Performance Assessment and Modeling of Routing Protocol in Vehicular Ad Hoc Networks Using Statistical Design of Experiments Methodology: A Comprehensive Study

Abstract

The performance assessment of routing protocols in vehicular ad hoc networks (VANETs) plays a critical role in testing the efficiency of the routing algorithms before deployment in real conditions. This research introduces the statistical design of experiments (DOE) methodology as an innovative alternative to the one factor at a time (OFAT) approach for the assessment and the modeling of VANET routing protocol performance. In this paper, three design of experiments methods are applied, namely the two-level full factorial method, the Plackett–Burman method and the Taguchi method, and their outcomes are comprehensively compared. The present work considers a case study involving four factors namely: node density, number of connections, black hole and worm hole attacks. Their effects on four measured outputs called responses are simultaneously evaluated: throughput, packet loss ratio, average end-to-end delay and routing overhead of the AODV routing protocol. Further, regression models using the least squares method are generated. First, we compare the main effects of factors resulted from the three DOE methods. Second, we perform analysis of variance (ANOVA) to explore the statistical significance and compare the percentage contributions of each factor. Third, the goodness of fit of regression models is assessed using the adjusted R-squared measure and the fitting plots of measured versus predicted responses. VANET simulations are implemented using the network simulator (NS-3) and the simulator of urban mobility (SUMO). The findings reveal that the design of experiments methodology offers powerful mathematical, graphical and statistical techniques for analyzing and modeling the performance of VANET routing protocols with high accuracy and low costs. The three methods give equivalent results in terms of the main effect and ANOVA analysis. Nonetheless, the Taguchi models show higher predictive accuracy.

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“…Using Taguchi's experimental design, S. Ajjaj [20] examines how factors affect the AODV protocol's routing overhead. The transmission range was shown to have the greatest impact on routing overhead, followed by the maximum connection and network size at various factor levels.…”

Secure AODV Routing Strategies in Smart Cities for Vehicular Communication

“…The source vehicle transmits an RREQ to navigate to the destination The RREP forwarded directly from target to source or via any subsequent vehicle capable of fulfilling the request that contains a valid route, has a destination sequence number that is equal to or greater than the one included in the RREQ message [23].…”

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

“…The core interest of this study is to explore incremental online learning algorithms in the context of attack detection in VANETs. More specifically, our research focuses on the detection of the black hole attack, estimated as the most damaging attacks that might compromise Ad hoc On Demand Distance Vector (AODV) routing protocol in VANETs in terms of data availability [7,8]. To this end, we have used data gathered from extensive simulations of realistic VANET scenarios based on two well-known simulators, namely SUMO [9] and NS-3 [10].…”