Richard J. Tomsett, G. Bent, Christopher Simpkin, I. Taylor, Daniel Harborne, A. Preece, R. Ganti
{"title":"Demonstration of Dynamic Distributed Orchestration of Node-RED IoT Workflows Using a Vector Symbolic Architecture","authors":"Richard J. Tomsett, G. Bent, Christopher Simpkin, I. Taylor, Daniel Harborne, A. Preece, R. Ganti","doi":"10.1109/SMARTCOMP.2019.00089","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00089","url":null,"abstract":"Traditional service-based applications, in fixed networks, are typically constructed and managed centrally and assume stable service endpoints and adequate network connectivity. Constructing and maintaining such applications in dynamic heterogeneous wireless networked environments, where limited bandwidth and transient connectivity are commonplace, presents significant challenges and makes centralized application construction and management impossible. In this demonstration we present an architecture which is capable of providing an adaptable and resilient method for on-demand decentralized construction and management of complex time-critical applications in such environments. The approach uses a Vector Symbolic Architecture (VSA) to compactly represent an application as a single semantic vector that encodes the service interfaces, workflow, and the time-critical constraints required. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected in a completely decentralized manner. There are a large number of workflow systems designed to work in various scientific domains, including support for the Internet of Things (IoT). One such workflow system is Node-RED, which is designed to bring workflow-based programming to IoT. The main focus of this demonstration is to show how we can migrate Node-RED workflows into a decentralized execution environment, so that such workflows can run on Edge networks.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128153458","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":"On the Use of LSTM Networks for Predictive Maintenance in Smart Industries","authors":"Dario Bruneo, Fabrizio De Vita","doi":"10.1109/SMARTCOMP.2019.00059","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00059","url":null,"abstract":"Aspects related to the maintenance scheduling have become a crucial problem especially in those sectors where the fault of a component can compromise the operation of the entire system, or the life of a human being. Current systems have the ability to warn only when the failure has occurred causing, in the worst case, an offline period that can cost a lot in terms of money, time, and security. Recently, new ways to address the problem have been proposed thanks to the support of machine learning techniques, with the aim to predict the Remaining Useful Life (RUL) of a system by correlating the data coming from a set of sensors attached to several components. In this paper, we present a machine learning approach by using LSTM networks in order to demonstrate that they can be considered a feasible technique to analyze the \"history\" of a system in order to predict the RUL. Moreover, we propose a technique for the tuning of LSTM networks hyperparameters. In order to train the models, we used a dataset provided by NASA containing a set of sensors measurements of jet engines. Finally, we show the results and make comparisons with other machine learning techniques and models we found in the literature.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114531712","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}
Antonio Arena, Alessio Bianchini, Pericle Perazzo, C. Vallati, G. Dini
{"title":"BRUSCHETTA: An IoT Blockchain-Based Framework for Certifying Extra Virgin Olive Oil Supply Chain","authors":"Antonio Arena, Alessio Bianchini, Pericle Perazzo, C. Vallati, G. Dini","doi":"10.1109/SMARTCOMP.2019.00049","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00049","url":null,"abstract":"Urban population is expected to continuously grow in size. The smart city concepts allows to handle the new challenges and issues created by this growth by applying a wide range of technologies that can provide citizens with a better living environment. Smart agriculture will play an important part of smart cities, as a sustainable and high quality food supply chain is crucial to facilitate the grow of human agglomerates. In this context, European laws imposes very strict requirements in the food industry, in order to ensure that food provenance is always guaranteed. Such fine-grained traceability can be only achieved by applying state-of-the-art technologies. In this paper, we present BRUSCHETTA, a blockchain-based application for the traceability and the certification of the Extra Virgin Olive Oil (EVOO) supply chain. EVOO is an emblematic food product for Italy, but it is also one of the most falsified ones. BRUSCHETTA provides a blockchain-based system to enforce the certification of this product by tracing its entire supply chain: from the plantation to the shops. The goal is to enable the final customer to access a tamper-proof history of the product, including the farming, harvesting, production, packaging, conservation, and transportation processes. BRUSCHETTA leverages Internet of Things (IoT) technologies in order to interconnect sensors dedicated to EVOO quality control, and to let them operate on the blockchain. We also provide a support for the correct tailoring of the BRUSCHETTA blockchain system, and we propose a mechanism for its dynamic auto-tuning to optimize it in case of high loads.