2020 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献

{"title":"SDN-Based Regulated Flow Routing in MANETs","authors":"Klement Streit, C. Schmitt, Carlo Giannelli","doi":"10.1109/SMARTCOMP50058.2020.00030","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00030","url":null,"abstract":"Already available WiFi direct and upcoming 5G Device-to-Device (D2D) communication mechanisms are paving the way for the development of Mobile Ad-hoc Networks (MANET) applications. This trend involves the cooperation of nearby mobile nodes in charge of dispatching messages. In addition, the consolidation of the Fog paradigm enables innovative scenarios characterized by the interaction of MANET and Edge nodes. For instance, tourists visiting a city form a MANET to share pictures while the municipality provides Internet connectivity via Edge devices. However, it is required to address specific issues stemming from the collaborative nature of D2D communication, ranging from limited node capabilities providing multi-hop networks to unreliable connectivity due to node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133255537","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":"Analytics-Aware Storage of Surveillance Videos: Implementation and Optimization","authors":"Min-Han Tsai, N. Venkatasubramanian, Cheng-Hsin Hsu","doi":"10.1109/SMARTCOMP50058.2020.00024","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00024","url":null,"abstract":"Increasingly more surveillance cameras in smart environments stream videos to storage servers for on-demand video analytics queries in the future. Unlike on-demand video services, in which maximizing the user-perceived video quality is the design objective, the considered storage servers aim to retain as much information as possible while offering enough space for incoming video clips. In this paper, we design, optimize, and implement an analytics-aware storage server on a smart campus testbed at NTHU, Taiwan, which consists of eight smart street lamps equipped with various sensors, network devices, analytics servers, and a storage server. We focus on the design and implementation of the storage server, and consider two key research problems: (i) how to efficiently determine the information amount of individual video clips and (ii) how to intelligently downsample individual video clips. More specifically, the first problem is to sample video frames from the stored video clips to analyze for approximations of the information amount without overloading the storage server. The resulting information amount is fed into the second problem to decide the video downsampling approaches for retaining as much information amount as possible without consuming excessive storage space. We propose two efficient algorithms to solve these two problems and compare their performance with the current practices via real experiments on our smart campus testbed. Our experiment results reveal the practicality and efficiency of our proposed design and algorithms, e.g., compared to the current practices, our storage server: (i) improves the per-request information amount by up to ~ 4 times, (ii) increases the total information amount by at most ~ 20%, (iii) boosts the number of saved video clips by up to ~ 35%, (iv) runs in real-time, and (v) scales well with larger storage space.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123387483","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":"Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media","authors":"Qi Chen, Wei Wang, Kaizhu Huang, Suparna De, Frans Coenen","doi":"10.1109/SMARTCOMP50058.2020.00051","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00051","url":null,"abstract":"Social media platforms such as Twitter are increasingly used to collect data of all kinds. During natural disasters, users may post text and image data on social media platforms to report information about infrastructure damage, injured people, cautions and warnings. Effective processing and analysing tweets in real time can help city organisations gain situational awareness of the affected citizens and take timely operations. With the advances in deep learning techniques, recent studies have significantly improved the performance in classifying crisis-related tweets. However, deep learning models are vulnerable to adversarial examples, which may be imperceptible to the human, but can lead to model's misclassification. To process multi-modal data as well as improve the robustness of deep learning models, we propose a multi-modal adversarial training method for crisis-related tweets classification in this paper. The evaluation results clearly demonstrate the advantages of the proposed model in improving the robustness of tweet classification.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122062514","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":"Multi-source Transfer Learning for Human Activity Recognition in Smart Homes","authors":"Hao Niu, D. Nguyen, Kei Yonekawa, Mori Kurokawa, Shinya Wada, K. Yoshihara","doi":"10.1109/SMARTCOMP50058.2020.00063","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00063","url":null,"abstract":"With the deployment of smart homes, we find that human activity recognition (HAR) is essentially important to many applications, e.g., child/senior care, intelligent information push and exercise promotion. Although it is always better to build HAR model for each smart home to resolve the practical problem that homes have different floorplans or adopted sensors, it is intractable to acquire labeled data for each home due to cost and privacy. We thus propose a method to transfer the HAR model from multiple labeled source homes to the unlabeled target home. Specifically, we first generate transferable representations for the sensors of these homes, based on which we build the HAR model using the data of labeled source homes. Then, we employ the built HAR model into the unlabeled target home. Experiment results on CASAS dataset illustrate that our proposed method outperforms baseline methods in general and also avoids potential negative transfer caused by using only one source home.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123899853","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":"Home Activity Recognition Using Aggregated Electricity Consumption Data","authors":"Kotaro Ishizu, Teruhiro Mizumoto, H. Yamaguchi, T. Higashino","doi":"10.1109/SMARTCOMP50058.2020.00068","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00068","url":null,"abstract":"In this paper, we propose a low-cost, non-invasive home activity recognition method using low-resolution power consumption data. Notably, we tackle the following two challenges. Firstly, we use only the time series of power consumption data aggregated per house and measured every few tens of seconds, which is usually used for demand monitoring by smart meters. We design a set of activities that can be recognized by such low-resolution data, and find out an appropriate feature set to train and test balanced random forest classifiers. Secondly, we consider the divergence of activity patterns seen in different households. Since supervised learning dedicated to each household is not a realistic solution, we arrange different classifiers trained by different household data in supervised learning, and present a method to automatically choose the best-fit classifier for the household of interest in the online phase. The experiment was conducted to collect aggregated power consumption data from eight real homes for 191 days. The result of activity recognition using the dataset shows that the proposed method achieved 70% recognition accuracy in identifying activities like cooking and sleeping, which is significant for non-invasive remote monitoring.