Naser Hossein Motlagh - Academia.edu (original) (raw)
Papers by Naser Hossein Motlagh
IEEE Pervasive Computing
This article has been accepted for inclusion in a future issue of this journal. Content is final ... more This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE Transactions on Industrial Informatics
Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enab... more Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting low-cost sensors' measurements accurately and detecting the calibrators' drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators' strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of lowcost sensors suffered from sensing accuracy.
IEEE Pervasive Computing, 2022
Electronics, 2021
The time-series forecasting is a vital area that motivates continuous investigate areas of intrig... more The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean ...
Near Field Communication (NFC) technology is a new wireless short range communication technique f... more Near Field Communication (NFC) technology is a new wireless short range communication technique for data transmission between intelligent devices such as mobile phones by integrating a small NFC reader into the cellular phones. This new technology supports the communication link within distance of up to 4 cm. NFC developed over Radio Frequency Identification (RFID), where it uses magnetic field induction to establish a communication link between devices. The main purpose of developing NFC is for the useful application it provides such as wireless payment and ticketing, electronic keys, identification and so on. Applying NFC for these matters is beneficial because of the peer-to-peer communication which exists behind it. For example this technology prepares the possibility of quick set up a Bluetooth or a WLAN connection without any manual configuration. Also wireless payments and identification will be applied worldwide in near future using contactless feature of NFC. The purpose of this thesis is to review the technical aspects of NFC technology such as Radio Frequency (RF) containing Modulation techniques, Underlying Protocol and Frame format, Applications and finally the Security of NFC will be discussed.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
IEEE Internet of Things Journal, 2019
Unmanned Aerial Vehicles (UAVs) are gaining much momentum due to the vast number of their applica... more Unmanned Aerial Vehicles (UAVs) are gaining much momentum due to the vast number of their applications. In addition to their original missions, UAVs can be used simultaneously for offering Value Added Internet of Things Services (VAIoTS) from the sky. VAIoTS can be achieved by equipping UAVs with suitable IoT payloads and organizing UAVs' flights using a central System Orchestrator (SO). SO holds the complete information about UAVs, such as their current positions, their amount of energy, their intended use-cases or flight missions, and their onboard IoT device(s). To ensure efficient VAIoTSs, there is a need for developing a smart mechanism that would be executed at the SO in order to take into account two major factors: i) the UAVs' energy consumption and ii) the UAVs' operation time. To effectively implement this mechanism, this paper presents three complementary solutions, named Energy Aware UAV Selection (EAUS), Delay Aware UAV Selection (DAUS), and Fair Trade-off UAV Selection (FTUS), respectively. These solutions use Linear Integer Problem (LIP) optimizations. While the EAUS solution aims to reduce the energy consumption of UAVs, the DAUS solution aims to reduce the operational time of UAVs. Meanwhile, FTUS uses a bargaining game to ensure a fair trade-off between the energy consumption and the operation time. The results obtained from the performance evaluations demonstrate the efficiency and the robustness of the proposed schemes. Each solution demonstrates its efficiency at achieving its planned goals.
IEEE Internet of Things Journal, 2016
Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since th... more Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented. Index Terms-Drone, Internet of Things (IoT), machine type communication (MTC), machine-to-machine (M2M), unmanned aerial system (UAS), unmanned aerial vehicle (UAV), unmanned aerial vehicle data processing. I. INTRODUCTION I N THE near future, millions of unmanned aerial vehicles (UAVs), also known as drones, are expected to be rapidly deployed in diverse sectors of our daily life performing wide-ranging activities from delivering a package to diving into water for a specific underwater operation [1].
2016 IEEE Global Communications Conference (GLOBECOM), 2016
This paper, presents a UAV-based integrative IoT platform that leverages UAVs to deliver differen... more This paper, presents a UAV-based integrative IoT platform that leverages UAVs to deliver different IoT services from height. One of the major tasks of the platform is to select the appropriate UAVs. This selection may be based on different criteria, such as UAVs equipment, energy budget, geographical proximity of the UAV to the area of interest, etc. For the selection mechanism, this paper proposes and formulates two Linear Integer Problem (LIP) optimization solutions by aiming at minimizing the energy consumption and shortening the UAV operation time. For the solutions, Energy Aware Selection of UAVs (EAS) and Delay Aware Selection of UAVs (DAS) are evaluated through simulation. The results show that if the objective is the energy, EAS is more efficient than DAS in case of total energy consumption by the UAVs. Additionally, if the time is the objective, DAS has higher performance than EAS in case of operation time. Index Terms-Unmanned Aerial Vehicle (UAV), UAV/Drone Selection Mechanism, and UAV-based IoT Platform.
