Thin Client Power Control via Usage Pattern Analysis
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Solution Overview
Problem
Managing power consumption and detecting unusual activities in thin client computing devices is challenging due to varying usage patterns across different user groups, which can lead to inefficiencies and potential security issues in client-server systems.
Innovation Solution
A system comprising a server computing device with a processor and storage, connected to multiple thin client devices, that analyzes messages from these devices to generate usage patterns, create automatic power control schedules, and monitor for unusual activities, using motion sensors to detect user presence and control power on/off based on predetermined times and schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual power control is used for thin client devices, then individual user preferences can be accommodated, but power management complexity increases and energy savings are reduced
Solution Approach 1:
The system enables thin client devices to automatically manage their own power states by monitoring local sensor data (motion, temperature, humidity) and making autonomous decisions about when to transition between active and sleep modes, eliminating the need for complex centralized manual control while achieving energy savings
Solution Approach 2:
The power management system dynamically adjusts power states based on real-time environmental conditions and usage patterns, allowing devices to adapt their power consumption levels automatically rather than following static manual control schedules
2Loss of energy
If centralized power control is implemented, then power savings can be maximized, but individual device usage patterns and user preferences cannot be accommodated
Solution Approach 1:
The system divides power management control into two segments: centralized policy framework from the server and localized autonomous decision-making at each thin client device, allowing both centralized energy optimization and individual device adaptability to coexist
Solution Approach 2:
Each thin client device is equipped with local sensors and autonomous control capabilities that allow it to adapt to its specific local environment and usage patterns, while still participating in the overall centralized power management framework
3Reliability
If continuous monitoring of all thin client activities is performed, then unusual activities can be detected, but system complexity and processing requirements increase
Solution Approach 1:
The system extracts and monitors only specific key activity indicators and unusual patterns from thin client operations, rather than continuously analyzing all activities, reducing processing complexity while maintaining security detection effectiveness
Solution Approach 2:
An intermediary monitoring layer is introduced that sits between the thin client devices and the central server, performing preliminary analysis of device activities and only reporting unusual patterns to the server, thereby reducing overall system complexity and processing requirements
4Ease of operation
If thin client devices remain in active state to ensure immediate responsiveness, then user satisfaction is improved, but power consumption increases
Solution Approach 1:
The system implements periodic monitoring of usage patterns and environmental conditions to determine optimal power state transitions, allowing devices to cycle between active and sleep modes based on predicted usage needs, balancing responsiveness with energy savings
Data Source
AI summary
Systems and methods for automatic power control and unusual activity detections for thin client computing devices. In operation, the server receives messages from the thin clients, with each message corresponding to a activity occurred at the thin clients. The server then analyzes the messages and generates usage patterns of the thin client computing devices. Based on the usage patterns, the server may generate automatic power control schedules for the computing devices, and control power of each of the computing devices based on the automatic power control schedules. Further, the server may monitor unusual activities occurred at the computing devices based on the messages and the usage patterns of the computing devices.


