Multiagent Routing Simulation with Partial Smart Vehicles Penetration

Kamiński, Bogumił; Kraiński, Łukasz; Mashatan, Atefeh; Prałat, Paweł; Szufel, Przemysław

doi:10.1155/2020/3152020

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Some broad research directions of smart mobility subdomain of smart city are -1)Drastic enhancement in road safety; 2) Improvement in city economy which is dependent on smart mobility to sustain the professional and daily life of citizens; 3) Keep an eye on vehicle mobility information with the help of GPS, Radar, and camera to communicate the real-time situation to drivers as an early warning system; 4) Smart and secure toll transaction system [131]; 5) Smart vehicle travel time reduction using routing algorithms [132]; 6) Curb pollution and energy consumption using car polling and bike sharing system [133] [134]; 7) Fuel economy is another sector of improvement that is possible in smart mobility systems. Out of all these areas, few highly cited recent work done by researchers in recent years are discussed in detail here.…”

Section: Research Directionsmentioning

confidence: 99%

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Arora,

Jain,

Yadav

et al. 2023

IEEE Open J. Commun. Soc.

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Smart City has seen a growing interest among government, researchers, and industrialists. Smart cities use digital technologies to enhance the quality of life for residents while promoting sustainability and efficient resource management. By integrating various technologies such as ICT, AI, and IoT, smart cities can improve the delivery of public services, optimize transportation systems, reduce energy consumption, and enhance public safety, among other benefits. The goal of smart cities is to create a more livable, efficient, and sustainable urban environment for residents, businesses, and visitors. The smart city focuses on automating different disciplines, including smart environment, smart home, smart economy, smart mobility, and, smart governance. Due to that Multi-Agent driven Smart city has received tremendous attention from the research community to get an intelligent solution to complex problems from different disciplines by subdividing responsibilities into multiple agents and empowering agents through AI. In this regard, it is vital to explore the usage of Multi-Agent in different critical application areas of smart cities to achieve an end-to-end intelligent urban area. In this paper, a detailed description of the Multi-Agent process for Smart city application areas is provided along with resources and future research directions. Four different application areas-Smart Home, Smart Governance, Smart Environment, and Smart Mobility are discussed in detail.

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Section: Research Directionsmentioning

confidence: 99%

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Arora,

Jain,

Yadav

et al. 2023

IEEE Open J. Commun. Soc.

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show abstract

“…The system's prediction and optimization mechanisms considerably reduced the cost of maintaining an asset over its full life. A multi-agent simulation framework for complicated urban transportation was proposed in [15] with the aim of lowering the overall journey duration and smart car adoption level. Another design for the distribution system in the electric power model was created in [16] utilizing an artificial neural network in order to obtain operational restrictions and decrease the incidence of faults.…”

Section: Related Workmentioning

confidence: 99%

Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks

Kumaraguru,

Kamalakkannan,

H L

et al. 2023

IJGI

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Terabytes of data are now being handled by an increasing number of apps, and rapid user decision-making is hampered by data analysis. At the same time, there is a rise in interest in big data analysis for social networks at the moment. Thus, adopting distributed multi-agent-based technology in an optimum way is one of the solutions to effective big data analysis for social networks. Studying the development of a social network helps users gain an understanding of interactions and relationships and guides them in making decisions. In this study, a method called Hessian Distributed Ant Optimized and Perron–Frobenius Eigen Centrality (HDAO-PFEC) is developed to analyze large amounts of data (i.e., Big Data) in a computationally accurate and efficient manner. Designing an adaptable Multi-Agent System architecture for large data analysis is the primary goal of HDAO-PFEC. Initially, using a Hessian Mutual Distributed Ant Optimization MapReduce model, comparable user interest tweets are produced in a computationally efficient manner. Eigen Vector Centrality is a measure of a node’s importance in a network (i.e., a social network), which allows association with other significant nodes (i.e., users), allowing for a greater effect on social networks. With this goal in mind, a MapReduce methodology in the Hadoop platform using Big Data, which enables quick and ordered calculations, is used in a distributed computing method to estimate the Eigen Vector Centrality value for each social network member. Lastly, extensive investigative experimental learning demonstrates the HDAO-PFEC method’s use and accuracy as well as its time and overhead on the well-known sentiment 140 dataset.

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“…For more realistic scenarios involving real-world networks of roads, in [14] we have developed a general simulation framework. An in-depth simulation-approach to the broadcasting problem will be the subject of an accompanying paper [13].…”

Section: Introductionmentioning

confidence: 99%

On Broadcasting Time in the Model of Travelling Agents

Reaz¹,

Kamiński²,

Mashatan³

et al. 2020

Preprint

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Consider the following broadcasting process run on a connected graph G = (V, E). Suppose that k ≥ 2 agents start on vertices selected from V uniformly and independently at random. One of the agents has a message that she wants to communicate to the other agents. All agents perform independent random walks on G, with the message being passed when an agent that knows the message meets an agent that does not know the message. The broadcasting time ξ(G, k) is the time it takes to spread the message to all agents.Our ultimate goal is to gain a better understanding of the broadcasting process run on real-world networks of roads of large cities that might shed some light on the behaviour of future autonomous and connected vehicles. Due to the complexity of road networks, such phenomena have to be studied using simulation in practical applications. In this paper, we study the process on the simplest scenario, i.e., the family of complete graphs, as in this case the problem is analytically tractable. We provide tight bounds for ξ(K n , k) that hold asymptotically almost surely for the whole range of the parameter k. These theoretical results reveal interesting relationships and, at the same time, are also helpful to understand and explain the behaviour we observe in more realistic networks.

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Multiagent Routing Simulation with Partial Smart Vehicles Penetration

Cited by 8 publications

References 32 publications

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms

Hessian Distributed Ant Optimized Perron–Frobenius Eigen Centrality for Social Networks

On Broadcasting Time in the Model of Travelling Agents

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