2019 IEEE International Conference on Smart Internet of Things (SmartIoT)最新文献_第9页

Pay-Per-Pollution: Towards an Air Pollution-Aware Toll System for Smart Cities 按污染付费:面向智慧城市的空气污染感知收费系统

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00063

S. R. Garzon, Axel Küpper

{"title":"Pay-Per-Pollution: Towards an Air Pollution-Aware Toll System for Smart Cities","authors":"S. R. Garzon, Axel Küpper","doi":"10.1109/SmartIoT.2019.00063","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00063","url":null,"abstract":"Today, citizens of urban areas suffer from the serious health consequences of traffic-related air pollution. A common way to regulate traffic and indirectly the emission of pollutants is to charge drivers for the use of the road network. However, today's toll systems put their focus mainly on the optimization of the traffic flow by the introduction of congestion-based road charges rather than taking the current air pollution levels into consideration. This paper introduces the concept of an air pollution-aware toll system that charges drivers based on the route they travel across a pollution-affected region. By dynamically dividing the region into pollution-affected areas according to predefined pollution levels, the price for a trip can be obtained from the sum of distance-based transit costs for each polluted area. The costs for a transit are thereby linked to the current pollution level in order to make the worsening of an already critical situation more costly for the polluter than when the situation is less critical on site. The technical and practical challenges and opportunities with respect to the setup of a sensor network, the processing of air pollution measurements and the dynamic pricing process are investigated and discussed in detail.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128925097","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: 5

Modeling and Evaluation of a Power-Aware Algorithm for IoT Bluetooth Low Energy Devices 物联网蓝牙低功耗设备的功耗感知算法建模与评估

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00014

M. Ruiz, Celia Garrido-Hidalgo, Damas P. Gruska, T. Olivares, Diego Hortelano, Luis Roda-Sanchez

{"title":"Modeling and Evaluation of a Power-Aware Algorithm for IoT Bluetooth Low Energy Devices","authors":"M. Ruiz, Celia Garrido-Hidalgo, Damas P. Gruska, T. Olivares, Diego Hortelano, Luis Roda-Sanchez","doi":"10.1109/SmartIoT.2019.00014","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00014","url":null,"abstract":"Power consumption is one of the main concerns in developing IoT Wireless Sensor Networks (WSNs) due to the limited amount of energy and the difficulty of recharging them. The energy consumption rate in WSNs varies greatly based on the protocols used. Therefore, developing energy-efficient protocols is an unavoidable issue in WSNs. In addition, it is highly advisable both, verify algorithms before their implementation and carry out performance evaluation prior to the deployment of novel algorithms in real environments. Thus, in this paper we study the problem of optimally controlling the use of sleep states in an energy-aware algorithm, the so-called SustainaBLE, to save energy in IoT Bluetooth Low Energy (BLE) devices. Timed Coloured Petri nets (TCPNs) have been used to obtain complete and unambiguous specifications as well as CPNTools to evaluate the correctness of the protocol and carry out performance evaluation. We present the TCPN formal model for SustainaBLE, which allows input parameters to fulfil the requirements of the system under study, and conduct a neat study of SustainaBLE by modifying the intervals in which the nodes remain in a sleep state. The results conform to those harvest from our testbed allowing us to conclude that we have got a suitable model for different performance evaluation without the need to deploy the system to be studied.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132575635","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: 2

Research on Situational Perception of Power Grid Business Based on User Portrait 基于用户画像的电网业务情境感知研究

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00061

Zhiyong Yu, Linlin Liu, Chen Chen, Weitao Zhang, X. Ju, Lei Zhang

引用次数: 5

Optical Fiber Defect Detection Method Based on DSSD Network 基于DSSD网络的光纤缺陷检测方法

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-08-01 DOI: 10.1109/SmartIoT.2019.00075

Shiman Wang, Liming Wu, Wenhao Wu, Junchao Li, Xinying He, Feiyang Song

引用次数: 4

A Fast Online Cascaded Regression Algorithm for Face Alignment 人脸对齐的快速在线级联回归算法

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-05-10 DOI: 10.1109/SmartIoT.2019.00057

Lin Feng, Caifeng Liu, Sheng-lan Liu, Huibing Wang

{"title":"A Fast Online Cascaded Regression Algorithm for Face Alignment","authors":"Lin Feng, Caifeng Liu, Sheng-lan Liu, Huibing Wang","doi":"10.1109/SmartIoT.2019.00057","DOIUrl":"https://doi.org/10.1109/SmartIoT.2019.00057","url":null,"abstract":"Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. In many real-time applications, the training data is obtained one by one or batch by batch. It results in that the static model limits its performance on sequential images with extensive variations. Therefore, the most critical and challenging aspect in this field is dynamically updating the tracker's models to enhance predictive and generalization capabilities continuously. In order to address this question, we develop a fast and accurate online learning algorithm for face alignment. Particularly, we incorporate on-line sequential extreme learning machine into a parallel cascaded regression framework, coined incremental cascade regression(ICR). To the best of our knowledge, this is the first incremental cascaded framework with the non-linear regressor. One main advantage of ICR is that the tracker model can be fast updated in an incremental way without the entire retraining process when a new input is incoming. Experimental results demonstrate that the proposed ICR is more accurate and efficient on sequential images compared with the recent state-of-the-art cascade approaches. Furthermore, the incremental learning proposed in this paper can update the trained model in real time.","PeriodicalId":240441,"journal":{"name":"2019 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122618529","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

Color Recognition for Rubik's Cube Robot 魔方机器人的颜色识别

2019 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2019-01-11 DOI: 10.1109/SmartIoT.2019.00048

Sheng-lan Liu, Dong Jiang, Lin Feng, Feilong Wang, Zhanbo Feng, Xiang Liu, Shuai Guo, Bingjun Li, Yuchen Cong

引用次数: 3