Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992960
S. Ahmadi, S. Rashad, H. Elgazzar
Intrusion detection systems (IDSs) monitor network traffics to find suspicious activities, such as an attack or illegal activities. These systems play an important role in securing computer networks. Due to availability of irrelevant or redundant features and big dimensionality of network datasets which results to an inefficient detection process, this study, focused on identifying important attributes in order to build an effective IDS. A majority vote system, using three standard feature selection methods, Correlation-based feature selection, Information Gain, and Chi-square is proposed to select the most relevant features for IDS. The decision tree classifier is applied on reduced feature sets to build an intrusion detection system. The results show that selected reduced attributes from the novel feature selection system give a better performance for building a computationally efficient IDS system.
{"title":"Efficient Feature Selection for Intrusion Detection Systems","authors":"S. Ahmadi, S. Rashad, H. Elgazzar","doi":"10.1109/UEMCON47517.2019.8992960","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992960","url":null,"abstract":"Intrusion detection systems (IDSs) monitor network traffics to find suspicious activities, such as an attack or illegal activities. These systems play an important role in securing computer networks. Due to availability of irrelevant or redundant features and big dimensionality of network datasets which results to an inefficient detection process, this study, focused on identifying important attributes in order to build an effective IDS. A majority vote system, using three standard feature selection methods, Correlation-based feature selection, Information Gain, and Chi-square is proposed to select the most relevant features for IDS. The decision tree classifier is applied on reduced feature sets to build an intrusion detection system. The results show that selected reduced attributes from the novel feature selection system give a better performance for building a computationally efficient IDS system.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123735577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992922
Chuck Easttom, Nagi Mei
Cybersecurity vulnerabilities in medical devices have been widely documented. These issues have been described in hacking conferences as well as bulletins from the United States Food and Drug Administration. These issues present a serious threat to implantable medical devices. While the literature is replete with discussions of these vulnerabilities, there is less literature on a broad-based solution that would be applicable to all such devices. In fact, there is a substantial gap in the literature regarding how to mitigate the threat to implanted medical devices. This paper describes a specific firmware solution which can apply to any implantable medical device in order to mitigate security concerns.
{"title":"Mitigating Implanted Medical Device Cybersecurity Risks","authors":"Chuck Easttom, Nagi Mei","doi":"10.1109/UEMCON47517.2019.8992922","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992922","url":null,"abstract":"Cybersecurity vulnerabilities in medical devices have been widely documented. These issues have been described in hacking conferences as well as bulletins from the United States Food and Drug Administration. These issues present a serious threat to implantable medical devices. While the literature is replete with discussions of these vulnerabilities, there is less literature on a broad-based solution that would be applicable to all such devices. In fact, there is a substantial gap in the literature regarding how to mitigate the threat to implanted medical devices. This paper describes a specific firmware solution which can apply to any implantable medical device in order to mitigate security concerns.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993050
Waleed Aldosari, M. Zohdy, Richard Olawoyin
Wireless Sensor Networks (WSNs) are susceptible to jamming attacks due to the shared wireless medium. The jammer can disrupt any specific or entire radio frequency based on its function and strategies. Locating the jammer location is very important against the jamming in the wireless network and restore the communication channel. To support the existing anti-jamming techniques, we proposed an algorithm based on the Extended Kalman filter (EKF) and power received to track the jammer. Detecting jammer location is the first step taking to defend such attacks. Besides, estimating jammer location supports a wide range of defense. Range-based jammer localization technique based on the received power is used in this work to detect the external malicious node location by designed the position, velocity, and acceleration approach of Extended Kalman filter. An extensive simulation conducted to evaluate the performance of EKF compares to the Virtual Force Iteration Localization (VFIL), Weighted Centroid Localization (WCL), and Centroid Localization algorithms (CL). The EKF proves to be of high efficiency in comparison to VFIL, WCL, and CL.
