The current air quality monitoring system cannot cover a large area, not real-time and has not
implemented big data analysis technology with high accuracy. The purpose of an integration Mobile
Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor
in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge
computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing.
VaaMSN is a package of air quality sensor, GPS, 4G Wi-Fi modem and single board computing. SEMAR
cloud computing has a time-series database for real-time visualization, Big Data environment and analytics
use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system
are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that
the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of
SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.
WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline MonitoringIJECEIAES
One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.
Review on Environment Monitoring System and Energy EfficiencyIJERA Editor
The Environment monitoring is one of the applications of wireless sensor network. The most serious environment pollution is air pollution because different air pollutant causes damage to human health and causes global warming. To avoid such effect on human health and climate change Environment monitoring systems are used. This paper provides the short overview of different environmental air pollution monitoring systems and Energy efficiency in WSN to reduced the power consumption of system.
WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline MonitoringIJECEIAES
One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.
Review on Environment Monitoring System and Energy EfficiencyIJERA Editor
The Environment monitoring is one of the applications of wireless sensor network. The most serious environment pollution is air pollution because different air pollutant causes damage to human health and causes global warming. To avoid such effect on human health and climate change Environment monitoring systems are used. This paper provides the short overview of different environmental air pollution monitoring systems and Energy efficiency in WSN to reduced the power consumption of system.
Preliminary study of wireless balloon network using adaptive position trackin...TELKOMNIKA JOURNAL
Limited resources in post-disaster areas, one of which is a communication where coordination needed for aid distribution in disaster areas. Wireless balloon technology as a solution for use in post-disaster areas. Bandwidth limitations and high delay in communication systems on wireless balloons create limitations in aid coordination, especially mobile device tracking on BPBD volunteers or officers. This research develops an effective communication system at the wireless balloon to track personal device officers in disaster areas that use cellular devices. This mobile device tracking system utilizes a wireless balloon using a publish-subscribe system on their mobile devices, namely volunteers as publishers and those responsible for disasters or communities as subscribers. To overcome the limitations of communication resources on cellular devices and wireless balloons using the Adaptive method on publish-subscribe called UM-Disaster. The results of this study, the UM-Disaster system for multi-cell tracking has an average efficiency of 40-63% for bandwidth and processor use on mobile devices at 51-70%.
In this paper prepared a systems to that amount the units of the fitness about structural elements within Reinforced Concrete (RC), at total times, partially atop the perfect coverage concerning sensors is provided. As a result, the records about the distances within the sensor’s near nodes and its sensing areas are the only want because concerning every sensor into the recent algorithms. Furthermore, based totally completely regarding the simulations, great improvement performs stay seen along the lifespan regarding a variety concerning existing lifespan maximization algorithms, anybody is a cease end result related to the newly proposed algorithm. The promoted sensor mark hard-ware trigger the PZT sensor and collect the responses acquires beyond the structural element. It moreover send collected information to an information middle because of similarly science yet analysis within an energy efficient manner using low power wireless verbal exchange technology. The brought ingress in conformity with and the evaluation atop the accrued information operate lie remotely executed by means of using a net interface. Performance effects showcase therefore a good deal the fractures great enough within consequence including purpose structural problems be able continue to be efficiently detected together with the promoter rule
This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for
about 58%.
Ferdin Joe John Joseph:: IoT Based Unified Approach to Predict Particulate Ma...Ferdin Joe John Joseph PhD
IoT Based Unified Approach to Predict Particulate Matter Pollution in Thailand presented in the International Conference on Recent Trends in IoT and Blockchain ICRTIB 2019.
This is particularly the case on e Health monitoring applications for chronic patients, Where Patients
monitoring refers to a continuous observation of patient’s condition (physiological and physical) traditionally
performed by one or several body sensors. The architecture for this system is based on medical sensors which
measure patients’ physical parameters by using wireless sensor networks (WSNs). These sensors transfer data
from patients’ bodies over the wireless network to the cloud environment. The system is aimed to prevent delays
in the arrival of patients’ medical information to the healthcare providers, Therefore, patients will have a high
quality services because the e heath smart system supports medical staff by providing real-time data gathering,
eliminating manual data collection, enabling the monitoring of huge numbers of patients. We underline the
necessity of the analysis of data quality on e-Health applications, especially concerning remote monitoring and
assistance of patients with chronic diseases.
A machine learning algorithm for classification of mental tasks.pdfPravinKshirsagar11
In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine.
