A topology-oblivious routing protocol for NDN-VANETs

Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent connectivity, which leads to failures of end-to-end paths between nodes. Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts. In this work, we propose an enhanced routing protocol of our previous topology-oblivious Multihop, Multipath, and Multichannel NDN for VANETs (MMM-VNDN) routing strategy that exploits several paths to achieve more efficient content retrieval. Our new enhanced protocol, i mproved MMM-VNDN (iMMM-VNDN), creates paths between a requester node and a provider by broadcasting Interest messages. When a provider responds with a Data message to a broadcast Interest message, we create unicast routes between nodes, by using the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN extracts and thus creates routes based on the MAC addresses from the strategy layer of an NDN node. Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

Distance-based schemes present one of the methods to avoid the broadcast problem in vehicular named data networks. However, such schemes overlook the most factor in performance evaluation which is the variation in received signal strength caused by the propagation model choice. Thus, they are evaluated under one propagation model while neglecting the effect of the others. This paper evaluates the impact of the propagation variation model over three distance-based schemes, namely rapid named data networking (RNDN), enhanced vehicle on named data networking (EVNDN) and opportunistic interest forwarding protocol (OIFP). Simulation experiments are performed over three propagation models. Simulation results show that Nakagami significantly degrades network performance. However, it has a noticeable positive effect over higher distance resulting in a higher interest satisfaction ratio as compared to the other models. The RNDN exhibits a higher number of retransmissions across the Nakagami. In contrast, a higher number of retransmissions is exhibited by EVNDN when compared to the other schemes over the Friis and random. The OIFP show a higher interest satisfaction ratio when compared to EVNDN and RNDN under all models. OIFP shows robustness towards the adverse fading effects resulting from the Nakagami and exhibits lower end to end delays

A Review on Forwarding Strategies in NDN based Vehicular Networks

Named Data Networking (NDN) is a model that has been proposed by many researchers to alter the long-established IP based networking model. It derives the content centric approach rather than host-based approach. This is gaining even more traction in the wireless network and is able to replace the conventional IP-based networking. Up to now, NDN has proven to be fruitful when used with certain limitations in vehicular networks. Vehicular networks deal with exchanging information across fast moving complex vehicle network topology. The sending and receiving of information in such a scenario acts as a challenge and thus requires an effective forwarding strategy to address this problem. Different research work has provided with multiple forwarding strategy that solves the current problem up to some limit but further research work is still longed for to get an optimum solution. This paper provides a brief survey on current existing forwarding strategies related to vehicular networks using NDN as well as providing information on various resources and technologies used in it

A Smart Forwarding in NDN VANET

Intelligent Transport System (ITS) applications rely on efficient forwarding or routing ofthe packet. However, routing or forwarding packet in Connected Vehicles is a challenging task and data retrieval rate can be very low due to highly dynamic topology andintermittent connectivity. Most of the routing solutions in the literature are location-based accompanied with limited flooding when location information is not available. For efficient communication and data retrieval in the vehicular network, we propose a hybrid forwarding solution, called CCLF. CCLF takes into account content-based connectivity information, i.e., Interest satisfaction ratio for each name prefix, in its forwarding decisions. To overcome the shortcomings of IP in mobile environment, CCLF is based ona data-centric network called Named Data Network (NDN). By keeping track of content connectivity and giving higher priority to vehicles with better content connectivity to forward Interests, CCLF not only reduces Interest flooding when location information is unknown or inaccurate, but also increases data fetching rate