Distributed Routing for Edge Computing Handoff Latency
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Solution Overview
Problem
Existing wireless networks are not well-suited for low-latency, high-bandwidth edge-based computing due to slow and overhead-heavy connection handoffs, particularly in real-time data processing scenarios like self-driving cars and autonomous drones, where centralized authorities introduce significant latency and congestion.
Innovation Solution
Implementing a distributed routing environment using blockchain for contract-based handoffs between wireless base stations, allowing for decentralized coordination of handoffs and data processing at edge-based data centers, reducing reliance on central authorities and minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized authorities (base station controllers or mobile switching centers) are used to coordinate handoffs between cellular towers, then connection handoff coordination is achieved, but significant latency and network overhead are introduced
Solution Approach 1:
The patent segments the centralized handoff coordination function into distributed components. Each base station independently determines handoff decisions based on local measurements and pre-configured parameters, eliminating the need for continuous centralized control. This segmentation reduces latency by removing communication delays to central authorities while maintaining reliable handoff coordination through distributed intelligence.
Solution Approach 2:
Base stations are empowered to make autonomous handoff decisions without requiring centralized authorization. The system implements self-service by enabling each base station to independently evaluate handoff conditions, select target cells, and execute handoffs based on pre-configured policies and real-time measurements, thereby eliminating waiting time for centralized decisions while ensuring coordinated operation.
2Power
If traditional public cloud data centers are used for data processing, then computing power is available, but transmission delays occur due to large geographic distances
Solution Approach 1:
The patent implements edge computing by deploying computing resources at the network edge (base stations and nearby servers) rather than centralized cloud data centers. This local quality approach places processing power geographically close to data sources and consumers, enabling real-time processing of high-bandwidth data streams while minimizing transmission latency imposed by speed of light limits over large distances.
3Ease of operation
If centralized authorities coordinate handoffs for high-bandwidth real-time data processing, then connection management is achieved, but the latency budget is exceeded
Solution Approach 1:
The system performs preliminary configuration of handoff parameters, target cell selections, and processing workflows before handoff events occur. Base stations pre-establish processing pipelines at edge computing nodes and configure handoff policies in advance, enabling rapid execution during actual handoff events without requiring real-time centralized coordination, thereby meeting stringent latency budgets while maintaining ease of operation.
Data Source
AI summary
Provided is a process including: advertising a plurality of values corresponding to computing components to peer nodes of a peer-to-peer network; storing the plurality of values in a tamper-evident, distributed ledger; determining a target data center in the distributed computing environment, wherein the target data center performs computations based on data sent from a mobile computing device, and wherein the target data center executes a peer node of the peer-to-peer network; determining a network path that is linked to the target data center based on a distance to the target data center; and transferring a packet from the target data center, wherein the packet traverses the network path and comprises one or more computation results from the target data center.