A Review of anomaly detection techniques in advanced metering infrastructurejournalBEEI
Advanced Metering Infrastructure (AMI) is a component of electrical networks that combines the energy and telecommunication infrastructure to collect, measure and analyze consumer energy consumptions. One of the main elements of AMI is a smart meter that used to manage electricity generation and distribution to end-user. The rapid implementation of AMI raises the need to deliver better maintenance performance and monitoring more efficiently while keeping consumers informed on their consumption habits. The convergence from analog to digital has made AMI tend to inherit the current vulnerabilities of digital devices that prone to cyber-attack, where attackers can manipulate the consumer energy consumption for their benefit. A huge amount of data generated in AMI allows attackers to manipulate the consumer energy consumption to their benefit once they manage to hack into the AMI environment. Anomalies detection is a technique can be used to identify any rare event such as data manipulation that happens in AMI based on the data collected from the smart meter. The purpose of this study is to review existing studies on anomalies techniques used to detect data manipulation in AMI and smart grid systems. Furthermore, several measurement methods and approaches used by existing studies will be addressed.
Web based Water Turbidity Monitoring and Automated Filtration System: IoT App...IJECEIAES
Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.
Design and Implementation of Portable Outdoor Air Quality Measurement System ...IJECEIAES
Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
Preliminary study of wireless balloon network using adaptive position trackin...TELKOMNIKA JOURNAL
Limited resources in post-disaster areas, one of which is a communication where coordination needed for aid distribution in disaster areas. Wireless balloon technology as a solution for use in post-disaster areas. Bandwidth limitations and high delay in communication systems on wireless balloons create limitations in aid coordination, especially mobile device tracking on BPBD volunteers or officers. This research develops an effective communication system at the wireless balloon to track personal device officers in disaster areas that use cellular devices. This mobile device tracking system utilizes a wireless balloon using a publish-subscribe system on their mobile devices, namely volunteers as publishers and those responsible for disasters or communities as subscribers. To overcome the limitations of communication resources on cellular devices and wireless balloons using the Adaptive method on publish-subscribe called UM-Disaster. The results of this study, the UM-Disaster system for multi-cell tracking has an average efficiency of 40-63% for bandwidth and processor use on mobile devices at 51-70%.
In this paper prepared a systems to that amount the units of the fitness about structural elements within Reinforced Concrete (RC), at total times, partially atop the perfect coverage concerning sensors is provided. As a result, the records about the distances within the sensor’s near nodes and its sensing areas are the only want because concerning every sensor into the recent algorithms. Furthermore, based totally completely regarding the simulations, great improvement performs stay seen along the lifespan regarding a variety concerning existing lifespan maximization algorithms, anybody is a cease end result related to the newly proposed algorithm. The promoted sensor mark hard-ware trigger the PZT sensor and collect the responses acquires beyond the structural element. It moreover send collected information to an information middle because of similarly science yet analysis within an energy efficient manner using low power wireless verbal exchange technology. The brought ingress in conformity with and the evaluation atop the accrued information operate lie remotely executed by means of using a net interface. Performance effects showcase therefore a good deal the fractures great enough within consequence including purpose structural problems be able continue to be efficiently detected together with the promoter rule
This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for
about 58%.
Ferdin Joe John Joseph:: IoT Based Unified Approach to Predict Particulate Ma...Ferdin Joe John Joseph PhD
IoT Based Unified Approach to Predict Particulate Matter Pollution in Thailand presented in the International Conference on Recent Trends in IoT and Blockchain ICRTIB 2019.
This is particularly the case on e Health monitoring applications for chronic patients, Where Patients
monitoring refers to a continuous observation of patient’s condition (physiological and physical) traditionally
performed by one or several body sensors. The architecture for this system is based on medical sensors which
measure patients’ physical parameters by using wireless sensor networks (WSNs). These sensors transfer data
from patients’ bodies over the wireless network to the cloud environment. The system is aimed to prevent delays
in the arrival of patients’ medical information to the healthcare providers, Therefore, patients will have a high
quality services because the e heath smart system supports medical staff by providing real-time data gathering,
eliminating manual data collection, enabling the monitoring of huge numbers of patients. We underline the
necessity of the analysis of data quality on e-Health applications, especially concerning remote monitoring and
assistance of patients with chronic diseases.
A machine learning algorithm for classification of mental tasks.pdfPravinKshirsagar11
In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine.
A Review of anomaly detection techniques in advanced metering infrastructurejournalBEEI
Advanced Metering Infrastructure (AMI) is a component of electrical networks that combines the energy and telecommunication infrastructure to collect, measure and analyze consumer energy consumptions. One of the main elements of AMI is a smart meter that used to manage electricity generation and distribution to end-user. The rapid implementation of AMI raises the need to deliver better maintenance performance and monitoring more efficiently while keeping consumers informed on their consumption habits. The convergence from analog to digital has made AMI tend to inherit the current vulnerabilities of digital devices that prone to cyber-attack, where attackers can manipulate the consumer energy consumption for their benefit. A huge amount of data generated in AMI allows attackers to manipulate the consumer energy consumption to their benefit once they manage to hack into the AMI environment. Anomalies detection is a technique can be used to identify any rare event such as data manipulation that happens in AMI based on the data collected from the smart meter. The purpose of this study is to review existing studies on anomalies techniques used to detect data manipulation in AMI and smart grid systems. Furthermore, several measurement methods and approaches used by existing studies will be addressed.
