UAV-MEC Resource Balancing for IoT Data Processing
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Solution Overview
Problem
Unmanned aerial vehicle (UAV) multi-access edge computing (MEC) devices face challenges in balancing computational loading due to insufficient processing capacity, leading to delayed data processing and reduced quality of service for Internet of Things (IoT) devices, especially in remote areas where conventional edge servers cannot be installed.
Innovation Solution
A method and system that dynamically assess the computational needs of IoT devices, identify resource shortages, and redistribute available UAV-MEC resources to the field location, allowing multiple UAV-MECs to communicate and request assistance through a network to complete tasks without the need for repeated trips to recharge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single UAV-MEC processes all data from IoT devices, then device complexity is reduced, but computational capacity becomes insufficient leading to processing delays
Solution Approach 1:
The patent segments the computational workload by introducing multiple UAV-MECs that each handle specific portions of data processing tasks. The system divides the total computational load into smaller sub-tasks distributed across multiple aerial nodes, enabling parallel processing and eliminating the bottleneck of a single overloaded device.
Solution Approach 2:
The patent transitions from a single-point computational model to a distributed spatial model by deploying multiple UAV-MECs at different locations. This dimensional shift from one central processor to multiple distributed processors enables simultaneous access to data from various IoT devices and eliminates processing delays through parallel computation.
2Power
If UAV-MECs make multiple trips to recharge, then processing capacity is maintained, but loss of time increases due to repeated travel
Solution Approach 1:
The patent merges the computational functions of multiple UAV-MECs into a unified distributed processing system. By combining the processing capacities of multiple aerial nodes, the system achieves sustained computational capability without requiring individual units to repeatedly return to base stations for recharging, as the collective system can maintain capacity through coordinated operation.
Solution Approach 2:
The patent ensures continuous data processing by maintaining an active distributed network of UAV-MECs that can offload tasks dynamically. The system continuously redistributes computational load across available aerial nodes, eliminating interruptions caused by recharge cycles and ensuring uninterrupted processing of IoT data streams.
3Power
If computational tasks are offloaded to cloud servers, then processing capacity increases, but loss of time increases due to data transmission distance
Solution Approach 1:
The patent introduces multiple UAV-MECs as intermediary nodes between IoT devices and the central cloud server. These aerial intermediaries perform initial data processing and computation locally, reducing the volume and criticality of data that must be transmitted to the cloud. This intermediary layer enables capacity enhancement without the full latency penalty of distant cloud processing.
Data Source
AI summary
Balancing computational loading across UAV-MEC devices by receiving information associated with a data processing task, determining a first computational loading associated with the data processing task relative to a capacity of a first device, sending the first computational loading and a first location to a second device, determining that the second device can support the first computational loading, deploying, the second device to the first location, and completing the first computational task using the first device and the second device.