Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques

Hybrid Ad-hoc NETwork (HANET) is a fusion of both the static and dynamic topologies. Each node of this network consists of low capacity battery. Because of heterogeneous characteristic of the topology, network parameters are imprecise in nature. This, in effect, the performance and lifetime of the network degrade. To overcome these issues, this paper proposes an optimized energy efficient routing (OE2R) method. This method is inspired by artificial intelligence techniques such as multiobjective optimization, geometric programming, aspiration level, and tolerance limit. The fusion of such stated artificial intelligence techniques provide an effective tool to optimize multiple conflict objectives and estimate imprecise parameters of the network, simultaneously. The proposed technique OE2R is simulated using LINGO optimization software. To justify the effectiveness of the proposed technique, its performance is compared with some existing methods such as approximate linear programming technique for average cost bounds and linear programming-based efficient message delivery approach in hybrid network in several passes of performance metrics. The simulation results show that the proposed OE2R method performs much better with the comparative methods and may be implemented as energy-efficient routing for HANET.