Dynamic IoT Bandwidth Allocation via Cloud API
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud-based IoT networks face challenges in dynamically allocating network bandwidth to IoT controllers due to static bandwidth limits, leading to bottlenecks as the number of sensors and their bandwidth requirements fluctuate.
Innovation Solution
Implementing an on-demand IoT bandwidth-allocation system that uses a cloud-management platform to dynamically adjust bandwidth allocations through an API, allowing IoT controllers to request additional bandwidth as needed based on changing sensor populations and requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed bandwidth allocation is used for cloud-based application servers, then network stability is maintained, but network performance deteriorates due to bottlenecks when sensor population changes
Solution Approach 1:
The patent implements dynamic bandwidth allocation that automatically adjusts network bandwidth based on real-time sensor population changes. The system monitors the number of connected sensors and dynamically provisions bandwidth resources through cloud-based virtualization, allowing the bandwidth allocation to adapt flexibly to varying network conditions rather than remaining fixed.
Solution Approach 2:
The system changes the bandwidth parameter dynamically based on sensor population metrics. When the number of sensors increases or decreases, the system automatically adjusts the allocated bandwidth parameter through API calls to the cloud management platform, ensuring optimal network performance for the current sensor load.
2Device complexity
If static bandwidth limits are imposed on virtual servers, then resource management is simplified, but adaptability deteriorates as sensors continuously enter and leave the network
Solution Approach 1:
The system implements self-service bandwidth allocation where the application server automatically monitors its own sensor population and triggers bandwidth provisioning requests without manual intervention. The server autonomously detects when bandwidth adjustments are needed and executes the provisioning through cloud APIs, eliminating the need for complex manual resource management while maintaining high adaptability.
Solution Approach 2:
The system establishes a feedback loop where the application server continuously monitors sensor population changes and uses this information to automatically trigger bandwidth adjustment requests. The cloud management platform receives these requests and provisions appropriate bandwidth, creating a closed-loop system that adapts automatically to changing conditions without complex manual configuration.
3Measurement precision
If manual bandwidth adjustment is required for each sensor change, then precise bandwidth control is achieved, but response time deteriorates due to delayed bandwidth allocation
Solution Approach 1:
The system performs preliminary bandwidth provisioning by automatically detecting sensor population changes and triggering bandwidth adjustment requests before performance degradation occurs. The application server continuously monitors sensor counts and proactively provisions bandwidth through cloud APIs, ensuring bandwidth is available in advance rather than waiting for manual intervention or performance issues to arise.
Solution Approach 2:
The system uses real-time feedback from sensor population monitoring to automatically trigger bandwidth adjustment requests. When the sensor count changes, the system immediately detects this through continuous monitoring and automatically provisions the appropriate bandwidth through cloud APIs, achieving both precise control and rapid response without manual intervention.
Data Source
AI summary
A method and associated systems for on-demand Internet of Things bandwidth allocation in response to changing sensor populations. An IOT sensor device adds itself to or deletes itself from a cluster of IOT sensors. A physical IOT controller that manages the cluster detects this change, identifies a resulting change in the cluster's bandwidth requirements, and stores this information in a local database. When such a sensor-population change satisfies a triggering condition, the controller requests that a cloud-based application server adjust the controller's bandwidth allocation. The server aggregates this and similar requests from all connected controllers in a global database, and when controller bandwidth requirements satisfy a second triggering condition, the server, using a standard API, asks the cloud-management platform to reprovision the server's virtual bandwidth allocation. The server then distributes the adjusted bandwidth among its IOT controllers, which in turn allocate their adjusted bandwidths among their sensor devices.