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Machine Learning-Enabled Power Scheduling in IoT-Based smart cities

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Computers, Materials & Continua 2021年第5期67卷 2449-2462页

作者： Nabeela Awan Salman Khan Mohammad Khalid Imam Rahmani Muhammad Tahir Nur Alam MD Ryan Alturki Ihsan Ullah Department of Computer Science Government Girls Degree College MardanKPK23200Pakistan Department of Electrical Energy System UETPeshawar25000Pakistan Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityKSA Department of smart Computing Kyungdong UniversityGlobal CampusGosung24764South Korea Department of Information Sciences College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Robotics Engineering Daegu Gyeongbuk Institute of Science and TechnologySouth Korea

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

关键词： PSO IBR machine learning IoT smart cities CDBR

Improving accuracy of automated 3-D building models for smart cities

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International Journal of Digital Earth 2019年第2期12卷 209-227页

作者： Byungyun Yang Jungil Lee Department of Geography DePaul UniversityChicagoILUSA Shinhan Aerial Survey SeoulKorea

Photorealistic three-dimensional(3-D)models are important tools for urban and disaster management planning because they contain useful visual and spatial information for assessing the situation and responding ***,it is crucial for 3-D city models to maintain a high level of accuracy in portraying 3-D geometric *** that,significant research with respect to 3-D city models has been conducted;however,it is still difficult to create accurate 3-D representations,particularly across a wide ***,this research is aimed at developing an automated 3-D city modeling application that utilizes a combination of aerial photographs,terrestrial light detection and ranging,and total station techniques for particularly dense urban *** enable the development of an automated 3-D city model,this research developed application software programmed in C++.This application enables users to generate 3-D images of ***,the 3-D city models are generated using the aerial *** positional accuracy of the model is highly improved by comparing building models produced using a photogrammetric plotting *** created using a combination of aerial photos and terrestrial instruments,the 3-D city model is quantitatively assessed for completeness and suitability for commercial or public use.

关键词： 3D city model GIS smart cities oblique aerial photographs disaster management

Deep Learning Method to Detect the Road Cracks and Potholes for smart cities

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Computers, Materials & Continua 2023年第4期75卷 1863-1881页

作者： Hong-Hu Chu Muhammad Rizwan Saeed Javed Rashid Muhammad Tahir Mehmood Israr Ahmad Rao Sohail Iqbal Ghulam Ali College of Civil Engineering Hunan UniversityChangsha410082China Department of CS University of OkaraOkara56310Pakistan Department of CS&SE Islamic International UniversityIslamabad44000Pakistan Department of IT Services University of OkaraOkara56310Pakistan Department of CS Government College UniversityFaisalabad38000Pakistan Department of Automation Science Beihang UniversityBeijing100191China

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance *** the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani *** proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

关键词： Road cracks and potholes CNN smart cities pothole crack detection decision support system

KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in smart cities

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Computers, Materials & Continua 2021年第8期68卷 1831-1845页

作者： Najla Al-Nabhan Maha AlDuhaim Sarah AlHussan Haifa Abdullah Mnira AlHaid Rawan AlDuhaishi Computer Science Department King Saud UniversityRiyadhSaudi Arabia

Simulation is a powerful tool for improving,evaluating and analyzing the performance of new and existing *** simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of *** are many types of traffic simulators that allow simulating traffic in modern *** most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic *** many cities of Saudi Arabia,traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of ***,employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private *** simulators are usually expensive,closed source and inflexible as they allow limited *** this project,we developed a local traffic simulator“KSUtraffic”for traffic modeling,planning and analysis with respect to different traffic control strategies and *** modeled information specified by GIS and real traffic ***,we designed experiments that manipulate simulation parameters and the underlying *** visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.

