EAI Endorsed Transactions on Smart Cities最新文献

{"title":"Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment","authors":"Abbas Abdullahi, Ameer Mohammed, Mathias Usman Bonet, A. El-Suleiman, Rabiu B. Ahmad, Teng D. Chollom","doi":"10.4108/eetsc.4385","DOIUrl":"https://doi.org/10.4108/eetsc.4385","url":null,"abstract":"As cities worldwide transform into smart, interconnected urban environments, the management of municipal waste emerges as a pressing challenge. This paper, titled \"Development of a Smart Waste Management System with Automatic Bin Lid Control,\" offers a sophisticated solution that integrates seamlessly within the concept of smart cities. This system harnesses the power of the Internet of Things (IoT) to optimize waste collection and enhance urban cleanliness. The primary aim of this project is to create a smart waste management system that extends beyond traditional waste bins. It introduces a network of intelligent waste containers equipped with automatic lid control mechanisms that operate based on real-time waste level data. When approaching these bins, the lid control mechanism automatically opens the bin only if the waste level is not full, thereby facilitating convenient waste disposal. In the context of smart cities, this innovative approach presents several advantages. It optimizes waste collection efficiency by prioritizing bins in need of immediate attention and ensures that waste containers are not prematurely emptied, reducing unnecessary waste disposal trips. Moreover, the system enables city authorities to gain insights into waste level trends, fostering data-driven and proactive waste management strategies for a cleaner, more sustainable urban environment.","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140690152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle counting application utilizing background subtraction method with large-scale camera data","authors":"Mien Puoc Doan, Vu The Tran, Sy Ngo Van","doi":"10.4108/eetsc.3211","DOIUrl":"https://doi.org/10.4108/eetsc.3211","url":null,"abstract":"In modern society, people are increasingly using cameras at home, in shops, and on the streets. Traffic systems have also invested in building more surveillance camera systems. The data collected by cameras contains valuable information for traffic regulation and recording traffic violations. The challenge is how to effectively use this data. In this article, we will discuss the use of real-time data from surveillance cameras on some roads in Da Nang City for vehicle counting using background subtraction methods. Additionally, we also tested the detection of red light violations to contribute to the development of a smart traffic system. So, the use of background subtraction in analyzing real-time data from surveillance cameras can greatly improve traffic management","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Contemporary Approach to Designing and Implementing Electronic Voting Systems (EVS)","authors":"A. Azameti, Samuel Chris Quist, G. Koi-Akrofi, Benedict C. Nwachuku","doi":"10.4108/eetsc.3896","DOIUrl":"https://doi.org/10.4108/eetsc.3896","url":null,"abstract":"This study delves into the potential of Electronic Voting Systems (EVS) in Ghana, to enhance transparent and trustworthy electoral processes. We presented a comprehensive framework highlighting trust, diaspora engagement, and human factors in voting. The study proposes a robust EVS framework for Ghana, emphasizing trust and accountability, preventing electoral fraud, and encouraging African governments to invest in IT and collaborate with experts in e-government and e-voting systems. We commence with a detailed systems analysis, identifying specific electoral challenges in Ghana. An artifact is designed and developed, and the artifact's effectiveness is demonstrated through Design Science Research Methodology (DSRM). We evaluated its alignment with the desired solution for Ghana's electoral issues. We emphasize the potential of EVS to address electoral challenges in Ghana and underscore the importance of proactive government policies, IT investments, and collaboration with IT experts. User acceptance testing achieved a remarkable 98% approval rate, showcasing the feasibility of implementing EVS at the national level. This research underlines the pivotal role of EVS in Ghana and advocates for visionary government policies and investments in IT. These measures can modernize electoral systems, align them with international standards, and promote democratic progress while preventing electoral fraud in Ghana and other African nations to avoid condemnation and punishment of unconstitutional transfers of power that are being challenged by meticulously planned takeovers. ","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"82 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Cyber Attacks using Machine Learning ‎based Intrusion Detection System for IoT Based Smart ‎Cities","authors":"Maria Nawaz Chohan, Usman Haider, Muhammad Yaseen Ayub, Hina Shoukat, Tarandeep Kaur Bhatia, Muhammad Furqan Ul Hassan","doi":"10.4108/eetsc.3222","DOIUrl":"https://doi.org/10.4108/eetsc.3222","url":null,"abstract":"The