Swarm intelligence inspired meta-heuristics for solving multi-constraint QoS path problem in vehicular ad hoc networks

Abstract

Applications and services which run on top of vehicular ad hoc networks range from human safety and traffic management to assisted driving and real time battlefield communication. Many of these applications may have low-elasticity QoS requirements. Characteristics like high mobility of vehicles, dynamic topology, inherent distributed nature and consequent lack of central coordination make it challenging to provide QoS support in such networks. Since, QoS provisioning and path finding can be potentially handled together, this work aims at exploiting such a possibility. Consequently, an attempt has been made to solve the multi-constraint QoS optimal path problem by using an innovative combination of swarm intelligence based optimization strategies and an especially designed cost function. This work also presents a detailed theoretical as well as experimental analysis of the proposed algorithms, probably first of its kind. Additionally, the analysis part also investigates the applicability issues and related solutions considering select algorithms in view of solving the given problem for vehicular ad hoc networks. Meta-heuristics derived in this process have been applied on realistic modelled vehicular ad hoc network topology and as the presented analysis would indicate results are encouraging.