Advanced Trends in Wireless Communications, 2011
To improve the performance of short-range wireless communications, channel quality must be improv... more To improve the performance of short-range wireless communications, channel quality must be improved by avoiding interference and multi-path fading. Frequency hopping spread spectrum (FHSS) is a transmission technique where the carrier hops from frequency to frequency. For frequency hopping a mechanism must be designed so that the data can be transmitted in a clear channel and avoid congested channels. Adaptive frequency hopping is a system which is used to improve immunity toward frequency interference by avoiding using congested frequency channels in hopping sequence. Mathematical modelling is used to simulate and analyze the performance improvement by using frequency hopping spread spectrum with popular modulation schemes, and also the hopping channel situations are investigated. In this chapter the focus is to improve wireless communication performance by adaptive frequency hopping which is implemented by selecting sets of communication channels and adaptively hopping sender's and receiver's frequency channels and determining the channel numbers with less interference. Also the work investigates whether the selected channels are congested or clear then a list of good channels can be generated and in practice to use detected frequency channels as hopping sequence to improve the performance of communication and finally the quality of service. The Fourier transform mathematical modules are used to convert signals from time domain to frequency domain and vice versa. The mathematical modules are applied to represent the frequency and simulate them in MATLAB and as result the simulated spectrums are analysed. Then a simple two-state Gilbert-Elliot Channel Model (Gilbert, 1960; Elliott, 1963) in which a two-state Markov chain with states named "Good" and "Bad" is used to check if the channels are congested or clear in case of interference. Finally, a solution to improve the performance of wireless communications by choosing and using "Good" channels as the next frequency hopping sequence channel is proposed. 2. Review of related theories 2.1 Spread spectrum Spread spectrum is a digital modulation technology and a technique based on principals of spreading a signal among many frequencies to prevent interference and signal detection. As
Int'l J. of Communications, Network and System Sciences, 2010
To improve the performance of short-range wireless communications, channel quality must be improv... more To improve the performance of short-range wireless communications, channel quality must be improved by avoiding interference and multi-path fading. Frequency hopping spread spectrum (FHSS) is a transmission technique where the carrier hops from frequency to frequency. For frequency hopping a mechanism must be designed so that the data can be transmitted in a clear channel and avoid congested channels. Adaptive frequency hopping is a system which is used to improve immunity toward frequency interference by avoiding using congested frequency channels in hopping sequence. In this paper mathematical modelling is used to simulate and analyze the performance improvement by using FHSS with popular modulation schemes, and also the hopping channel situations are investigated.
IEEE Internet of Things Journal
Humans tend to spend most of their life indoors making the quality of indoor environments essenti... more Humans tend to spend most of their life indoors making the quality of indoor environments essential for human health and well-being. While several solutions for monitoring the indoor environment have been proposed, ranging from infrastructure-based monitoring solutions to cameras, these tend to require separate installation making the sensors difficult to maintain and upgrade. In this paper, we introduce the idea of using smart plants as an easy-to-deploy and affordable solution for monitoring the indoor environment. Plants are typically deployed close to humans and they increasingly are placed in containers that integrate sensors, such as soil moisture, temperature, humidity, and CO 2 sensors. We demonstrate how these sensors can be used as an alternative technology for monitoring-and enriching-indoor spaces without needing to install proprietary sensors or other technology. Specifically, we show how smart plants can be used to estimate overall CO 2 accumulation, occupancy information, and whether people use protective face masks or not. We also establish a research roadmap for the use of smart plants to monitor indoor environments.