Web based Water Turbidity Monitoring and Automated Filtration System: IoT App...IJECEIAES
Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.
Design and Implementation of Portable Outdoor Air Quality Measurement System ...IJECEIAES
Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.
Air pollution monitoring system using LoRa modul as transceiver systemTELKOMNIKA JOURNAL
Air pollution is a disaster that can indirectly interfere with human health, Indonesia is the third highest country in the world that has pollution levels, one of the types of pollution that threatens public health is the increase of CO, NO2 and SO2 level in the air. With the increasing level of air pollution in the city, it requires a device that can monitor air pollution in a real time. By integrating air sensor and Raspberry Pi as data processor and using LoRa module as transceiver module, then the process of transmitting data from transmitter to receiver can be done directly without connected internet. In a test, the system can transmit intensity data information by wireless system on Line Of Sight (LOS) scemes at a maximum distance of 1.7 Km and Non Line Of Sight (NLOS) scheme at a distance of 400 meters with a average delay is 2 second.
Air Quality Monitoring and Control System in IoTijtsrd
Air pollution that refers to the contamination of the air, irrespective of indoors or outside. A physical, biological or chemical alteration to the air in the atmosphere can be termed as pollution. It occurs when any harmful gases, dust, smoke enters into the atmosphere and makes it difficult for plants, animals, and humans to survive as the air becomes dirty. Proposed system considers pollution due to automobiles and provide a real time solution which is not just monitors pollution levels but also take into consideration control measures for reducing traffic and industrial zone in highly polluted areas. The solution is provided by a sensor based hardware module which can be placed along roads and plants. These modules can be placed on lamp posts and they transfer information about air quality wirelessly to cloud server. The proposed system also provides about air quality information through a mobile application which enables commuters to take up routes where air quality is good. Soe Soe Mon | Thida Soe | Khin Aye Thu "Air Quality Monitoring and Control System in IoT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26554.pdfPaper URL: https://www.ijtsrd.com/computer-science/embedded-system/26554/air-quality-monitoring-and-control-system-in-iot/soe-soe-mon
Implementation of environmental monitoring based on KAA IoT platformjournalBEEI
Wireless sensor network (WSN) is a key to access the internet of things (IoT). The popularity of IoT and the prediction that there will be more devices connected to the Internet cause difficulties in integrating and making connected devices. The problem of IoT implementation are the lack of real-time data collection, processing, and the inability to provide continuous monitoring. To overcome these problems, this paper proposes an IoT device for monitoring environmental conditions through the IoT KAA platform that can be monitored anywhere and anytime in real time. The end device node consists of several sensors such as as temperature, humidity, carbon monoxide (CO) and carbon dioxide (CO2) sensors. The collected data from the end device node will be transmitted via a communication based on IEEE 802.15.4 to Raspberry Pi gateway, then sent to the KAA cloud server and saved into the database. The environmental data can be accessed via a web-based sensor application. We Analize the performance evaluation in terms of transaction, availability, data transfer, response time, transaction rate, throughput, and concurrency. The experimental result shows that the use of KAA IoT platform is better than that without platform.
Real-time monitoring system for weather and air pollutant measurement with HT...journalBEEI
This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.
A survey paper on Pollution Free Smart Cities using Smart Monitoring System b...IJSRD
The world we live in had evolved into a world where almost every family has at least one vehicle in their possession to be safe we could assume that each family has on average two vehicles. Now due to the increase in the number of vehicles in the road there is that much of an increase in the pollutant levels being increased onto the environment. The major sources of air pollution are CO, NOx and HC and these are a major degradation factor to the environment. To tackle this problem this paper talks about a monitoring system which is placed on the automobile to measure the pollutant levels emerging from the vehicle. On top of that some safety features have been added as well for the driver and all of the people traveling in the vehicle in the form of an alcohol sensor and a seat belt sensor. How this works is that the sensors are placed on the vehicle and can be connected through the users android device. The android device through a WiFi connection is connected to the server, the server is connected to a database and that database has the administrator PC to view and monitor the data. The safety feature is the addition of the alcohol sensor which is connected to the ignition of the vehicle and how it works is that if the driver has any alcohol and it is detected by the sensor then automobile will not start, hence adding an additional safety measure to the vehicle on top of pollution monitoring. This project works on an IoT environment and there is a lot of scope with smart devices in the future.
