2021 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第8页

A Novel Architecture for the Smart Management of Wastewater Treatment Plants 污水处理厂智能管理的新架构

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00080

Giovanni Cicceri, R. Maisano, N. Morey, S. Distefano

引用次数: 0

Mining the Stream of News for City Areas Profiling: a Case Study for the City of Rome 挖掘信息流用于城市区域分析:以罗马市为例

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00066

A. Bechini, Alessandro Bondielli, José Luis Corcuera Bárcena, P. Ducange, F. Marcelloni, Alessandro Renda

{"title":"Mining the Stream of News for City Areas Profiling: a Case Study for the City of Rome","authors":"A. Bechini, Alessandro Bondielli, José Luis Corcuera Bárcena, P. Ducange, F. Marcelloni, Alessandro Renda","doi":"10.1109/SMARTCOMP52413.2021.00066","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00066","url":null,"abstract":"Tracking and profiling changes in the occurrence of notable events in a city, in terms of what happens in the different areas and how possible changes are perceived, is an important issue in the context of smart cities: in fact, it may be helpful in developing applications to help administrations and citizens alike. In this paper, we propose an approach to provide time-sensitive snapshots of events within the different areas of a city, and the city as a whole. To probe inside neighborhoods and communities, we propose to use articles in online newspapers, as they represent an accessible source of information on what notable events actually happen, and on the most relevant topics at a given moment in time. We adopt an approach to group up articles by means of clustering, and to automatically assign labels to clusters by analyzing their content. The outcomes of this procedure, repeated along a certain timespan, are able to describe the temporal evolution of notable events in specific city areas. In this paper we show the effectiveness of the proposed methodology by reporting a case study for the city of Rome, over an investigation span of few years, which includes also the Covid-19 pandemic period.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414030","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

User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization 基于多目标优化的智慧城市以用户为中心的分布式路径规划

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00031

F. Tiausas, J. P. Talusan, Yu Ishimaki, H. Yamana, H. Yamaguchi, Shameek Bhattacharjee, Abhishek Dubey, K. Yasumoto, Sajal K. Das

{"title":"User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization","authors":"F. Tiausas, J. P. Talusan, Yu Ishimaki, H. Yamana, H. Yamaguchi, Shameek Bhattacharjee, Abhishek Dubey, K. Yasumoto, Sajal K. Das","doi":"10.1109/SMARTCOMP52413.2021.00031","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00031","url":null,"abstract":"The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877230","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

Assemble, Control, and Test (ACT): A Management Framework for Indoor IoT Systems 组装、控制和测试(ACT):室内物联网系统的管理框架

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00073

E. Perry, Qingshuang Han

引用次数: 0

Scalable Real-Time Analytics for IoT Applications 物联网应用的可扩展实时分析

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00084

Khalid Mahmood, T. Risch

引用次数: 1

ChatterHub: Privacy Invasion via Smart Home Hub ChatterHub:通过智能家居中心侵犯隐私

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00045

Omid Setayeshfar, Karthika Subramani, Xingzi Yuan, Raunak Dey, Dezhi Hong, K. H. Lee, In Kee Kim

{"title":"ChatterHub: Privacy Invasion via Smart Home Hub","authors":"Omid Setayeshfar, Karthika Subramani, Xingzi Yuan, Raunak Dey, Dezhi Hong, K. H. Lee, In Kee Kim","doi":"10.1109/SMARTCOMP52413.2021.00045","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00045","url":null,"abstract":"Smart-home devices promise to make users’ lives more convenient. However, at the same time, such devices increase the possibility of breaching users’ privacy as they are tightly connected to the users’ daily lives and activities. To address privacy invasion through smart-home devices, we present ChatterHub. This novel approach accurately identifies smart-home devices’ activities with minimal monitoring of encrypted traffic in the home network. ChatterHub targets devices that can only connect to the Internet through a centralized smart-home hub (e.g., Samsung SmartThings) using Zigbee or Z-wave. Specifically, ChatterHub passively eavesdrops on encrypted network traffic from the hub and leverages machine learning techniques to classify events and states of smart-home devices. Using ChatterHub, an adversary can identify smart-home devices’ specific activities without prior knowledge of the target smart home (e.g., list of deployed devices, types of communication protocols). We evaluated the accuracy and efficiency of ChatterHub in three real-world smart-home environments, and the evaluation results show that an attacker can successfully disclose smart-home devices’ behaviors with over 88% F1 score. We further demonstrate that ChatterHub successfully recognizes privacy-sensitive activities, including open and close of a smart door lock and turn on and off of smart LED. Additionally, to mitigate the threats posed by ChatterHub, we introduce two approaches, packet padding and random sequence injection. These mitigation approaches can effectively prevent threats from ChatterHub with only 9.2MB of additional network traffic per day.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124254749","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: 3

STAR: A Scalable Self-taught Learning Framework for Older Adults’ Activity Recognition STAR:老年人活动识别的可扩展自学框架