Air pollution is a major issue in urban areas. High population density is exposed with excess ant... more Air pollution is a major issue in urban areas. High population density is exposed with excess anthropogenic emissions impacting environment and health effects. Each year approximately 4.2 million deaths are attributed to exposure to ambient air pollution1. To perform air quality monitoring in urban areas, there is a need to measure the pollution with high resolution. Indeed, highly accurate and reliable air quality monitoring stations have been established to continuously monitor the air pollution. However, these monitoring stations are expensive and complex in operation and maintenance. Therefore, it is not feasible to deploy these stations massively in urban areas (Lagerspetz et al., 2019). Alternatively, dense deployment of low-cost air quality sensors in urban areas allows detecting the pollution hot spots in real-time. Nevertheless, these low-cost sensors suffer from sensing accuracy (Motlagh et al., 2020). In this paper, we present data analysis of two identical lowcost sensor...
2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020
Air pollution is a contributor to approximately one in every nine deaths annually. To counteract ... more Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying Mega...
Transportation Research Part D-transport and Environment, 2021
Transit activities are a significant contributor to a person’s daily exposure to pollutants. Curr... more Transit activities are a significant contributor to a person’s daily exposure to pollutants. Currently obtaining accurate information about the personal exposure of a commuter is challenging as existing solutions either have a coarse monitoring resolution that omits subtle variations in pollutant concentrations or are laborious and costly to use. We contribute by systematically analysing the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters. Through extensive experiments carried out in the Helsinki metropolitan region, we demonstrate that low-cost sensors can capture the overall exposure with sufficient accuracy, while at the same time providing insights into variations within transport modalities. We also demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors. For example, we demonstrate that location within the vehicle carriage ...
Proceedings of the 6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, 2020
Underwater plastic pollution is a significant global concern, affecting everything from marine ec... more Underwater plastic pollution is a significant global concern, affecting everything from marine ecosystems to climate change and even human health. Currently, obtaining accurate information about aquatic plastic pollutants at high spatial and temporal resolution is difficult as existing methods are laborious (e.g., dive surveys), restricted to a subset of plastics (e.g., aerial imaging for floating debris), have limited resolution (e.g., beach surveys), or are unsuited for aquatic environments (e.g., wireless sensing or Fourier-transform infrared spectroscopy). We propose PENGUIN, a work-in-progress AUV-based solution for identifying and classifying aquatic plastic pollutants. PENGUIN has been designed as the first system that can both recognize pollutants and classify them according to specifics of the material. We present the overall design of PENGUIN, introducing the different components of the architecture, and presenting current status of development. We also present results of ...
![Research paper thumbnail of [WiP] A Novel Method for Big Data Analytics and Summarization Based on Fuzzy Similarity Measure](https://attachments.academia-assets.com/81225133/thumbnails/1.jpg)
](2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), 2018
The use of Internet of Things (IoT) sensors and devices is on a sharp rise with the help of infra... more The use of Internet of Things (IoT) sensors and devices is on a sharp rise with the help of infrastructures like 5G cellular networks. However, with this swift expansion come major challenges such as management of the vast data collected by these IoT devices. In the paper, we propose a method which takes advantage of fuzzy similarity to significantly simplify the big data analysis and management for human operators and machines. A use case in the area of smart buildings was utilized to illustrate its potential application. A comparison is made between the ideal situation and the data collected from an office room. The considered environmental factors in this research are temperature, humidity, and workplace lighting and then the collected data was utilized to make triangular fuzzy numbers and after that, we compare them with an efficient fuzzy similarity measure. In addition to data summarization and abstraction, this method also protects the main information from inaccuracy. The ad...
Marine pollution is a growing worldwide concern, affecting health of marine ecosystems, human hea... more Marine pollution is a growing worldwide concern, affecting health of marine ecosystems, human health, climate change, and weather patterns. To reduce underwater pollution, it is critical to have access to accurate information about the extent of marine pollutants as otherwise appropriate countermeasures and cleaning measures cannot be chosen. Currently such information is difficult to acquire as existing monitoring solutions are highly laborious or costly, limited to specific pollutants, and have limited spatial and temporal resolution. In this article, we present a research vision of large-scale autonomous marine pollution monitoring that uses coordinated groups of autonomous underwater vehicles (AUV)s to monitor extent and characteristics of marine pollutants. We highlight key requirements and reference technologies to establish a research roadmap for realizing this vision. We also address the feasibility of our vision, carrying out controlled experiments that address classificati...
