Genetic Algorithm-Based Interest Forwarding for Information Centric Vehicular Networks

Abstract

The information centric vehicular network (ICVN) is envisioned as an alternative to IP-based vehicular network in order to extend the content oriented retrieval in to vehicular communication. In such network, named contents are retrieved through circulation of interest packets. Hence, there is a need of efficient interest forwarding techniques for timely retrieval of data. So far, the flooding of interest is the most intuitive forwarding approach in ICVN. But it suffers from broadcast storm and generates extensive signaling overhead which is not affordable in bandwidth constrained ICVN. In this article, a guided genetic algorithm (GA)-based interest forwarding (GABIF) technique is proposed for ICVN. The algorithm selects a set of neighbors to forward interest packets rather than flooding an interest to all its neighbors. To select the set of neighbors, the content seeker (a vehicle) performs a GA-based analysis on the list of neighbors and finds the optimized set of vehicles that have maximum possibility of holding the searched content. Every vehicle in the topology maintains a table of its neighbors along with the content availability probability and the cost of acquiring content from those sources. These parameters are later used to generate the optimized solution by applying GA. The proposed approach is simulated in $ns$-3-based $ndnSim$-2.0 and compared with other three exiting approaches. Observation shows a better performance of GABIF in many aspects including interest success rate, content retrieval time, protocol overhead, and server hit ratio.