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00037

S. R. Ramamurthy, Indrajeet Ghosh, A. Gangopadhyay, E. Galik, Nirmalya Roy

{"title":"STAR: A Scalable Self-taught Learning Framework for Older Adults’ Activity Recognition","authors":"S. R. Ramamurthy, Indrajeet Ghosh, A. Gangopadhyay, E. Galik, Nirmalya Roy","doi":"10.1109/SMARTCOMP52413.2021.00037","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00037","url":null,"abstract":"Activity Recognition (AR) in older adults living with Neurocognitive disorders caused by diseases such as Alzheimer’s is still a challenging research problem. The inherent natural variation in performing an activity increases while repeating the same activity for an older adult, let alone the variation introduced when another older adult performs the same activity. Moreover, the challenges in acquiring the labeled data while preserving the privacy, availability of annotators with domain knowledge, aversion towards cameras even for a minimal amount of time for ground truth data collection, and psychological and mental health status make AR for older adults challenging. In this paper, we postulate a self-taught learning-based approach that helps recognize activities with variations that are not being directly seen during the training phase. We hypothesize that the features extracted using deep architectures from unlabeled data instances can learn general underlying representations of activities efficiently and help improve activity classification in a supervised setting, although the data instances in labeled data do not follow the generative distribution of that of unlabeled data. We posit real data from a retirement community center using our in-house SenseBox infrastructure and survey-based assessments concurrently done by a clinical evaluator to study the relationship between activities and functional/behavioral health of older adults. We evaluate our proposed self-taught learning-based approach, STAR, using the presented in-house Alzheimer’s Activity Recognition (AAR) dataset acquired in a real-world deployment in 25 homes which outperforms the state-of-the-art algorithm by about 20%.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125035153","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: 3

Tutorial #2 [2 abstracts] 教程#2[2个摘要]

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00012

引用次数: 0

Mining Social Media to Understand User Opinions on IoT Security and Privacy 挖掘社交媒体，了解用户对物联网安全和隐私的看法

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-08-01 DOI: 10.1109/SMARTCOMP52413.2021.00056

A. Sriram, Yanyan Li, A. Hadaegh

{"title":"Mining Social Media to Understand User Opinions on IoT Security and Privacy","authors":"A. Sriram, Yanyan Li, A. Hadaegh","doi":"10.1109/SMARTCOMP52413.2021.00056","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00056","url":null,"abstract":"Internet of Things (IoT), as an emerging technology, has attracted lots of attentions in recent years, but also faced criticism regarding its security and privacy. Some user interviews have been done in the past to understand the security and privacy concerns of end users on IoT devices, but they were limited in the number of participants. To better understand user opinions on IoT security and privacy, we conducted a larger user study using Twitter and Reddit data. We collected more than 6 million tweets and reddit submissions using query search keywords such as drone security, smart camera privacy and etc and labeled them using flair pre-trained en-sentiment model. We performed sentiment analysis using BERT, a transformer-based model, and a neural network classifier. Our sentiment analysis results have shown that Twitter users are more positive towards IoT security and privacy, whereas Reddit users are more negative. On certain categories such as smart refrigerator, smart TV, drone, speaker, voice assistant, fitness tracker, and smartwatch, both users express negative sentiment. We also conducted a longitudinal study to understand how user opinions change over time. A continuous upward trend has been identified for both positive and negative sentiments on Reddit users. We further analyzed users’ specific concerns through topic modeling particularly related to smart lock, smart camera, and drone.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879902","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

TRANSIT-GYM: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems 公交-体育馆:公交系统分析的仿真与评估引擎

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-06-30 DOI: 10.1109/SMARTCOMP52413.2021.00030

Ruixiao Sun, Rongze Gui, H. Neema, Yuche Chen, Juliette Ugirumurera, Joseph Severino, Philip Pugliese, Aron Laszka, A. Dubey

{"title":"TRANSIT-GYM: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems","authors":"Ruixiao Sun, Rongze Gui, H. Neema, Yuche Chen, Juliette Ugirumurera, Joseph Severino, Philip Pugliese, Aron Laszka, A. Dubey","doi":"10.1109/SMARTCOMP52413.2021.00030","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00030","url":null,"abstract":"Public-transit systems face a number of operational challenges: (a) changing ridership patterns requiring optimization of fixed line services, (b) optimizing vehicle-to-trip assignments to reduce maintenance and operation codes, and (c) ensuring equitable and fair coverage to areas with low ridership. Optimizing these objectives presents a hard computational problem due to the size and complexity of the decision space. State-of-the-art methods formulate these problems as variants of the vehicle routing problem and use data-driven heuristics for optimizing the procedures. However, the evaluation and training of these algorithms require large datasets that provide realistic coverage of various operational uncertainties. This paper presents a dynamic simulation platform, called TRANSIT-GYM, that can bridge this gap by providing the ability to simulate scenarios, focusing on variation of demand models, variations of route networks, and variations of vehicle-to-trip assignments. The central contribution of this work is a domain-specific language and associated experimentation tool-chain and infrastructure to enable subject-matter experts to intuitively specify, simulate, and analyze large-scale transit scenarios and their parametric variations. Of particular significance is an integrated microscopic energy consumption model that also helps to analyze the energy cost of various transit decisions made by the transportation agency of a city.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123447209","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