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RESEARCH PRODUCT

Fog Computing based traffic Safety for Connected Vulnerable Road Users

subject

description

Annually, millions of people die and many more sustain non-fatal injuries because of road traffic crashes. Despite multitude of countermeasures, the number of causalities and disabilities owing to traffic accidents are increasing each year causing grinding social, economic, and health problems. Due to their high volume and lack of protective-shells, more than half of road traffic deaths are imputed to vulnerable road users (VRUs): pedestrians, cyclists and motorcyclists. Mobile devices combined with fog computing can provide feasible solutions to protect VRUs by predicting collusions and warning users of an imminent traffic accident. Mobile devices’ ubiquity and high computational capabilities make the devices an important components of traffic safety solutions. Fog computing has features that suits to traffic safety applications as it is an extension of cloud computing that brings down computing, storage, and network services to the proximity of end user. Therefore, in this thesis, we have proposed an infrastructure-less traffic safety architecture that depends on fog computing and mobile devices possessed by VRUs and drivers. The main duties of mobile devices are extracting their positions and other related data and sending cooperative awareness message to a nearby fog server using wireless connection. The fog server estimates collision using a collision prediction algorithm and sends an alert message, if an about-to-occur collision is predicted. Evaluation results shows that the proposed architecture is able to render alerts in real time. Moreover, analytical and performance evaluations depict that the architecture outperforms other related road safety architectures in terms of reliability, scalability and latency. However, before deploying the architecture, challenges pertaining to weaknesses of important ingredients of the architecture should be treated prudently. Position read by mobile devices are not accurate and do not meet maximum position sampling rates traffic safety applications demand. Moreover, continuous and high rate position sampling drains mobile devices battery quickly. From fog computing’s point of view, it confronts new privacy and security challenges in addition to those assumed from cloud computing. For aforementioned challenges, we have proposed new solutions: (i) In order to improve GPS accuracy, we have proposed an efficient and effective two-stage map matching algorithm. In the first stage, GPS readings obtained from smartphones are passed through Kalman filter to smooth outlier readings. In the second stage, the smoothed positions are mapped to road segments using online time warping algorithm. (ii) position sampling frequency requirement is fulfilled by an energy efficient location prediction system that fuses GPS and inertial sensors’ data. (iii) For energy efficiency, we proposed an energy efficient fuzzy logic-based adaptive beaconing rate management that ensures safety of VRUs. (iv) finally, privacy and security issues are addressed indirectly using trust management system. The two-way subjective logic-based trust management system enables fog clients to evaluate the trust level of fog servers before awarding the service and allows the servers to check out the trustworthiness of the service demanders. Engaging omnipresent mobile device and QoS-aware fog computing paradigm in active traffic safety applications has the potential to reduce overwhelming number of traffic accidents on VRUs.