A survey paper on Pollution Free Smart Cities using Smart Monitoring System b...IJSRD
The world we live in had evolved into a world where almost every family has at least one vehicle in their possession to be safe we could assume that each family has on average two vehicles. Now due to the increase in the number of vehicles in the road there is that much of an increase in the pollutant levels being increased onto the environment. The major sources of air pollution are CO, NOx and HC and these are a major degradation factor to the environment. To tackle this problem this paper talks about a monitoring system which is placed on the automobile to measure the pollutant levels emerging from the vehicle. On top of that some safety features have been added as well for the driver and all of the people traveling in the vehicle in the form of an alcohol sensor and a seat belt sensor. How this works is that the sensors are placed on the vehicle and can be connected through the users android device. The android device through a WiFi connection is connected to the server, the server is connected to a database and that database has the administrator PC to view and monitor the data. The safety feature is the addition of the alcohol sensor which is connected to the ignition of the vehicle and how it works is that if the driver has any alcohol and it is detected by the sensor then automobile will not start, hence adding an additional safety measure to the vehicle on top of pollution monitoring. This project works on an IoT environment and there is a lot of scope with smart devices in the future.
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
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Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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HDFS, Yarn, and Map Reduce [5]. The researchers also built a SEMAR’s extension for water
quality monitoring in real-time with integrating Internet of Things (IoT) and Big Data analytic [6].
The data result is classified using a machine learning algorithm. In addition to SEMAR
research, there are also other studies on the application of the Internet of Things for
environmental monitoring, namely research on location-based environmental monitoring using
GPS sensors and Big Data Technology to detect coral reef damage [7] and research on
wireless sensor network integration with cloud computing for monitoring. Water quality
environment that collaborates with smart aeration systems [8].
However, the system is only implemented in water environmental monitoring and is not
used for extensive area monitoring. Therefore, the SEMAR’s extension can be improved for
other environmental monitoring such as air quality condition monitoring. This system can
combine with a mobile sensor [9] to improve the area of monitoring. This journal has five
sections that organized as follows: Section 1 presents about Intro of the research, section 2
presents the related works and previous study of the research, section 3 presents the detail of
our system design that we used in our research, section 4 presents the results and discussion
of the experimental and the implementation of our research. And for section 5 we present
conclusions including the recommendation of the future work that will be conducted for the
extensions of our project research.
2. Related Works
The study and previous works about the air quality monitoring smart system that have
been developed using various technology depend on the protocol and also using the real-time
system. In 2016, researchers from India have conducted a study of implement Internet of Things
on Smart Pollution Detection with AWS IOT Cloud, where the air quality sensor and GPS
installed on the vehicle [10]. This system used for tracking and detecting air pollutants in urban,
they don’t use machine learning or any classification.
Mobile Enterprise Sensor Bus (M-ESB) is a research from China which used to urban
environment sensing such as road condition and air pollution. M-ESB send the results of the
data sensor that installed on a bus to the server and stored into a database, the output
generated from this system is a display in the form of electronic maps website [11]. In 2017,
Zhihan Lv and team [12] have conducted the study about Big Data Analytics challenges and the
future topics of Big Data development. The result of this study shows that trends of IoT-Big Data
platform are the retrieval data process that focused more and more on streaming and multiple
sensors data. MapReduce and machine learning algorithm used in the analysis method of Big
Data. In this research, we also conducted the research by utilized an IoT-Big Data platform.
3. Research Method
Figure 1 shows the IoT reference model [13] which consists of seven sections. This
model used as a reference for the design of our system.
Figure 1. IoT reference model.
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Figure 1 describes the layering systems of IoT Reference Model. The system grouped
according to the IoT reference model consisting of physical devices and controllers (1),
connectivity (2), edge computing (3), data accumulation (4), data abstraction (5), application (6),
collaboration and process (7). The overall system design shows in Figure 2. Figure 2 shows the
overall system divided into several layers according to the IoT reference model [13] in Figure 1.
We will explain briefly about implementation according to layering system in IoT Reference
Model in the next sub-section.
Figure 2. Architecture of integration VaaMSN with SEMAR cloud system
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3.1. Physical Devices and Controller
Physical Devices and Controllers are part of architecture IoT that used to detect air
quality condition. The device has been equipped with the air quality sensors such as Shinyei
PPD42 Particulate Matter Detector for particulate, MQ7 for carbon monoxide, MQ135 for sulfur
dioxide, MQ131 for ozone and MiCS 2714 for nitrogen dioxide. The device consists of an air
quality sensor which is controlled by a microcontroller [14]. The data collection process started
with air quality measurement using air quality sensors based on wireless sensor network [15].
Wireless sensor networks are used because they could be implemented in urban areas [16].
This controller collects the data and performs the conversion process for the air quality unit
ug/m3. The converted data sent to another device for processing to the server via Wi-Fi
network.In this research, physical devices and controllers are installed on top of the vehicle and
retrieving data from the sensors every 5 seconds.
