Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT (1910.08296v1)

Abstract: Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is non-convex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

• The paper introduces a joint computation and communication design using UAVs to reduce energy consumption in IoT mobile edge computing systems.
• It decomposes a complex non-convex optimization into sub-problems solved via Lagrangian duality and successive convex approximation for effective resource allocation.
• Extensive simulations validate that the UAV-enabled framework outperforms traditional setups by dynamically optimizing trajectories and relay operations.

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

The paper under discussion presents a comprehensive paper on Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems within the Internet of Things (IoT) framework. It addresses the pressing need for efficient computation resource management in IoT devices constrained by limited local computing capabilities. The authors propose a novel system architecture where a UAV equipped with a MEC server serves latency-critical computation tasks from various terminal devices (TDs) over time slots. The UAV's dual role—performing computations and acting as a relay to an Access Point (AP)—is leveraged to reduce energy and enhance the computing ability of traditional MEC systems.

Problem Formulation and System Overview

The proposed framework is set against the backdrop of increased computation demands put forth by IoT applications such as AR, VR, and autonomous driving, straining the capacities of local IoT devices. The optimization problem at the heart of the paper aims to minimize total energy consumption spanning communication, computation, and UAV flight operations. This is achieved by intelligently allocating computation bits, scheduling time slots, optimizing power, and designing UAV trajectories.

Within the system model, the UAV communicates via Orthogonal Frequency-Division Multiple Access (OFDMA), dividing its interactions into distinct phases for local computing, UAV-based computation, and relaying to the AP. The formulation is challenging due to its inherent non-convex nature. The problem is tackled through decomposition into manageable sub-problems, one focusing on energy minimization for a fixed trajectory and the other on trajectory optimization. The former is addressed via Lagrangian duality, and the latter employs a Successive Convex Approximation (SCA) algorithm.

Numerical Results and Implications

The authors validate their approach with extensive numerical simulations. Results indicate that the proposed UAV-assisted system significantly reduces total energy consumption compared to benchmark setups, including straight-flight trajectories, no-AP configurations, and local-only computations. Interestingly, numerical results underscore the role of AP cooperation in scenarios with high computation requirements, showcasing improved energy efficiency.

One of the standout outcomes is the relationship between UAV trajectory designs and system parameters. The UAV's ability to dynamically tailor its path enables it to hover strategically, optimizing relay communications to the AP based on task intensity and position relative to TDs. Such adaptability points to the promising future of UAV deployments in real-time MEC frameworks, especially when fine-tuned to exploit spatial and temporal attributes effectively.

Future Directions

The insights presented in the paper pave the way for intriguing avenues of future research. Key areas include further exploring advanced relay strategies, integrating security protocols to protect offloaded data against eavesdropping, and extending the model to multi-UAV scenarios for wider coverage and redundancy. Additionally, as IoT ecosystems continue to expand, there is potential in leveraging machine learning for predictive resource allocation, enabling even more nuanced UAV trajectory planning and resource utilization.

In summary, the paper offers a detailed examination of UAV capabilities in augmenting MEC systems for IoT, opening up actionable avenues for deploying UAV technology in practical communication and computing infrastructures. The methodological rigor and the promise of enhanced system efficiency render this paper a valuable contribution for researchers and practitioners keen on advancing UAV-assisted MEC solutions.