world’s dynamics is evolving with artificial intelligence (AI) and the results are smart products. A smart city has smart city is collection of smart innovations powered with AI and internet of things (IoTs). Along with the ease and comfort that the concept of a smart city pointed at, many security concerns are being raised that hinders the path of its flourishment. An Intrusion Detection System (IDS) monitors the whole network traffic and alerts in case of any anomaly. A Machine Learning-based IDS intelligently senses the network threats, takes decisions about data packet legibility and alarm the user. Researchers have deployed various ML techniques to IDS to improve the detection accuracy. This work presents a comparative analysis of various ML algorithms trained over UNSW-NB15 dataset. ADA Boost, Linear Support Vector Machine (LSVM), Auto Encoder Classifier, ‎Quadratic Support Vector Machine (QSVM) and Multi-Layer Perceptron algorithms are being employed in the stimulation. ADA Boost showed an excellent accuracy of 98.3% in the results.","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121043380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applied Design and Methodology of Delivery Robots Based on Human–Robot Interaction in Smart Cities","authors":"Wing-ting Law, Kam-wah Fan, Ki-sing Li, Tiande Mo","doi":"10.4108/eetsc.2649","DOIUrl":"https://doi.org/10.4108/eetsc.2649","url":null,"abstract":"This paper proposes an optimised design of an autonomous delivery robot while adopting the latest technologies from the different branching fields of robotics, artificial intelligence, and tele-communication. As a prospective representation of a user-centric robot design, the proposal is design with the major focus on maximizing users’ satisfaction throughout every human–robot interaction (HRI) touchpoints. By the use of sensor fusion techniques along with the deployment of an image-detection-based technique accompanying the point-cloud-detection-based path-planning methodology, the robot delivery would be optimised with effective path-planning and obstacle avoidance capability. With the extension of 5G connectivity, it is proposed that the real-time status update and video stream would enable greater efficiency in terms of remote monitoring and centralised robot administration.","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117351551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Evaluation of ARIMA and FB-Prophet Forecasting Methods in the Context of Endemic Diseases: A Case Study of Gedaref State in Sudan","authors":"H. Ali Hussein, Mukhtar M. E. Mahmoud, Haroun A. Eisa","doi":"10.4108/eetsc.v7i2.3023","DOIUrl":"https://doi.org/10.4108/eetsc.v7i2.3023","url":null,"abstract":"Today, artificial intelligence is a key tool for turning a city into a smart city, and advances in information and communication technology (ICT) have led to the development of smart cities with many different parts. Smart Health is one of these components and is used to improve healthcare by providing services such as disease forecasting, early diagnosis, and others. There are various machine learning algorithms available now that can help with S-Health services, but which is better for disease forecasting? Gedaref State, for example, has some of Sudan's heaviest rains, and malaria and pneumonia are widespread throughout the year. Predicting future trends for these diseases has been a major focus for researchers in order for Gedaref's administration and the state's ministry of health to design effective ways to prevent and control the development of these diseases, as well as to prepare an adequate stock of medicine. As a result, it is necessary to establish a trustworthy and accurate forecasting model to aid Gedaref's government in developing economic and medical strategies for dealing with these diseases, as well as taking action on medical resource allocation. This study uses a time series dataset collected from the state's ministry of health to estimate malaria and pneumonia as common diseases in Gedaref state, Sudan, five months later. To comprehend the overall number of cases of diseases, two forecasting methodologies, namely the ARIMA and Prophet models, are applied to the disease's dataset. The performance of the ARIMA and FB-Prophet forecasting systems in predicting malaria and pneumonia diseases in Gedaref State is compared in this study. The data was collected from the state's ministry of health between January 2017 and December 2021. The results reveal that the ARIMA technique outperforms the FB-Prophet forecasting method in both malaria (RMSE: 182.8, MAE: 141.6, MAPE: 0.0057, and MASE: 0.0537) and pneumonia (RMSE: 1400.3, MAE: 1001.4, MAPE: 0.0513, and MASE: 0.9136).","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126720233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Welcome to the first issue of Volume 7 of the EAI Endorsed Transactions on Smart Cities","authors":"Mohammad Derawi","doi":"10.4108/eetsc.v7i1.3389","DOIUrl":"https://doi.org/10.4108/eetsc.v7i1.3389","url":null,"abstract":"","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134562450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Propaganda