Development of collision avoidance application using internet of things (loT) technology for vehicle-to-vehicle (v2v) and vehicle-to-infrastructure (v21) communication system

Rising number of road accidents have been a common issue that needs to be given attention where most of it causes fatal injury and death. 30% of accidents are involving rear-to-end crashes meanwhile more than 900,000 cases in a year are related to rearblind-spots. Even though safety improvements have been upgraded such as introduction of Assistance Driving Assistance System (ADAS), yet the numbers are still on its endangering path. To solve this issue, Vehicle Ad-hoc Networks (VANET) system is invented to ensure a safer environment for drivers and pedestrians. Vehicle-toInfrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication System is one of the technologies created under VANET . This dissertation presented the new V2V and V2I system that is applicable to avoid collisions with development of On-Board Unit (OBU) and Roadside Unit (RSU) prototype using Internet of Things (IoT) technology. Single-Board Computers (SBC) is integrated with sensors such as GPS, LiDAR and ultrasonic for OBU while DHT22, CO gas sensor, PM sensor and rain sensor for RSU. Both OBU and RSU connected to internet via 4G module integrated on the SBC which also function as Apache-MySQL-PHP (AMP) server. Location Tracker, Forward Collision Warning (FCW) and Blind Spot Warning (BSW) application is embedded into OBU located in a vehicle known as a Subject Vehicle (SV). All testing involved with obstacle vehicle known as Host Vehicle (HV) executed at Universiti Malaysia Pahang (UMP) Pekan campus. Finding shows that OBU‟s location is as accurate as 0.0124% in latitude while 0.0084% in longitude in real-time at 60 km/h. Such GPS accuracy allow FCW application to generate alert at CP of 80% to the driver. FCW developed is tested at different speed of SV and HV and findings shows that alert is generated at a safe distance and sufficient time for the driver to react. Throughout the field testing, the new TTC has been successfully formulated and verified where the real time distance has been subtracted to 1 meter over current speed. Collision percentage (CP) of 80% is still generated even though the average lagging time (LT) delay of SBC is recorded at 1.3 seconds. The new formulated TTC and CP proven that the driver has ample time to respond to the generated alert, e.g., for the case of HV is at 0 km/h and SV is at 60 km/h, alert is generated at CP of 84.04% with TTC recorded at 2.4s, which is almost aligned with recommendation of International Organizations of Standardization 2013 stating 2.6s is the best time for driver to react. Even though there was a slight delay with the alerts, with consideration of 1m safe distance and 1.3s LT, driver was able to pull off a safe braking after the alert to slow down SV thus to avoid collision from happening. For BSW application, promising results by having 1 second delay in detecting blinded HV at the constant span of 40km/h speed limit between SV and HV which is an enabler to the safe lane changing operation. The presence of host vehicle (HV) or any obstacles is detected in the blinded area of SV. In contrast to OBU, RSU is developed to monitor the weather which in turn influenced the road conditions and eventually lead to the traffic status monitoring. The RSU‟s sensors are sensitively detected the haze, rain, temperature and humidity accurately. Therefore, this system is potentially to produce Variable Speed Limit (VSL) based on the environment conditions. Speed Limit information from the RSU can be accessed through the OBU inside the vehicles using internet from the 4G technology. Implementation of IoT technology has proven to assist the drivers in avoiding collisions thuspotential to reduce the road accidents

Real-time and predictive analytics of air quality with IoT system: A review

Environmental pollution particularly due to the emission of combus-tible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early pre-caution or preventive measure can be taken in eliminating potential health risk which may be done via Smart Environmental Monitoring system with the Internet of Things (IoT), which is cost-effective and efficient way to control air pollution and curb climate change, IoT applications along with Machine Learning(ML) can make the data prediction in real-time. ML can be used to predict the previous and current data obtained by sensors. This review describes the existence of an inte-grated research field in the development of the environmental monitoring system and ML method. The findings of this review interestingly show that (i) various communication module is used for environmental monitoring system. (ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can as-sist in the development of real-time, and continuous high precision environmen-tal monitoring systems. v) Machine Learning (ML) Regression algorithm is suit-able for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting

Real-time and predictive analytics of air quality with IoT system: a review

Environmental pollution particularly due to the emission of combustible gas from industry, haze, and vehicles, that has always been a major concern. Continuous monitoring of the air quality is hence essential to ensure early precaution or preventive measure can be taken in eliminating potential health risk which may be done via Smart Environmental Monitoring system with the Internet of Things (IoT), which is cost-effective and efficient way to control air pollution and curb climate change, IoT applications along with Machine Learning(ML) can make the data prediction in real-time. ML can be used to predict the previous and current data obtained by sensors. This review describes the existence of an integrated research field in the development of the environmental monitoring system and ML method. The findings of this review interestingly show that (i) various communication module is used for environmental monitoring system. (ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can assist in the development of real-time, and continuous high precision environmental monitoring systems. (v) Machine Learning (ML) Regression algorithm is suitable for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting

DSRC technology in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) IoT system for Intelligent Transportation System (ITS): a review

Intelligent Transportation System (ITS) consisting of Vehicle Ad-hoc Networks (VANET) offers a major role in ensuring a safer environment in cities for drivers and pedestrians. VANET has been classified into two main parts which are Vehicle to Infrastructure (V2I) along with Vehicle to Vehicle (V2V) Communication System. This technology is still in development and has not been fully implemented worldwide. Currently, Dedicated Short Range Communication (DSRC) is a commonly used module for this system. This paper focuses on both V2V and V2I latest findings done by previous researcher and describes the operation of DSRC along with its architecture including SAE J2735, Basic Safety Message (BSM) and different type ofWireless Access in Vehicular Environment (WAVE) which is being labeled as IEEE 802.11p. Interestingly, (i) DSRC technology has been significantly evolved from electronic toll collector application to other V2V and V2I applications such as Emergency Electronics Brake Lights (EEBL), Forward Collision Warning (FCW), Intersection Moving Assist (IMA), Left Turn Assist (LTA) and Do Not Pass Warning (DNPW) (ii) DSRC operates at different standards and frequencies subject to the country regulations (e.g. ITS-G5A for Europe (5.875–5.905 GHz), US (5.850– 5.925 GHz), Japan (755.5–764.5MHz) and most other countries (5.855–5.925 GHz)) where the frequencies affected most on the radius of coverage

DSRC Technology in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) IoT System for Intelligent Transportation System (ITS): A Review

Intelligent Transportation System (ITS) consisting of Vehicle Ad-hoc Networks (VANET) offers a major role in ensuring a safer environment in cities. For drivers and pedestrians. VANET has been classified into two main parts which are Vehicle to Infrastructure (V2I) along with Vehicle to Vehicle (V2V) Communication System. This technology is still in development and has not been fully implemented worldwide. Currently, Dedicated Short Range Communication (DSRC) is a commonly used module for this system. This paper focuses on both V2V and V2I latest findings done by previous researcher and describes the operation of DSRC along with its architecture including SAE J2735, Basic Safety Message (BSM) and different type of Wireless Access in Vehicular Environment (WAVE) which is being labeled as IEEE 802.11p. Interestingly, (i) DSRC technology has been significantly evolved from electronic toll collector application to other V2V and V2I applications such as Emergency Electronics Brake Lights (EEBL), Forward Collision Warning (FCW), Intersection Moving Assist (IMA), Left Turn Assist (LTA) and Do Not Pass Warning (DNPW) (ii) DSRC operates at different standards and frequencies subject to the country regulations (e.g. ITS-G5A for Europe (5.875–5.905 GHz), US (5.850–5.925 GHz), Japan (755.5–764.5 MHz) and most other countries (5.855–5.925 GHz)) where the frequencies affected most on the radius of coverage