Group-Based Data Transfer in M2M Systems
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Solution Overview
Problem
Managing data flow and interoperability among diverse devices in IoT systems is challenging due to the lack of unified abstraction in M2M networks, leading to inefficient data communication and bandwidth issues, especially as the number of interconnected devices increases.
Innovation Solution
Implementing a group computing framework with a cast-converge programming model that abstracts devices into groups based on attributes and rules, allowing for efficient data filtering and processing within defined groups, thereby reducing unnecessary data broadcasts and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data broadcasting is implemented in M2M networks, then device connectivity and communication coverage are improved, but data redundancy and bandwidth consumption increase significantly
Solution Approach 1:
The patent segments devices into groups based on their attributes (e.g., device type, location, function) and enables selective data broadcasting to specific groups rather than all devices. This is achieved through group identifiers and filtering mechanisms that allow the system to divide the complete device population into manageable subsets, reducing redundant transmissions while maintaining connectivity for relevant devices.
Solution Approach 2:
The patent implements local quality by tailoring data broadcast characteristics to specific device groups. Different groups receive customized data based on their attributes, with control over broadcast parameters (frequency, content, timing) adjusted locally for each group. This allows optimization of bandwidth usage for each segment while maintaining overall system reliability.
2Loss of information
If manual configuration of device groups is used, then data filtering precision is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent enables automatic group assignment where devices autonomously determine their group memberships based on their attributes. The system automatically configures group definitions and data filtering rules without requiring manual intervention. Devices self-identify, self-categorize, and self-configure their data reception parameters, eliminating complex manual setup while maintaining precise data filtering through attribute-based automatic classification.
3Ease of operation
If all devices receive all data broadcasts, then communication simplicity is maintained, but energy consumption and processing overhead increase
Solution Approach 1:
The patent applies partial action by having devices selectively receive and process only the data relevant to their group, rather than all devices processing all broadcasts. The system maintains simplicity through automatic filtering mechanisms that operate at the protocol level, while significantly reducing energy consumption by preventing unnecessary data reception and processing for devices outside their designated groups.
Data Source
AI summary
A particular device is provided with a communications module to receive signals of a plurality of devices within range of the particular device and further provisioned with grouping logic. The grouping logic is executable by one or more processors to determine from each of the signals a respective identifier for each of the plurality of devices, determine, based at least in part on the identifiers, that a particular subset of the plurality of devices are also included with the particular device in a particular one of a plurality of defined groups, and converge data received from the particular subset of devices based on the particular group.