Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City最新文献_第3页

NB-IoT Based Waste Monitoring and Collection Planning System 基于NB-IoT的垃圾监控与收集规划系统

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512615

Martin Katura, Liang Wu, Chen Fu, Wei Liu

{"title":"NB-IoT Based Waste Monitoring and Collection Planning System","authors":"Martin Katura, Liang Wu, Chen Fu, Wei Liu","doi":"10.1145/3512576.3512615","DOIUrl":"https://doi.org/10.1145/3512576.3512615","url":null,"abstract":"Increasing Municipal Solid Waste (MSW) generation has become a major challenge in Namibia particularly in the capital city of Windhoek. Lack of finances and sufficient data in waste management system is a limiting factor to improving the efficiency of MSW management system in Windhoek city. IoT technology provides cost saving solutions that enables municipal authorities and companies to implement solution using less finances. Moreover, IoT enable the municipal and responsible authorities to capture relevant data that provides more insight into the waste management system i.e. in planning an efficient waste collection process. In this paper, the author proposes a waste monitoring and collection planning system based on narrow-band Internet of Things (NB-IoT). NB-IoT communication technology provides competitive advantage in flexibility and coverage while ensuring scalability and reliable service. The proposed solution will design and implement a smart bin prototype and a web-based waste monitoring application, capable of performing real time monitoring of waste fill level and detecting foul smell from the garbage bin. Moreover, the system provides effective decision-making support in waste collection planning. This will allow the municipal City of Windhoek to collect waste materials on time in order to avoid garbage bins from overflowing and keep the city clean.","PeriodicalId":278114,"journal":{"name":"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114541174","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}

引用次数: 0

Analysis on the development of Art and Design in Local Colleges and Universities in the Internet Era: Taking Shaanxi Province as an example 互联网时代地方高校艺术设计发展探析——以陕西省为例

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512625

Li Bin, N. Ahmad, Darliana

引用次数: 0

Digital fingerprint extraction method of IOT devices based on Cryptography 基于密码学的物联网设备数字指纹提取方法

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512628

Yan Zhang, Lei Zhang, Feng Yang, Siting Gu

引用次数: 0

Towards foggy image optimization: dark channel prior via RGB-splitted processing 对雾图像优化:暗通道优先通过rgb分割处理

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512579

Yang Liu, Zhining Xu, Cheng-Hsien Li, Caidong Yang, Yongqiang Xie, Zhongbo Li

引用次数: 0

Improvement of collision detection using Time Elastic Band algorithm 改进的时间弹性带碰撞检测算法

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512592

Wanyu Dai, Xianghua Ma

引用次数: 1

Exploring and Analyzing Facebook as crowdsourcing platform for traffic updates using Selenium Support Vector Machine and Non-parametric LDA 使用Selenium支持向量机和非参数LDA对Facebook作为流量更新的众包平台进行探索和分析

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512617

Leodivino Lawas, Ken Gorro, Elmo Ranolo, A. Ilano

{"title":"Exploring and Analyzing Facebook as crowdsourcing platform for traffic updates using Selenium Support Vector Machine and Non-parametric LDA","authors":"Leodivino Lawas, Ken Gorro, Elmo Ranolo, A. Ilano","doi":"10.1145/3512576.3512617","DOIUrl":"https://doi.org/10.1145/3512576.3512617","url":null,"abstract":"Traffic is a major problem in the Philippines. Facebook is one of the social media platforms that is commonly used by Filipinos. Machine learning is a field of computer science that allows computers to perform tasks like human beings. In this study, the proponents explored Facebook as a source of traffic updates and as a source of traffic information. In this paper, as a partial result, a machine learning model was created to classify Facebook posts as related to traffic. To gather Facebook posts, a total of 1000 respondents were asked for consent to scrape their public post using the username link and selenium. The Support vector machine model was trained with 3000 Facebook posts. The SVM model was only trained to 3 classes {Road accident, Road activities and Other}. The SVM model was evaluated using 10-cross fold validation. The result shows that the accuracy is 76% and the recall is 69%. To analyze the narrative of the corpus, the Hierarchical Dirichlet Process model was created with the log-likelihood of -4.06 with 10 topic models. The following are the narratives of the corpus: {Traffic Management, Immediate Emergency Response, Seeking help, Busses causes majority of accidents.}","PeriodicalId":278114,"journal":{"name":"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131598657","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}

