2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)最新文献

{"title":"An Agnostic Data-Driven Approach to Predict Stoppages of Industrial Packing Machine in Near","authors":"G. Filios, Ioannis Katsidimas, S. Nikoletseas, Stefanos H. Panagiotou, Theofanis P. Raptis","doi":"10.1109/DCOSS49796.2020.00046","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00046","url":null,"abstract":"As data awareness in manufacturing companies increases with the deployment of sensors and Internet of Things (IoT) devices, data-driven maintenance and prediction have become quite popular in the Industry 4.0 paradigm. Machine Learning (ML) has been recognised as a promising, efficient and reliable tool for fault detection use cases, as it allows to export important knowledge from monitored assets. Scientists deal with issues such as the small amount of data that indicate potential problems, or the imbalance which exists between the standard process data and the data inadequacy of the systems to make a high precision forecast. Currently, in this context, even large industries are not able to effectively predict abnormal behaviors in their tools, processes and equipment, when adopting strategies to anticipate crucial events. In this paper, we propose a methodology to enable prediction of a packing machine’s stoppages in manufacturing process of a large industry, by using forecasting techniques based on univariate time series data. There are more than 100 reasons that cause the machine to stop, in a quite big production line length. However, we use a single signal, concerning the machines operational status to make our prediction, without considering other fault or warning signals, hence its characterization as \"agnostic\". A workflow is presented for cleaning and preprocessing the data, and for training and evaluating a predictive model. Two predictive models, namely ARIMA and Prophet, are applied and evaluated on real data from an advanced machining process used for packing. Training and evaluation tests indicate that the results of the applied methods perform well on a daily basis. Our work can be further extended and act as reference for future research activities that could lead to more robust and accurate prediction frameworks.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114358914","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":"DCOSS 2020 Breaker Page","authors":"","doi":"10.1109/dcoss49796.2020.00003","DOIUrl":"https://doi.org/10.1109/dcoss49796.2020.00003","url":null,"abstract":"","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130476921","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":"Experimentation with Local Intrusion Detection in IoT Networks Using Supervised Learning","authors":"Christiana Ioannou, V. Vassiliou","doi":"10.1109/DCOSS49796.2020.00073","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00073","url":null,"abstract":"In this paper we are experimenting with an intrusion detection system (IDS) for IoT. The IDS under consideration is employing a machine learning techniques for detecting novel at-tacks in the IoT network. We examine detection based on Support Vector Machines (SVM). The detection models were trained and evaluated for Selective Forward and Blackhole network routing layer attacks using IoT-testbed data and achieved up to 99.8% Accuracy rates and 100% Recall values.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121926236","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":"Joint Workshop on Emerging Topics in Sensor Systems - Message from Chairs","authors":"","doi":"10.1109/dcoss49796.2020.00014","DOIUrl":"https://doi.org/10.1109/dcoss49796.2020.00014","url":null,"abstract":"","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125895201","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 multidimensional human-centric framework for environmental intelligence: Air pollution and noise in smart cities","authors":"Andreas Bardoutsos, G. Filios, Ioannis Katsidimas, T. Krousarlis, S. Nikoletseas, Pantelis Tzamalis","doi":"10.1109/DCOSS49796.2020.00036","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00036","url":null,"abstract":"For the important problem of increasing levels of air pollution and noise in urban and rural areas, we propose a holistic, multi-dimensional approach to gather, monitor and analyze heterogeneous data sources of air pollutants and noise indicators, into an integrated, intelligent computational system. Although several interesting approaches have been developed for monitoring pollution and noise, however the challenge remains for even more detailed, precise, large scale monitoring.To overcome the limitations of current systems, we envision an integrated approach to human-centric environmental intelligence, bringing together modern IoT technology and the human factor. In particular, our approach emphasizes selected behavioural and health aspects, and the complementary use of sensing technology with citizen engagement and crowdsourcing methods. The proposed system will collect diverse data from heterogeneous sources, such as mobile and static wireless sensor networks, crowdsourcing, citizen questionnaires and social media analytics, to continuously combine objective estimations with subjective perception of air quality and noise. With the use of advanced AI and Deep Learning algorithms, our system will be able to estimate air pollutants concentration and noise levels in micro-scale with adequate precision over large urban-scale environments. Furthermore, tracking of behavioral and psychological users’ input, as well as personal exposure to pollution, will allow studying the impact of air quality and noise on the users’ daily habits and the interplay of ambient conditions with behavioural factors, towards an active engagement of citizens in a hybrid techno-social manner. A reference architecture for the realization of this human-centric environmental intelligence approach is presented. Also, a planned implementation at the city of Patras, Greece is discussed. To the best of our knowledge, this is one of the first holistic, multifaceted approaches to a surveillance system for air quality and noise in urban areas.