Edge Computing Containers for IoT Latency Reduction
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Solution Overview
Problem
Cloud-centric IoT systems face scalability and latency issues with large IoT devices and low-latency applications due to bandwidth connectivity and round-trip delays, making them unsuitable for massive IoT deployments.
Innovation Solution
Implementing edge computing using container technology in edge computing devices to offload computation-intensive tasks, form local private cloud networks, and provide IoT services, thereby reducing latency and improving scalability by processing data closer to the device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-centric architecture is used for IoT systems, then centralized management and resource pooling are achieved, but latency increases due to round-trip delays and bandwidth connectivity issues
Solution Approach 1:
The patent segments the centralized cloud architecture into distributed edge computing nodes deployed throughout the network. Each edge device runs containerized applications locally, breaking the monolithic cloud structure into smaller, geographically distributed units that process data closer to IoT devices, thereby reducing round-trip latency while maintaining management capabilities through orchestration systems.
Solution Approach 2:
The patent introduces a new dimensional layer (edge layer) between IoT devices and the cloud, creating a multi-tier architecture. This edge dimension enables local processing of computation-intensive tasks while maintaining connectivity to centralized cloud services, effectively adding spatial proximity as a new dimension for reducing latency without sacrificing centralized management benefits.
2Quantity of substance
If cloud-centric architecture is used for IoT systems, then resource pooling is achieved, but scalability is limited for large IoT deployments
Solution Approach 1:
The patent segments the monolithic cloud resource pool into distributed resource pools at edge locations. Each edge device maintains local resources for immediate processing while connecting to broader cloud resources when needed, enabling incremental scaling by adding individual edge nodes rather than requiring proportional increases to centralized cloud capacity.
Solution Approach 2:
The patent implements dynamic resource allocation where edge devices can adaptively scale their local computing capacity based on real-time demands. Containers can be dynamically instantiated, scaled, or migrated across edge devices, allowing the system to flexibly adapt to varying IoT deployment sizes and workloads without rigid predefined resource allocations.
3Power
If computation-intensive tasks are processed in the cloud, then centralized processing power is utilized, but bandwidth connectivity requirements increase
Solution Approach 1:
The patent extracts computation-intensive processing tasks from the centralized cloud and places them in containerized environments at edge devices. This extraction removes the need for continuous high-bandwidth connectivity for heavy computations, as processing occurs locally at the edge while maintaining lighter connectivity requirements for coordination and data synchronization.
Solution Approach 2:
The patent performs preliminary processing of data at edge devices before transmitting to the cloud. By pre-processing, filtering, and analyzing data locally using containerized applications, the system reduces the volume and complexity of data requiring bandwidth-intensive transmission to centralized cloud resources, thereby reducing overall bandwidth connectivity requirements.
Data Source
AI summary
Methods and systems for improving the performance and functioning of a network and its components may include configuring an edge computing device to activate a long range (LoRa) Wide Area Network (WAN) gateway bridge as a container in a container platform operating on the edge computing device. The edge computing device may use the LoRa WAN gateway bridge to form a local private cloud network and communicate with LoRa end devices.