Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach

Abstract

Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.

5G SLAM Using the Clustering and Assignment Approach with Diffuse Multipath

“…In order to enable real-time execution, there is a need for faster solutions for both the channel estimation and the SLAM filter. The solutions could be either in the form of new algorithms, or by offloading the computation to more powerful edge computing systems, where edge servers can provide high-performance computing capability closer to end users [68][69][70].…”

Stochastic Coded Offloading Scheme for Unmanned Aerial Vehicle-Assisted Edge Computing

“…We model the phase one (UAV type allocation) and phase two (task allocation) to complete applications defined by an application owner, e.g., road traffic monitoring [11] while considering various uncertainties. Since each edge server has limited computation capability, by deploy many edge servers at the BS, we can use constraints (53) and (54) from Appendix A to ensure that there will be enough computation resources to support the computation required by each UAV. The following sets are used to denote time slots, UAV types, mobile charging stations, and BSs.…”

Optimization and Communication in UAV Networks

“…However, the latter do not necessarily always have the capacity to offload data to an edge server. In such a case, mobile edge servers can go to them thanks to the deployment of UAV-assisted Multi-access Edge Computing systems, which raises new challenging optimization and networking issues as addressed in [ 9 , 10 ].…”

“…Reference [ 9 ] proposes to provide an Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) system based on a usage-based pricing policy for allowing the exploitation of the servers’ computing resources while the authors of [ 10 ] introduce the DRUID-NET perspective, aiming to adapt to a rapidly varying demand by applying different tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory combined.…”