2022 32nd Conference of Open Innovations Association (FRUCT)最新文献

{"title":"Ground Level Mobile Signal Prediction Using Higher Altitude UAV Measurements and ANN","authors":"Ibtihal Al Saadi, N. Tarhuni, M. Mesbah","doi":"10.23919/FRUCT56874.2022.9953813","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953813","url":null,"abstract":"Testing the mobile network signal strength is essential for evaluating actual user experience. This procedure is done by measurement campaign, where a person or a group of people walk or drive through the target area holding a measuring equipment. However, this is not suitable to do in hard-to-reach areas. In order to minimize human involvement and to reduce resources, labour, and time consumed, an alternative approach for physical assessment of cellular coverage and quality evaluating is needed. In this work, we used a drone to measure mobile network signal strength to generate a two-dimensional coverage map for difficult-to-reach areas. A machine learning algorithm is used to estimate the signal strength in other locations within the area to generate a dense 2D coverage map. The measurements were done on Sultan Qaboos University Campus, Muscat, Oman. Our finding shows that a drone equipped with a low-cost signal strength measuring device and an artificial neural network (ANN) algorithm are able to generate an accurate dense map of mobile signal strength in a flexible and cost-effective manner. The ANN was capable of predicting the signal strength at the ground from measurement at higher altitudes with an accuracy of 97%.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127642972","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":"Data and Location Privacy of Smart Devices over Vehicular Cloud Computing","authors":"Hani Al-Balasmeh, M. Singh, Raman Singh","doi":"10.23919/FRUCT56874.2022.9953812","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953812","url":null,"abstract":"In this paper, we have addressed the problem of data and location privacy in smart devices over vehicular cloud computing (VCC). We proposed a framework to identify and register the smart IoT GPS devices over VCC service and allow the users to monitor their devices in real time. The proposed framework divides into three parts: First, data anonymization of users' information over VCC, by masking the original data of the user and replaced with fake data. The proposed technique will remove the user identity and other linkers to identify the users. Second, proposed a technique using asymmetric cryptography (RSA) technique, the proposed technique provides location privacy of users' trajectories before requesting point of interest (POI) from location-based services (LBS). Third, secure communication between users and the VCC, based on Token-based authentication by authenticating the trusted users while requesting a location from the VCC service. The proposed framework shows the efficiency and reliability of responding to user trajectories from different sources of IoT GPS devices and datasets.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130312952","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":"Method for Automated Data Collection for 3D Reconstruction","authors":"M. Zaslavskiy, Roman Shestopalov, Alexander Grebenshchikov, Danil Korenev, Evgeny Shkvirya","doi":"10.23919/FRUCT56874.2022.9953825","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953825","url":null,"abstract":"The paper presents an automated method to guide a human operator during RGB image shot for improving the quality of further 3D reconstruction for low-rise outdoor objects and a use case for method application. The method provides automation as a three-step process: local analysis of images performed during the shooting, global analysis, and recommendations performed after the shooting. Method steps filter out defective images, approximate future 3D-model using Structure-from-Motion (SfM), and map it to human operator trajectory estimation to identify object areas that will have low resolution on the final 3D model, requiring additional shooting. Method structure does not require any sensors except RGB camera and inertial sensors and does not rely on any external backend, which lowers the hardware requirement. The authors implemented the method and use-case as an Android application. The method was evaluated by experiments on outdoor datasets created for this study. The evaluation shows that the local analysis stage is fast enough to perform during the shooting process and that the local analysis stage improves the quality of the final 3D model.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130626655","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":"Motor Activity Sensorics for mHealth Support of Human Resilience in Daily Life","authors":"A. Meigal, L. Gerasimova-Meigal, G. Rego, Dmitry G. Korzun","doi":"10.23919/FRUCT56874.2022.9953829","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953829","url":null,"abstract":"The quality of life depends on the human resilience to stresses and other negative impacts from the environment and society. We assume that the human resilience can be effectively supported by motor activity. The problem is that people reduce the motor activity (the human mobility) although they are potentially able to move (the human motility). In this paper, we consider human motor activity sensorics and study the concept of mobile Health (mHealth) system to digitally support the mobility of a person during her/his daily life. The sensorics is based on inertial sensors of a smartphone that accompanies the person. The smartphone evaluates various motor tests for the human activity. The collected statistics provide an interesting picture to motivate the person to more activity. We introduce the concept model that interrelates human resilience and motor activity. We discuss possible digital support of human resilience based on testing the human activity. In sum, this study contributes our concept of smartphone-based mHealth system that digitally supports the motor activity of a person in daily life subject to increase the human resilience.