Offload Computing Protocol for IoT Battery Optimization
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Solution Overview
Problem
IoT devices, such as mobile and wearable devices, face limitations in memory, battery reserve, and network bandwidth, making compute-intensive tasks infeasible and battery-draining due to their constrained resources, while also lacking sufficient processing power for complex tasks.
Innovation Solution
The Offload Computing Protocol (OCP) enables these devices to outsource computing tasks to nearby, less constrained IoT devices with greater memory, processing power, and battery life, using available network transport layers like Bluetooth or Wi-Fi, allowing for efficient data transfer and computation offloading while minimizing battery usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If compute-intensive tasks are executed on constrained IoT devices, then processing capability is improved, but battery consumption increases significantly
Solution Approach 1:
The patent extracts the compute-intensive task execution from the constrained IoT device and relocates it to a less constrained device with greater processing power and battery capacity. The OCP enables the constrained device to outsource task execution while retaining task management and result reception capabilities, thereby avoiding direct battery consumption for heavy computation.
Solution Approach 2:
The OCP acts as an intermediary protocol that facilitates communication and coordination between the constrained IoT device and the less constrained device. It manages task offloading, data transfer, and result retrieval, enabling the constrained device to leverage external processing power without direct battery expenditure on compute-intensive operations.
2Power
If more processing power is used in constrained IoT devices, then task execution capability is improved, but device complexity and cost increase
Solution Approach 1:
The less constrained device serves multiple functions: it acts as a compute resource for multiple different constrained IoT devices, a data storage repository, and a communication hub. This multi-functionality amortizes the cost of having high processing power across multiple uses, making the system more efficient and reducing the need for each individual constrained device to have high processing capabilities.
Solution Approach 2:
The system enables constrained IoT devices to access external processing power on-demand without requiring permanent local processing infrastructure. Devices can dynamically offload tasks to available less constrained devices in the network, effectively serving themselves with external resources rather than maintaining expensive local processing power.
3Use of energy by moving object
If compute tasks are offloaded to external devices, then battery consumption is reduced, but network dependency increases
Solution Approach 1:
The OCP implements dynamic task offloading decisions based on real-time network conditions, device availability, and task characteristics. The system can adaptively choose when to offload tasks and when to execute them locally, balancing battery conservation with network reliability concerns. This dynamic approach allows the system to respond to changing network conditions rather than relying statically on external resources.
Data Source
AI summary
Systems and methods for are provided for offloading computing tasks from constrained devices. An example apparatus includes an offload computing protocol (OCP) enabled device. The OCP enabled device includes OCP extensions to the operating system to enable the offloading of computing tasks. A proximity locator may use a radio transceiver to locate an OCP device that can accept a computing task. The OCP enabled device may include an OCP bundle comprising code and data, wherein the OCP bundle is to be sent to the OCP device.