引用次数: 0

Research on signal scheme optimization and transportation efficiency based on road transportation 基于道路运输的信号方案优化与运输效率研究

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512651

Yi Lu, Tangyi Guo

引用次数: 0

A Geometric Feature Based Traversable Area Extraction and Evaluation Method for 3D Point Clouds 基于几何特征的三维点云可遍历区域提取与评价方法

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512582

Yaobin Li, Ruibin Guo, Hui Zhang

引用次数: 0

Traffic Matrix Prediction with Attention-based Recurrent Neural Network 基于注意力的递归神经网络交通矩阵预测

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512594

Maliang Zhang, Yingpeng Sang, Weizheng Li, Chaoxin Cai, Jinghao Huang

{"title":"Traffic Matrix Prediction with Attention-based Recurrent Neural Network","authors":"Maliang Zhang, Yingpeng Sang, Weizheng Li, Chaoxin Cai, Jinghao Huang","doi":"10.1145/3512576.3512594","DOIUrl":"https://doi.org/10.1145/3512576.3512594","url":null,"abstract":"Traffic matrix (TM) shows the traffic volume of a network. Therefore, TM prediction is of great significance for network management. Attention mechanism has been successful in many sub-domains of machine learning, such as computer vision and natural language processing, and it performs particularly well on time series data. In this work, we first introduce attention mechanisms into the traffic matrix prediction field by proposing an attention-based deep learning model for traffic matrix prediction. This model is composed of two parts, encoder and decoder. We use a recurrent neural network (RNN) architecture as the encoder and our decoder has an attention layer and a linear layer. Attention mechanism allows the model to have better memory ability, so the model can concentrate on those important data regardless of distance. We also reduce the time consumption of our model using GPU-based parallel acceleration. Finally, we evaluate the effectiveness of our model on a real world TM dataset, and the results show our implementations on the proposed model perform better than the baseline models.","PeriodicalId":278114,"journal":{"name":"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114828918","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}

引用次数: 0

Factors Influencing Spatial Deprivation of Urban Public Transit——The Perspective of Public Transit Resource Allocation 城市公共交通空间剥夺的影响因素——基于公共交通资源配置的视角

Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City Pub Date : 2021-12-22 DOI: 10.1145/3512576.3512660

Yuanyuan Zhang, Chengkun Li, Zehui Chen

{"title":"Factors Influencing Spatial Deprivation of Urban Public Transit——The Perspective of Public Transit Resource Allocation","authors":"Yuanyuan Zhang, Chengkun Li, Zehui Chen","doi":"10.1145/3512576.3512660","DOIUrl":"https://doi.org/10.1145/3512576.3512660","url":null,"abstract":"Based on the allocation of public transit resources, the study firstly used the cluster analysis algorithm to classify the public transit deprivation level into four levels based on the accessibility of public transit. Then, a multivariate logistic regression model between public transit resource allocation and deprivation level was constructed and reversely tested, indicating that the model's accuracy in predicting the fairness level was as high as 76.52%. By using the model, an empirical study was conducted on the public transit deprivation of the downtown area in Guangzhou City. The results show that the public transit deprivation in Guangzhou is affected by the level of public transit resources, and it increases spatially from the center to the outside in a layer structure, with the northeast of the city presenting severe deprivation. With the decrease of station and line coverage and station service area coverage, the more public transit resources per capita, the lower the degree of public transit deprivation. The U-shaped relationship between the station and line occupancy and station occupancy and the intensity of traffic deprivation indicates that although the increase of both can alleviate traffic deprivation to a certain extent, the over-concentration of public transit resources will reduce its spatial allocation efficiency and lead to a decreased equity. The decreased location quotient within the coverage of 300m and 500m of the stations will aggravate the traffic deprivation.","PeriodicalId":278114,"journal":{"name":"Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124438998","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}

引用次数: 0