Browsing by Subject "Internet of things"

Now showing 1 - 9 of 9
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    A machine learning based approach to identify geo-location of Twitter users
    (Association for Computing Machinery, 2017) Onan A.
    Twitter, a popular microblogging platform, has attracted great attention. Twitter enables people from all over the world to interact in an extremely personal way. The immense quantity of user-generated text messages become available on Twitter that could potentially serve as an important source of information for researchers and practitioners. The information available on Twitter may be utilized for many purposes, such as event detection, public health and crisis management. In order to effectively coordinate such activities, the identification of Twitter users' geo-locations is extremely important. Though online social networks can provide some sort of geo-location information based on GPS coordinates, Twitter suffers from geo-location sparseness problem. The identification of Twitter users' geo-location based on the content of send out messages, becomes extremely important. In this regard, this paper presents a machine learning based approach to the problem. In this study, our corpora is represented as a word vector. To obtain a classification scheme with high predictive performance, the performance of five classification algorithms, three ensemble methods and two feature selection methods are evaluated. Among the compared algorithms, the highest results (84.85%) is achieved by AdaBoost ensemble of Random Forest, when the feature set is selected with the use of consistency-based feature selection method in conjunction with best first search. © 2017 ACM.
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    WALRUS: A retro communication gadget based on internet of things technologies
    (2018) Öztürk, Övünç; Küçük, Yunus Emre; Yalnız, Ahmet; Öztürk, Övünç; Fakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
    In this work, a new system depending on a device that can encode and decode push-button signals, modulated using Morse code conventions, were developed to build a low-cost communication medium based on Internet of Things (IoT). The proposed system consists of two parts: a base station and handheld terminals. The base station is a single board computer with a web application based on Node.js. Handheld terminals are small battery powered devices, developed using MCU's, that can communicate with the base station over the wireless network. They can encode and decode Morse code, and convert to text or speech depending on the configuration of the terminal, which can be extended by using different add-ons, such as an OLED screen or a text to speech module. Communication between terminals is orchestrated by the base station using IoT Technologies like MQTT. The handheld terminals can be used by disabled people as a mean for private conversation, or a gadget for entertainment purposes. The system is an uncomplicated and low-cost communication medium and implemented to find alternative use cases for the IoT technologies.
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    Internet of things based smart energy management for smart home
    (Korean Society for Internet Information, 2019) Taştan M.
    Thanks to internet, as one of indispensable parts of our lives, many devices that we use in our daily lives like TV, air conditioner, refrigerator, washing machine, can be monitored and controlled remotely by becoming more intelligent via Internet of Things (IoT) technology. Smart Home applications as one of the elements of smart cities, are individually the most demanded application without question. In this study, Smart Energy Management (SEM) system, based on NodeMCU and Android, has been designed for SEM, which is a part of the smart home application. With this system, household energy consumption can be monitored in real time, as well as having the ability to record the data comprising of operation times and energy consumption information for each device. Additionally, it is ensured to meet the energy needs on a maximized level possible, during the hours when the energy costs are lower owing to the SEM system. The Android interface provides the users with the opportunity to monitor and change their electricity consumption habits in order to optimize the energy efficiency, along with the opportunity to draw up of a daily and weekly schedule. © 2019 KSII.
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    A case study for block-based linked data generation: Recipes as jigsaw puzzles
    (SAGE Publications Ltd, 2020) Öztürk Ö.; Özacar T.
    This article is a proof-of-concept case study to evaluate the functionality of a block metaphor–based linked data generator. In this work, we chose to produce linked data repository of recipes, which provide a medium for people to share their regional and healthy recipes with the masses. However, the same approach can also be adapted easily to other domains. Therefore, the applicability of our approach extends well beyond the food domain that we are considering in this article. As a medium for information sharing and understanding between heterogeneous systems, ontologies will play an important role in the realisation of the Internet of things (IoT) vision. Therefore, an ontology-based recipe repository would also be one of the basic blocks of a smart kitchen environment. However, building ontologies is a challenging task, especially for users who are not conversant in the ontology building languages. This article proposes an approach that can be used even by non-experts and facilitates the sharing and searching of recipe data. In our case, we exploit the features of the block paradigm to publish recipes in Linked Data format. In this way, users do not have to know the OWL (Web Ontology Language) syntax and the text input is kept minimal. As far as we know, this article is the first study that produces linked data using Blockly in the literature. We also conducted a user-based evaluation of the proposed approach using the System Usability Scale (SUS) questionnaire. © The Author(s) 2019.