IEEE Pervasive Computing
This article has been accepted for inclusion in a future issue of this journal. Content is final ... more This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE Transactions on Industrial Informatics
Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enab... more Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcting low-cost sensors' measurements accurately and detecting the calibrators' drift. The proposed calibration method uses Bayesian framework to establish white-box and black-box calibrators. We evaluate the method in a controlled experiment under different types of smoking events. The calibration results show that the method accurately estimates the aerosol mass concentration during the smoking events. We show that black-box calibrators are more accurate than white-box calibrators. However, black-box calibrators may drift easily when a new smoking event occurs, while white-box calibrators remain robust. Therefore, we implement both of the calibrators in parallel to extract both calibrators' strengths and also enable drifting monitoring for calibration models. We also discuss that our method is implementable for other types of lowcost sensors suffered from sensing accuracy.
IEEE Pervasive Computing, 2022
Electronics, 2021
The time-series forecasting is a vital area that motivates continuous investigate areas of intrig... more The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean ...
Near Field Communication (NFC) technology is a new wireless short range communication technique f... more Near Field Communication (NFC) technology is a new wireless short range communication technique for data transmission between intelligent devices such as mobile phones by integrating a small NFC reader into the cellular phones. This new technology supports the communication link within distance of up to 4 cm. NFC developed over Radio Frequency Identification (RFID), where it uses magnetic field induction to establish a communication link between devices. The main purpose of developing NFC is for the useful application it provides such as wireless payment and ticketing, electronic keys, identification and so on. Applying NFC for these matters is beneficial because of the peer-to-peer communication which exists behind it. For example this technology prepares the possibility of quick set up a Bluetooth or a WLAN connection without any manual configuration. Also wireless payments and identification will be applied worldwide in near future using contactless feature of NFC. The purpose of this thesis is to review the technical aspects of NFC technology such as Radio Frequency (RF) containing Modulation techniques, Underlying Protocol and Frame format, Applications and finally the Security of NFC will be discussed.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
IEEE Internet of Things Journal, 2019
Unmanned Aerial Vehicles (UAVs) are gaining much momentum due to the vast number of their applica... more Unmanned Aerial Vehicles (UAVs) are gaining much momentum due to the vast number of their applications. In addition to their original missions, UAVs can be used simultaneously for offering Value Added Internet of Things Services (VAIoTS) from the sky. VAIoTS can be achieved by equipping UAVs with suitable IoT payloads and organizing UAVs' flights using a central System Orchestrator (SO). SO holds the complete information about UAVs, such as their current positions, their amount of energy, their intended use-cases or flight missions, and their onboard IoT device(s). To ensure efficient VAIoTSs, there is a need for developing a smart mechanism that would be executed at the SO in order to take into account two major factors: i) the UAVs' energy consumption and ii) the UAVs' operation time. To effectively implement this mechanism, this paper presents three complementary solutions, named Energy Aware UAV Selection (EAUS), Delay Aware UAV Selection (DAUS), and Fair Trade-off UAV Selection (FTUS), respectively. These solutions use Linear Integer Problem (LIP) optimizations. While the EAUS solution aims to reduce the energy consumption of UAVs, the DAUS solution aims to reduce the operational time of UAVs. Meanwhile, FTUS uses a bargaining game to ensure a fair trade-off between the energy consumption and the operation time. The results obtained from the performance evaluations demonstrate the efficiency and the robustness of the proposed schemes. Each solution demonstrates its efficiency at achieving its planned goals.
IEEE Internet of Things Journal, 2016
Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since th... more Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented. Index Terms-Drone, Internet of Things (IoT), machine type communication (MTC), machine-to-machine (M2M), unmanned aerial system (UAS), unmanned aerial vehicle (UAV), unmanned aerial vehicle data processing. I. INTRODUCTION I N THE near future, millions of unmanned aerial vehicles (UAVs), also known as drones, are expected to be rapidly deployed in diverse sectors of our daily life performing wide-ranging activities from delivering a package to diving into water for a specific underwater operation [1].