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478371","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":"Model-Based Operator Placement for Data Processing in IoT Environments","authors":"A. C. F. D. Silva, Pascal Hirmer, B. Mitschang","doi":"10.1109/SMARTCOMP.2019.00084","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00084","url":null,"abstract":"The advances of the Internet of Things (IoT) lead to further challenges for data processing. Besides deriving meaningful information from a high amount of raw data, processing data in a timely manner is required as well, in order to enable the development of reactive IoT applications. Usually, the processing of IoT data is done in cloud-based infrastructures, which provide on-demand resources to process the data as needed. However, this affects timely processing, since sending data to off-premise cloud infrastructures increases latency and network traffic. In this paper, we propose a method to process data streams primarily on-premise in IoT environments, i.e., data is processed near to their data sources and the processing power already provided by IoT devices in the environment is explored.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126187989","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":"Cooperative Learning for Multi-perspective Image Classification","authors":"Nicholas Nordlund, H. Kwon, L. Tassiulas","doi":"10.1109/SMARTCOMP.2019.00032","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00032","url":null,"abstract":"Data gathered from dense sensor networks is often highly correlated across collocated sensors. For example, in video surveillance networks, multiple cameras can observe the same object from multiple angles. Despite the spatial and temporal dependencies between video frames from different cameras, the deep learning algorithms used in today's video analytics problems treat all frames as independent inputs to image classifiers and object detectors. The outputs of these classifiers and detectors on multiple frames are then fused to extract information about the underlying sensor region. We present a cooperative learning framework that allows sensors to train deep learning systems on their own local data and compressed insights from neighboring sensors' input data. This system fuses sensor data before classification to allow learning agents to more naturally handle correlated inputs and cooperate with neighboring sensors with minimal communication costs.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817464","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}
Geng Li, Akrit Mudvari, Kerim Gokarslan, Patrick Baker, S. Kompella, Franck Le, K. Marcus, J. Tucker, Y. Yang, Paul Yu
{"title":"Magnalium: Highly Reliable SDC Networks with Multiple Control Plane Composition","authors":"Geng Li, Akrit Mudvari, Kerim Gokarslan, Patrick Baker, S. Kompella, Franck Le, K. Marcus, J. Tucker, Y. Yang, Paul Yu","doi":"10.1109/SMARTCOMP.2019.00035","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00035","url":null,"abstract":"Existing software-defined SDx architectures highly depend on a centralized control plane and hence can face substantial reliability challenges in software-defined coalition (SDC) settings, in which the centralized control plane can be weakly connected to the data plane, or even disconnected from the data plane due to high dynamicity. On the contrary, distributed control planes (e.g., OLSRv2) provide autonomy but lose flexibility and global policy guarantees. In this paper, we present Magnalium, a novel system to achieve high reliability in SDC networks by composing multiple control planes in real-time. Magnalium introduces a novel, unified composition framework that uses a distributed verification to systematically generate forwarding rules in accordance with desired policy requirements. Magnalium also introduces several supporting components to address challenges in wireless environment and resource management. We conduct data-driven simulations, showing that Magnalium benefits from both centralized and distributed control planes and even reduces downtime by 65% over the most reliable individual control plane.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122343161","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}
Konstantinos Poularakis, Qiaofeng Qin, K. Marcus, K. Chan, K. Leung, L. Tassiulas
{"title":"Hybrid SDN Control in Mobile Ad Hoc Networks","authors":"Konstantinos Poularakis, Qiaofeng Qin, K. Marcus, K. Chan, K. Leung, L. Tassiulas","doi":"10.1109/SMARTCOMP.2019.00038","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00038","url":null,"abstract":"Software defined networking (SDN) can be beneficial in mobile ad hoc networks (MANETs) to increase flexibility, provide programmability and simplify management. The high dynamics in mobile networks, however, raise new reliability challenges to the conventional centralized control plane of SDN. To increase reliability, methods such as placing multiple controllers in the network have been considered that add redundancy in the control plane in a brute force manner. However, these methods cannot by themselves fundamentally solve the reliability problem. To address this issue, this paper complements the controller placement methods with a new architecture that has a hybrid structure splitting the routing decision logic between the controllers and the data plane nodes. Specifically, the controllers can break the routing path into segments, similar to the segment routing technique, and broadcast the list of segment labels to the data plane nodes. The latter are able to make the actual forwarding decisions for each segment in a distributed manner, e.g., by running an existing MANET protocol like OLSR. Experiments on a testbed built from commercial mobile devices with integrated SDN functionality highlight the feasibility and benefits of the proposed architecture.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133197984","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}