3.2. Connectivity
Through the Wi-Fi signal from a 4G modem, Physical devices and controllers send data
to the edge computing layer for processing. The 4G modem is also used to connect between
Single Board Computing (SBC) on layers the edge computing and cloud computing. The
throughput of this 4G modem is about 20 Mbps.
3.3. Edge Computing
Smart car hub consists of SBC, Camera, GPS, display and Wi-Fi 4G modem an Edge
Computing [17]. The data collected by the sensor is combined with GPS data in JSON format,
then send the data through the MQTT protocol.
Algorithm 1. Single Board Computing
1: Begin:
2: MQTT Connect
3: loop:
4: Read sensor data
5: Read GPS Sensor
6: Add Sensor data and GPS Sensor to JSON Format
7:
Send data to the server using MQTT (topic,
data)
8: End
SBC is the main controller to connect VaaMSN system with cloud computing system,
where there are other devices connected to it such as Camera that serves to take picture
condition around the car, as well as a display screen that serves as a user interface. in this
research, Raspberry Pi 3 type B is used as SBC, Raspberry Pi sends a line of air quality data
through the topic 'airsensor' to MQTT broker on cloud computing. The data lines transmitted
has formats like 'air quality data, latitude, longitude and current time'.
3.4. Data Accumulation
Data accumulation is a process of storing data which sent by edge computing to the No
SQL database as a Big data platform. Air quality data in JSON form, received by MQTT
Subscriber with topic 'airsensor'. The data is used for the prediction process to determine the air
quality index in accordance with the rules in Section 2.8. Air Quality Index results obtained from
predicted results are stored in Cassandra that serves as No SQL database with schemas
consist of a current timestamp, air quality data, latitude, longitude, label.
Cassandra was chosen because it has stable performance, strong security, operational
simplicity for the lowest total cost of ownership and best scalability of NoSQL platforms [18].
The data with topic 'airsensor' stored on Cassandra through an Application Programming
Interface (API) web service and the use of the Representative State Transfer (REST)
communication architecture method that runs on the Node.js service in microservice for data
storage with IP address 202.182.58.11.
3.5. Data Abstraction
This layer defines the management of the data flow for Big Data server and real-time
visualization. We built two connector applications the first connector is based on Node.Js that
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subscribed into MQTT Broker [19]. This connector function is to get data from MQTT Broker,
distributed data to Cassandra database server by using RESTful API and send data to Influx
DB visualization server as backend. The second connector is based on Python programming
language. This connector function subscribes the data from MQTT Broker to Machine Learning
server, and send the predicted data to MQTT Broker with a different topic. The IP addresses of
microservices are 202.182.58.10 for InfluxDB as real-time visualization, 202.182.58.12 for
MQTT Broker and connector, 202.182.58.14 for RESTful API and Cassandra database server.
3.6. Applications
The application layer consists of 1) Learning Process, 2) Real-time classification, 3)
Real-time visualization in the form of a table, map, and graph.
3.6.1. Learning Process
The process of Machine Learning is used to build the classification model. This process
is used to provide a classification model before performing the classification process in real-
time. Thus, the level of confidence in classification results can affect the accuracy level of the
generated model.
In the training process of the dataset, we used Scikit-learn [20] for conducting the
training process of the dataset. Support Vector Machine [21-23] and Decision Tree [24-26] are
used as classification algorithms for this research. The best method between the two would be
selected by comparing the results of the algorithm.
Support Vector Machine (SVM) used for regression and classification algorithm [20],
this algorithm has been implemented for big data classification [23]. In SVM, this classification is
performed by giving a training vectors 𝑦𝑖 ∈ 𝑅 𝑛
, 𝑖 = 1, … , 𝑙, with an indicator vector 𝑦𝑖 ∈ 𝑅𝑙
such
that 𝑦𝑖 ∈ {1,-1} to solves the following primal optimization problem:
min
𝑤,𝑏,𝜉
1
2
𝑤 𝑇
𝑤 + 𝐶 ∑ 𝜉𝑖
𝑛
𝐼=1
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 ∶ 𝑦𝑖(𝑤 𝑇
𝜙(𝑥𝑖) + 𝑏) ≥ 1 − 𝜉𝑖, (1)
𝜉𝑖 ≥ 0, 𝑖 = 1, … , 𝑙,
Due to the high dimensional possibilities of the vector variables w, where 𝐶 > 0 is the
parameter of regularization and 𝜙(𝑥𝑖) map xi to the higher dimension. we can solve the
following double problem:
min
𝛼
1
2
𝛼 𝑇
𝑄 𝛼 + 𝑒 𝑇
𝛼
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑦 𝑇
𝛼 = 0 (2)
0 ≤ 𝛼𝑖 ≤ 𝐶. 𝑖 = 1, … . , 𝑙,
after problem (2) is solved, using the primal-dual relationship, the optimal w satisfies:
𝑤 = ∑ 𝑦𝑖 𝛼𝑖 𝜙(𝑥𝑖)𝑙
𝐼=1 (3)
The decision function of the classification becomes:
𝑓(𝑤 𝑇
𝜙(𝑥) + 𝑏) = 𝑠𝑖𝑔𝑛 (∑ 𝑦𝑖 𝛼𝑖 𝐾(𝑥𝑖, 𝑥) + 𝑏l
𝑖=1 ) (4)
The original SVM can be classified into two classes. Proper multiclass methods are
required when dealing with more than two-class classification problems. In this case, combine
several binary classifiers with two methods. The first method is 'One on one' means applying a
comparison of inter-class pairs. The second method is 'One against the other' means
comparing one class with all the other classes.
The decision tree is a machine learning algorithm that uses tree decisions such as trees
and the possible subsequent impact, which involves the results of events, resource costs, and
utilities. Decision Tree is one of the best classifiers when considering classification accuracy,
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this algorithm studies the classification function which includes the dependent attribute
(variable) given by the value of the independent attribute (input) (variable).
Some of the most well-known decision tree algorithms are C4.5, CART and Naive
Bayes Tree [24]. This research uses a CART that stands for Classification and Regression
Trees. CART analysis is a form of binary recursive partitioning and can handle numerical and
categorical variables [24-26]. The impurity level of accepted data can be measured by CART,
also it can construct a binary tree where each internal node produces two classes for the
accepted attribute. The way of how the tree constructed by selecting the attribute recursively
use an attribute which has the lowest Gini Index. Attribute with the lowest Gini Index value is
obtained by calculating the Gini Index value in each attribute. Gini Index is calculated based on
the formula below, where the probability of the 𝑖 𝑡ℎ
class for 𝑐 target classes of a given attribute
is 𝑃𝑖, meanwhile, 𝑃𝑖 is the probability of class 𝑖 [24].
𝐺𝑖𝑛𝑖 = 1 − ∑ (𝑃𝑖)2𝑐
𝑖=1 (5)
The accuracy of the classifier algorithm was evaluated by divides the dataset into two
subsets are about 70% for the training set and the remaining 30% for the test set. The training
set is used to build the classification model. While the measurement of the built classification
model performance used test set. The method used is called the hold-out method. Learning
procedure is shown in Algorithm 2.
Algorithm 2. Single Board Computing
1: Method Linear SVM, Decision Tree
2: Begin:
3: sensorIndo Air Quality Sensor
4: loop:
5: Retrieve Dataset
6: Machine Learning Training (method)
7: Machine Learning Testing (method)
8: Calculate MSE, Mislabel, Accuracy
9: Save model
10: End
This research uses Air Pollution Standard Index Range (ISPU), this rule is used by the
current Republic of Indonesia government to determine the quality category [3] as shown in
Table 1. Parameters of the Air Pollution Standards Index include carbon monoxide (CO),
nitrogen dioxide (NO2), particulate (PM10), ozone (O3), and sulfur dioxide (SO2). The 'Range'
column in Table 1 refers to the measured air quality index formulation values in determining the
formulation be explained in Table 2.
Table 1. Air Pollution Standard Index
Category Range
Good 0-50
Moderate 51-100
Not Healthy 101-199
Very Not Healthy 200-299
Hazardous 300-more
Table 2 describes the groupings of values of each air quality parameter in ug/m3 units
to be formulated to determine the air quality index. The 'Air Quality Index' column shows the
maximum value of the air quality index with the air quality parameter conditions in accordance
with the air quality parameter columns in the same row.
Determining the air quality index by the value grouping rules and value constraints can
be used in (6) as set out in Table 2.
I =
Ia−Ib
Xa−Xb
(Xx-Xb) + Ib (6)
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Explanation:
I : Air pollution index are counted.
Ia : Air pollution index upper limit.
Ib : lower ground air pollution index Ambient upper limit.
Xa: Ambient upper bound.
Xb: Ambient lower bound.
Xx : Real ambient content of measurement results.
Table 2. Air Quality Index
Air Quality Index
PM10 SO 𝟐 CO O3 NO 𝟐
ug/m3 ug/m3 ug/m3 ug/m3 ug/m3
50 50 80 5 120 -
100 150 365 10 235 -
200 350 800 17 400 1130
300 420 1600 34 800 2260
400 500 2100 36 1000 3000
500 600 2620 57,5 1200 3750
In determining the ISPU, if there are several air quality parameters that are measured
then, the data used is the air quality parameter with the highest ISPU value. For example, if the
data obtained SO 𝟐 = 71, NO 𝟐 = 55, PM10 = 91 then the reported data is ISPU worth 91, Air
Quality is “Moderate” and the dominant parameter is PM10.
