A compendium of radio resource management in UAV-assisted next generation computing paradigms

Abstract

Unmanned aerial vehicles (UAVs) have huge potential in empowering new applications in different areas ranging from military to medical applications and traffic control to the entertainment information industry. There has been overwhelming interest in improving UAVs and multi-UAVs frameworks to collaborate and complete all missions efficiently. UAVs can be connected to IoT devices at any time and fulfill their requirements. Because of the constrained onboard resources, there is a need for radio resource management (RRM) in UAV correspondent situations. Optimization plays a significant role in the efficient utilization of these resources and provides services to the network’s edge. This survey presents a comprehensive overview of RRM optimization techniques, including cloud, fog, mobile edge computing (MEC), and cloudlet for UAVs. Further, the mathematical modeling of objectives and constraints discussed in the literature is also presented here. A summary of the challenges while using these computing paradigms is explored. Future research directions on the UAV-assisted network are introduced. In short, this survey provides key guidelines of how various radio resources in different environments are analyzed and optimized using different algorithms and strategies.