Decentralized Clustering in Wireless Sensor Networks
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Solution Overview
Problem
Current clustering methods in wireless sensor networks are inefficient, as they often select cluster heads based on probability functions, require excessive message exchange, and are not scalable for large networks, leading to energy wastage and reduced network lifetime.
Innovation Solution
A decentralized clustering method where nodes calculate a scoring system to select cluster heads based on merit, ensuring each node belongs to only one cluster, with cluster heads distributed evenly to reduce communication costs and energy consumption, and the clustering process is completed within a predetermined finite time, independent of network size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If cluster head selection methods take into account nodes' energy to select the best cluster heads, then network lifetime is extended, but a large number of messages need to be exchanged between various nodes during such clustering approaches
Solution Approach 1:
The patent segments the cluster head selection process into multiple phases: initial cluster formation phase where nodes exchange messages to form clusters, and subsequent phases where clusters operate independently. This segmentation allows energy-intensive message exchange to occur only once during initialization, rather than continuously, thereby extending network lifetime while controlling energy consumption.
Solution Approach 2:
The patent performs preliminary cluster formation and cluster head selection before the main network operation begins. Nodes exchange messages and establish clusters in advance, so that during normal operation, the energy-intensive selection process does not need to repeat. This preliminary action reduces ongoing energy consumption while maintaining network lifetime extension benefits.
2Reliability
If centralized approaches are used to gather all information for each node, then the best clusters can be created, but gathering all information for each node is a time and energy-consuming task and not applicable to large-scale WSNs
Solution Approach 1:
The patent divides the network into multiple clusters, with each cluster operating semi-independently under its own cluster head. This segmentation allows local decision-making within each cluster rather than requiring centralized collection of information from all nodes in the entire network. The result is high-quality clusters formed through local optimization without the time and energy costs of global information gathering.
Solution Approach 2:
The patent implements local quality by allowing each cluster to optimize its own composition and operations independently based on local conditions, rather than requiring uniform global optimization. Each cluster head makes decisions based on information from its own cluster members, enabling fast, energy-efficient cluster formation with high local quality that scales to large networks.
3Loss of energy
If probability functions are used to select cluster heads, then the clustering process does not require a lot of energy, but the appropriate cluster head is not chosen to maximize the network lifetime
Solution Approach 1:
The patent performs preliminary energy-aware cluster head selection during the initial cluster formation phase, where nodes exchange information about their energy levels and suitability for cluster head roles. This preliminary assessment ensures that the most energy-appropriate nodes are selected as cluster heads before the main network operation begins, maximizing network lifetime without requiring continuous energy-intensive reselection processes.
Solution Approach 2:
The patent implements a self-service mechanism where nodes autonomously evaluate their own energy levels and suitability for cluster head roles, and this information is shared with neighboring nodes during cluster formation. This self-assessment approach allows energy-optimal cluster head selection without requiring complex centralized evaluation or continuous monitoring, balancing low energy consumption with extended network lifetime.
Data Source
AI summary
An improved method of clustering wireless sensor networks includes selecting cluster heads in the network based on a score calculated for each node. The score is calculated based on one or more predetermined criteria of each node and determines which node is selected as a cluster head among one or more nodes in the vicinity of each other. As a result, the score of each cluster head node at the time of its selection is higher than or the same as its cluster node members. Moreover, the cluster heads may be selected such that they are not located in close proximity to each other and are fairly distributed in the environment. The duration of this decentralized clustering process is finite and predetermined and does not depend on the number of nodes and the size of the network.