M2M Device Management via Correlation Analysis
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Solution Overview
Problem
Machine to machine (M2M) networks face inefficiencies due to limited resources and collection of valueless data, leading to wasted power, storage, and network bandwidth, necessitating effective management to optimize data collection and resource utilization.
Innovation Solution
A method and apparatus for efficient device management in M2M networks that calculates correlation values between devices based on location and data samples, controlling operations of devices with higher correlation values to prevent unnecessary data collection, using a correlation analysis processor and operation control processor to determine and implement operation policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If M2M devices continuously collect and transmit data, then data availability is improved, but network bandwidth consumption increases and resources are wasted
Solution Approach 1:
The system performs preliminary correlation analysis between M2M devices before data collection to identify devices with high correlation values. By pre-determining which devices are redundant, the system can selectively control data collection, ensuring data availability from representative devices while avoiding unnecessary data gathering from correlated devices, thus reducing network bandwidth consumption.
Solution Approach 2:
The invention extracts and identifies redundant M2M devices from the network by calculating correlation values between devices. Once devices with high correlation values are extracted from the active data collection pool, the system maintains data availability through representative devices while eliminating unnecessary data transmission from redundant devices, thereby reducing network bandwidth consumption.
2Loss of information
If more M2M devices are deployed to collect diverse data, then data coverage is improved, but device deployment cost increases
Solution Approach 1:
The system continuously monitors and calculates correlation values between M2M devices, providing feedback on device redundancy. This feedback mechanism allows the network to identify when sufficient data coverage has been achieved through existing devices, preventing unnecessary deployment of additional devices and optimizing the balance between data coverage and deployment cost.
Solution Approach 2:
The invention changes the operational parameters of M2M devices based on correlation analysis results. By adjusting which devices remain active for data collection and which are placed in sleep mode or deactivated, the system maintains adequate data coverage while reducing the effective number of deployed devices, thereby lowering operational and deployment costs.
3Reliability
If M2M devices operate continuously to ensure data collection, then data collection reliability is improved, but device power consumption increases
Solution Approach 1:
The system implements periodic correlation analysis and selectively activates M2M devices based on current network conditions and correlation values. Instead of continuous operation, devices are activated periodically when needed, with correlation analysis determining which devices should be active. This periodic operation mode maintains data collection reliability while significantly reducing overall device power consumption.
Solution Approach 2:
M2M devices autonomously adjust their operational states based on correlation values and network conditions. Devices with low correlation values maintain higher activity levels, while devices with high correlation values are selectively deactivated or placed in sleep mode. This self-service mechanism ensures data collection reliability is maintained through appropriate device activation while minimizing overall power consumption across the network.
Data Source
AI summary
The present disclosure is related to device management in a machine to machine (M2M) network. Particularly, the present disclosure relates to performing an efficient device management based on correlations between M2M devices in the M2M network.