Energy Harvesting Mobile Edge Computing Resource Allocation
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Solution Overview
Problem
Conventional batteries face challenges in providing long-term battery life and reliable power supply for mobile devices, especially in scenarios where rechargeable batteries or grid power are impractical, leading to instability in computing performance when integrated with energy harvesting in mobile edge computing systems.
Innovation Solution
A distributed task offloading and computing resources management method based on energy harvesting, utilizing a perturbation Lyapunov optimization algorithm to dynamically allocate resources and optimize task offloading strategies, ensuring battery energy stability and efficient computing resource allocation across multiple mobile edge computing servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If conventional batteries are used in mobile devices, then the device can operate with stable power supply, but the battery capacity is limited and cannot provide long-term battery life
Solution Approach 1:
The patent combines conventional batteries with energy harvesting technology in a hybrid power supply system. The mobile device integrates both traditional battery power and harvested energy from environmental sources, allowing the device to extend operational duration while maintaining power supply stability through coordinated management of both power sources.
Solution Approach 2:
The system performs preliminary energy harvesting and storage actions to prepare for future power needs. By continuously harvesting energy from the environment and storing it in advance, the system ensures extended battery life while maintaining stable power availability when needed, resolving the contradiction between duration and reliability.
2Productivity
If centralized optimization method is used in MEC system, then resource allocation can be optimized globally, but the method is no longer suitable for distributed MEC scenarios with thousands of heterogeneous IoT applications
Solution Approach 1:
The patent segments the centralized optimization problem into distributed sub-problems that can be solved independently at each mobile device and MEC server. By dividing the global resource allocation task into local decision-making units, the system achieves efficient resource allocation for thousands of heterogeneous IoT applications without requiring complex centralized coordination.
Solution Approach 2:
Each mobile device and MEC server autonomously makes optimization decisions based on local information and global pricing signals. The distributed agents independently manage their own resource allocation and task offloading decisions, eliminating the need for complex centralized control while maintaining high resource allocation efficiency across the entire MEC system.
3Productivity
If task offloading is performed to MEC server, then computing performance is improved, but energy consumption and computational delay need to be balanced
Solution Approach 1:
The patent implements dynamic task offloading decisions that adapt to changing system conditions including energy availability, computational load, and pricing signals. The mobile device dynamically adjusts the amount of data to offload based on real-time battery energy levels and harvested energy, optimizing the balance between computing performance improvement and energy consumption.
Solution Approach 2:
The system uses feedback from energy harvesting status, battery levels, and computational performance metrics to continuously adjust offloading decisions. The mobile device monitors its energy state and computational needs, receiving pricing feedback from MEC servers, and dynamically optimizes the trade-off between offloading for performance and retaining tasks for energy conservation.
4Adaptability or versatility
If distributed task offloading strategy is developed, then the system can handle heterogeneous IoT applications, but different MDs have different requirements in computing offload delay and energy consumption
Solution Approach 1:
The patent applies local quality by allowing each mobile device to have customized offloading strategies tailored to its specific requirements for delay and energy consumption. Different MDs can prioritize different objectives based on their application needs, with the system providing localized optimization for each device while maintaining overall system efficiency through market-based coordination.
Data Source
AI summary
A distributed task offloading and computing resources management method based on energy harvesting is provided, including: establishing a task local computing model and an edge cloud computing model; establishing a device maximum benefit objective function based on the perturbation Lyapunov optimization algorithm and a mobile edge computing server maximum benefit objective function; pre-selecting, by the device based on a pre-screening criteria, a mobile edge computing server for task offloading; calculating an optimal task size strategy for performing task offloading by the device to the selected mobile edge computing server by using a Lagrange multiplier algorithm and a KKT condition; obtaining an optimal quotation strategy of the mobile edge computing server for the device in each of time slots; and obtaining a solution of the optimal task size strategy meeting a Stackelberg equilibrium and a solution of the optimal dynamic quotation strategy meeting the Stackelberg equilibrium as a resource allocation strategy.