2016 IEEE Global Communications Conference (GLOBECOM), 2016
This paper, presents a UAV-based integrative IoT platform that leverages UAVs to deliver differen... more This paper, presents a UAV-based integrative IoT platform that leverages UAVs to deliver different IoT services from height. One of the major tasks of the platform is to select the appropriate UAVs. This selection may be based on different criteria, such as UAVs equipment, energy budget, geographical proximity of the UAV to the area of interest, etc. For the selection mechanism, this paper proposes and formulates two Linear Integer Problem (LIP) optimization solutions by aiming at minimizing the energy consumption and shortening the UAV operation time. For the solutions, Energy Aware Selection of UAVs (EAS) and Delay Aware Selection of UAVs (DAS) are evaluated through simulation. The results show that if the objective is the energy, EAS is more efficient than DAS in case of total energy consumption by the UAVs. Additionally, if the time is the objective, DAS has higher performance than EAS in case of operation time. Index Terms-Unmanned Aerial Vehicle (UAV), UAV/Drone Selection Mechanism, and UAV-based IoT Platform.
Advanced Trends in Wireless Communications, 2011
To improve the performance of short-range wireless communications, channel quality must be improv... more To improve the performance of short-range wireless communications, channel quality must be improved by avoiding interference and multi-path fading. Frequency hopping spread spectrum (FHSS) is a transmission technique where the carrier hops from frequency to frequency. For frequency hopping a mechanism must be designed so that the data can be transmitted in a clear channel and avoid congested channels. Adaptive frequency hopping is a system which is used to improve immunity toward frequency interference by avoiding using congested frequency channels in hopping sequence. Mathematical modelling is used to simulate and analyze the performance improvement by using frequency hopping spread spectrum with popular modulation schemes, and also the hopping channel situations are investigated. In this chapter the focus is to improve wireless communication performance by adaptive frequency hopping which is implemented by selecting sets of communication channels and adaptively hopping sender's and receiver's frequency channels and determining the channel numbers with less interference. Also the work investigates whether the selected channels are congested or clear then a list of good channels can be generated and in practice to use detected frequency channels as hopping sequence to improve the performance of communication and finally the quality of service. The Fourier transform mathematical modules are used to convert signals from time domain to frequency domain and vice versa. The mathematical modules are applied to represent the frequency and simulate them in MATLAB and as result the simulated spectrums are analysed. Then a simple two-state Gilbert-Elliot Channel Model (Gilbert, 1960; Elliott, 1963) in which a two-state Markov chain with states named "Good" and "Bad" is used to check if the channels are congested or clear in case of interference. Finally, a solution to improve the performance of wireless communications by choosing and using "Good" channels as the next frequency hopping sequence channel is proposed. 2. Review of related theories 2.1 Spread spectrum Spread spectrum is a digital modulation technology and a technique based on principals of spreading a signal among many frequencies to prevent interference and signal detection. As
Int'l J. of Communications, Network and System Sciences, 2010
To improve the performance of short-range wireless communications, channel quality must be improv... more To improve the performance of short-range wireless communications, channel quality must be improved by avoiding interference and multi-path fading. Frequency hopping spread spectrum (FHSS) is a transmission technique where the carrier hops from frequency to frequency. For frequency hopping a mechanism must be designed so that the data can be transmitted in a clear channel and avoid congested channels. Adaptive frequency hopping is a system which is used to improve immunity toward frequency interference by avoiding using congested frequency channels in hopping sequence. In this paper mathematical modelling is used to simulate and analyze the performance improvement by using FHSS with popular modulation schemes, and also the hopping channel situations are investigated.
IEEE Internet of Things Journal
Humans tend to spend most of their life indoors making the quality of indoor environments essenti... more Humans tend to spend most of their life indoors making the quality of indoor environments essential for human health and well-being. While several solutions for monitoring the indoor environment have been proposed, ranging from infrastructure-based monitoring solutions to cameras, these tend to require separate installation making the sensors difficult to maintain and upgrade. In this paper, we introduce the idea of using smart plants as an easy-to-deploy and affordable solution for monitoring the indoor environment. Plants are typically deployed close to humans and they increasingly are placed in containers that integrate sensors, such as soil moisture, temperature, humidity, and CO 2 sensors. We demonstrate how these sensors can be used as an alternative technology for monitoring-and enriching-indoor spaces without needing to install proprietary sensors or other technology. Specifically, we show how smart plants can be used to estimate overall CO 2 accumulation, occupancy information, and whether people use protective face masks or not. We also establish a research roadmap for the use of smart plants to monitor indoor environments.