{"title":"Tracking the Mobile Jammer in Wireless Sensor Networks Using Extended Kalman Filter","authors":"Waleed Aldosari, M. Zohdy, Richard Olawoyin","doi":"10.1109/UEMCON47517.2019.8993050","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993050","url":null,"abstract":"Wireless Sensor Networks (WSNs) are susceptible to jamming attacks due to the shared wireless medium. The jammer can disrupt any specific or entire radio frequency based on its function and strategies. Locating the jammer location is very important against the jamming in the wireless network and restore the communication channel. To support the existing anti-jamming techniques, we proposed an algorithm based on the Extended Kalman filter (EKF) and power received to track the jammer. Detecting jammer location is the first step taking to defend such attacks. Besides, estimating jammer location supports a wide range of defense. Range-based jammer localization technique based on the received power is used in this work to detect the external malicious node location by designed the position, velocity, and acceleration approach of Extended Kalman filter. An extensive simulation conducted to evaluate the performance of EKF compares to the Virtual Force Iteration Localization (VFIL), Weighted Centroid Localization (WCL), and Centroid Localization algorithms (CL). The EKF proves to be of high efficiency in comparison to VFIL, WCL, and CL.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126625076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper is based on Electronic Vehicle System, like any other electric vehicle, with certain additional, new features which will take electronic vehicles to a whole new level. Speed management, thereby contributing to traffic management has always been a very big challenge to us. The safety and the smooth flow of traffic is very much essential. To overcome these huddles, it is necessary to device a smart vehicle system, which will be responsible to regulate the speed of the vehicle and manage the traffic. Firstly, to control the speed, we need to device a three condition layer, which is based on Data Analytics, Machine Learning, Deep Learning and IOT. This three-layered System will get the maximum speed of the car, beyond which the driver will be unable to drive. Obtaining the maximum speed limit will depend on factors like the prescribed maximum speed limit of the particular road, the car is running on, the present traffic density of the road and the traffic situation as per the nearby traffic pole, the position of the other cars with respect to the concerned car. All these major factors will attribute to obtaining a safe maximum speed limit, as the three-layered system will work simultaneously, enabling the driver to drive safely within this limit. Since speed management can be reached, this will attribute to the management of the traffic. The knowledge of IOT is necessary, which will connect the car and the nearby traffic pole. Once they are connected, the car will receive signals and updates, regarding the traffic situation and based on the color of the signal the car will adjust it's speed or stop, accordingly. These are the major aspects of this proposed new vehicle system. Thus, the System can prove to be very much beneficial.
{"title":"SPEED CONTROLLING & TRAFFIC MANAGEMENT SYSTEM (SCTMS)","authors":"Aneesh Kar, Soujanya Syamal, Suvraneel Chatterjee, Antarika Basu, Himadri Nath Saha, Srijata Choudhuri","doi":"10.1109/UEMCON47517.2019.8993092","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993092","url":null,"abstract":"This paper is based on Electronic Vehicle System, like any other electric vehicle, with certain additional, new features which will take electronic vehicles to a whole new level. Speed management, thereby contributing to traffic management has always been a very big challenge to us. The safety and the smooth flow of traffic is very much essential. To overcome these huddles, it is necessary to device a smart vehicle system, which will be responsible to regulate the speed of the vehicle and manage the traffic. Firstly, to control the speed, we need to device a three condition layer, which is based on Data Analytics, Machine Learning, Deep Learning and IOT. This three-layered System will get the maximum speed of the car, beyond which the driver will be unable to drive. Obtaining the maximum speed limit will depend on factors like the prescribed maximum speed limit of the particular road, the car is running on, the present traffic density of the road and the traffic situation as per the nearby traffic pole, the position of the other cars with respect to the concerned car. All these major factors will attribute to obtaining a safe maximum speed limit, as the three-layered system will work simultaneously, enabling the driver to drive safely within this limit. Since speed management can be reached, this will attribute to the management of the traffic. The knowledge of IOT is necessary, which will connect the car and the nearby traffic pole. Once they are connected, the car will receive signals and updates, regarding the traffic situation and based on the color of the signal the car will adjust it's speed or stop, accordingly. These are the major aspects of this proposed new vehicle system. Thus, the System can prove to be very much beneficial.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121537530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992925
Shireesha Chandra, A. Varde, Jiayin Wang
The digital universe is expanding at a very fast pace generating massive datasets. In order to keep up with the processing and storage needs for this big data, and to discover knowledge, we need scalable infrastructure and technologies that can access data from multiple disks simultaneously. Cloud computing provides paradigms for data analytics over such huge datasets. While SQL continues to be popular among database and data mining professionals, in recent years Hive has established itself as a rapidly advancing technology for big data which makes it highly suitable for use over the cloud. In this paper, we present investigatory research on Hive and SQL with a detailed case study between them, considering cloud data management and mining. Our work here constitutes a thorough scrutiny, focusing on processing Hive queries on cloud infrastructure considering three different approaches, and also delving into SQL processing on the cloud with similar approaches. Real datasets are used for conducting various operations using Hive and SQL. This paper conducts performance comparisons of the two technologies and explains the environment in which one is preferred over the other for processing and analyzing data. It provides recommendations for cloud data analytics, based on the case study.
