the DRL algorithm Proximal Policy Optimization (PPO) is implemented in five different neural network models including the state-of-the-art Transformer network which can accurately model spatial dependence and capture the persistence of sequential data. To validate the feasibility of the agent, simulation experiments are conducted in hypothetical road network and real-time road map. The experiments utilize waiting time, fuel consumption, and CO2 emission as key simulation metrics due to their significant impact on traffic congestion. However, the main goal is to alleviate traffic congestion by minimizing waiting time. Results demonstrate substantial reductions in waiting times for both networks, with reductions of 26.35% and 26.31% observed for the two masking strategies in the hypothetical scenario, and decreases of 5.86% and 6.86% recorded for the real-time road map, highlighting significant improvements in congestion alleviation efforts.
LiDAR technology has emerged as a pivotal tool in intelligent Transportation systems (ITS), providing unique capabilities that have significantly transformed roadside traffic applications. However, this transformation...
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Blockchain-Enabled Supply Chain Management with Role-Based Access control and AES Encryption will use blockchain technology in order to enable safe, transparent, and decentralized supply chain management. Using smart ...
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As the number of vehicles rises, there is a problem with traffic congestion on the roads. This problem is characterized by slower speeds, greater time spent travelling, and more congestion in the traffic lanes. In add...
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There are a lot of people in India and other countries around the world who are physically disabled and use wheelchairs to move around. This gesture-controlled wheelchair project aims to provide an innovative solution...
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Implementing a PIλDμ controller is challenging while implementing in Industry 4.0 because of computational limitations in programmable logic controllers (PLC). The Oustaloup Approximation (OA) method is used in this...
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The rapid growth of the Internet of Things (IoT) has created a pressing need for efficient service allocation methods to manage the multitude of connected devices. Edge computing has become essential to fulfill the lo...
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ISBN:
(纸本)9783031814037;9783031814044
The rapid growth of the Internet of Things (IoT) has created a pressing need for efficient service allocation methods to manage the multitude of connected devices. Edge computing has become essential to fulfill the low-latency and high-bandwidth demands of IoT applications. This paper investigates the use of game theory as a framework for optimizing service allocation in edge computing environments. By treating the interactions between IoT devices and edge servers as a strategic game, we propose strategies to achieve optimal allocation and resource utilization. Our approach tackles key challenges such as minimizing latency, improving energy efficiency, and balancing load. Experimental results indicate that game-theoretic methods greatly improve the performance and scalability of IoT systems in edge computing, positioning them a promising solution for future applications.
With the rapid development and widespread application of cloud computing, container technology has become a hot topic for enterprises and research institutions in recent years. This article proposes an automated conta...
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