2020 International Conference on Omni-layer Intelligent Systems (COINS)最新文献

{"title":"Resampling and Data Augmentation For Equines’ Behaviour Classification Based on Wearable Sensor Accelerometer Data Using a Convolutional Neural Network","authors":"Anniek Eerdekens, M. Deruyck, Jaron Fontaine, L. Martens, E. D. Poorter, D. Plets, W. Joseph","doi":"10.1109/COINS49042.2020.9191639","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191639","url":null,"abstract":"Monitoring horses’ behaviors through sensors can yield important information about their health and welfare. Sampling frequency majorly affects the classification accuracy in activity recognition and energy needs for the sensor. The aim of this study was to evaluate the effect of sampling rate reduction of a tri-axial accelerometer on the recognition accuracy by resampling a 50 Hz experimental dataset to four lower sampling rates (5 Hz, 10 Hz, 12.5 Hz and 25 Hz). Also, in this work we investigate the ‘reality gap’ that incorporates changes in the data that are primarily characterized as sensor rotations or measurement noise through various data augmentation techniques such as rotation and jittering. Finally, another factor influencing activity recognition are the subjects themselves and therefore the model is evaluated on different horse types. A deep learning-based approach for activity detection of equines is proposed to automatically classify 2238 manually annotated 2 s samples tri-axial accelerometer leg data data of seven different activities performed by six different subjects. The raw data are preprocessed and fed into a convolutional neural network (CNN) from which features are extracted automatically by using strong computing capabilities. Furthermore, the neural network was intentionally designed to minimize running time, enabling us to imagine the future use of the built model in embedded constrained devices. The complexity of these automatic learning techniques can be decreased while achieving high accuracies using ten-fold-cross validation using a computationally less intensive received signal length data (99.32% at 5 Hz vs 99.74% at 25 Hz). This indicates that sampling at 5 Hz with a 2 s window will offer advantages for activity surveillance thanks to decreased energy requirements, since validation time decreases 16-fold (784 microseconds at 50 Hz to 48 microseconds at 5 Hz). Moreover, in this work we show that rotating the training or validation signal with 10 degrees over the X, Y and Z-axis increases the generalization capabilities of our model (99.61 % vs 99.93%) while adding small amounts of noise (smaller than 0.3 standard deviation (STD)) does not decrease the classification accuracy under 99%. Finally, the performance and ability of the model to generalize is validated on data from unseen horses at the cost of only 4.1% and 2.45% reduction in accuracy when validated on a pony and a lame horse, respectively.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116862149","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":"Deep Reinforcement Learning for the management of Software-Defined Networks in Smart Farming","authors":"R. Alonso, Inés Sittón-Candanedo, Roberto Casado-Vara, Javier Prieto, J. Corchado","doi":"10.1109/COINS49042.2020.9191634","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191634","url":null,"abstract":"The Internet of Things and the millions of devices that generate and collect data through sensors to send it to the Cloud are part of the life of users in many contexts, including smart farming and precision agriculture scenarios. This volume of data is stored and processed in the Cloud, with the purpose of obtaining knowledge and valuable information for organizations. Edge Computing has emerged to reduce the costs associated with transferring, processing and storing data from IoT environments in the Cloud. This paradigm allows data to be pre-processed at the edge of the network before they are sent to the Cloud, obtaining shorter response times and maintaining service even during communication breakdowns between the IoT and Cloud layers. Furthermore, there is a increasing trend to shared physical network resources among diverse user entities through Software-Defined Networks and Network Function Virtualization with the aim to reduce costs. In this sense, smart mechanisms are required to optimize virtual dataflows in the networks, as Deep Reinforcement Learning techniques. This paper proposes a Double Deep-Q Learning approach to manage virtual dataflows in SDN/NFV using an Edge-IoT architecture, formerly applied in smart farming and Industry 4.0 scenarios.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123096864","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":"The Hashgraph Protocol: Efficient Asynchronous BFT for High-Throughput Distributed Ledgers","authors":"L. Baird, Atul Luykx","doi":"10.1109/COINS49042.2020.9191430","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191430","url":null,"abstract":"Atomic broadcast protocols are increasingly used to build distributed ledgers. The most robust protocols achieve byzantine fault tolerance (BFT) and operate in asynchronous networks. Recent proposals such as HoneyBadgerBFT (ACM CCS ‘16) and BEAT (ACM CCS ‘18) achieve optimal communication complexity, growing linearly as a function of the number of nodes present. Although asymptotically optimal, their practical performance precludes their use in demanding applications. Further performance improvements to HoneyBadgerBFT and BEAT are not obvious as they run two separate sub-protocols for broadcast and voting, each of which has already been optimized. We describe how hashgraph — an asynchronous BFT atomic broadcast protocol (ABFT) — departs in structure from prior work by not using communication to vote, only to broadcast transactions. We perform an extensive empirical study to understand how hashgraph’s structure affects performance. We observe that hashgraph can improve latency by an order of magnitude over HoneyBadgerBFT and BEAT, while keeping throughput constant with the same number of nodes; similarly, throughput can increase by up to an order of magnitude while maintaining latency. Furthermore, we test hashgraph’s capability for high performance, and conclude that it can achieve sufficiently high throughput and low latency to support demanding practical applications.