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132882978","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":"Overweight Object Transportation with a Set of Collaborative Robots","authors":"Miguel Castorena, N. Doan, Benjamin Gillmore, Jimmy Lahn, Joshua Lorenzen, Oscar Morales-Ponce","doi":"10.1109/DCOSS49796.2020.00057","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00057","url":null,"abstract":"We study the box pushing problem with a set of robots. Robots and relocated objects are placed on opposite sides in such a way that robots have a direct view of the objects. We consider a rather weak model where robots do not have access to a localization system and are disoriented. Further, robots do not know the weight of the boxes nor of their own pushing capabilities. Robots do have access to a wireless device to communicate with other robots. The objective is to design a distributed algorithm that lets the robots self-coordinate to move the objects. We propose a synchronous algorithm that allows the robots to self-coordinate to relocate the box. We use Timed Input/Output Automata to describe the algorithms and show that the algorithm correctly completes the task. Then we extend the algorithm to deal with obstacles and robot failures. We implement the algorithm in SyRof (a testbed built at California State University Long Beach.) The testbed consists of four robots equipped with omnidirectional wheels to simulate drones and an autopilot that provides a synchronous system. The resulting implementation allows the robots to complete the task successfully.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129532112","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":"Enhancing the Reliability of IoT Data Marketplaces through Security Validation of IoT Devices","authors":"Yoonjong Na, Yejin Joo, Heejo Lee, Xiangchen Zhao, Kurian Karyakulam Sajan, G. Ramachandran, B. Krishnamachari","doi":"10.1109/DCOSS49796.2020.00050","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00050","url":null,"abstract":"IoT data marketplaces are being developed to help cities and communities create large scale IoT applications. Such data marketplaces let the IoT device owners sell their data to the application developers. Following this application development model, the application developers need not deploy their own IoT devices when developing IoT applications; instead, they can buy data from a data marketplace. In a marketplace-based IoT application, the application developers are making critical business and operation decisions using the data produced by seller’s IoT devices. Under these circumstances, it is crucial to verify and validate the security of IoT devices.In this paper, we assess the security of IoT data marketplaces. In particular, we discuss what kind of vulnerabilities exist in IoT data marketplaces using the well-known STRIDE model, and present a security assessment and certification framework for IoT data marketplaces to help the device owners to examine the security vulnerabilities of their devices. Most importantly, our solution certifies the IoT devices when they connect to the data marketplace, which helps the application developers to make an informed decision when buying and consuming data from a data marketplace. To demonstrate the effectiveness of the proposed approach, we have developed a proof-of-concept using I3 (Intelligent IoT Integrator), which is an open-source IoT data marketplace developed at the University of Southern California, and IoTcube, which is a vulnerability detection toolkit developed by researchers at Korea University. Through this work, we show that it is possible to increase the reliability of a IoT data marketplace while not damaging the convenience of the users.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116052518","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":"(POSTER) Overtake: Opportunistic Routing and Concurrent Transmissions for TSCH","authors":"Laura Harms, O. Landsiedel","doi":"10.1109/DCOSS49796.2020.00032","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00032","url":null,"abstract":"In this paper, we present Overtake, an opportunistic routing protocol for Time-Slotted Channel Hopping (TSCH). Overtake combines (1) opportunistic routing, (2) concurrent transmissions and (3) TSCH. We show that this novel combination enables low-latency, central scheduling withstanding node failures. Our initial results show its ability to withstand node failures of up to 40% of nodes of a flow while keeping minimal latency.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"32 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123204565","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":"Shadow-based Hand Gesture Recognition in one Packet","authors":"S. Hazra, Martina Brachmann, Thiemo Voig","doi":"10.1109/DCOSS49796.2020.00018","DOIUrl":"https://doi.org/10.1109/DCOSS49796.2020.00018","url":null,"abstract":"The ubiquity of wirelessly connected sensing devices in IoT applications provides the opportunity to enable various types of interaction with our digitally connected environment. Currently, low processing capabilities and high energy costs for communication limit the use of energy-constrained devices for this purpose. In this paper, we address this challenge by exploring the new possibilities highly capable deep neural network classifiers present. To reduce the energy consumption for transferring continuously sampled data, we propose to compress the sensed data and perform classification at the edge. We evaluate several compression methods in the context of a shadow-based hand gesture detection application, where the classification is performed using a convolutional neural network. We show that simple data reduction methods allow us to compress the sensed data into a single IEEE 802.15.4 packet while maintaining a classification accuracy of 93%. We further show the generality of our compression methods in an audio-based interaction scenario.","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131440523","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":"DCOSS 2020 Foreword","authors":"","doi":"10.1109/dcoss49796.2020.00005","DOIUrl":"https://doi.org/10.1109/dcoss49796.2020.00005","url":null,"abstract":"","PeriodicalId":198837,"journal":{"name":"2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132063753","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}