关键词： Simulation and modelling smart cities smart infrastructure traffic management and planning intelligent transportation

Energy-Efficient and Blockchain-Enabled Model for Internet of Things (IoT) in smart cities

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Computers, Materials & Continua 2021年第3期66卷 2509-2524页

作者： Norah Saleh Alghamdi Mohammad Ayoub Khan College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computing and Information Technologies University of BishaBisha67714Saudi Arabia

Wireless sensor networks(WSNs)and Internet of Things(IoT)have gained more popularity in recent years as an underlying infrastructure for connected devices and sensors in smart *** data generated from these sensors are used by smart cities to strengthen their infrastructure,utilities,and public *** are suitable for long periods of data acquisition in smart *** make the networks of smart cities more reliable for sensitive information,the blockchain mechanism has been *** key issues and challenges of WSNs in smart cities is efficiently scheduling the resources;leading to extending the network lifetime of *** this paper,a linear network coding(LNC)for WSNs with blockchain-enabled IoT devices has been *** consumption of energy is reduced for each node by applying *** efficiency and the reliability of the proposed model are evaluated and compared to those of the existing *** from the simulation demonstrate that the proposed model increases the efficiency in terms of the number of live nodes,packet delivery ratio,throughput,and the optimized residual energy compared to other current techniques.

关键词： IoT blockchain WSN smart cities LNC

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in smart cities

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Computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed Hamza Hadeel Alsolai Jaber S.Alzahrani Mohammad Alamgeer Mohamed Mahmoud Sayed Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic *** the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic *** Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past *** Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of *** Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction *** this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart ***,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization ***,BiGRUmodel is employed for effective forecasting of traffic in smart ***,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU *** proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models

Optimizing Traffic Signals in smart cities Based on Genetic Algorithm

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Computer Systems Science & Engineering 2022年第1期40卷 65-74页

作者： Nagham A.Al-Madi Adnan A.Hnaif Al-Zaytoonah University of Jordan Faculty of Science and Information TechnologyAmman11733Jordan

Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic *** condition affects travel demand on the streets of *** study aims to improve an intelligent road traffic management system(IRTMS)derived from the human community-based genetic algorithm(HCBGA)to mitigate traffic signal congestion in Amman,Jordan’s capital *** parameters considered for IRTMS are total time and waiting time,and fixed timers are still used for *** contrast,the enhanced system,called enhanced-IRTMS(E-IRTMS),considers additional important parameters,namely,the speed performance index(SPI),speed reduction index(SRI),road congestion index(R i),and congestion period,to enhance IRTMS decision.A significant reduction in congestion period was measured using E-IRTMS,improving by 13% compared with that measured using ***,the IRTMS result surpasses that of the current traffic signal system by approximately 83%.This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI,SRI,and R_(i) compared with IRTMS.

关键词： Genetic algorithm traffic signal congestion smart cities

Deep Learning Enabled Intelligent Healthcare Management System in smart cities Environment

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Computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

smart Object Detection and Home Appliances Control System in smart cities

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Computers, Materials & Continua 2021年第4期67卷 895-915页

作者： Sulaiman Khan Shah Nazir Habib Ullah Khan Department of Computer Science University of SwabiKPPakistan Department of Accounting and Information Systems College of Business and EconomicsQatar UniversityDohaQatar

During the last decade the emergence of Internet of Things(IoT)based applications inspired the world by providing state of the art solutions to many common *** traffic management systems to urban cities planning and development,IoT based home monitoring systems,and many other smart *** of these facilities,most of these IoT based solutions are data driven and results in small accuracy values for smaller *** order to address this problem,this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart *** hybrid model consists of;convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like;Raspberry Pi,Amazon Web services cloud,and GSM modems for remotely accessing and controlling of the home *** android application is developed and deployed on Amazon Web Services(AWS)cloud for the remote monitoring of home appliances.A GSM device and Message queuing telemetry transport(MQTT)are integrated for communicating with the connected IoT devices to ensure the online and offline *** object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network *** applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the *** many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34%compared to the conventional recognition model(k nearest neighbours)recognition rate of 76%.

关键词： Hybrid deep learning model IoT smart cities home appliances control system and Amazon web services