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130983167","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 47 F2/bit Charge-Sharing based Sequence-dependent PUF with a Permutative Challenge","authors":"Kai-Uwe Müller, Alexander Stanitzki, R. Kokozinski","doi":"10.1109/COINS49042.2020.9191427","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191427","url":null,"abstract":"Small sensor and actor nodes are often excluded from security mechanisms because of the lack of performance for cryptographic applications or the lack of a non-volatile memory to store the secret keys for such applications. Physical Unclonable Functions (PUFs) provide a good way for a secure key storage, but are also not necessarily lightweight in terms of area and power consumption. A PUF concept based on a capacitor array is described, which uses the a passive charge sharing technique and is able to accept a high number of challenges as input. By using pair building, an 8-stage array is able to derive up to 20160 bits of key material with an area use of $47mathrm{F}^{2} /$bit in a 350nm CMOS technology.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061082","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":"enerDAG – Towards a DLT-based Local Energy Trading Platform","authors":"C. Groß, Mark Schwed, Stefan Müller, O. Bringmann","doi":"10.1109/COINS49042.2020.9191415","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191415","url":null,"abstract":"Due to decreasing costs and less by-effects of renewables energy the energy production is getting more and more decentralized and therefore leads to more bottom-up loads on the grid. To reduce the stress on the grid local energy grids with smart energy trading are a possible solution. This paper describes the goals and concept of our flexible resilient local energy trading platform called enerDAG and how it can easily be extended through smart contracts. At the end enerDAG gets critically analyzed if it solves the critical requirements for a scalable smart grid platform. Nodes using this platform can get a financial advantage through cheaper local energy which might also lead to even more investment in own regenerative energy systems.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128508317","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":"Towards IoT-Driven Predictive Business Process Analytics","authors":"Erfan Elhami, Abolfazl Ansari, Bahareh J. Farahani, F. S. Aliee","doi":"10.1109/COINS49042.2020.9191422","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191422","url":null,"abstract":"Predictive business process monitoring is concerned with predicting the process-related Key Performance Indicators (KPIs) and forecasting the future behavior of the process in realtime. Despite the amount of work contributed by researches to this field of research, the performance of existing solutions is not desirable for practical settings. Indeed, these approaches are typically context-unaware and lack generality. However, in real-life use cases, business processes are not isolated from the surrounding working environment, and thus they are influenced by many contextual events, such as events generated by IoT devices. To the best of our knowledge, there is no comprehensive study addressing the integration of contextual events with the process prediction. This paper proposes a holistic context-aware methodology for predictive process monitoring by incorporating IoT data. Moreover, we present a systematic method to integrate the contextual events in the runtime process using Business Process Management System} (BPMS) capabilities. We also introduce a predictive model based on Deep Neural Networks (DNN) to forecast the next activity. Finally, we evaluate our solution using a case study in the aviation industry.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131138519","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":"HAC-T and Fast Search for Similarity in Security","authors":"Jonathan J. Oliver, Muqeet Ali, Josiah Hagen","doi":"10.1109/COINS49042.2020.9191381","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191381","url":null,"abstract":"Similarity digests have gained popularity for many security applications like blacklisting/whitelisting, and finding similar variants of malware. TLSH has been shown to be particularly good at hunting similar malware, and is resistant to evasion as compared to other similarity digests like ssdeep and sdhash. Searching and clustering are fundamental tools which help the security analysts and security operations center (SOC) operators in hunting and analyzing malware. Current approaches which aim to cluster malware are not scalable enough to keep up with the vast amount of malware and goodware available in the wild. In