Every year nearly 1.5 million people are dying in traffic collisions around the world, due to the unexpected behavior of pedestrians while crossing the road. To address this problem an augmentation function for predicting the crash risk of the active pedestrian is proposed. The augmentation function has several functions like pre-crash scenario, vehicle trajectory, and pedestrian trajectory. In a Pre-Crash scenario, pedestrian movement such as entering the road boundary or not is detected. The input comes from the sensor which is located at the head of the car. After the pre-crash scenario vehicle trajectory is used to control the speed of the vehicle. Then the Markov IRW model-based pedestrian trajectory finding is used to predict the state of each pedestrian. The states are categorized into three types: Running, Walking and Standing. In this model, the pedestrian types whether the pedestrian is a child or young or old aged people is predicted. And the crash risk is evaluated based on the Monte Carlo algorithm that calculates the minimum detection range for active pedestrians. If the crash risk is exceeded the threshold limit then an augmentation function is activating the evasive action.
Clinical methods for checking domesticated animals’ wellbeing are lacking, as they give just irregular data and required a lot of asset interest as far as time and veterinary aptitude. The creature wellbeing observing framework that is assigning equipment which will mount on the creature body, at present there are no such frameworks which will give on current status of the creature. At present to distinguish the wellbeing status of creature we needed to sit tight for veterinary mastery which set aside long effort for its appearance. The framework won’t just improve singular creature wellbeing, yet it likewise distinguishes and forestalls far and wide infections, regardless of whether it started from common causes or from natural assaults. Such a framework would help in early conclusion of sicknesses. The framework comprises of various sensors for example Temperature sensor, Heart rate sensor. The gadget is vital just as supportive for the medical care of creatures.
Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.
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