3.6.2 Real-time Classification
Through the data model that has been generated by the learning process and analytics
on large-scale data can be used to create a real-time classification system. Therefore, although
data is used on a large scale with a large number of sensor nodes, the system able still perform
the analysis process. The purpose of using this system is to bypass the data distribution delay
from VaaMSN (edge computing) to data storage and visualization.
The process of real-time classification and learning process using a scikit-learn that
runs in the python environment. Air quality data sent by VaaMSN on the topic of 'airsensor'
through MQTT communication, then the data is converted into JSON format so that it can be
used in classification to generate air quality index prediction from received data. The results of
the process are numerical from 0 to 4 representing the categories in Table 1, sequentially
starting from good, medium, bad, very bad and hazardous. The result is stored in a variable
called 'label and put into JSON previously received data so that the data contains 'air quality
data, latitude, longitude and the current time and labels'. The combined data be re-submitted
with the topic "airsensoranalytic" for use in real-time visualization.
3.6.3 Real-Time Visualization
Visualization stage start from the connector sends the data into InfluxDB by using
‘writepoint()’ function on Node.Js. We use InfluxDB for time-series database [27], the data
collected by InfluxDB as arranged by time-series, then send to Grafana [28]. The Grafana
generated graphical interfaces such as a table, graph, and maps. Data schemes are
“{current timestamp, sensor id, pm10, co, so2, 03, no, latitude, longitude, latitude, label}”. We
built three type of visualization, Figure 3 (a) is a table, show data of air monitoring sensors with
latitude, longitude and index quality air monitoring, Figure 3 (b-f) is a Graph show the time
series data with a line chart, and Figure 3 (g) is a Map show point of VaaMSN.
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3.7. Collaboration and Processes
This layer provides feedback to users performed by the system. The system will send a
notification if there is bad air quality in an Air Quality Parameter area. If there is a label value
exceeding bad characteristics of the data sent on the 'airqualityanalytic' topic, the website
visualization system built able to provide a notification to the user in the form of a warning that
the current air condition is hazardous or not healthy. Not only using the website to push
notification but also can be using Mesosfer platform. The Mesosfer is Mobile Backend Platform
as a Service that provides several features to help and simplify the creation of the internet of
things system and enabling users to speed up the development process. This platform used to
send mobile notifications to alert the user about air quality condition. When air quality is in
unhealthy, very unhealthy and hazardous condition system would send data to Mesosphere
RestAPI, then the data becomes notification sent by Mesosfere to the user's cell. The data
submitted is 'air quality data', 'air quality condition', 'current time' and 'data location'.
4. Results and Discussion
In this results sections, we have done some experiment and present the
implementation both of software and hardware development. The experiment has been given
the results that performed as well and using the analytical test showed how the system that we
built works well. Several tests performed including real-time visualization testing and
comparative performance evaluation of SVM algorithms [29] and linear Decision Tree using
their default parameters where we use datasets from sensors according to the given rules.
4.1. Results of Confusion Matrix
Table 3 shows the confusion matrix of the result of the training model that has been
built from the Linear SVM algorithm, Table 4 shows the confusion matrix for the DT algorithm.
The SVM experiment results that the number of data error are between 22 until 127 from
around 21.804 data. It is mean that the error percentage is about 0.2% to 0.5%. The confusion
matrix of SVM can be seen in Table 3.
Table 3. Confusion Matrix of Support Vector Machine Dataset Training.
Predicted Class
Good Moderate Not Healthy Very Unhealthy Hazardous
Actual Class
Good 4716 22 0 0 0
Moderate 22 4110 0 0 0
Not Healthy 11 100 4129 0 0
Very Unhealthy 0 11 127 3932 18
Hazardous 0 0 3 85 4518
Table 4. Confusion Matrix of Decision Tree Dataset Training.
Predicted Class
Good Moderate Not Healthy Very Unhealthy Hazardous
Actual Class
Good 4732 6 0 0 0
Moderate 3 4331 1 0 0
Not Healthy 0 12 4225 3 0
Very Unhealthy 0 0 3 4079 6
Hazardous 0 0 0 5 4601
The Decision Tree algorithm results in experimental results that the number of data
error is between 3 until 12 from around 21.804 data. It is mean that the error percentage is
almost 0%. Table 4 showed that the confusion matrix of the Decision Tree algorithm. The
experiment showed that Decision tree is better than SVM with higher accuracy of the predicted
label.
4.2. Classification Results
The second experiment we measure the acceleration of the classification result. To
measure the acceleration we calculate the accuracy rate and MSE (Mean Squared Error).
