Demand-Driven Priority Data Structure for Wireless Network Efficiency
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Solution Overview
Problem
In harsh radio environments, existing data dissemination methods in ad-hoc wireless networks are inefficient due to redundant data transmissions and lack of optimization for broadcast mediums, leading to significant inefficiencies in data delivery.
Innovation Solution
A Demand-Driven Priority Data Structure (DDPDS) is implemented, which prioritizes data units based on their importance, connectivity type, and transmission rate among nodes, using a significance factor to schedule data dissemination efficiently, minimizing redundancy and optimizing data delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If epidemic algorithms or gossip algorithms are used for data retransmission, then data can be disseminated among nodes, but redundant data transmissions occur leading to inefficiency
Solution Approach 1:
The patent maintains a data structure at each node that pre-records which data units have been received by which nodes. Before retransmission, the system queries this data structure to identify exactly which nodes are missing which data units, enabling targeted retransmission rather than random or blanket retransmission of all data
Solution Approach 2:
The patent implements a feedback mechanism where nodes report back to the source node about which data units they have successfully received. This feedback information is stored in the data structure and used to determine subsequent retransmission needs, allowing the system to adapt retransmission behavior based on actual network conditions and node states
2Ease of operation
If random neighbor selection is used in gossip algorithms, then data dissemination can proceed, but redundant transmissions occur and energy is wasted
Solution Approach 1:
The data structure pre-calculates and stores the reception status of all data units across all nodes before retransmission begins. This preliminary information allows the source node to identify exactly which nodes need which data units, eliminating the need for random neighbor selection and associated redundant transmissions
Solution Approach 2:
Each node maintains its own record of received data units and can independently determine what data it still needs. This self-service approach allows nodes to request only the specific data units they are missing rather than participating in energy-wasting random exchanges
3Reliability
If point-to-point solutions are used for data transmission, then direct node-to-node communication is achieved, but optimization for broadcast medium is lost
Solution Approach 1:
The patent designs a data structure and control mechanism that serves multiple functions: it tracks individual node reception status for reliable point-to-point delivery, aggregates this information for network-wide optimization, and enables the source node to make informed decisions about whether to use unicast or broadcast transmissions based on the collective state of all nodes
Data Source
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AI summary
A system and method for optimizing the efficiency of data sharing among a set of radio nodes in a radio environment is provided. The technique includes identifying a data unit and assigning it a class, identifying a connectivity type, computing a significance factor based on connectivity type and assigned class, mapping the significance factor to a priority factor, and scheduling output of the data unit based on the priority factor. A scheduler algorithm can be used for scheduling output. The class can be assigned based on which nodes of the set of radio nodes possess copies of the data unit at a given time. An arbitration process can be used to determine the priority factor. A significance factor weighting algorithm, based on how many nodes will be receiving the data and on how much a node needs the data, can be used to determine the significance factor.