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116581396","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-Tenant Management in Secured IoT Based Solutions","authors":"Kerem Aytaç, Ömer Korçak","doi":"10.23919/FRUCT56874.2022.9953817","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953817","url":null,"abstract":"The existence of Internet of Things (IoT) can be noticed in many different areas. There are lots of IoT implementations in different domains which facilitate existing workloads and solve many kinds of struggles within that specific domain. The excessive growth in IoT based solutions for both home and industry applications caused developers to pay great attention over the requirements such as reusability, modifiability, and interoperability with a robust security. Many IoT products should present a core feature which serves plenty of customers from different domains and provide domain-specific features for each of them. This approach also forces the IoT products to be multi-tenant supported to satisfy the requirements. Multi-tenancy brings more strict security considerations, organizational approaches, policies, roles, and identity managements. In this paper, we propose a solution for implementing multi-tenancy and tenant management in an IoT product, and drill down to details in order to disclose how to make that product modular and give the ability to serve unlimited number of vertical solutions.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428821","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":"Thesis Review and Analysis Automated System","authors":"Jean Patrick Lostaunau, Armando Soto, Alfredo Barrientos","doi":"10.23919/FRUCT56874.2022.9953855","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953855","url":null,"abstract":"In this article, we propose the design, construction, and validation of a technological solution with the ability to automate the process of reviewing and analyzing thesis paper by using natural language processing and a deep learning training algorithm. This project seeks to be a proof of concept because current solutions in the academic field focus on abstracting and analyzing scientific articles but not in theses. Another point to consider is that these solutions are in English language. The resulting language model was compared with other language models based on the Transformers architecture. The result of this comparison gives us an objective for future research on the A.S.T.R.A. project.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128000763","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 Straightforward and Efficient Approach to Secure Smart Home Communication using Identify-Based Cryptosystems","authors":"M. M. Rathore, Sushil S. Chaurasia, Dhirendra Shukla, Elmahdi Bentafat","doi":"10.23919/FRUCT56874.2022.9953822","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953822","url":null,"abstract":"With the growing practical implementation of smart home, the attacks on smart homes are proportionally increasing. Residents can only be benefited from smart home technology if they and their home-assets are secured against cyber-attacks. A number of PKI-based communication security models have been proposed for data authentication and confidentiality in smart homes. However, it is not convenient for a home device with the limited capacity to store, verify, and manage public keys (certificates) of all other devices. Identity-based cryptography (IBC) is one of the asymmetric cryptographic solutions that does not require certificates. However, due to the central storage of the secret at the key generation center (KGC), the security fully relies on the KGC in IBC environment. Thus, to resolve these issues while providing the security to smart homes, in this paper, we proposed a straightforward and light-weight security model based on IBC, wheel pairing, and elliptic curves. The proposed model performs distributed key generation where the main secret is generated by all participating home devices, instead of a central KGC. We designed a complete protocol, which illuminates the fundamental steps of new device enrollment, distributed key generation, device to device encryption, data integrity, and entity authentication. Moreover, the commitment procedure is introduced that ensures no party can change its partial-secret after he has committed to it. The elliptic curve cryptography (ECC) based Diffie-Hellman (DH) model is deployed for session key generation for device to device data encryption, whereas IBC-based private key is used for signatures. Finally, the feasibility of the model is evaluated by implementing the system on various numbers of IoT machines, while considering them as home devices. Also, the security of the proposed model is verified technically and formally by a software verification tool called Automated Validation of Internet Security Protocols and Applications (AVISPA) against popular known attacks.