Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975952
Mohamed S. Abdalzaher, Mahmoud M. Salim, H. A. Elsayed, M. Fouda
Smartness and IoT along with machine learning (ML) lead the research directions nowadays. Smart city, smart campus, smart home, smart vehicle, etc; or if we call it “Smart x” will change how the world entities interact among themselves. This paper provides an ML benchmarking as well as a taxonomy that divides its models into linear and non-linear ones based on the problem type (classification or regression), the targeted security issue, the kind of IoT network, and the used evaluation measure. On the other hand, security algorithms enhanced with ML play a significant role to govern the new era of communication. This paper also provides a case study to apply the ML methods to IoT smart campus (SC) as a model to reach a secured IoT system for data collection and manipulation with guided research directions.
{"title":"Machine Learning Benchmarking for Secured IoT Smart Systems","authors":"Mohamed S. Abdalzaher, Mahmoud M. Salim, H. A. Elsayed, M. Fouda","doi":"10.1109/IoTaIS56727.2022.9975952","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975952","url":null,"abstract":"Smartness and IoT along with machine learning (ML) lead the research directions nowadays. Smart city, smart campus, smart home, smart vehicle, etc; or if we call it “Smart x” will change how the world entities interact among themselves. This paper provides an ML benchmarking as well as a taxonomy that divides its models into linear and non-linear ones based on the problem type (classification or regression), the targeted security issue, the kind of IoT network, and the used evaluation measure. On the other hand, security algorithms enhanced with ML play a significant role to govern the new era of communication. This paper also provides a case study to apply the ML methods to IoT smart campus (SC) as a model to reach a secured IoT system for data collection and manipulation with guided research directions.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125999545","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975924
Yazan Taib, Mohamad Qasim, Basel Al Hayek, A. Kabalan, Wessam Shehieb, Peter Yacoub, K. Arshad, Khaled Assaleh
Thyroid Stimulating Hormone (TSH) levels produced by the thyroid gland control various crucial bodily functions. It is important to monitor and control the production level of TSH in a human body. State-Of-The-Art (SOTA) focused on the detection of TSH levels, but the focus was on the functionality only rather than patient’s comfort, convenience or cost. In this research, we propose a non-invasive method of collecting and monitoring patient data associated with symptoms of TSH at the comfort and convenience of the patient with minimal cost. This helps primary diagnoses of the potential patient based on a probabilistic outcome of the proposed algorithm. Four main symptoms are prominent in most cases of abnormal levels of TSH, excessive sweating/dry skin, irregular heart rate, neck swelling and weight change. This paper proposes a framework for the primary diagnosis of TSH by monitoring the most common symptoms, such as response from (1) galvanic skin sensor for the detection of sweat/dry skin, (2) heart rate sensor, (3) image processing module for the swollen neck and, (4) a questionnaire to know of any sudden weight changes of the patient. The proposed system has been developed and tested on patients and obtained promising preliminary results.
{"title":"Primary Diagnosis of Thyroid Stimulating Hormone Using a Non-Invasive Method","authors":"Yazan Taib, Mohamad Qasim, Basel Al Hayek, A. Kabalan, Wessam Shehieb, Peter Yacoub, K. Arshad, Khaled Assaleh","doi":"10.1109/IoTaIS56727.2022.9975924","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975924","url":null,"abstract":"Thyroid Stimulating Hormone (TSH) levels produced by the thyroid gland control various crucial bodily functions. It is important to monitor and control the production level of TSH in a human body. State-Of-The-Art (SOTA) focused on the detection of TSH levels, but the focus was on the functionality only rather than patient’s comfort, convenience or cost. In this research, we propose a non-invasive method of collecting and monitoring patient data associated with symptoms of TSH at the comfort and convenience of the patient with minimal cost. This helps primary diagnoses of the potential patient based on a probabilistic outcome of the proposed algorithm. Four main symptoms are prominent in most cases of abnormal levels of TSH, excessive sweating/dry skin, irregular heart rate, neck swelling and weight change. This paper proposes a framework for the primary diagnosis of TSH by monitoring the most common symptoms, such as response from (1) galvanic skin sensor for the detection of sweat/dry skin, (2) heart rate sensor, (3) image processing module for the swollen neck and, (4) a questionnaire to know of any sudden weight changes of the patient. The proposed system has been developed and tested on patients and obtained promising preliminary results.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127621694","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9976001
Rijo Jacob, Alastair Nisbet
Securing a wireless digital scene for IoT based digital investigations has been problematic. Amongst the most pressing issues is the increasingly challenging task of identifying IoT devices. Practical difficulties arise from the rapid introduction, growing variety and likeness to ordinary physical objects of IoT devices. The mostly indistinguishable digital evidence sources are often overlooked during the forensic process of identification. The alternative means of IoT device identification, including from the Device Fingerprinting and Indoor Localisation areas, are lacking for the need of investigators to be able to accomplish the task of identification. To assist the search operations, including for IoT devices, a suitable approach is to reconstruct wireless sensing deployments ahead of the identification task. This, however, will require investigators to harness the communications of IoT devices. This paper apprises the salient features and capabilities desirable for an effective IoT monitoring and modelling system. A model of the envisaged system is light-weight and suited for both forensic and law enforcement purposes. The underlying principles of the contemporary model are three discrete aspects of IoT device communications, namely, monitorability, traceability and discoverability.
