IoT Based Automated Car

Now a day internet of things has been great attention, because it is allows objects to be sensed and controlled remotely and standardization are being actively conducted. Network of physical things which communicate with each other and passes the data with each other. A whole array of physical ?things? ? from people and places through cars and computers to domestic appliances and production machinery ? is being equipped with embedded electronics systems, software and sensors. As we can see that now a day?s car automation creates lot of attention in IoT. There are many challenge generated during implantation of system. In this paper we are controlling the different car function like over the internet. User can control his car anywhere in world, just basic 2G internet connectivity required. After completing this system car user can get flexibility because use can access his car over worldwide

IoT based Smart Hospital for Secure Healthcare System

Now a day, with the rapid use of internet and implementation as well as development of medical sensor for healthcare applications, Internet of Things (IoT) has gained raising popularity. IoT is the paradigm of connectivity, sensor connected with the embedded system. All sensor and device connected to each other so transmission and communication between those sensors become easily. In healthcare system the medical data are sensitive in nature so without considering security and privacy is worthless. Cloud computing is the most important paradigm in IT-health. All the medical data of the patient as well as the doctor and patient personal information store in local mode as well as cloud, so whenever it needed the data will be easily available.Patient medical data is stored in system as well as cloud, so malicious attack and unwanted access may cause a harmful to patient health. Security is most important and crucial part of healthcare. The access control policy is based on right to access of medical data and privilege to authorized entity which is directly and indirectly connected with the patient health

DBGC:Dimension-Based Generic Convolution Block for Object Recognition

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs

Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions : A Systematic Review

Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions

Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.

QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization

Air pollution has emerged as a major concern of the current century. In recent times, fellow researchers have conducted numerous researches in the area ofair quality monitoring. Still, air quality monitoring remains an open research area due to various challenges such as sophisticated topology design, privacy and security, power backup, large memory requirements and deployment of such systems at resource-constrained sites. The proposed research work is an attempt to address the issues of communication topology design, assessment of the Quality of Service(QoS) levels against accuracy, sensing through put and power consumption optimization. In the undertaken work, the proposed IoT based Air Quality Monitoring system has been deployed at indoor and outdoor sites to measure air quality parameters such as PM10, PM2.5, carbon monoxide, temperature and humidity. The proposed system is also tested at variety of quality of service levels at the indoor and outdoor sites. The conducted experiments have also recorded accuracy in terms ofreliable delivery of the messages under employed protocol.

COVIDSAVIOR : A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus