Li-Fi Based Automatic Traffic Signal Control for Emergency Vehicles

Shanmughasundaram, R; Vadanan, S. Prasanna; Dharmarajan, Vivek

doi:10.1109/icaecc.2018.8479427

Cited by 25 publications

(6 citation statements)

References 5 publications

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“…Trust region policy optimization (TRPO) can be defined as a gradient descent-based technique for scalable policy optimization, and it is used throughout the training process to optimise the policy parameter of the MLP and maximise the predicted discounted return. Policy gradient methods are more stable throughout training and don't require a model, in contrast to value iteration approaches [19][20][21].…”

Section: Suggested Load Balancing Methodsmentioning

confidence: 99%

Enhancing Hybrid System Based on Reinforcement Learning

2024

IJIES

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Light fidelity (Li-Fi) can be defined as a type of wireless technology that sends data via light waves through LED light bulbs. Light fidelity (Li-Fi) offers high-speed data transmission capabilities and a large unlicensed bandwidth, making it a promising technology for the future. However, factors including interference, wall reflection, and blocking may cause the quality of a light fidelity (Li-Fi) channel to vary from one part of a room to another. Another type of wireless communication technology that offers broad coverage and slow transmission rates is wireless fidelity (Wi-Fi). Since the electromagnetic spectrums regarding such two technologies do not overlap, there is a possibility for building a hybrid Wi-Fi and Li-Fi network that provides seamless and high throughput global communication. One wireless fidelity access point (Wi-Fi AP) and four light fidelity access points (Li-Fi Aps) make up the downlink hybrid system we discuss in this work. Finding an access point (AP) assignment method that will increase long-term system throughput at the same time as still guaranteeing users' pleasure and fairness is challenging. Thus, the authors suggest using a reinforcement learning (RL) algorithm. The algorithm aims to balance the load of multiple access points (APs) by considering both the LiFi and Wi-Fi channels. The results obtained using MATLAB code for a hybrid system based on reinforcement learning (RL) and a standard solution set size (SSS) access technology called TDMA. The system was evaluated in terms of throughput and user satisfaction for 5 users. According to the results, the RL-based hybrid system achieved a throughput of up to 210 Mbps and an SSS of 180 Mbps. Additionally, the user satisfaction was reported to be 100%.

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Section: Suggested Load Balancing Methodsmentioning

confidence: 99%

Enhancing Hybrid System Based on Reinforcement Learning

2024

IJIES

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“…LiFi technology is subject to some constraints, including visual impairment and a restricted coverage area caused by the specific properties of light propagation [10]. These limitations make LiFi technology restricted in some application scenarios.…”

Section: Challenges Of Lifi Technologymentioning

confidence: 99%

LiFi Technology Application and Prospect in the Field of Medical

Shen

2024

HSET

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Light Fidelity (LiFi) technology utilizes visible light as a transmission medium with a large spectrum and resistance to electromagnetic interference. Due to its unique characteristics, the use of light emitted by light-emitting diodes (LED) to transmit data makes it very safe when transmitting data. LiFi technology plays an integral role in many fields, such as aerospace, underwater communications, defense and security, and medicine. This paper introduces the basic working principle of LiFi. LiFi technology is used as a modulation technique for data transmission, and the current status of LiFi technology in the medical field is briefly analyzed, along with the challenges and difficulties encountered in this field. Examples include a medical monitoring system based on LiFi technology and the transmission of images in the operating theater using LiFi technology. In the medical field, LiFi technology also faces challenges and difficulties, such as ensuring the stability and reliability of communication and achieving efficient signal processing and data transmission. The potential of multiple-input multiple-output (MIMO) technology for in-depth research is described.

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“…en, the traffic near the intersection point is controlled to allow the vehicle. For emergency vehicles routing nearby traffic signals, a novel method using the Li-Fi protocol was proposed in reference [25]. With some fixed modulated frequency, the Li-Fi model is fixed in emergency and nonemergency vehicles and the systems of traffic light control.…”

Section: Literature Reviewmentioning

confidence: 99%

BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

Ahmed

Frikha

Rahebi

2022

Mobile Information Systems

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This article proposes an ambulance vehicle routing approach in smart cities. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). It aims to take transfer the patients confidentially, accurately, and quickly. The type of CNN used in this research is a residual network (ResNet). The node method is responsible for creating the city map. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The driver feeds the data that contain the ambulance vehicle’s node position and the accident location to the BA-CNN vehicle routing algorithm. The algorithm then obtains the shortest path to reach the location of the accident by the driver. When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. The offline data are then trained by utilizing a neural network. The neural network is used for finding the shortest routes between source and destination. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV.

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Li-Fi Based Automatic Traffic Signal Control for Emergency Vehicles

Cited by 25 publications

References 5 publications

Enhancing Hybrid System Based on Reinforcement Learning

Enhancing Hybrid System Based on Reinforcement Learning

LiFi Technology Application and Prospect in the Field of Medical

BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

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