{"title":"Designing a Forensic Investigation Framework for IoT Monitoring and Modelling","authors":"Rijo Jacob, Alastair Nisbet","doi":"10.1109/IoTaIS56727.2022.9976001","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9976001","url":null,"abstract":"Securing a wireless digital scene for IoT based digital investigations has been problematic. Amongst the most pressing issues is the increasingly challenging task of identifying IoT devices. Practical difficulties arise from the rapid introduction, growing variety and likeness to ordinary physical objects of IoT devices. The mostly indistinguishable digital evidence sources are often overlooked during the forensic process of identification. The alternative means of IoT device identification, including from the Device Fingerprinting and Indoor Localisation areas, are lacking for the need of investigators to be able to accomplish the task of identification. To assist the search operations, including for IoT devices, a suitable approach is to reconstruct wireless sensing deployments ahead of the identification task. This, however, will require investigators to harness the communications of IoT devices. This paper apprises the salient features and capabilities desirable for an effective IoT monitoring and modelling system. A model of the envisaged system is light-weight and suited for both forensic and law enforcement purposes. The underlying principles of the contemporary model are three discrete aspects of IoT device communications, namely, monitorability, traceability and discoverability.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122008685","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975983
R. Avanzato, F. Beritelli
Mortality caused by cardiovascular diseases (CVDs) has been steadily increasing over the years. For this reason, numerous studies have addressed this issue, introducing innovative techniques for automatic detection of heart disease using ECG/PCG signals and convolutional neural networks (CNNs). The present paper proposes a system for automatic diagnosis of heart disease (five pathology classes) using electrocardiogram (ECG) signals and CNNs. Specifically, ECG signals are passed directly to an appropriately trained CNN network. The database comprises a combination of two public datasets: MIT-BIH Arrhythmia and MIT-BIH Atrial Fibrillation database. The results obtained from testing the network show average classification accuracy of about 93% when a 2second ECG signal is fed to the network; conversely, applying a post-processing filter results in about 100% accuracy after around 38 seconds.
{"title":"Heart disease recognition based on extended ECG sequence database and deep learning techniques","authors":"R. Avanzato, F. Beritelli","doi":"10.1109/IoTaIS56727.2022.9975983","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975983","url":null,"abstract":"Mortality caused by cardiovascular diseases (CVDs) has been steadily increasing over the years. For this reason, numerous studies have addressed this issue, introducing innovative techniques for automatic detection of heart disease using ECG/PCG signals and convolutional neural networks (CNNs). The present paper proposes a system for automatic diagnosis of heart disease (five pathology classes) using electrocardiogram (ECG) signals and CNNs. Specifically, ECG signals are passed directly to an appropriately trained CNN network. The database comprises a combination of two public datasets: MIT-BIH Arrhythmia and MIT-BIH Atrial Fibrillation database. The results obtained from testing the network show average classification accuracy of about 93% when a 2second ECG signal is fed to the network; conversely, applying a post-processing filter results in about 100% accuracy after around 38 seconds.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764016","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975971
M. A. Murti, C. Setianingsih, Iga Narendra, Kenneth Angelo, Muchlis Aryomukti, Arasy Bazwir, Reviandi Naufal Kurniawan
Electricity has become one of the main human needs today because all environments, whether at home, at work, or in factories, use electrical energy. Every year the use of electricity always increases, this cause an increase in electricity prices which in turn makes electricity expensive. With the increase in tariffs, this should be an impetus for the public to be aware of saving electricity use. This study aims to compare the two models using two algorithms, namely LSTM and Prophet, then measure the level of accuracy and draw conclusions using the statistical metrics Mean Absolute Error (MAE) method to forecast electricity consumption in a period of thirty days or about one month. The datasets used are the consumption of electricity use in Germany during the period 2006 – 2017. This data includes the total daily consumption of electricity in GWh, daily column in day – month – year format, wind power production in GWh, production solar power in GWh, as well as the total sum of wind and solar power production in GWh. In this case, the researcher only uses daily column data in the format of days – months – years and data on total daily consumption of electricity as parameters to estimate electricity use for the next month. This data is provided by Open Power System Data (OPSD) and is available on the “kaggle.com” website. The data used in this study is very useful for time series analysis. Based on the results of testing with the LSTM algorithm with the SGD optimizer, the MAE value is 0.198987. The test results with the Prophet algorithm produce an MAE with a value of 40.
