Mobile-Assisted Edge Computing via CRI Auction
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Solution Overview
Problem
The existing edge computing paradigms face challenges in ensuring low latency for IoT applications due to a supply-demand mismatch at the network edge, where static edge nodes often lack sufficient resources to handle peak demands, leading to increased waiting times and decreased user experience.
Innovation Solution
A Mobile-Assisted edge computing framework is introduced, utilizing mobile devices as edge nodes through a Credible, Reciprocal, and Incentive (CRI) auction mechanism to dynamically allocate and process user requests, leveraging the idle resources and mobility of mobile devices like smart cars, UAVs, and robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static edge nodes are used to process user requests, then service provisioning is simplified, but resource capacity is insufficient to meet peak demand
Solution Approach 1:
The patent introduces mobile edge nodes that can dynamically move to different locations based on user demand distribution. This transforms the static edge computing infrastructure into a dynamic system where computing resources can be relocated to match peak demand areas, thereby increasing resource capacity adaptability without requiring permanent deployment of additional static nodes throughout the network.
Solution Approach 2:
Mobile devices are utilized as multi-functional edge nodes that can serve multiple purposes: they provide computing resources for edge processing, move adaptively to different regions based on demand, and supplement the static edge node infrastructure. This universal approach allows existing mobile devices to fulfill multiple roles in the edge computing ecosystem.
2Quantity of substance
If more static edge nodes are deployed to meet peak demand, then resource capacity increases, but construction cost increases
Solution Approach 1:
The patent leverages idle computing resources already present in mobile devices (smartphones, tablets, vehicles) as edge nodes. Instead of requiring deployment of new infrastructure, the system utilizes existing devices that would otherwise be underutilized, thereby increasing total resource capacity without incurring additional construction or deployment costs.
Solution Approach 2:
The system changes the operational parameters of existing mobile devices by activating their computing resources for edge processing tasks. This transforms devices from purely consumer-grade equipment into dual-purpose units that also provide infrastructure services, effectively increasing resource capacity without physical expansion of the network infrastructure.
3Loss of time
If services are pre-cached at static edge nodes, then response latency is reduced, but caching space is limited
Solution Approach 1:
The patent replaces static caching at fixed edge nodes with dynamic caching on mobile devices. Services and data can be cached on mobile edge nodes that move to locations where they are most needed, allowing the caching capacity to follow demand patterns dynamically rather than being constrained by fixed storage capacity at static locations.
Solution Approach 2:
The system adds a spatial dimension to caching by utilizing mobile devices that can physically relocate. Instead of being constrained to caching at fixed geographic locations with limited space, the system distributes caching across multiple mobile units that can move to different regions, effectively expanding total caching capacity by utilizing the mobility dimension.
4Quantity of substance
If user requests are scheduled to remote cloud for processing, then resource capacity is sufficient, but processing delay increases
Solution Approach 1:
The patent introduces mobile edge nodes as intermediary computing resources between static edge nodes and remote cloud data centers. When static edge nodes cannot handle requests locally, mobile edge nodes provide intermediate processing capacity closer to users than the remote cloud, reducing latency while supplementing rather than replacing the cloud infrastructure.
Solution Approach 2:
The patent segments the edge computing infrastructure into multiple layers: static edge nodes for baseline service provisioning, mobile edge nodes for peak demand and dynamic resource supplementation, and remote cloud for overflow handling. This segmentation allows requests to be processed at the most appropriate level, minimizing latency by handling most requests at edge levels rather than routing all to remote cloud.
Data Source
AI summary
A Mobile-Assisted edge computing framework including: one or more requests to be processed; an operator configured to assign the one or more requests to a static edge node; and a cloud configured to cache and pre-fetch services for the one or more requests; wherein the one or more requests include handled requests and unhandled requests, the static edge node is configured to process the handled requests, and one or more mobile edge nodes are configured to process the unhandled requests; and wherein the static edge node is configured to auction the unhandled requests to the one or more mobile edge nodes, the auction including: generating a candidate set of the one or more mobile edge nodes; assigning an unhandled request to a target mobile edge node of the candidate set; and processing the unhandled request via the target mobile edge node.