Ad-hoc Network Node Grouping via Weight-Based Master Election
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Solution Overview
Problem
Existing methods for organizing nodes in large-scale ad-hoc networks are costly in terms of resources and limited in forming large groups, as they require each node to transmit weight information to many others, increasing resource consumption and system time.
Innovation Solution
A method where nodes calculate and store information about the most significant node with the highest weight, transmitting this information step-by-step, allowing nodes to determine a master node without needing to know all other nodes' weights, and repeating this process to form larger groups efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each node transmits weight information to all other nodes in the network, then the master node can be accurately determined, but the resource consumption and system time increase significantly
Solution Approach 1:
The network is divided into clusters with master nodes, and the weight information propagation is segmented into multiple rounds. Each node only needs to transmit its weight information to a limited number of neighbors in each round, rather than broadcasting to all nodes simultaneously. This segmentation reduces the transmission overhead while ensuring that weight information eventually reaches all necessary nodes through iterative propagation.
Solution Approach 2:
Nodes pre-calculate their weight values based on local parameters (mobility, transmission power, coverage area, battery power) before the master node election process. This preliminary calculation avoids the need for nodes to exchange and process complete weight information from all other nodes, as each node already has its own weight ready for comparison through limited local exchanges.
2Loss of information
If each node transmits weight information to all other nodes, then complete network awareness is achieved, but the method is only suitable for small groups of nodes
Solution Approach 1:
The network is organized into clusters of nodes, each with a master node. This segmentation allows the network to scale to large sizes by dividing it into multiple manageable groups. Each cluster operates semi-independently, with master nodes handling inter-cluster communication, enabling the overall network to grow without requiring every node to communicate with every other node.
Solution Approach 2:
The patent introduces a hierarchical dimension to the network structure, organizing nodes into clusters with master nodes that operate at a higher level. This dimensional change from a flat all-to-all communication model to a hierarchical clustered model enables scalability to large networks while maintaining information completeness through the hierarchical information flow.
3Reliability
If complete weight information is propagated across the network, then accurate master node selection is ensured, but the complexity of the organization method increases
Solution Approach 1:
Nodes pre-calculate their weight values based on local parameters before the election process. This preliminary action simplifies the subsequent master node selection by eliminating the need for complex real-time information exchange and processing, as each node already has its weight determined and ready for comparison through simple local message passing.
Solution Approach 2:
The master node election process is structured as a periodic iterative procedure where weight information is propagated in discrete rounds. In each round, nodes exchange information with their neighbors and update their knowledge of the most significant node. This periodic structure simplifies the complexity by breaking down the continuous information exchange into manageable discrete steps, making the overall process more tractable and easier to implement.
Data Source
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AI summary
The method comprises a step (130) of calculating, for each node, a weight representing the suitability of this node to be the master node of a grouping, and a step (190) of electing a master node of a grouping, such that the weight of this master node is greater than or equal to the weights of the other nodes of the grouping. The method also comprises a step (140, 180) of storing, in each node, an item of information (14) containing an identifier of the node of highest weight known by this node and the value of this highest weight, a step (150) of transmitting, by each node to nodes adjacent to this node, the information item (14) stored in this node, and a step (170) of comparing, for each node, information items (14) transmitted to this node during the transmitting step (150) with the item of information stored in this node, so as to determine a new node of highest weight known by this node. The steps of storing (180), transmitting (150) and comparing (170) are repeated successively a predetermined number (k) of times, on completion of which the step (190) of electing a master node is carried out, in the course of which a node (M) whose stored item of information contains its own identifier is chosen to be a master node.