UAV Cooperative Task Offloading Using Second-Price Auction
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Solution Overview
Problem
Existing methods for task unloading in remote areas using unmanned aerial vehicles (UAVs) do not effectively address energy consumption and task unloading delay, and fail to consider optimal assignment to multiple UAVs and ground users, leading to inefficient task unloading and suboptimal quality of service.
Innovation Solution
A task unloading method based on UAV cooperation, utilizing a second price auction algorithm to optimize task assignment by establishing local and UAV unloading models, considering energy consumption, time delay, and hovering models, to determine an optimal unloading solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tasks are unloaded to unmanned aerial vehicles in remote areas, then communication quality and service quality are improved, but energy consumption of the unmanned aerial vehicles increases
Solution Approach 1:
The patent implements dynamic task unloading decisions by continuously monitoring system state (energy levels, task queues, channel conditions) and adjusting unloading parameters in real-time. The MEC server on the UAV dynamically determines whether to accept new tasks based on current energy reserves and computational load, transforming the static energy-consumption problem into a dynamic optimization process that adapts to changing conditions.
Solution Approach 2:
The patent changes key operational parameters including transmission power levels, computational resource allocation ratios, and task unloading thresholds based on system state. By adjusting these parameters dynamically, the system optimizes the balance between communication quality (improved through higher power and better resource allocation) and energy consumption (controlled through adaptive threshold adjustments).
2Productivity
If tasks are unloaded to unmanned aerial vehicles, then task processing capability is improved, but task unloading delay increases
Solution Approach 1:
The patent implements preliminary actions by pre-positioning MEC servers on UAVs in strategic locations before tasks arrive, and by pre-allocating computational resources and establishing communication channels in advance. This allows the system to rapidly accept and process tasks without the overhead of setting up infrastructure at the moment of task arrival, thereby reducing unloading delay while maintaining high processing capability.
Solution Approach 2:
The patent introduces the UAV with MEC server as an intermediary between ground users and the core network. This intermediary provides edge computing capabilities locally, enabling rapid task processing without requiring long-distance communication with remote base stations, thus improving both processing capability and reducing delay through localized computation.
3Adaptability or versatility
If multiple unmanned aerial vehicles are used for task unloading, then system capacity and coverage are improved, but task assignment complexity increases
Solution Approach 1:
The patent segments the task assignment problem into hierarchical levels: individual UAVs independently manage their local task queues and make basic acceptance/rejection decisions, while a centralized coordinator handles inter-UAV task redistribution and resource allocation. This segmentation reduces overall system complexity by distributing decision-making authority while maintaining coordinated multi-UAV operation and expanded system capacity.
Solution Approach 2:
The patent implements partial task unloading where not all tasks are offloaded to UAVs, and not all UAVs are actively processing tasks simultaneously. The system selectively assigns tasks to subsets of available UAVs based on current conditions, avoiding the full complexity of managing all possible UAV-task combinations while still achieving enhanced system capacity through selective multi-UAV utilization.
4Use of energy by moving object
If ground users unload tasks locally, then energy consumption is reduced, but service quality and processing capability deteriorate
Solution Approach 1:
The patent introduces the UAV with MEC server as an intermediary that ground users can access for task processing. Instead of requiring users to choose between local processing (low energy, low capability) and remote cloud processing (high capability, high energy), the UAV intermediary provides edge computing services that achieve high processing capability with lower energy consumption by eliminating long-distance data transmission and enabling localized computation.
Solution Approach 2:
The patent substitutes the mechanical approach of local device processing with a wireless edge computing approach. Instead of relying on ground user device computational power (which is limited and energy-intensive for complex tasks), the system replaces it with remotely hosted but locally-accessible MEC services on UAVs, achieving better performance with lower user-side energy consumption through wireless communication and remote computation.
Data Source
AI summary
A task unloading method based on unmanned aerial vehicle cooperation is provided. The method includes: establishing an unmanned aerial vehicle cooperation task unloading system; establishing, for each of ground users, a local task unloading model of the ground user; establishing, for each of the ground users, an unmanned aerial vehicle task unloading model of the ground user; establishing a hovering model of the unmanned aerial vehicles; and calculating an optimal task unloading assignment solution by using a second price auction algorithm to perform task unloading. With the method, task unloading is performed based on unmanned aerial vehicle mobile edge computing, improving the quality of service and the service experience of the users.