{"title":"A Hive and SQL Case Study in Cloud Data Analytics","authors":"Shireesha Chandra, A. Varde, Jiayin Wang","doi":"10.1109/UEMCON47517.2019.8992925","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992925","url":null,"abstract":"The digital universe is expanding at a very fast pace generating massive datasets. In order to keep up with the processing and storage needs for this big data, and to discover knowledge, we need scalable infrastructure and technologies that can access data from multiple disks simultaneously. Cloud computing provides paradigms for data analytics over such huge datasets. While SQL continues to be popular among database and data mining professionals, in recent years Hive has established itself as a rapidly advancing technology for big data which makes it highly suitable for use over the cloud. In this paper, we present investigatory research on Hive and SQL with a detailed case study between them, considering cloud data management and mining. Our work here constitutes a thorough scrutiny, focusing on processing Hive queries on cloud infrastructure considering three different approaches, and also delving into SQL processing on the cloud with similar approaches. Real datasets are used for conducting various operations using Hive and SQL. This paper conducts performance comparisons of the two technologies and explains the environment in which one is preferred over the other for processing and analyzing data. It provides recommendations for cloud data analytics, based on the case study.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116202973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993094
R. Parekh, Kushal Jain, David Luu, K. George
With the increasing popularity of wireless devices, the need to develop ways to charge, or to expand the battery life, also increases. This paper proposes a hybrid rectification circuit for collecting energy from nearby Wi-Fi networks. The circuit scavenges the energy emitted, but not used, by routers when they send information through the air, and stores the energy within a capacitor. The proposed architecture consists of two main building blocks: a bridgeless converter, and a diode bridge. The incident power on the antenna of the harvester is passed through the network of the LC lumped circuit, which helps in rectifying and boosting the voltage level. The second building block takes the output of the first block and further rectifies it, helping to further enhance voltage. The proposed system has been tested by using two distinct capacitive loads: 0.47 uF and 1000 uF. It was found that in the case of 1000 uF, the capacitor was able to charge 2.47 V within a span of 16.5 hours when kept directly next to the transmitting source antenna. In the case of 0.47 uF, it was able to charge within a span of 12 hours when kept 10 feet away from the transmitting source. The energy stored was used to light an LED, which was successful in the proximity circuit, albeit briefly.
{"title":"Scavenging Residual Energy from Wi-Fi Sources Using a Rectenna Circuit","authors":"R. Parekh, Kushal Jain, David Luu, K. George","doi":"10.1109/UEMCON47517.2019.8993094","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993094","url":null,"abstract":"With the increasing popularity of wireless devices, the need to develop ways to charge, or to expand the battery life, also increases. This paper proposes a hybrid rectification circuit for collecting energy from nearby Wi-Fi networks. The circuit scavenges the energy emitted, but not used, by routers when they send information through the air, and stores the energy within a capacitor. The proposed architecture consists of two main building blocks: a bridgeless converter, and a diode bridge. The incident power on the antenna of the harvester is passed through the network of the LC lumped circuit, which helps in rectifying and boosting the voltage level. The second building block takes the output of the first block and further rectifies it, helping to further enhance voltage. The proposed system has been tested by using two distinct capacitive loads: 0.47 uF and 1000 uF. It was found that in the case of 1000 uF, the capacitor was able to charge 2.47 V within a span of 16.5 hours when kept directly next to the transmitting source antenna. In the case of 0.47 uF, it was able to charge within a span of 12 hours when kept 10 feet away from the transmitting source. The energy stored was used to light an LED, which was successful in the proximity circuit, albeit briefly.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125247112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993045
Joseph Kamel, Farah Haidar, I. B. Jemaa, Arnaud Kaiser, B. Lonc, P. Urien
Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by Road-Side Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.
{"title":"A Misbehavior Authority System for Sybil Attack Detection in C-ITS","authors":"Joseph Kamel, Farah Haidar, I. B. Jemaa, Arnaud Kaiser, B. Lonc, P. Urien","doi":"10.1109/UEMCON47517.2019.8993045","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993045","url":null,"abstract":"Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by Road-Side Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8993082
Yash Parikh, Eman Abdelfattah
This paper investigates six machine-learning models to determine which algorithm would effectively predict buzz on Twitter. Different classifiers are applied such as Stochastic Gradient Descent, Support Vector Machines, Logistic Regression, Deep Neural Networks, Random Forests and Extra Trees on a Twitter dataset. This dataset contains features with users and author engagement over a certain period. After tests conducted on all the algorithms, we concluded that Extra Trees model outperforms the other models.