this paper, we present techniques which allow for fast search and clustering of TLSH hash digests which can aid analysts to inspect large amounts of malware/goodware. Our approach builds on fast nearest neighbor search techniques to build a tree-based index which performs fast search based on TLSH hash digests. The tree-based index is used in our threshold based Hierarchical Agglomerative Clustering (HAC-T) algorithm which is able to cluster digests in a scalable manner. Our clustering technique can cluster digests in O (n logn) time on average. We performed an empirical evaluation by comparing our approach with many standard and recent clustering techniques. We demonstrate that our approach is much more scalable and still is able to produce good cluster quality. We measured cluster quality using purity on 10 million samples obtained from VirusTotal. We obtained a high purity score in the range from 0.97 to 0.98 using labels from five major anti-virus vendors (Kaspersky, Microsoft, Symantec, Sophos, and McAfee) which demonstrates the effectiveness of the proposed method.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132829872","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":"COLAW: Cooperative Location Proof Architecture for VANETs based on Witnessing","authors":"Philippos Barabas, Emanuel Regnath, S. Steinhorst","doi":"10.1109/COINS49042.2020.9191402","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191402","url":null,"abstract":"Vehicular applications heavily rely on location information to improve road safety and efficiency as well as to provide a personalized driving experience through a variety of location-based services. To determine their position, vehicles depend on different technologies like GPS, which might be unreliable or vulnerable to interference or spoofing. In the safety-critical vehicular world, a secure mechanism must be in place which guarantees the accuracy and trustworthiness of location information to the service that requires it. In this work we propose COLAW, a COoperative Location proof Architecture based on Witnessing that leverages the distributed nature of vehicular ad-hoc networks to create verifiable and secure location proofs. The evaluation of COLAW shows that it is possible for a group of neighboring vehicles to generate secure location proofs for each other with a significantly lower message overhead than previously proposed approaches and that the protocol’s performance can be further improved, by taking certain environmental parameters and road conditions into consideration.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997851","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":"Distributed Ledger and Smart Contract Based Approach for IoT Sensor Applications","authors":"Christoph Lehnert, Grischan Engel, Thomas Greiner","doi":"10.1109/COINS49042.2020.9191409","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191409","url":null,"abstract":"Security and traceability of smart sensor data in centrally organized IoT-architectures require a third party of trust. In order to overcome this issue, Distributed Ledger Technologies (DLT) apply consensus mechanisms. Current approaches suggest DLT-based IoT-architectures which are static and only provide limited data precision in specific applications. Thus, they rely on custom tokens and additional technologies such as SQL databases. In addition, the design of the applied smart contracts (sc) allow unauthorized access. In contrast, in this paper an adaptable, scalable and purely DLT-based IoT-architecture for secure and decentral software services is proposed. It employs sc for the secure and decentralized interaction between users, software services and IoT devices, such as smart sensors. Thereby, sc are adjustable and their access is controlled by an address comparison of authorized wallets. Finally, a case-study on a sc based software service for an industrial smart temperature sensor demonstrates applicability and benefits of the proposed approach.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125072531","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":"Towards Safer Roads: A Deep Learning-Based Multimodal Fatigue Monitoring System","authors":"M. Hashemi, Bahareh J. Farahani, F. Firouzi","doi":"10.1109/COINS49042.2020.9191418","DOIUrl":"https://doi.org/10.1109/COINS49042.2020.9191418","url":null,"abstract":"The human factor has been documented as the primary contributor to road accidents bringing outrageous costs, such as property damage, disabling injuries, and loss of life. To mitigate accident-related costs and to enhance driver safety, particularly during unfavorable driving conditions, the transportation industry strives to integrate IoT and Deep Learning technologies. In this work, we propose a holistic IoT-based multimodal technique to monitor driver fatigue by exploiting the facial and physiological information of the driver. A novel deep neural network is designed to classify the eye and mouth states. The results of the classification are fed into the cloud to be fused with other data sources (e.g., health records) in order to assess the corresponding driver risk accurately. Experimental results on various datasets show that the proposed mouth classification and eye state detection solution results in 99.5% and 99.01% accuracy, respectively.","PeriodicalId":350108,"journal":{"name":"2020 International Conference on Omni-layer Intelligent Systems (COINS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132061239","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}