PV-Alert: A fog-based architecture for safeguarding vulnerable road users

Abstract

High volumes of pedestrians, cyclists and other vulnerable road users (VRUs) have much higher casualty rates per mile; not surprising given their lack of protection from an accident. In order to alleviate the problem, sensing capabilities of smartphones can be used to detect, warn and safeguard these road users. In this research we propose an infrastructure-less fog-based architecture named PV-Alert (Pedestrian-Vehicle Alert) where fog nodes process delay sensitive data obtained from smartphones for alerting pedestrians and drivers before sending the data to the cloud for further analysis. Fog computing is considered in developing the architecture since it is an emerging paradigm that has paramount of advantages for low-latency applications which demand mobility support, geo-distribution and location awareness. We have also defined an algorithm for accident prediction and alerting traffic accidents involving vehicles and pedestrians. PV-Alert can be deployed quickly since it is based only on users' smartphones and doesn't require special infrastructure. Simulation results show that the proposed architecture is able to render alerts in real time whichever the wireless communication technology used to connect smartphones with fog nodes (Wi-Fi or LTE). Moreover, analytical and performance evaluations depict that the new architecture outperforms other related architectures in terms of reliability, scalability and latency.

A two-way trust management system for fog computing

“…The proposed trust management algorithm runs under applications that require trusted communications. For instance, let us take a traffic safety application for Vulnerable Road Users (VRU) proposed in [5]. In this application drivers and VRUs, which could be pedestrians, cyclists and powered two-wheelers send their geolocation and other associated data periodically to fog servers for collision risk prediction.…”

“…The value is returned from the latency response-time model produced using log-normal distribution [56] with the mean and standard deviation of 60 ms and 20 ms, respectively. The mean and standard deviation is taken from our previous work on fog computing-based traffic safety architecture for VRUs [5].…”

“…The parameters of the model are obtained from packet loss observation of links of a good and bad node [58]. Hence, the probability of transferring from good state to the bad state, and the probability of transferring from the bad state to the good state are generated based on the PDR result of fog computing environment as experimented in [5].…”

“…By exploiting the characteristics of fog computing, many novel IoT applications and services have been suggested. Traffic safety [5], e-Health [6], web content delivery [7], augmented reality [8], and big data analysis [9] are some of the applications that suits fog computing. Moreover, establishment of consortiums like OpenFog [10] by hi-tech giant companies and renowned academic institutions to define an architecture for fog computing and build operational models and testbeds indicates wide acceptance of the computing paradigm.…”

An Energy Efficient Smartphone Sensors’ Data Fusion for High Rate Position Sampling Demands

“…For instance, ETSI has enacted maximum and minimum time interval between beacon generations to be 1s and 0.1s for collision risk warning systems. Despite the fact that smartphone-based solutions are proposed for traffic safety by warning about-to-occur collisions based on positions of vehicles and vulnerable road users (VRU) [3] [4], present smartphones' position sampling rate do not meet the minimum time interval positions have to be sampled.…”