Environmental Monitoring Under Uncertainty Using Smart Vehicular Ad Hoc Network

Khilar

Dash

et al. 2022

Preprint

COVID-19 pandemic has created an emergency across the globe. The number of corona positive cases and death cases are still rising across the world. The government of all countries is taking various steps to control the infection of COVID-19. One of the steps to control the spreading of the coronavirus is to quarantine. But the number of active cases at the quarantine center is increasing day by day. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for the monitoring of people at the quarantine center by two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase involves component NIBs, RSUs, and vehicles. For the transmission of data from the quarantine center to the observation center, an effective route is determined using route value. The route-value is dependent on factors like density, shortest path, delay, vehicular data carrying delay, and attenuation. The analysis of health data is done by using multi-class SVM. Finally, simulation results are obtained for the two phases. The simulation results obtained for the health data transmission phase is E2E delay, number of network gaps, and packet delivery ratio. The health data analysis phase determines parameters such as precision, recall, accuracy, etc.

Section: Applications Of Vanetmentioning

confidence: 99%

Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Khilar

Dash

et al. 2022

Preprint

“…β l ℝ represents the regression coefficients that are not known. From Equation ( 20), p l ðxÞ is determined as mentioned in Equation (21).…”

Section: Lr As a Classifiermentioning

confidence: 99%

“…The delay for VANET is determined by various external factors such as the density of cars on the route, the number of network gaps, and the existence of impediments such as buildings and trees. Finally, environmental parameter analysis using VANET 20,21 is a beneficial application. Table 1 provides an overall assessment of VANET applications and challenges.…”

Section: Literature Surveymentioning

confidence: 99%

A heterogeneous fault diagnosis approach to enhance performance of connected vehicles

Swain

et al. 2022

Int J Communication

Evolution of wireless access technology, availability of smart sensors, and reduction in the size of the set up of the communication system have engrossed many researchers toward vehicular ad hoc network (VANET).Vehicle-to-vehicle and vehicle-to-access-point communication in a vehicular environment facilitates the deployment of VANET for many different purposes. The success of any application implemented in a VANET relies on timely and accurate data dissemination across the nodes of the network. Implementation of any application is not going to be fruitful if the communication unit transmits incorrect sensor data due to the presence of a fault. This article focuses on the automatic detection of hard and soft faults for vehicular sensors and the classification of faults into permanent, intermittent, and transient faults using cloud-based VANET. For the cloud service, ThingSpeak cloud is used. At the RSU of the VANET, hard fault detection is performed, and for this purpose, a time-out strategy is proposed. The observation center, after receiving sensor status data over a vehicular cloud, does soft failure detection. The soft fault is identified by utilizing a comparative-based technique during soft fault diagnosis. Soft faults are categorized using two machine learning algorithms: Support vector machine and logistic regression. The effectiveness of the suggested work is assessed using performance metrics like fault detection accuracy, false alarm rate, false positive rate, precision, accuracy, recall, and F1 score.

“…25 Convenience application of VANET includes automatic toll tax collection through VANET, 26,27 diversion of the route during road traffic congestion or due to road accident, 28,29 and to identify the location for the available parking slot in the mall. 30,31 Finally, the productive application of VANET includes environmental parameter analysis through VANET, 32,33 secure automatic toll tax collection, 26 and automatic fire monitoring through the urban region. 34 Looking at the wide application of VANET, transmission of environmental parameters from the location of measurement to the observation center through VANET is considered.…”

Section: Literature Surveymentioning

confidence: 99%

Environmental monitoring through Vehicular Ad Hoc Network: A productive application for smart cities

Khilar

Swain

2021

Int J Communication

Industrial growth, technological development, and increased human activities for comfort and luxurious life increase different environmental parameters like temperature, humidity, carbon monoxide, methane, and smoke. The value of these parameters are increasing steadily which has adverse effects on human life and human health and is also the cause of various disease like cancers and lung disease. It is now essential to monitor the environmental parameters of a region frequently. For the automatic continuous monitoring of environmental parameters of any location, this paper proposes to use vehicular ad hoc network (VANET), which is one of the promising subclasses of mobile ad hoc network (MANET). Automatic monitoring of environmental parameters of any location includes measurement of environmental parameters of a location, transmission of environmental parameters from the measured location to the observation center, and analysis of the parameters at the observation center. Two aspects are discussed for the transmission of parameters from the location of measurement to the observation center: first by vehicular communication and second by vehicular cloud. The analysis of the environmental parameters at the observation center is done using probabilistic neural network (PNN).Apart from this, different generic network parameters like end-to-end delay, number of hops, and network gaps for the proposed work are computed for the transmission of different environmental parameters and is compared with different VANET routing protocols like geographic source routing (GSR), anchor-based street and traffic-aware routing (A-STAR), and peripheral nodebased geographic distance routing (P-GEDIR). Vehicular motion is created using Simulation of Urban MObility (SUMO), and the network parameter values, like end-to-end delay and number of hops, are obtained using OMNET ++. A testbed experiment is conducted for the classification of sensor data which is adoptable in practice. K E Y W O R D SMANET, probabilistic neural network, VANET, vehicular cloud A productive application of VANET.