{"title":"Forecasting Electricity Consumption using Long Short Term Memory and Prophet Algorithm","authors":"M. A. Murti, C. Setianingsih, Iga Narendra, Kenneth Angelo, Muchlis Aryomukti, Arasy Bazwir, Reviandi Naufal Kurniawan","doi":"10.1109/IoTaIS56727.2022.9975971","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975971","url":null,"abstract":"Electricity has become one of the main human needs today because all environments, whether at home, at work, or in factories, use electrical energy. Every year the use of electricity always increases, this cause an increase in electricity prices which in turn makes electricity expensive. With the increase in tariffs, this should be an impetus for the public to be aware of saving electricity use. This study aims to compare the two models using two algorithms, namely LSTM and Prophet, then measure the level of accuracy and draw conclusions using the statistical metrics Mean Absolute Error (MAE) method to forecast electricity consumption in a period of thirty days or about one month. The datasets used are the consumption of electricity use in Germany during the period 2006 – 2017. This data includes the total daily consumption of electricity in GWh, daily column in day – month – year format, wind power production in GWh, production solar power in GWh, as well as the total sum of wind and solar power production in GWh. In this case, the researcher only uses daily column data in the format of days – months – years and data on total daily consumption of electricity as parameters to estimate electricity use for the next month. This data is provided by Open Power System Data (OPSD) and is available on the “kaggle.com” website. The data used in this study is very useful for time series analysis. Based on the results of testing with the LSTM algorithm with the SGD optimizer, the MAE value is 0.198987. The test results with the Prophet algorithm produce an MAE with a value of 40.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125048161","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975953
F. Z. A. Mudrikah, Istikmal, Bagus Aditya
Forest and land fires are an increasingly common problem in Indonesia. Fire cases that often occur require a system that is able to detect fires and provide information to users remotely to reduce the impact of fires. Along with the development of hardware technology such as computers, the use of GIS seems to be an effective shortcut in analyzing an event. Kubernetes is an open source platform for managing containerized application workloads, offering declarative configuration and automation. This research designs a Google Maps API System tool for forest and land fires using a real-time database on microservices infrastructure with outputs in the form of fire locations and the results of sensor readings used. Broadly speaking, the processes that occur in the design of the location of forest fire points will be detected by sensors. Then firebase will store forest fire data which will simultaneously be updated on the website. Clients can see the point of forest fires through a browser on their respective desktops. Based on the results of the performance tests that have been carried out, it can be concluded that the use of Kubernetes microclusters can provide advantages when compared to those built monolithically, because Kubernetes microclusters have several advantages, namely having the Horizontal Pod Autoscaler feature, and the Kubernetes microcluster manages components and related services well. Then for each test performed, there was no significant change in memory usage. In the analysis of the results of the comparison data with 7 tests that have been carried out there are 6 tests which mean that the service built with the Kubernetes microcluster is superior to the monolithic one, namely hits per second 2354 ms, latency 3599 ms, response code 720 success code, cpu utilization 13.84%, error rate error rate 0.00%, and throughput 112/sec.