{"title":"Comparison of Machine Learning Models to Predict Twitter Buzz","authors":"Yash Parikh, Eman Abdelfattah","doi":"10.1109/UEMCON47517.2019.8993082","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8993082","url":null,"abstract":"This paper investigates six machine-learning models to determine which algorithm would effectively predict buzz on Twitter. Different classifiers are applied such as Stochastic Gradient Descent, Support Vector Machines, Logistic Regression, Deep Neural Networks, Random Forests and Extra Trees on a Twitter dataset. This dataset contains features with users and author engagement over a certain period. After tests conducted on all the algorithms, we concluded that Extra Trees model outperforms the other models.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132717354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992980
Misbah Ahmad, Imran Ahmed, A. Adnan
In video surveillance system, one of the important task is to detect person. In recent years, different computer vision and deep learning algorithms have been developed, which provides robust person detection results. Majority of these developed techniques focused on frontal and asymmetric views. Therefore, in this paper, person detection has been performed from a significantly changed perspective i.e. overhead view. A deep learning model i.e. YOLO (You Look Only Once) has been explored in the context of person detection from overhead view. The model is trained on frontal view data set and tested on overhead view person data set. Furthermore, overhead view person counting has been performed using information of classified bounding box. The YOLO model produces significantly good results with TPR of 95% and FPR up to 0.2%.
在视频监控系统中，人员检测是一项重要任务。近年来，不同的计算机视觉和深度学习算法被开发出来，提供了鲁棒的人检测结果。这些已开发的技术大多集中在正面和非对称视图上。因此，在本文中，人的检测已经从一个显著改变的角度进行，即俯视图。一种深度学习模型，即YOLO (You Look Only Once)，已经在俯视视角的人检测环境中进行了探索。该模型在正面视图数据集上进行训练，在俯视图人数据集上进行测试。在此基础上，利用分类边界框信息进行了俯视图人员计数。YOLO模型的TPR可达95%，FPR可达0.2%，效果显著。
{"title":"Overhead View Person Detection Using YOLO","authors":"Misbah Ahmad, Imran Ahmed, A. Adnan","doi":"10.1109/UEMCON47517.2019.8992980","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992980","url":null,"abstract":"In video surveillance system, one of the important task is to detect person. In recent years, different computer vision and deep learning algorithms have been developed, which provides robust person detection results. Majority of these developed techniques focused on frontal and asymmetric views. Therefore, in this paper, person detection has been performed from a significantly changed perspective i.e. overhead view. A deep learning model i.e. YOLO (You Look Only Once) has been explored in the context of person detection from overhead view. The model is trained on frontal view data set and tested on overhead view person data set. Furthermore, overhead view person counting has been performed using information of classified bounding box. The YOLO model produces significantly good results with TPR of 95% and FPR up to 0.2%.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114192045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/UEMCON47517.2019.8992950
Ushashi Chowdhury, Pranjal Chowdhury, Sourav Paul, Anwesha Sen, Partho Protim Sarkar, S. Basak, Abari Bhattacharya
Now-a-days we can see that human life is becoming very fast. Moreover, the city life is getting very busy day- by-day. So in the daily busy schedule it is becoming very difficult for the parents to monitor their children closely. This paper discusses about a smart wearable device like a wristband which tracks the child from time to time to ensure their safety. If any problem occurs it would alert parents through the cell phone so that they can take immediate action. This paper focus on the SMS text enabled communication. Parents can send SMS with some keywords and the device reply back. The device can detect the child's approximate location, it can detect the body temperature and the surrounding temperature, humidity and also the heartbeat of a child. For the emergency situation, the device would have some measures like an alarm buzzer, SOS light which will notify the bystanders to help the child. So this paper is all about the safety and security of a child to help them to recover from any type of difficulty.
{"title":"Multi-sensor Wearable for Child Safety","authors":"Ushashi Chowdhury, Pranjal Chowdhury, Sourav Paul, Anwesha Sen, Partho Protim Sarkar, S. Basak, Abari Bhattacharya","doi":"10.1109/UEMCON47517.2019.8992950","DOIUrl":"https://doi.org/10.1109/UEMCON47517.2019.8992950","url":null,"abstract":"Now-a-days we can see that human life is becoming very fast. Moreover, the city life is getting very busy day- by-day. So in the daily busy schedule it is becoming very difficult for the parents to monitor their children closely. This paper discusses about a smart wearable device like a wristband which tracks the child from time to time to ensure their safety. If any problem occurs it would alert parents through the cell phone so that they can take immediate action. This paper focus on the SMS text enabled communication. Parents can send SMS with some keywords and the device reply back. The device can detect the child's approximate location, it can detect the body temperature and the surrounding temperature, humidity and also the heartbeat of a child. For the emergency situation, the device would have some measures like an alarm buzzer, SOS light which will notify the bystanders to help the child. So this paper is all about the safety and security of a child to help them to recover from any type of difficulty.","PeriodicalId":187022,"journal":{"name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114220539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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