Hierarchical Context Handling for M2M Communication
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Solution Overview
Problem
Current machine-to-machine (M2M) communication architectures in mobile wireless networks, such as GPRS and LTE, face inefficiencies due to duplicative data storage and signaling overload, as they were designed for human users and do not effectively manage the unique characteristics of device communications, leading to increased costs and resource inefficiencies as the number of connected devices grows.
Innovation Solution
Implementing a hierarchical structure for information storage and context handling in the core network, where group-level information is shared among devices and device-specific information can override or supplement group-level data, reducing duplication and enhancing resource management, supported by a virtual optimized core (VOC) that consolidates core network functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional flat architecture is used for M2M communication, then individual device handling is straightforward, but signaling overhead increases and network resources are wasted due to duplicative data storage for each device
Solution Approach 1:
The patent segments the information storage structure into hierarchical levels (global, regional, local) instead of using a flat per-device storage approach. This segmentation allows common information to be stored at higher levels and only device-specific variations at lower levels, reducing overall storage requirements and signaling overhead while maintaining the ability to handle individual device requirements.
Solution Approach 2:
The patent introduces a hierarchical dimension to the information storage architecture, transforming the traditional two-dimensional (device-by-device) storage approach into a multi-dimensional hierarchical structure. This adds levels of abstraction (global, regional, local) that enable efficient aggregation and sharing of common information across multiple devices, reducing duplication and resource consumption.
2Reliability
If device-specific information is stored for every device, then individual device management is precise, but data duplication increases and storage efficiency decreases
Solution Approach 1:
The patent merges information storage across multiple devices by identifying and consolidating common information at global and regional levels. Instead of storing identical device-specific information for each device, the system combines common elements into shared repositories, keeping only unique variations at the local device level, thereby reducing total storage volume while maintaining precise device management capability.
Solution Approach 2:
The patent uses selective copying where common information is copied to higher hierarchical levels for shared storage, rather than duplicating entire device profiles. This allows efficient replication of common data elements while avoiding redundant storage of identical information, reducing overall data volume while preserving device-specific management precision.
3Productivity
If hierarchical information storage is implemented, then resource efficiency improves and data duplication is reduced, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a dynamic hierarchical storage system where the level at which information is stored can adapt based on device characteristics, group memberships, and information types. This dynamic approach allows the system to automatically optimize the hierarchical level for each piece of information, balancing resource efficiency with implementation complexity by only creating hierarchical structures when beneficial.
Solution Approach 2:
The patent creates universal information storage structures at higher hierarchical levels that serve multiple devices and functions simultaneously. These global and regional information repositories provide multi-functional support for various device groups, reducing the need for separate storage structures and lowering overall system complexity despite the hierarchical organization.
Data Source
AI summary
A hierarchical data structure for signaling and data handling at a packet core network is defined. A hierarchical data structure is applied to both persistent and dynamic information to support a type of devices that share the same characteristics. Subscription database, context information, and policy rule information is comprised of group level information and device level information. Group level information contains the information the devices that belong to the group share. Device level information contains the device specific information that is different from group level information. When a device belonging to a group requests access to a packet network or performs a procedure that changes the mobility or session state of the device, context management unit works with authentication unit, subscription database unit, and policy decision unit to construct a right information for the device to handle the control and user data traffic.