IoT Edge Task Management for DNN Battery Drain

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Solution Overview

Problem

In the context of 6G IoT edge networks, IoT devices face challenges in running deep neural network (DNN) tasks due to limited processing capabilities, leading to battery drainage and latency issues when offloading tasks to edge devices, which may run out of battery before completing tasks, and require efficient load balancing without restarting.

Innovation Solution

A method and system for task management in IoT-edge networks that involves assigning DNN tasks to edge devices, monitoring their battery levels, triggering alarms when thresholds are met, and dynamically transferring tasks to nearby edge devices to avoid battery depletion and latency, using a load balancing mechanism that prioritizes tasks and optimizes energy consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If DNN tasks are offloaded to edge devices to overcome limited processing capabilities of IoT devices, then computational capability is improved, but battery drainage increases

Engineering Contradiction:

Improvecomputational capabilityVSAvoidbattery drainage

Core Design Contradiction:

PowerVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary battery level assessment of edge devices before offloading DNN tasks. The IoT device evaluates the battery status of candidate edge devices and selects only those with sufficient battery levels, preventing task failure due to battery depletion and reducing the need for task re-offloading

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts task offloading decisions based on real-time battery levels of edge devices. The selection of edge devices for task execution is not static but adapts to changing battery conditions, ensuring optimal energy utilization and task completion

Inventive Principle:

Principle #15Dynamics

2Speed

If DNN tasks are assigned to edge devices for execution, then processing speed is improved, but reliability decreases due to battery depletion before task completion

Engineering Contradiction:

Improveprocessing speedVSAvoidtask completion reliability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where edge devices report their battery status to the IoT device. This continuous monitoring allows the system to make informed decisions about task assignment and potential task migration, ensuring reliable task completion

Inventive Principle:

Principle #23Feedback

3Reliability

If tasks are transferred between edge devices to avoid battery depletion, then task completion reliability is improved, but latency increases due to task migration

Engineering Contradiction:

Improvetask completion reliabilityVSAvoidtask migration latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary battery level assessment and identifies suitable edge devices before task offloading. By pre-evaluating battery levels and maintaining a list of qualified edge devices, the system minimizes the time required for task migration decisions and reduces overall latency

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically selects the most appropriate edge device for task execution based on real-time battery levels and device availability. This dynamic selection optimizes the balance between task completion reliability and migration latency by choosing the best available option

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20220383116A1METHOD AND SYSTEM FOR TASK MANAGEMENT IN AN INTERNET OF THINGS (IoT) EDGE NETWORK

Publication Date: 2022.12.01 SAMSUNG ELECTRONICS CO LTD

AI summary

A method and system of task management in an internet of things (IoT)-edge network may be provided. The method may include assigning at least one deep neural network (DNN) task from an IoT device to a first edge device; determining whether the first edge device satisfies one of a first predetermined criteria and a second predetermined criteria during execution of the at least one DNN task; triggering an alarm to the IoT device based on the determination; identifying a second edge device subsequent to the triggering of the alarm; determining whether to transfer the at least one DNN task to the second edge device or to execute the at least one DNN task on the first edge device, based on determining that the first device satisfies the second predetermined criteria; and transferring the at least one DNN task to the second edge device, based on determining that the first device satisfies the first predetermined criteria.