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124018650","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 Scalable Distributed System for Precision Irrigation","authors":"M. Amoretti, Dario Lodi Rizzini, Gabriele Penzotti, S. Caselli","doi":"10.1109/SMARTCOMP50058.2020.00074","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00074","url":null,"abstract":"The level of adoption of Precision Agriculture (PA) technologies is still very different from one country to another and from one region to another in the same country. A major challenge is to develop PA from a best practice pursued by a minority of enlightened farmers to a widespread practice with sizeable impact on the use of environmental resources. One of the obstacles hindering PA is the lack of quantitative data readily accessible to farmers to guide their daily operations. In this paper, we present the information system developed within project POSITIVE to support and enhance precision irrigation across the whole Emilia-Romagna region. To this purpose, the POSITIVE information system establishes a service transforming satellite and sensor data into biophysical parameters and vegetation indices with full regional coverage. These data are automatically fed into a public irrigation advisory service (Irrinet+) thereby enabling precision irrigation and fertigation on a regional scale. Irrigation maps can be sent as advice to farmers or directly commanded to registered irrigation machines. The architecture of the distributed information system and the open protocols developed to achieve scalability and enable interaction of multiple heterogeneous components are reported in the paper.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114891476","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":"Missing Data Not At Random: Characterization of Targeted Interference in Wireless Networks","authors":"A. M. Chandran","doi":"10.1109/SMARTCOMP50058.2020.00059","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00059","url":null,"abstract":"Communication systems include data collection and estimation during their operations. At the receiver, the data can be missed due to various reasons such as channel conditions, malicious attack, failure at the receiver. Some of these conditions occur at random, but sometimes, their occurrences are not random. These occurrences can be due to the precise placement of interference to impede communication between the devices. There are different mechanisms proposed in the literature to address this data loss, by requesting retransmission, spreading the signal, etc. A different approach is by using statistical analysis to mine data received to estimate the data points that are missed not at random. In the statistical study, data that are missed not at random manifest during data collection when one or more subjects involved in the survey skip their responses for one or more data fields due to social, economic, and health reasons. These missed responses are filled by imputation from the responses collected from other subjects. Similarly, in a wireless network, data lost from a particular node that is under attack can be considered as data missed not at random and can be estimated from the data collected from the surrounding nodes.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126173330","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":"Targeted Learning for the Dynamic Selection of Channel Estimation Methodology","authors":"A. M. Chandran, M. Zawodniok, A. Adekpedjou","doi":"10.1109/SMARTCOMP50058.2020.00055","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00055","url":null,"abstract":"The explosive expansion of collected data - in terms of dimensionality, diversity, volume - increases more rapidly than we can analyze to draw useful conclusions, make informed decisions, and provide specific recommendations. Various fields such as medical, healthcare, aviation, telecommunication require new tools to process the data which they collect to process effectively and economically and benefit from the estimated quantities that were learned from the data itself. In particular, there are different methodologies proposed and used in telecommunications to estimate the channel coefficients of different types of channels. All these methodologies are grounded based on the assumption of the statistical property of the channel. However, a flexible solution that can dynamically deploy different methods based on the received signal yields higher performance and maintained over time. In this paper, we propose to apply targeted learning and explore the suitable parameters for a communication system. The initial results demonstrate it is possible to distinguish and identify the best methodology to fit the current channel conditions.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127822860","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":"Smart Healthy Intelligent Room: Headcount through Air Quality Monitoring","authors":"Giovanni Cicceri, Carlo Scaffidi, Zakaria Benomar, S. Distefano, A. Puliafito, Giuseppe Tricomi, Giovanni Merlino","doi":"10.1109/SMARTCOMP50058.2020.00071","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00071","url":null,"abstract":"In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"129 Pt 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131171209","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":"Mitigating Privacy Leak by Injecting Unique Noise into the Traffic of Smart Speakers","authors":"Rikuta Furuta, H. Ochiai, H. Esaki","doi":"10.1109/SMARTCOMP50058.2020.00093","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00093","url":null,"abstract":"In recent years, in the Internet, it is common to encrypt communication lines for the assumption that the contents of communication are eavesdropped, but even if the communication lines are secure, there are many cases in which the possibility of the contents of communication being leaked to a third party by a side-channel attack is not taken into account. Although it is important that the contents of all communication are not known by the third party, the information related to privacy may be leaked unintentionally by only encrypting traffics. In this study, we made smart speakers, an IoT device that has started to penetrate into our daily lives, to perform eight kinds of activities, and used their traffic data to estimate their activities with CNN, and we were able to estimate the activities with 98% accuracy. As a counter measure, we propose a method to reduce the accuracy of estimation by adding dummy packets to their communication traffic as noise. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model. In this study, we made smart speakers, an IoT device that has started to penetrate into our daily lives, to perform eight kinds of activities, and used their traffic data to estimate their activities with CNN, and we were able to estimate the activities with 98% accuracy. As a counter measure, we propose a method to reduce the accuracy of estimation by adding dummy packets to their communication traffic as noise. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132101946","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}