TOPOLOGY OPTIMIZATION IN IoT NETWORKS USING GRAPH THEORY

Abstract

The invention presents a novel method for optimizing the topology of Internet of Things (IoT) networks using graph theory. By modelling the network as a graph and applying algorithms such as the minimum spanning tree (MST) and shortest path algorithms, the system minimizes latency, improves routing efficiency, and reduces energy consumption. The system dynamically adapts to changes in the network, ensuring continuous optimization and robustness against failures. This invention provides a scalable solution for managing large-scale IoT networks with minimal energy usage and maximum connectivity.

Specification

Description:FIELD OF THE INVENTION
The present invention relates to the field of Internet of Things (IoT) networks, specifically focusing on network topology optimization using mathematical techniques from graph theory. This invention aims to improve connectivity, reduce latency, and enhance the efficiency of data transmission in large-scale IoT environments.

BACKGROUND OF THE INVENTION
With the exponential growth of IoT devices, efficient management of network topology has become essential. IoT networks often face challenges such as high latency, inefficient routing, and unreliable connections due to the distributed nature of devices. Traditional methods are based on heuristic or probabilistic approaches, which are insufficient to handle the complexity and dynamics of modern IoT networks.
There is a need for an intelligent and scalable approach to optimize network topology, minimizing latency while ensuring reliable data transmission. Graph theory provides a robust framework to model the network efficiently and optimize its structure, improving data transmission efficiency, fault tolerance, and energy consumption.

SUMMARY OF THE INVENTION
The present invention provides a method for optimizing IoT network topology using graph theory. This system models IoT devices and their interconnections as a graph, where nodes represent devices and edges represent communication links. The system optimizes the network based on the following objectives:
Minimized Latency: Reducing the time required for data to travel between nodes.
Optimized Routing Paths: Identifying the most efficient and reliable paths for data transmission.
Improved Network Robustness: Enhancing the network's fault tolerance against node or link failures.
Energy Efficiency: Reducing power consumption by minimizing unnecessary communication and optimizing device wake-up schedules.
This invention applies graph-theoretic algorithms, including minimum spanning trees (MST), shortest path algorithms, network flow algorithms, and Eulerian circuits, to achieve optimal topology. It also supports dynamic re-optimization based on real-time changes in the network.

DETAILED DESCRIPTION OF THE INVENTION
1. System Architecture
The system architecture consists of:
A network graph model, where nodes represent devices and edges represent communication links.
A centralized server that runs graph-theory-based optimization algorithms.
A real-time update module that dynamically re-optimizes the network based on changes in device locations or network conditions.
2. Graph-Theory Algorithms for Optimization
Minimum Spanning Tree (MST):
Reduces communication costs by minimizing the total number of edges while maintaining connectivity without cycles.
Shortest Path Algorithm (Dijkstra):
Identifies the most efficient paths between any two devices, ensuring reduced latency.
Max Flow-Min Cut Algorithm:
Ensures maximum data flow through the network while minimizing bottlenecks.
Eulerian Path/Circuit:
Ensures efficient traversal of the network by covering all nodes with minimal path repetition, conserving energy.
3. Dynamic Topology Adaptation
The system monitors the state of the network continuously. In case of new device addition, removal, or failure, the system dynamically re-optimizes the topology using real-time data to maintain performance. The changes are instantly reflected, ensuring uninterrupted connectivity.
4. Energy Efficiency Optimization
The invention reduces energy consumption by:
Eliminating redundant communication links through the MST algorithm.
Optimizing wake-up schedules of IoT devices, ensuring devices are active only when needed to extend battery life.
5. Network Robustness and Fault Tolerance
The system ensures network reliability through redundancy. In case of node or link failure, the system identifies alternate paths for data transmission using edge-connectivity and flow algorithms, ensuring fault tolerance.
, Claims:WE CLAIMS
1. A method for optimizing IoT network topology using graph theory, wherein the network is modeled as a graph with nodes representing devices and edges representing communication links, the method comprising:
a. Applying a minimum spanning tree (MST) algorithm to minimize communication costs;
b. Utilizing a shortest path algorithm to reduce data transmission latency;
c. Implementing network flow algorithms to enhance the efficiency and reliability of data flow.
The method of claim 1, wherein the MST algorithm dynamically adjusts the topology based on real-time changes, including the addition or removal of devices.
The method of claim 1, wherein energy efficiency is achieved by minimizing redundant communication links and optimizing device wake-up schedules.
The method of claim 1, wherein fault tolerance is ensured by identifying alternative routing paths during node or connection failures.
A system for real-time optimization of IoT network topology, comprising:
a. A server running graph-theory-based algorithms; and
b. A module for dynamic updates of the network based on real-time data.

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