The Role of Deep Learning in Parking Space Identification and Prediction Systems

In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic congestion.Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space.This explains the need to predict the availability of parking spaces.Recently,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction systems.However,no work reviews DL approaches applied to solve parking identification and prediction problems.Inspired by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking spaces.Ataxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are presented.Moreover,several open research challenges are outlined.This work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction problems.Moreover,there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain.This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities.This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.

Relevant Visual Semantic Context-Aware Attention-Based Dialog

The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image contents.However,it faces challenges in overcoming visual semantic limitations,particularly in obtaining sufficient context from visual and textual aspects of images.This paper proposes a new visual dialog dataset called Diverse History-Dialog(DS-Dialog)to address the visual semantic limitations faced by the existing dataset.DS-Dialog groups relevant histories based on their respective Microsoft Common Objects in Context(MSCOCO)image categories and consolidates them for each image.Specifically,each MSCOCO image category consists of top relevant histories extracted based on their semantic relationships between the original image caption and historical context.These relevant histories are consolidated for each image,and DS-Dialog enhances the current dataset by adding new context-aware relevant history to provide more visual semantic context for each image.The new dataset is generated through several stages,including image semantic feature extraction,keyphrase extraction,relevant question extraction,and relevant history dialog generation.The DS-Dialog dataset contains about 2.6 million question-answer pairs,where 1.3 million pairs correspond to existing VisDial’s question-answer pairs,and the remaining 1.3 million pairs include a maximum of 5 image features for each VisDial image,with each image comprising 10-round relevant question-answer pairs.Moreover,a novel adaptive relevant history selection is proposed to resolve missing visual semantic information for each image.DS-Dialog is used to benchmark the performance of previous visual dialog models and achieves better performance than previous models.Specifically,the proposed DSDialog model achieves an 8% higher mean reciprocal rank(MRR),11% higher R@1%,6% higher R@5%,5% higher R@10%,and 8% higher normalized discounted cumulative gain(NDCG)compared to LF.DS-Dialog also achieves approximately 1 point improvement on R@k,mean,MRR,and NDCG compared to the original RVA,and 2 points improvement compared to LF andDualVD.These results demonstrates the importance of the relevant semantic historical context in enhancing the visual semantic relationship between textual and visual representations of the images and questions.

Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT

In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)approach.The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly rainfall.MADQN is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)approaches.MADQN enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia.Investigation is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic network.Our proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

COLLABORATIVE INTELLIGENCE FOR VEHICULAR INTERNET OF THINGS

Future vehicular Internet-of-Things(IoT)systems feature a large number of devices and multi-access environments where different types of communication,computing,and storage resources must be efficiently utilized.At the same time,novel services such as cooperative autonomous driving and intelligent transportation systems(ITS),that demand unprecedented high accuracy,ultra-low latency,and large bandwidth,are emerging.

Reinforcement learning models for scheduling in wireless networks

The dynamicity of available resources and net- work conditions, such as channel capacity and traffic charac- teristics, have posed major challenges to scheduling in wire- less networks. Reinforcement learning （RL） enables wire- less nodes to observe their respective operating environment, learn, and make optimal or near-optimal scheduling deci- sions. Learning, which is the main intrinsic characteristic of RL, enables wireless nodes to adapt to most forms of dynamicity in the operating environment as time goes by. This paper presents an extensive review on the application of the traditional and enhanced RL approaches to various types of scheduling schemes, namely packet, sleep-wake and task schedulers, in wireless networks, as well as the advantages and performance enhancements brought about by RL. Addi- tionally, it presents how various challenges associated with scheduling schemes have been approached using RL. Finally, we discuss various open issues related to RL-based schedul- ing schemes in wireless networks in order to explore new re- search directions in this area. Discussions in this paper are presented in a tutorial manner in order to establish a founda- tion for further research in this field.