Air pollution is a major issue in urban areas. High population density is exposed with excess ant... more Air pollution is a major issue in urban areas. High population density is exposed with excess anthropogenic emissions impacting environment and health effects. Each year approximately 4.2 million deaths are attributed to exposure to ambient air pollution1. To perform air quality monitoring in urban areas, there is a need to measure the pollution with high resolution. Indeed, highly accurate and reliable air quality monitoring stations have been established to continuously monitor the air pollution. However, these monitoring stations are expensive and complex in operation and maintenance. Therefore, it is not feasible to deploy these stations massively in urban areas (Lagerspetz et al., 2019). Alternatively, dense deployment of low-cost air quality sensors in urban areas allows detecting the pollution hot spots in real-time. Nevertheless, these low-cost sensors suffer from sensing accuracy (Motlagh et al., 2020). In this paper, we present data analysis of two identical lowcost sensor...
2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020
Air pollution is a contributor to approximately one in every nine deaths annually. To counteract ... more Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying Mega...
Transportation Research Part D-transport and Environment, 2021
Transit activities are a significant contributor to a person’s daily exposure to pollutants. Curr... more Transit activities are a significant contributor to a person’s daily exposure to pollutants. Currently obtaining accurate information about the personal exposure of a commuter is challenging as existing solutions either have a coarse monitoring resolution that omits subtle variations in pollutant concentrations or are laborious and costly to use. We contribute by systematically analysing the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters. Through extensive experiments carried out in the Helsinki metropolitan region, we demonstrate that low-cost sensors can capture the overall exposure with sufficient accuracy, while at the same time providing insights into variations within transport modalities. We also demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors. For example, we demonstrate that location within the vehicle carriage ...
Proceedings of the 6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, 2020
Underwater plastic pollution is a significant global concern, affecting everything from marine ec... more Underwater plastic pollution is a significant global concern, affecting everything from marine ecosystems to climate change and even human health. Currently, obtaining accurate information about aquatic plastic pollutants at high spatial and temporal resolution is difficult as existing methods are laborious (e.g., dive surveys), restricted to a subset of plastics (e.g., aerial imaging for floating debris), have limited resolution (e.g., beach surveys), or are unsuited for aquatic environments (e.g., wireless sensing or Fourier-transform infrared spectroscopy). We propose PENGUIN, a work-in-progress AUV-based solution for identifying and classifying aquatic plastic pollutants. PENGUIN has been designed as the first system that can both recognize pollutants and classify them according to specifics of the material. We present the overall design of PENGUIN, introducing the different components of the architecture, and presenting current status of development. We also present results of ...
2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA), 2018
The use of Internet of Things (IoT) sensors and devices is on a sharp rise with the help of infra... more The use of Internet of Things (IoT) sensors and devices is on a sharp rise with the help of infrastructures like 5G cellular networks. However, with this swift expansion come major challenges such as management of the vast data collected by these IoT devices. In the paper, we propose a method which takes advantage of fuzzy similarity to significantly simplify the big data analysis and management for human operators and machines. A use case in the area of smart buildings was utilized to illustrate its potential application. A comparison is made between the ideal situation and the data collected from an office room. The considered environmental factors in this research are temperature, humidity, and workplace lighting and then the collected data was utilized to make triangular fuzzy numbers and after that, we compare them with an efficient fuzzy similarity measure. In addition to data summarization and abstraction, this method also protects the main information from inaccuracy. The ad...
Marine pollution is a growing worldwide concern, affecting health of marine ecosystems, human hea... more Marine pollution is a growing worldwide concern, affecting health of marine ecosystems, human health, climate change, and weather patterns. To reduce underwater pollution, it is critical to have access to accurate information about the extent of marine pollutants as otherwise appropriate countermeasures and cleaning measures cannot be chosen. Currently such information is difficult to acquire as existing monitoring solutions are highly laborious or costly, limited to specific pollutants, and have limited spatial and temporal resolution. In this article, we present a research vision of large-scale autonomous marine pollution monitoring that uses coordinated groups of autonomous underwater vehicles (AUV)s to monitor extent and characteristics of marine pollutants. We highlight key requirements and reference technologies to establish a research roadmap for realizing this vision. We also address the feasibility of our vision, carrying out controlled experiments that address classificati...