Fog Based Computation Offloading for Swarm of Drones

TL;DR

This paper proposes a fog computing framework for swarm of drones to reduce latency and energy consumption for computation-intensive tasks, using a genetic algorithm for optimal task offloading.

Contribution

Introducing a fog computing-based offloading system for drone swarms with a genetic algorithm for energy-efficient task allocation under latency and reliability constraints.

Findings

Effective task completion with minimal energy consumption

Reduced latency and improved reliability in drone swarm computing

Genetic algorithm outperforms baseline methods in simulations

Abstract

Due to the limited computing resources of swarm of drones, it is difficult to handle computation-intensive tasks locally, hence the cloud based computation offloading is widely adopted. However, for the business which requires low latency and high reliability, the cloud-based solution is not suitable, because of the slow response time caused by long distance data transmission. Therefore, to solve the problem mentioned above, in this paper, we introduce fog computing into swarm of drones (FCSD). Focusing on the latency and reliability sensitive business scenarios, the latency and reliability is constructed as the constraints of the optimization problem. And in order to enhance the practicality of the FCSD system, we formulate the energy consumption of FCSD as the optimization target function, to decrease the energy consumption as far as possible, under the premise of satisfying the latency and reliability requirements of the task. Furthermore, a heuristic algorithm based on genetic algorithm is designed to perform optimal task allocation in FCSD system. The simulation results validate that the proposed fog based computation offloading with the heuristic algorithm can complete the computing task effectively with the minimal energy consumption under the requirements of latency and reliability.