Asymmetric Consortium Blockchain and Homomorphically Polynomial-Based PIR for Secured Smart Parking Systems

In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.

A Novel Green IoT-Based Pay-As-You-Go Smart Parking System

The better management of resources and the potential improvement in trafc congestion via reducing the orbiting time for parking spaces is crucial in a smart city,particularly those with an uneven correlation between the increase in vehicles and infrastructure.This paper proposes and analyses a novel green IoT-based Pay-As-You-Go(PAYG)smart parking system by utilizing unused garage parking spaces.The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’pricing portfolio with a garage’s current demand.Malta,the world’s fourth-most densely populated country,is considered as a case of a smart city for the implementation of the proposed approach.The results obtained conrm that apart from having a high potential system in such countries,the pricing generated correctly forecasts the demand for a particular garage at a specic time of the day and year.The proposed PAYG smart parking system can effectively contribute to the macro solution to trafc congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.

Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

BLECA: A Blockchain-Based Lightweight and Efficient Cross-Domain Authentication Scheme for Smart Parks

Smart parks serve as integral components of smart cities,where they play a pivotal role in the process of urban modernization.The demand for cross-domain cooperation among smart devices from various parks has witnessed a significant increase.To ensure secure communication,device identities must undergo authentication.The existing cross-domain authentication schemes face issues such as complex authentication paths and high certificate management costs for devices,making it impractical for resource-constrained devices.This paper proposes a blockchain-based lightweight and efficient cross-domain authentication protocol for smart parks,which simplifies the authentication interaction and requires every device to maintain only one certificate.To enhance cross-domain cooperation flexibility,a comprehensive certificate revocation mechanism is presented,significantly reducing certificate management costs while ensuring efficient and secure identity authentication.When a park needs to revoke access permissions of several cooperative partners,the revocation of numerous cross-domain certificates can be accomplished with a single blockchain write operation.The security analysis and experimental results demonstrate the security and effectiveness of our scheme.

Intelligent Parking System

The searching of parking burns a lot of barrels of the world’s oil every day. Car parking problem is a major contributor in congestion of traffic and has been, still a major problem with increasing vehicle size in the luxurious segment and also confines parking spaces in urban cities. The rapid growth in the number of vehicles worldwide is intensifying the problem of the lack of parking space. As the global population continues to urbanize, without a well-planned, convenience-driven retreat from the car, these problems will worsen in many countries. The current unmanaged car parks and transportation facilities make it difficult to accommodate the increasing number of vehicles in a proper, convenient manner so it is necessary to have an efficient and smart parking system. Smart parking management systems are capable of providing extreme level of convenience to the drivers. In this paper, a proposed web App system, named “Park Easy” is based on the usage of smart phones, sensors monitoring techniques with a camera which is used as a sensor to take photos to show the occupancy of cars parks. By implementing this system, the utilization of parking spaces will increase. It allocates available parking space to a given driver to park their vehicle, renew the availability of the parking space when the car leaves and compute the charges due. Smart parking App, “Park Easy”, will also enable most important techniques to provide all the possible shortage route for parking from any area of the city mainly, it helps to predict accurately and sense spot/vehicle occupancy in real-time.

Research and Practice of Smart Power Consumption

As a vital part of a smart grid, smart power consumption enables real-time interaction between consumers and the grid. With improved management of the demand side and energy efficiency, smart power consumption plays an important role in emission reduction as well as the economic operation of the power grid. Meanwhile, it enhances the power grid to a larger scale infrastructure by the two-way transmission of energy and information. This paper introduces the research, practice and vision of smart power consumption in China.

Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning

Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking spaces.And the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy challenging.Therefore,this paper proposes a deep learning model for predicting block-level parking occupancy.First,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature selection.The results show that different types of POI data should consider different buffer radii.Then based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking occupancy.The results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of LSTM.When there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.