Table 5 shows that Decision tree algorithms offer a better accuracy rate by 0.99839479 when
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compared to the accuracy level of the SVM algorithm that around 0.98170061. However,
according to Table 5, both algorithms show good performance with an accuracy rate over 90%
and MSE is 0.00268096 for decision tree algorithms and 0.03076293 for SVM algorithm.
Figure 4 shows the curve shape of the ROC (Receiver Operating Curve) which
represents the Validation of the class model. ROC is a curve that compares graphs on the
vertical axis of TPR (True Positive Rate) with FPR (False Positive Rate) existing on the
horizontal axis of ROC. The area under the ROC curve called AUC. AUC is rated from 0 to 1
and gets better when it approaches 1. From the experiments performed, the decision tree
algorithm has 100% accuracy in all classes, while the SVM algorithm has an accuracy of around
98%. These AUC results are better compared to the use of multilabel classifier [30] which
produces AUC of around 0.71.
Table 5. Comparison of Two Algorithms on the Dataset.
Features Algorithm Mislabel Accuracy MSE
Air Quality
Support Vector Machine 399 / 21804 0.98170061 0.03076293
Decision Tree 35 / 21804 0.99839479 0.00268096
(a) (b)
Figure 4. Receiver Operating Characteristics: (a) Using SVM, and (b) Using Decision Tree.
From Figure 4 (a) is showed ROC graphical results of SVM algorithm SVM accuracy
value of each class about 97% to 99%. Meanwhile, Figure 4 (b) is show the ROC graphics
results from the decision tree algorithm, obtained from the image of the accuracy value of each
class is about 100%.
4.3. Purpose System Implementation
Experiments are used to test system integration from sensor readings to database
storage and real-time visualization. Air quality data obtained by air quality sensor devices is that
transmitted through communication to cloud computing. MQTT Broker distributes data to the
prediction system to determine the air quality index of the data. The data that has been sent
back after the data added with air quality index use another topic to MQTT Broker and accepted
by MQTT Customer and forwarded to InfluxDB connector for real-time visualization. Data
received by InfluxDB displayed in real-time using graphical interface graphics that can be
viewed on the website at IP address 202.182.58.10.
The test is performed by installing an air quality sensor device on top of the vehicle and
SBC in the vehicle. Vehicles are driven on the highway for collecting air quality data in the road.
Figure 5 (a) shows the air quality sensor device and Figure 5 (b) shows the SBC as Smart Car
Hub. Figure 6 shows real-time visualization on dashboard when the vehicle is in motion, a
maker's visualization on the world map representing air quality data, the color of the marker
shows the air quality data with the provision is green for good condition, blue for medium
condition, yellow for unhealthy, red for very no healthy and black for hazardous.
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(a) (b)
Figure 5. Devices: (a) air quality sensor, and (b) smart car Hub
Figure 6. Dashboard.
5. Conclusion
In this research, the integration of VaaMSN and SEMAR for the air quality monitoring
system has been implemented successfully. From the experiment show that the data from
VaaMSN is sent to Big Data platform and visualized in real-time. The two algorithms that we
used for analytical have been given the result that the estimation of accurate is more than 90%
and achieves MSE is 0.00268096 for DT algorithm and SVM algorithm about 0.03076293. That
means we achieve a good result in this experiment. In the future, the integration of VaaMSN
and SEMAR is expected to be used in the road environmental monitoring which detects holes
and road damage using cameras and other road condition sensors in real-time classification.
6. Acknowledgment
This research is funded by KEMERISTEKDIKTI from “Applied Leading Research in
Higher Education (in Indonesia Penelitian Terapan Unggulan Perguruan Tinggi)” in 2018
scheme with the number: 09/PL14/PG.1/SP2P/2018 and the title is “Integrated as a Mobile
Sensor Network Vehicle Implementation with Smart Environment Monitoring and Analytics in
Real-time (SEMAR) system as Road Surface Monitoring and Environment to support Smart City
(in Indonesia Implementation Vehicle as a Mobile Sensor Network terintegrasi dengan Smart
Environment Monitoring and Analytics in Real-time (SEMAR) system sebagai Pemantauan
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Permukaan Jalan dan Lingkungan untuk mendukung Smart City)”. This research is also funded
by DITJEN PENGUATAN INOVASI from "Innovation Strengthening Program (in Indonesia
Program Penguatan Inovasi)" in 2018 scheme with the number: 08/PPK/SK/INOVASI
INDUSTRI-DII/III/2018 with the title is "Development of Fin-Iot (Financial Technology and
Internet of Things) Platforms as Supporters of Digital-Based Payments for Small Medium
Enterprise (in Indonesia Pengembangan Platform Fin-Iot (Financial Technology Dan Internet Of
Things) Sebagai Pendukung Pembayaran Berbasis Digital Untuk Small Medium Enterprise))"
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