{"title":"Design of a Geographic Information System for Forest and Land Fires Based on a Real-Time Database on Microservices Infrastructure","authors":"F. Z. A. Mudrikah, Istikmal, Bagus Aditya","doi":"10.1109/IoTaIS56727.2022.9975953","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975953","url":null,"abstract":"Forest and land fires are an increasingly common problem in Indonesia. Fire cases that often occur require a system that is able to detect fires and provide information to users remotely to reduce the impact of fires. Along with the development of hardware technology such as computers, the use of GIS seems to be an effective shortcut in analyzing an event. Kubernetes is an open source platform for managing containerized application workloads, offering declarative configuration and automation. This research designs a Google Maps API System tool for forest and land fires using a real-time database on microservices infrastructure with outputs in the form of fire locations and the results of sensor readings used. Broadly speaking, the processes that occur in the design of the location of forest fire points will be detected by sensors. Then firebase will store forest fire data which will simultaneously be updated on the website. Clients can see the point of forest fires through a browser on their respective desktops. Based on the results of the performance tests that have been carried out, it can be concluded that the use of Kubernetes microclusters can provide advantages when compared to those built monolithically, because Kubernetes microclusters have several advantages, namely having the Horizontal Pod Autoscaler feature, and the Kubernetes microcluster manages components and related services well. Then for each test performed, there was no significant change in memory usage. In the analysis of the results of the comparison data with 7 tests that have been carried out there are 6 tests which mean that the service built with the Kubernetes microcluster is superior to the monolithic one, namely hits per second 2354 ms, latency 3599 ms, response code 720 success code, cpu utilization 13.84%, error rate error rate 0.00%, and throughput 112/sec.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126302063","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975894
Saeid Sadeghi Vilni, Mohammad Moltafet, Markus Leinonen, M. Codreanu
We consider a status update system consisting of one source, one butter-aided transmitter, and one receiver. The source randomly generates status update packets and the transmitter sends the packets to the receiver over an unreliable channel using a hybrid automatic repeat request (HARQ) protocol. The system holds two packets: one packet in the butter, which stores the last generated packet, and one packet currently under service in the transmitter. At each time slot, the transmitter decides whether to stay idle, transmit the last generated packet, or retransmit the packet currently under service. We aim to find the optimal actions at each slot to minimize the average age of information (AoI) of the source under a constraint on the average number of transmissions. We model the problem as a constrained Markov decision process (CMDP) problem and solve it for the known and unknown learning environment as follows. First, we use the Lagrangian approach to transform the CMDP problem to an MDP problem which is solved with the relative value iteration (RVI) for the known environment and with deep Q-learning (DQL) algorithm for the unknown environment. Second, we use the Lyapunov method to transform the CMDP problem to an MDP problem which is solved with DQL algorithm for the unknown environment. Simulation results assess the effectiveness of the proposed approaches.
{"title":"Average AoI Minimization in an HARQ-based Status Update System under Random Arrivals","authors":"Saeid Sadeghi Vilni, Mohammad Moltafet, Markus Leinonen, M. Codreanu","doi":"10.1109/IoTaIS56727.2022.9975894","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975894","url":null,"abstract":"We consider a status update system consisting of one source, one butter-aided transmitter, and one receiver. The source randomly generates status update packets and the transmitter sends the packets to the receiver over an unreliable channel using a hybrid automatic repeat request (HARQ) protocol. The system holds two packets: one packet in the butter, which stores the last generated packet, and one packet currently under service in the transmitter. At each time slot, the transmitter decides whether to stay idle, transmit the last generated packet, or retransmit the packet currently under service. We aim to find the optimal actions at each slot to minimize the average age of information (AoI) of the source under a constraint on the average number of transmissions. We model the problem as a constrained Markov decision process (CMDP) problem and solve it for the known and unknown learning environment as follows. First, we use the Lagrangian approach to transform the CMDP problem to an MDP problem which is solved with the relative value iteration (RVI) for the known environment and with deep Q-learning (DQL) algorithm for the unknown environment. Second, we use the Lyapunov method to transform the CMDP problem to an MDP problem which is solved with DQL algorithm for the unknown environment. Simulation results assess the effectiveness of the proposed approaches.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131775404","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975931
Sebastián Marzetti, V. Gies, V. Barchasz, H. Barthélemy, H. Glotin
In this paper, a comparison between analog and digital processing focused on ultra-low power embedded artificial intelligence is proposed. Several works developed before [1] –[6] demonstrate that features extraction and high sampling rate ADC are the most energy expensive tasks in fully digital embedded machine learning applications. Therefore, in this work analog and digital processing are compared, showing that under some conditions, analog processing is at least 30 times more efficient in terms of power consumption without taking into account the additional effect of the reduction of analog-to-digital sampling rate. Two case studies are presented: to set these ideas on a simple example, first order filter implementations using analog and digital circuits are first compared. Then, two techniques of spectrum analysis using digital FFT and analog filter benches are presented and discussed. Finally, a rule defining the situations where analog is more relevant than digital processing is proposed. This one can be used for intelligent Internet of Things (IoT) autonomous systems working on small batteries such as a single CR2032 coin cell for a very long time.