V. Salapura, D. Wood, S. Witherspoon, Keith Grueneberg, E. Bertino, A. A. Jabal, S. Calo
{"title":"Generative Policy Framework for AI Training Data Curation","authors":"V. Salapura, D. Wood, S. Witherspoon, Keith Grueneberg, E. Bertino, A. A. Jabal, S. Calo","doi":"10.1109/SMARTCOMP.2019.00092","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00092","url":null,"abstract":"Policy-based mechanisms are used to implement desired autonomic behavior of a managed system in a distributed environment. For modern dynamically changing systems, policy-based mechanisms tend to be too rigid, and quickly lose their efficacy when conditions of the autonomous system change during its operation. In this paper, we propose a generative policy framework that can generate policies for an autonomous system when conditions change. For changed conditions, the policy generation manager dynamically generates new set of policies optimized for the new situation. As a use case, we demonstrate how our generative policy framework generates policies for selecting optimal data for an AI model training. The policies are dynamically generated based on the availability and trustworthiness of data in a coalition environment.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"32 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133489609","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}
Nachiket Tapas, Giovanni Merlino, F. Longo, A. Puliafito
{"title":"Blockchain-Based Publicly Verifiable Cloud Storage","authors":"Nachiket Tapas, Giovanni Merlino, F. Longo, A. Puliafito","doi":"10.1109/SMARTCOMP.2019.00076","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00076","url":null,"abstract":"Cloud storage adoption, due to the growing popularity of IoT solutions, is steadily on the rise, and ever more critical to services and businesses. In light of this trend, customers of cloud-based services are increasingly reliant, and their interests correspondingly at stake, on the good faith and appropriate conduct of providers at all times, which can be misplaced considering that data is the \"new gold\", and malicious interests on the provider side may conjure to misappropriate, alter, hide data, or deny access. A key to this problem lies in identifying and designing protocols to produce a trail of all interactions between customers and providers, at the very least, and make it widely available, auditable and its contents therefore provable. This work introduces preliminary results of this research activity, in particular including scenarios, threat models, architecture, interaction protocols and security guarantees of the proposed blockchain-based solution.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116718294","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 Novel Two-Step Fall Detection Method Using Smartphone Sensors","authors":"John C. Dogan, M. Hossain","doi":"10.1109/SMARTCOMP.2019.00083","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2019.00083","url":null,"abstract":"A smartphone-based fall detection system has two major advantages over a traditional fall detection system that comes as a separate device: (1) the phone can automatically send messages to or call the emergency contact person when a fall is detected and (2) a user does not need to carry an extra device. This paper presents a novel two-step fall detection method which uses data extracted from smartphone sensors to detect falls. A fall can happen in many ways. A person can fall while he/she is walking, jogging, sitting, or even sleeping. Patterns of all falls are not the same. It is important to identify the type of falls to precisely distinguish it from non-falls (normal activities). Hence, our method first identifies the correct type of falls by performing multi-class classification. In the second step, this method produces a binary decision based on the multiclass prediction. We collected data from 10 users to evaluate our proposed fall detection method. Each user performed five normal activities–namely, walking, jogging, standing, sitting, lying, and also fell after performing each activity. We performed experiments with five common smartphone sensors: accelerometer, gyroscope, magnetometer, gravity, and linear acceleration. We tested five machine learning classifiers–namely, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest, and Naive Bayes. Our two-step fall detection method achieved the maximum accuracy of 95.65% and the maximum area under ROC curve (AUC) of 0.93, both with the gyroscope sensor and Support Vector Machine classifier.","PeriodicalId":253364,"journal":{"name":"2019 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114275887","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}