Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Optimizing Underwater IoT Routing with Multi-Criteria Decision Making and Uncertainty Weights

Ali Karkehabadi, Mitra Bakhshi, Seyed Behnam Razavian

TL;DR

The paper tackles energy- and latency-sensitive routing in underwater IoT by extending RPL with uncertainty-aware, multi-criteria decision making (SWARA and fuzzy SWARA) to select optimal parents under mobility and depth constraints. It introduces RPLUW, an underwater routing framework with DRL-assisted DODAG root lists, Trickle-based stability, and optimized IPv6 ND to handle dynamic links. A stepwise graph-construction method and MADM-based parent selection, weighted by multiple metrics (e.g., hops, energy, ARSSI, latency, ETX, link delivery, depth), yield substantial performance gains, including higher PDR and lower latency, compared to existing approaches. The results demonstrate the practical potential of integrating uncertain weighting into underwater routing, with implications for energy efficiency and QoS in challenging aquatic environments.

Abstract

Effective data routing is vital in the Internet of Things (IoT) paradigm, especially in underwater mobile sensor networks where inefficiency can lead to significant resource consumption. This article presents an innovative method designed to enhance network performance and reduce resource usage, while also accurately determining component weights in these networks, ensuring quality service. Building upon previous research on multi-criteria decision-making systems in coastal RPL networks, our method involves key adaptations for underwater environments. It integrates comprehensive network features to identify the optimal parent node for each sensor, employing the fuzzy SWARA decision-making approach under uncertain conditions. This method takes into account various factors including hops, energy, ARSSI rate, delay, ETX, link delivery rate, and depth to determine the most effective parent node assignment. Through simulation, our approach demonstrates marked improvements in network performance compared to existing solutions. These advancements are significant, offering a new direction in enhancing underwater IoT communications and suggesting wider applications for IoT systems facing similar challenges.

Optimizing Underwater IoT Routing with Multi-Criteria Decision Making and Uncertainty Weights

TL;DR

The paper tackles energy- and latency-sensitive routing in underwater IoT by extending RPL with uncertainty-aware, multi-criteria decision making (SWARA and fuzzy SWARA) to select optimal parents under mobility and depth constraints. It introduces RPLUW, an underwater routing framework with DRL-assisted DODAG root lists, Trickle-based stability, and optimized IPv6 ND to handle dynamic links. A stepwise graph-construction method and MADM-based parent selection, weighted by multiple metrics (e.g., hops, energy, ARSSI, latency, ETX, link delivery, depth), yield substantial performance gains, including higher PDR and lower latency, compared to existing approaches. The results demonstrate the practical potential of integrating uncertain weighting into underwater routing, with implications for energy efficiency and QoS in challenging aquatic environments.

Abstract

Effective data routing is vital in the Internet of Things (IoT) paradigm, especially in underwater mobile sensor networks where inefficiency can lead to significant resource consumption. This article presents an innovative method designed to enhance network performance and reduce resource usage, while also accurately determining component weights in these networks, ensuring quality service. Building upon previous research on multi-criteria decision-making systems in coastal RPL networks, our method involves key adaptations for underwater environments. It integrates comprehensive network features to identify the optimal parent node for each sensor, employing the fuzzy SWARA decision-making approach under uncertain conditions. This method takes into account various factors including hops, energy, ARSSI rate, delay, ETX, link delivery rate, and depth to determine the most effective parent node assignment. Through simulation, our approach demonstrates marked improvements in network performance compared to existing solutions. These advancements are significant, offering a new direction in enhancing underwater IoT communications and suggesting wider applications for IoT systems facing similar challenges.
Paper Structure (19 sections, 14 equations, 9 figures, 3 tables)

This paper contains 19 sections, 14 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Theoretical Background
  3. Speed of Sound in Water
  4. Underwater Frequency Link Quality Criterion
  5. Delay Time Model
  6. Calculating Node Depth
  7. Frequency Attenuation or Absorption Model
  8. The Proposed RPLUW Method
  9. System Model
  10. Handling Node Displacement in Underwater RPL Networks
  11. Efficient Trickle Timer Mechanism
  12. Optimized IPv6 Neighbor Discovery
  13. Timers in the RPLUW Method
  14. Research Methodology
  15. RPLUW Network Graph Construction Method
  16. ...and 4 more sections

Figures (9)

  • Figure 1: Formed graph with limit degree $\gamma = 1$
  • Figure 2: A network with several underwater sensors
  • Figure 4: RPL protocol structure in two normal sensor network environments and underwater sensor
  • Figure 5: Framework of Methodology
  • Figure 6: Average end-to-end latency of network packets in the case of fixed and mobile nodes
  • ...and 4 more figures