{"title":"Comparing Analog and Digital Processing for Ultra Low-Power Embedded Artificial Intelligence","authors":"Sebastián Marzetti, V. Gies, V. Barchasz, H. Barthélemy, H. Glotin","doi":"10.1109/IoTaIS56727.2022.9975931","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975931","url":null,"abstract":"In this paper, a comparison between analog and digital processing focused on ultra-low power embedded artificial intelligence is proposed. Several works developed before [1] –[6] demonstrate that features extraction and high sampling rate ADC are the most energy expensive tasks in fully digital embedded machine learning applications. Therefore, in this work analog and digital processing are compared, showing that under some conditions, analog processing is at least 30 times more efficient in terms of power consumption without taking into account the additional effect of the reduction of analog-to-digital sampling rate. Two case studies are presented: to set these ideas on a simple example, first order filter implementations using analog and digital circuits are first compared. Then, two techniques of spectrum analysis using digital FFT and analog filter benches are presented and discussed. Finally, a rule defining the situations where analog is more relevant than digital processing is proposed. This one can be used for intelligent Internet of Things (IoT) autonomous systems working on small batteries such as a single CR2032 coin cell for a very long time.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114833140","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975911
Alexandra Hidalgo, T. Serif, Tor-Morten Grønli, G. Ghinea
The IoT (Internet-of-Things) era is in full swing, with the transformation of all everyday objects extending the internet to communicate. The need to focus on UX (user experience) design of IoT services stems from the complex sequence of interactions between users from physical systems to virtual ones. This study aims to contribute to the definition of new IoT UX standards that go beyond the standard website and mobile guides that exist today. In attaining a better knowledge in the domain of UX and IoT, we focus on learning how UX designers are approaching IoT solutions and how user interactions are evolving with the use of IoT systems. Accordingly a study exploring the perspectives on the topic encompassing qualitative interviews with five UX designers and questionnaires administered to 65 IoT users was undertaken. Based on this, a series of guidelines are drafted and reviewed by UX designers to gain validity of the results produced thus creating a positive impact for not only users of IoT services but the designers behind the scenes creating the experiences.
{"title":"On User Experience in The Internet of Things","authors":"Alexandra Hidalgo, T. Serif, Tor-Morten Grønli, G. Ghinea","doi":"10.1109/IoTaIS56727.2022.9975911","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975911","url":null,"abstract":"The IoT (Internet-of-Things) era is in full swing, with the transformation of all everyday objects extending the internet to communicate. The need to focus on UX (user experience) design of IoT services stems from the complex sequence of interactions between users from physical systems to virtual ones. This study aims to contribute to the definition of new IoT UX standards that go beyond the standard website and mobile guides that exist today. In attaining a better knowledge in the domain of UX and IoT, we focus on learning how UX designers are approaching IoT solutions and how user interactions are evolving with the use of IoT systems. Accordingly a study exploring the perspectives on the topic encompassing qualitative interviews with five UX designers and questionnaires administered to 65 IoT users was undertaken. Based on this, a series of guidelines are drafted and reviewed by UX designers to gain validity of the results produced thus creating a positive impact for not only users of IoT services but the designers behind the scenes creating the experiences.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133871048","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}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975866
Vincent, Sharlene Regina, Kartika Purwandari, F. Kurniadi
With the increasing use of the Internet of Things (IoT) as a means of communication in the 21st century, many news media now rely on the internet as an online news publication platform. News headlines are often made as attractive as possible to entice the reader’s curiosity and thus increase the views of a news article. One of the many tactics employed is the use of clickbait. This research involves creating a model to detect headlines that contain clickbait. The model can act as a classifier between real news and clickbait-filled headlines with a Natural Language Processing (NLP) method approach. Bidirectional Long Short-Term Memory (Bi-LSTM), Decision Tree, and K-Nearest Neighbor (KNN) are all methods that can be used to distinguish actual news headlines from clickbait-laden headlines. This work is preliminary as research in this field is still being conducted, and improvements to the accuracy of these systems are still improving.
{"title":"Clickbait Headline Detection Using Supervised Learning Method","authors":"Vincent, Sharlene Regina, Kartika Purwandari, F. Kurniadi","doi":"10.1109/IoTaIS56727.2022.9975866","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975866","url":null,"abstract":"With the increasing use of the Internet of Things (IoT) as a means of communication in the 21st century, many news media now rely on the internet as an online news publication platform. News headlines are often made as attractive as possible to entice the reader’s curiosity and thus increase the views of a news article. One of the many tactics employed is the use of clickbait. This research involves creating a model to detect headlines that contain clickbait. The model can act as a classifier between real news and clickbait-filled headlines with a Natural Language Processing (NLP) method approach. Bidirectional Long Short-Term Memory (Bi-LSTM), Decision Tree, and K-Nearest Neighbor (KNN) are all methods that can be used to distinguish actual news headlines from clickbait-laden headlines. This work is preliminary as research in this field is still being conducted, and improvements to the accuracy of these systems are still improving.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133904239","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}
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