Biblio
Recently, several cross-layer protocols have been designed for vehicular networks to optimize data dissemination by ensuring internal communications between routing and MAC layers. In this context, a cross-layer protocol, called TDMA-aware Routing Protocol for Multi-hop communications (TRPM), was proposed in order to efficiently select a relay node based on time slot scheduling information obtained from the MAC layer. However, due to the constant evolution of cyber-attacks on the routing and MAC layers, data dissemination in vehicular networks is vulnerable to several types of attack. In this paper, we identify the different attack models that can disrupt the cross-layer operation of the TRPM protocol and assess their impact on performance through simulation. Several new vulnerabilities related to the MAC slot scheduling process are identified. Exploiting of these vulnerabilities would lead to severe channel capacity wastage where up to half of the free slots could not be reserved.
It is hard to set up an end-to-end connection between source and destination in Opportunistic Networks, due to dynamic network topology and the lack of infrastructure. Instead, the store-carry-forward mechanism is used to achieve communication. Namely, communication in Opportunistic Networks relies on the cooperation among nodes. Correspondingly, Opportunistic Networks have some issues like long delays, packet loss and so on, which lead to many challenges in Opportunistic Networks. However, malicious nodes do not follow the routing rules, or refuse to cooperate with benign nodes. Some misbehaviors like black-hole attack, gray-hole attack may arbitrarily bloat their delivery competency to intercept and drop data. Selfishness in Opportunistic Networks will also drop some data from other nodes. These misbehaviors will seriously affect network performance like the delivery success ratio. In this paper, we design a Trust-based Routing Protocol (TRP), combined with various utility algorithms, to more comprehensively evaluate the competency of a candidate node and effectively reduce negative effects by malicious nodes. In simulation, we compare TRP with other protocols, and shows that our protocol is effective for misbehaviors.