GA-based Energy Aware Path Planning Framework for Aerial Network Assistance

Abstract

Aerial networks have enormous potential to assist terrestrial communications under heavy traffic requests for a predictable duration. However, such potential for improving both the performance and the coverage through the use of drones can face a major challenge in terms of power limitation. Hence, we consider the energy consumption characteristic of the components in such networks to provide energy aware flight path planning. For this purpose, a flight path planning scheme is proposed on an underlying topology graph that models the energy consumption of path traversals in the aerial network. In the proposed model, we offer to seek for the minimum energy consumption on a global problem domain during the entire operational time. Thus, we provide a concrete problem formulation and implement a flight path planning with Genetic Algorithms (GA) approach. Moreover, a novel end-system initiated handover procedure is illustrated to preserve connectivity of terrestrial users in the network architecture. In the end, the evaluation of the proposed model is conducted under three different scales of social event scenarios. A comparison with a dummy path planning scheme without energy awareness concerns is presented according to a set of parameters. The evaluation outcomes show that the proposed model is able to save 20% energy consumption, provides 15% less number of terrestrial replenishment, and 18% more average endurance for the topology. Besides, another energy aware path planning scheme in the literature offering a deployment with Bellman Ford algorithm is also included in the evaluation to evaluate the feasibility of the proposed framework for the enhanced problem domain.