Abstract
Vehicular Ad Hoc Networks (VANETs) are among the main enablers for future Intelligent Transportation Systems (ITSs) as they facilitate information sharing, which improves road safety, traffic efficiency, and provides passengers’ comfort. Due to the dynamic nature of VANETs, vehicles need to exchange the Cooperative Awareness Messages (CAMs) more frequently to maintain network agility and preserve applications’ performance. However, in many situations, broadcasting at a high rate leads to congest the communication channel, rendering VANET unreliable. Existing broadcasting schemes designed for VANET use partial context variables to control the broadcasting rate. Additionally, CAMs uncertainty, which is context-dependent has been neglected and a predefined fixed certainty threshold has been used instead, which is not suitable for the highly dynamic context. Consequently, vehicles disseminate a high rate of unnecessary CAMs which degrades VANET performance. A good broadcasting scheme should accurately determine which and when CAMs are broadcasted. To this end, this study proposes a Context-Aware Adaptive Cooperative Awareness Messages Broadcasting Scheme (CA-ABS) using combinations of Adaptive Kalman Filter, Autoregression, and Sequential Deep Learning and Fuzzy inference system. Four context variables have been used to represent the vehicular context, namely, individual driving behaviors, CAMs uncertainty, vehicle density, and traffic flow. Kalman Filter and Autoregression are used to estimate and predict the CAMs messages respectively. The deep learning model has been constructed to estimate the CAMs’ uncertainties which is an important context variable that has been neglected in the previous research. Fuzzy Inference System takes context variables as input and determines an accurate broadcasting threshold and broadcasting interval. Extensive simulations have been conducted to evaluate the proposed scheme. Results show that the proposed scheme improves the CAMs delivery ratio and decreases the CAMs prediction errors.
Highlights
Every year, millions of people lose their lives and properties due to traffic accidents[1]
This paper proposes a Context-Aware Cooperative Awareness Messages Broadcasting Scheme (CA-ABS) that improves the CAMs delivery ratio and reduces neighboring CAMs prediction error
The proposed scheme is compared with four related schemes, namely, the DSA-adaptive broadcasting rate (ABR)[28], PPBR[17], PPBR[30], TSABR which is a fuzzy-logic based broadcasting scheme proposed by [22] augmented by Kalman Filter prediction model inspired by [27], and the baseline broadcasting scheme which is the standard 802.11p scheme assisted by Kalman Filter prediction model which is inspired by [27]
Summary
Millions of people lose their lives and properties due to traffic accidents[1]. When vehicle density or/and traffic flow increases the number of contentions on the channel increases, which increases CAMs drop ratio and, negatively affects applications' accuracy To sum up, both broadcasting approaches (micro- or macro-based) have considered the vehicular context only partially, leading to an increase in the number of unnecessary broadcasting which congests the communication channel and degrades VANETperformance. This paper proposes a Context-Aware Cooperative Awareness Messages Broadcasting Scheme (CA-ABS) that improves the CAMs delivery ratio and reduces neighboring CAMs prediction error Both microscopic and macroscopic variables have been considered to represent the vehicular context and considered for broadcasting. 1) A context-aware CAMs broadcasting scheme that uses both microscopic (individual driving behavior) and macroscopic variables (CAMs uncertainty, vehicle density, and the traffic flow) to represent the vehicular context and adapt the broadcasting decision according to the given situation.
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