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    A low-cost air quality monitoring system based on Internet of Things for smart homes
    (IOS Press BV, 2022) Taştan M.
    Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies. © 2022 - IOS Press. All rights reserved.
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    Cybersecurity for Internet of Things: Intrusion Detection with Machine Learning and Dimension Reduction
    (Institute of Electrical and Electronics Engineers Inc., 2023) Ozturk-Birim S.; Gunduz-Cure M.
    The IoT connects an increasing number of devices to the Internet, simplifying lives but also exposing new vulnerabilities to cyberattacks. The intrusion detection system (IDS) helps detect and prevent attacks on IoT networks. This study aims to develop an intrusion detection and classification system using machine learning and dimension reduction techniques on two datasets. Performance metrics, dataset characteristics, and critical aspects are analyzed. The RF and XGB methods were used to classify attacks in Bot-IoT and Ton-IoT datasets, with and without dimension reduction. Precision, recall, and fl were used to measure classification performance. XGB outperformed in multiclass classification, while RFC excelled in binary classification. The use of PCA reduced computation time for XGB in binary classification but increased it for RFC. XGB showed decreased computation time and a slight performance impact in multiclass classification. © 2023 IEEE.
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    Energy-Aware Task Scheduling for Digital Twin Edge Networks in 6G
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bozkaya E.; Bilen T.; Erel-Ozcevik M.; Ozcevik Y.
    With the recent surge in the Internet of Things (IoT) devices and applications, computation offloading services in Mobile Edge Computing (MEC) have provided the significant potential to upcoming 6G networks for a better Quality of Service (QoS). However, IoT devices are typically resource and energy-constrained, so this challenge can be compensated by incorporating energy-efficient approaches into the solution. Digital Twin is a candidate technology to reshape the future of the industry and energy-efficiently manage tremendous growth in data traffic at the network edge. Thus, we propose a Digital Twin Edge Network (DTEN) architecture for energy-aware task scheduling. More specifically, we formulate an energy optimization problem and identify a set of computation strategies to minimize both the task processing time and energy consumption. Due to being NP-hard, we compare it by Warehouse Location Problem (WLP) and solve it with the genetic algorithm-based approach in an energy and time-efficient manner. To achieve these, we present our digital twin-assisted energy-aware task scheduling algorithm by using both real-time and historical data in virtualization and service layers. After this, IoT devices can compute their tasks locally or offload to the edge/cloud server with the assistance of digital twins of the physical assets. Simulations are carried out to show the superiority of the proposed energy-aware task scheduling algorithm in terms of the task processing time and consumed energy in DTEN. © 2023 IEEE.
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    Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network
    (Elsevier B.V., 2023) Bozkaya E.; Erel-Özçevik M.; Bilen T.; Özçevik Y.
    The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm. © 2023 Elsevier B.V.
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    The output performance evaluations of multilayered piezoelectric nanogenerators based on the PVDF-HFP/PMN-35PT using various layer-by-layer assembly techniques
    (Springer, 2024) Paralı L.
    Multilayered Poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) and lead magnesium niobate lead titanate Pb (Mg1/3Nb2/3) O3–PbTiO3 (PMN-35PT) composition-based piezoelectric nanogenerators (PNGs) were fabricated as series, parallel, and combined series-parallel connections using various layer-by-layer assembly techniques. Supporting the theoretical approaches with experimental results shows that the fabricated four-layered PNG with parallel connections (4L-P) reached an open-circuit voltage of 0.4 V (VRMS) and a maximum electrical power of 0.3 µW (PRMS) by drawing a current (IRMS) of 1.46 µA under a resistive load of 140.2 KΩ. Increasing the capacitance and decreasing the impedance with the fabrication of the four-layer PNG by connecting the layers in parallel connection with the support of the impedance matching process led to an increase in electrical output. With the use of an impedance matching system, the piezoelectric performance tests revealed that the 4L-P-based PNG had a 6.7 times greater electrical power efficiency (72.92 µW) at the vibrational frequency of 20 Hz compared to that of the single-layered PNG (10.82 µW). Furthermore, the multilayer PNG was successfully used as a wearable sensor for the monitoring of human body motions in real time on an IOT (Internet of Things) platform. © The Author(s) 2024.