Fuzzy Logic based Model for Energy Consumption Trust Estimation in Electric Vehicular Networks

Abstract

Electric vehicles emerged new applications that are strongly related to the energy constraints such as the identification of the optimal path toward the vehicle's destination or toward the nearest recharging station, selection of the path where vehicle recovers extra energy, estimation of the need to recharge according to the actual battery state and the traffic state, etc. However, in electric vehicular networks, vehicles may provide wrong energy information due to sensors' failure, selfish or malicious reasons. Therefore, energy-related information trustworthiness needs to be evaluated in order to preserve the quality of the presented applications. In this paper, we address the energy-related information trustworthiness to discriminate between credible and erroneous values. Therefore, we propose a new fuzzy-based trust model that deals with the information uncertainties. This model aims at detecting the wrong energy information that mismatches with the vehicle's behavior and ensure that only trustworthy and plausible energy-information are handled. Results prove the performance of the proposed model and its capabilities to deal with several kinds of threats in different traffic densities with high precision.

A multi-level study for trust management models assessment in VANETs

“…Li and Song (2016) showed that the proposed model ensures a high preciseness score regardless the network's density. Simulations in Souissi et al (2018) proved that the proposed trust model is resilient to many kinds of threats at different traffic densities. Oluoch (2015) showed that as the density goes up, the probability of detecting safety events increases simultaneously.…”

“…As well, the solution introduced in Li and Song ( 2016) ensures a low complexity since the adopted theory (Dempster-Shafer theory) can effectively meet with real-time applications as proved in Raya et al (2008). Regarding the fuzzy-based trust approaches, the implementation of such models is simple (Soleymani et al, 2017;Souissi et al, 2018). Simulation results presented by Hu et al (2015) show that the required time for trust estimation is high.…”

“…P laczek and Bernas (2016) proved that the combination of these rules enhances the detection of malicious data and reduces the delay. In Souissi et al (2018), they introduced a fuzzy-based trust model for the energy consumption trust estimation in electric vehicular network. The presented model studies the correlation between the vehicle's behaviour (acceleration and deceleration) and the consumed energy.…”

“…• Scalability: Ensure that the proposed solution performs well whenever the network's size increases. Hu et al (2015), Soleymani et al (2017), Li and Song (2016), Souissi et al (2018), Oluoch (2015 and Yao et al (2017) were interested in this criterion. Hu et al (2015) proved that the higher the density is, the better the Relay Ratio is and the lower the delay is.…”

T-AODV: A trust-based routing against black-hole attacks in VANETs

“…Although, latency and data sparsity is the major demerits of this policy [11,15,16]. Energy-related information in electric vehicular networks and a central processing entity is proposed in [20]. It discriminates between credible and erroneous values and addresses malevolent vehicles to disseminate this false information based on a fuzzy trust model.…”