Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Optimized Topology Control for IoT Networks using Graph-based Localization

Indrakshi Dey, Nicola Marchetti

TL;DR

This paper tackles the problem of topology design and control through the designed graph-realization concept, where end-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph.

Abstract

The key research question we are addressing in this paper, is how local distance information can be integrated into the global structure determination, in the form of network graphs realization for IoT networks. IoT networks will be pervading every walk of life over the next few years with the aim of improving quality of life and enhancing surrounding living conditions, while balancing available resources, like energy and computational power. As we deal with massive number of heterogeneous devices contributing to each IoT network, it is of paramount importance that the IoT network topology can be designed and controlled in such a way that coverage and throughput can be maximized using a minimum number of devices, while tackling challenges like poor link quality and interference. We tackle the above-mentioned problem of topology design and control through our designed graph-realization concept. End-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph. The stitching is done in a way that transmit power and information rate are optimized while reducing error probability.

Optimized Topology Control for IoT Networks using Graph-based Localization

TL;DR

This paper tackles the problem of topology design and control through the designed graph-realization concept, where end-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph.

Abstract

The key research question we are addressing in this paper, is how local distance information can be integrated into the global structure determination, in the form of network graphs realization for IoT networks. IoT networks will be pervading every walk of life over the next few years with the aim of improving quality of life and enhancing surrounding living conditions, while balancing available resources, like energy and computational power. As we deal with massive number of heterogeneous devices contributing to each IoT network, it is of paramount importance that the IoT network topology can be designed and controlled in such a way that coverage and throughput can be maximized using a minimum number of devices, while tackling challenges like poor link quality and interference. We tackle the above-mentioned problem of topology design and control through our designed graph-realization concept. End-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph. The stitching is done in a way that transmit power and information rate are optimized while reducing error probability.
Paper Structure (23 sections, 38 equations, 8 figures, 2 algorithms)

This paper contains 23 sections, 38 equations, 8 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Motivation
  3. Related Works
  4. Contributions
  5. Network Graph Modelling
  6. Graph-embedding for End-nodes
  7. Sub-graph Reflection
  8. Sub-graph Rotation
  9. Sub-graph Translation
  10. Graph-embedding for Central and End-nodes
  11. Sub-graph Embedding for the Global Graph
  12. Aligning Sub-graphs and Sewing them together
  13. Topology Design and Control
  14. Information Coding for Transmission
  15. Transmit Power Allocation
  16. ...and 8 more sections

Figures (8)

  • Figure 1: A conceptual overview of proposed optimized topology control. Different colors represent different types of IoT nodes.
  • Figure 2: Variation in the number of iterations needed by different topology control algorithms as the number of nodes within the network increases; the nodes are considered to be randomly distributed over a 5 $\times$ 5 km$^2$ square area.
  • Figure 3: Comparative variation in average transmission range per node within the network, over different values of average received signal energy per node in an IoT network consisting of 100 nodes randomly distributed over a 5 × 5 km$^2$ square area.
  • Figure 4: Comparative variation in average energy supply remaining in each node within the network over different values of average transmit signal power per node in an IoT network consisting of 100 nodes randomly distributed over a 5 × 5 km$^2$ square area.
  • Figure 5: Comparative variation in average error probability in transmission per node within the network, over different values of average transmit signal power per node in an IoT network consisting of 100 nodes randomly distributed over a 5 × 5 km$^2$ square area.
  • ...and 3 more figures