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127789383","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-kernel Convolutional Neural Network for Wrist Pulse Signal Classification","authors":"Xiaofei Chen, Hua Xu, P. Qian, Yunfeng Xu, Fufeng Li, Shengwang Li","doi":"10.23919/FRUCT56874.2022.9953841","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953841","url":null,"abstract":"Wrist pulse is one kind of biomedical signals, it is affected not only by the heart beatings, but also by the conditions of nerves, organs, muscles, skin, etc. Therefore, wrist pulse signals can reflect a person's physical state and it has been widely used in health status analysis. However, previous works mainly use traditional machine learning methods to analyze wrist pulse signal. Because wrist pulse signal is high-dimensional and complex, it is difficult for traditional machine learning methods to learn effective information from them. This study aims to explore the utilizing of deep learning methods on wrist pulse signal analysis. We propose a novel multi-kernel Convolutional Neural Network for wrist pulse signal classification. Our model can handle multiple kinds of input features and each of them will pass through a convolutional neural network that has three different sizes of convolution kernel to capture multi-scale information in different time steps. We compare our method with traditional ma-chine learning methods on two tasks: Coronary Atherosclerotic Heart Disease Classification and Traditional Chinese Medicine Constitution yin deficiency and yang deficiency Classification. Besides, we also research the influence of different input features and different channels on wrist pulse signal analysis. The results show that our model significantly improves the performance on the two tasks, which proves the deep learning method is more suitable to deal with complex wrist pulse data.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126389903","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":"Framework to Improve the Traceability of the Coffee Production Chain in Perú by Applying a Blockchain Architecture","authors":"Alejandro Garcia, Javier Dávila, Lenis Wong","doi":"10.23919/FRUCT56874.2022.9953846","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953846","url":null,"abstract":"The value of coffee is decreasing in the international market caused by the poor traceability that coffee cooperatives offer to the rest of the public in the production chain. There are paper records or storage in centralized databases of the details of the product that is delivered and received at each stage, generating high cost and inefficiency in paper-based processes, fraud, corruption and errors in both processes. Other investigations pose the problem proposing solutions with opportunities to improve. A framework is proposed to improve the traceability of the coffee production chain using blockchain. The proposed framework is composed of 3 phases: (1) Blockchain architecture design, (2) Smart contracts design and (3) Web application design. Blockchain technology is used to guarantee immutable traceability of the production chain. The proposed framework was validated in a coffee cooperative located in Cajamarca (Peru) applying 2 experiments and a survey based on expert opinion. The results show that with the proposed framework, time of process and the number of errors can be reduced in the defined scenarios, where in turn a reduction of 99.87% of the time in the generation of coffee traceability is evidenced. In addition, the results of the survey show that the “performance”, “traceability parameters” and “usability” of the system have an average value of 4.6 (Value close to 5).","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124942569","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":"Classification and Analysis of Adversarial Machine Learning Attacks in IoT: a Label Flipping Attack Case Study","authors":"M. Abrishami, S. Dadkhah, E. P. Neto, Pulei Xiong, Shahrear Iqbal, S. Ray, A. Ghorbani","doi":"10.23919/FRUCT56874.2022.9953823","DOIUrl":"https://doi.org/10.23919/FRUCT56874.2022.9953823","url":null,"abstract":"With the increased usage of Internet of Things (IoT) devices in recent years, various Machine Learning (ML) algorithms have also developed dramatically for attack detection in this domain. However, the ML models are exposed to different classes of adversarial attacks that aim to fool a model into making an incorrect prediction. For instance, label manipulation or label flipping is an adversarial attack where the adversary attempts to manipulate the label of training data that causes the trained model biased and/or with decreased performance. However, the number of samples to be flipped in this category of attack can be restricted, giving the attacker a limited target selection. Due to the great significance of securing ML models against Adversarial Machine Learning (AML) attacks particularly in the IoT domain, this research presents an extensive review of AML in IoT. Then, a classification of AML attacks is presented based on the literature which sheds light on the future research in this domain. Next, this paper investigates the negative impact levels of applying the malicious label-flipping attacks on IoT data. We devise label-flipping scenarios for training a Support Vector Machine (SVM) model. The experiments demonstrate that the label flipping attacks impact the performance of ML models. These results can lead to designing more effective and powerful attack and defense mechanisms in adversarial settings. Finally, we show the weaknesses of the K-NN defense method against the random label flipping attack.","PeriodicalId":274664,"journal":{"name":"2022 32nd Conference of Open Innovations Association (FRUCT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123389406","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}