Detection And Challenges Managing Smart Cities Information On Social Media","authors":"Pir Noman Ahmad, K. Khan","doi":"10.4108/eetsc.v7i2.2925","DOIUrl":"https://doi.org/10.4108/eetsc.v7i2.2925","url":null,"abstract":"Misinformation, false news, and various forms of propaganda have increased as a consequence of the rapid spread of information on social media. The Covid-19 spread deeply transformed citizens' day-to-day lives due to the overview of new methods of effort and access to facilities based on smart technologies. Social media propagandistic data and high-quality information on smart cities are the most challenging elements of this study. As a result of a natural language processing perspective, we have developed a system that automatically extracts information from bi-lingual sources. This information is either in Urdu or English (Ur or Eng), and we apply machine translation to obtain the target language. We explore different neural architectures and extract linguistic layout and relevant features in the bi-lingual corpus. Moreover, we fine-tune RoBERTa and ensemble BiLSM, CRF and BiRNN model. Our solution uses fine-tuned RoBERTa, a pretrained language model, to perform word-level classification. This paper provides insight into the model's learning abilities by analyzing its attention heads and the model's evaluation results.","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133173928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of Blockchain for COVID-19: A Systematic Review, Applications and Possible Challenges","authors":"Shah Hussain Badshah, Muhammad Imad, Muhammad Abul Hassan, Naimullah, S. Khan, Farhatullah, Sana Ullah, Syed Haider Ali","doi":"10.4108/eetsc.v7i1.2827","DOIUrl":"https://doi.org/10.4108/eetsc.v7i1.2827","url":null,"abstract":"Smart city is emerging application in which many Internet of Things (IoT) devices are embedded to perform overall monitoring and perform processing automatically. In smart city the authenticity is key problem and many users in the in smart city has faced challenges during COVID-19. The COVID-19 epidemic, a deadly virus, first appeared in the globe in 2019. The World Health Organization (WHO) states that it is almost certainly feasible to contain this virus in its early phases if some precautions are taken. To contain the infection, most nations declared emergencies both inside and outside their borders and prohibited travel. Artificial intelligence and blockchain are being used in smart city applications to monitor the general condition in the nation and reduce the mortality rate. Blockchain has also made it possible to safeguard patient medical histories and provide epidemic tracking. AI also offers the ideal, wanted answer for correctly identifying the signs. The primary goal of this study is to fully investigate blockchain technology and artificial intelligence (AI) in relation to COVID-19. A case study that was recently developed to identify and networked pathogens acquired important knowledge and data. Additionally, AI that can handle massive quantities of medical data and perform difficult jobs will be able to reduce the likelihood of intricacy in data analysis. Lastly, we highlight the present difficulties and suggest potential paths for addressing the 19 diseases in future circumstances.","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122356392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Machine Learning based Intrusion Detection System (IDS) for Smart cities networks","authors":"Muhammad Yaseen Ayub, Usman Haider, Ali Haider, Muhammad Tehmasib Ali Tashfeen, Hina Shoukat, Abdul Basit","doi":"10.4108/eetsc.v7i1.2825","DOIUrl":"https://doi.org/10.4108/eetsc.v7i1.2825","url":null,"abstract":"INTRODUCTION: Internet of Things (IoT) along with Cloud based systems are opening a new domain of development. They have several applications from smart homes, Smart farming, Smart cities, smart grid etc. Due to IoT sensors operating in such close proximity to humans and critical infrastructure, there arises privacy and security issues. Securing an IoT network is very essential and is a hot research topic. Different types of Intrusion Detection Systems (IDS) have been developed to detect and prevent an unauthorized intrusion into the network.OBJECTIVES: The paper presents a Machine Learning based light, fast and reliable Intrusion Detection System (IDS).METHODS: Multiple Supervised machine learning algorithms are applied and their results are compared. Algorithms applied include Linear Discriminant analysis, Quadratic Discriminant Analysis, XG Boost, KNN and Decision Tree.RESULTS: Simulation results showed that KNN Algorithm gives us the highest accuracy, followed by XG Boost and Decision Tree which are not far behind.CONCLUSION: A fast, secure and intelligent IDS is developed using machine learning algorithms. The resulting IDS can be used in various types of networks especially in IoT based networks.","PeriodicalId":356944,"journal":{"name":"EAI Endorsed Transactions on Smart Cities","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}