A review of research on urban parking prediction

The rapid growth of urban traffic has intensified daily congestion,affecting both traffic flow and parking.Accurate parking prediction plays a vital role in effectively managing limited parking resources and is essential for the successful implementation of advanced intelligent systems.In an effort to comprehensively assess the latest developments in parking prediction,we curated a dataset of 639 articles spanning from 2010 to the present,using the Scopus database.Initially,we performed a bibliometric analysis utilizing VOSviewer software.These findings not only illuminate emerging trends within the parking prediction field but also provide strategic guidance for its progression.Subsequently,we categorized advancements in three focal areas:behavior prediction,demand prediction,and parking space prediction.A comprehensive overview of the present research status and future directions was then provided.The findings underscore the substantial progress achieved in current parking prediction models,achieved through diverse avenues like multi-source data integration,multi-variable feature extraction,nonlinear relationship modeling,deep learning techniques application,and ensemble model utilization.These innovative endeavors have not only pushed the theoretical boundaries of parking prediction but also significantly heightened the precision and applicability of predictive models in practical scenarios.Prospective research should explore avenues such as processing unstructured parking datasets,developing predictive models for small-scale data,mitigating noise interference in parking data,and harnessing potent platform fusion techniques.This study's significance transcends guiding and catalyzing advancement in academic and practical domains;it holds paramount relevance across academic research,technological innovation,decision-making support,business applications,and policy formulation.

Biding strategy of vehicle parking for participating in energy and spinning reserve markets

In this paper a model for suggesting a smart parking that involves a set of electric cars is presented to auction the management ability and correct parking planning in reserve spinning market, secondary energy market and grid. Parking interest under various scenarios is analyzed and its effective results are presented by a valid model. Besides, particle swarm optimization algorithm is used for calculating maximum benefit.

Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus plants.However,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus plants.The current study introduced the concept of smart learning in this setting to increase interest and motivation for learning.Convolutional neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each species.The scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as benchmarks.The performance of the model was presented in terms of accuracy,F1-score,precision,and recall values.The results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of 0.9954.The best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception V3.In addition,the number of total parameters was reduced by approximately 1.80–2.19 times.These findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.

Hierarchical Multi-objective Planning for Integrated Energy Systems in Smart Parks Considering Operational Characteristics

An integrated energy system(IES)is considered to be an important supporting technology for emission reduction because it can effectively improve the efficiency of energy utilization and promote its sustainable development.Considering the uncertainties and operational conditions,this paper establishes a bilevel multi-objective optimization model for IES for the Smart Park from the standpoint of economy,technology and environment.The upper level with one objective reflects the economic cost composed of investment,operating and maintenance,etc.The lower level constructs three objectives,including pollution emission,operation costs and renewable energy utilization.Simultaneously,various equality and inequality constraints are addressed to satisfy the technical requirements.In addition,an improved MOEA/D-MC-DC algorithm(Multi Objective Evolutionary Algorithm through Decomposition Based on Monte Carlo and Decoupled Coding,MOEA/D-MC-DC)is presented for handling the complex and nonlinear bilevel multi-objective optimization problems with constraints.A genetic algorithm(GA)is used to solve the upper single objective,while MOEA is employed to cope with the multi-objectives of the lower level.Using three typical IESs in the Smart Park as examples,several simulations are carried out to verify the efficiency,applicability and universality of the proposed model and optimization algorithm.The results show that the proposed method can effectively optimize the configuration of an IES in various Smart Parks.

An Optimal Charging/Discharging Strategy for Smart Electrical Car Parks

This paper presents a smart electrical car park model where the power flows among electrical vehicles(EVs)as well as between EVs and the main grid.Based on this model,an optimal charging/discharging scheme is proposed.The fluctuation of hourly electricity rates is considered in this strategy to select a proper charging/discharging rate for each EV with less expenditure during each charging period.The proposed smart electrical car park is able to buy or sell electricity in the form of active and/or reactive power,i.e.kWh and/or kVARh,from or to the main grid to improve the power quality.According to the current state of charge of the EV’s battery bank,customers and the grid demands,a control center makes the decisions and sends the instructions of specific charging/discharging mode to each charging station.The performance of the proposed charging/discharging algorithm is simulated in Matlab.A comparison between the proposed and the unregulated charging/discharging strategies has been implemented.The results demonstrate that the proposed scheme can achieve better economic profits for EV customers and increase the commercial benefits for the car park owner.