An Analysis of Data Dribble in IoT Devices Using Fuzzy Networks

Abstract

It is impossible to ignore this information centre segment because it is closely related to the Web of Material. Wireless sensor networks, which operate in a variety of situations, are essential to the web of Contents. They serve as the fundamental framework for the Internet of Things network in many important domains, including manufacturing operations, environmental tracking, as well as the artificially intelligent neighbourhood.Nevertheless, a multitude of obstacles which are for instance, security and technical issues arise alongside such integrated sensor networks, particularly when trying to scale them up and deploy them seamlessly. The inherent challenges of WSNs largely arise as a result of their constrained and defective nodes which can be attributed to power and computation while the bandwidth is limited. Included among the main technology challenges in WSNs guided by IoT bartered with the weighty data transmission with an energy concern cost the balance with the longevity of the deployment, as well as the security robustness against future threats. Most importantly, such issues need to be solved to eliminate any constraints for the integration of low-powered IoT devices. Energy efficiency remains an important design consideration in IoT-enabled WSNs as sensor nodes are for the most part powered by batteries with limited capacities. Network Layer Global Unique VLAN ID Confabulating features which are typical for uniform WSNs do deliver part of the answer, but few can cope with issues related to the complexity of heterogeneous.

Introduction

Overview

As the Internet of Things (IoT) grows, efficient and secure data routing becomes increasingly important due to the resource constraints of IoT devices and sensitivity of transmitted data. This research addresses these concerns by exploring advanced routing strategies that combine energy efficiency, security, and trust management within Wireless Sensor Networks (WSNs), which form the backbone of many IoT systems.

Key Issues in IoT Routing

• Traditional routing protocols often prioritize hop count or shortest path, neglecting energy usage, trustworthiness, and adaptability.

• IoT networks are dynamic, diverse, and operate under tight power and processing constraints, requiring innovative protocols for long-term operation.

• Sensor nodes deplete energy quickly and are often non-replaceable, making energy-aware routing critical.

Proposed Solution: TERP (Trust-related Energy Efficient Routing Policy)

• TERP is a trust-based routing model designed to improve upon conventional protocols by:

  • Evaluating node behavior and past interactions to establish trust.

  • Enhancing secure, reliable, and energy-aware data transmission.

• TERP dynamically selects routes based on trust metrics and energy efficiency, ensuring data integrity and network robustness.

Three-Phase Routing Framework

1. Phase I – Multi-path Link Routing Protocol (MLRP)
   Secure data transmission through neighbor discovery, encrypted communication (key distribution), and multi-path routing.

2. Phase II – H-TEEN Protocol
   Focuses on load balancing and energy optimization by clustering nodes, extending network lifespan.

3. Phase III – Data Storage and Management
   Emphasizes reliable storage, capacity planning, and data redundancy, ensuring integrity and availability.

Alternative Routing Model: EEG (Energy-Efficient Geographic Routing)

• Uses fuzzy logic and Harris Hawks Optimization (HHO) to select optimal routes based on six factors:

  • Delay, Distance, Power consumption, QoS, Cost, and Trust.

• Incorporates geographic location for routing decisions and adapts to node conditions like energy level, link quality, and congestion.

Fuzzy Logic-Based Optimization Framework

• Input Parameters: Node energy, distance to destination, link quality, network congestion, and mobility.

• Fuzzy Inference System: Uses rules to evaluate the best routing path (e.g., if node energy is high and link quality is strong, choose as next hop).

• De-fuzzification: Converts fuzzy outputs into specific routing decisions.

• This model enables intelligent, context-aware routing in dynamic IoT settings.

Simulation and Testing Tools

IoT simulators are essential for evaluating routing protocols. Popular ones include:

1. Cooja + Contiki OS: Ideal for WSNs with real hardware integration.

2. OMNeT++: Modular and extendable but lacks default IoT protocol support.

3. NS-3: Supports cognitive-level IoT simulation.

4. QualNet: High-fidelity commercial simulator, excellent for cybersecurity and performance analysis.

Specialized Routing Protocol: FLEA-RPL

• A fuzzy logic-based enhancement of RPL, using metrics like Residual Energy Ratio (RER), Expected Transmission Count (ETX), and load.

• Dynamically adjusts routing based on load balancing and traffic optimization.

• Ensures better parent node selection in DODAG (Destination-Oriented Directed Acyclic Graph) structures for efficient IoT data transmission.

Applications and Benefits

• Urban IoT Architectures: Enable smart governance in transportation, healthcare, waste management, and public safety.

• Scalability & Adaptability: Proposed methods handle large-scale, dynamic networks with diverse requirements.

• Security & Trust: Improved data confidentiality, integrity, and protection from attacks like DDoS or eavesdropping.

• Energy Efficiency: Extends network lifespan by optimizing power consumption and load distribution.

•  

Conclusion

This study uses the conventional RPL custom to deal with the difficulties of IoT networking protocols. The initial change that is suggested is the Fuzzy Logic-based Energy-Aware RPL (FLEA-RPL) protocol, which uses fuzzy good judgement to optimize advice selection based on ETX, Load, and RER metrics. Simulation results indicate that FLEA-RPL greatly increases solidarity in their lives. The next protocol that is presented is the IoT Multilayer Energy-Aware RPL (MCEA-RPL), in which the ecosystem is divide. By utilising fuzzy logic in conjunction with RSSI and PER data to determine the transition value, the 0.33 protocol, IoT Enhanced Mobility Support RPL (EM-RPL), enhances connectivity overall. EM-RPL rapidly identifies change channels to minimise path breakdowns along with improving data transfer when the transfer of information cost surpasses a certain amount. The outcomes of the experiment show that EM-RPL enhances node movement. Future studies should build on the current examination, particularly by tackling the usage of a limited number of sink nodes for data succession, which streamlines the neighbourhood structure, even though those standards enhance the IoTorganisation lifetime. It is crucial to control energy use across nodes as internet of things gadgets proliferate. The primary objective of this research is to enhance the RPL standard (Routing Protocol for Low-Power, Lossy Networks) by tackling issues in the IoT routing protocols. system that assesses important indicators including network load along with projected message count (ETX). Additionally, the residual energy ratio (RER) is used to identify the best data transmission method. According to the simulation results, FLEA-RPL increases energy efficiency, which enhances overall performance. Energy Awareness at Several Levels RPL (MCEA-RPL): This protocol groups networks into comparable-sized groups. It optimises path selection using fuzzy logic in accordance with RER and ETX parameters. The data collected by the cluster head node is aggregated and sent to the sink node. Simulation results indicate that MCEA-RPL significantly extends the network lifetime compared to traditional RPL protocols by balancing power consumption among nodes. Advanced Movement Support RPL (EM-RPL):. EM-RPL addresses mobility challenges in IoT networks by applying fuzzy logic to measures such as received signal strength indicator (RSSI) and packet error rate (PER) to Calculate delivery value. Simulations show that EM-RPL reduces interference and improves data transfer efficiency. Added support for moving nodes. Summary of participation: These proposed protocols share the goal of extending the lifetime of IoT networks, and increasing energy efficiency. And increasing the ability to adapt to dynamic conditions such as node movement, etc., although current research reduces the complexity of the network architecture by using a limited number of sink nodes for data collection. But future work could expand on this matter. A framework to explore various IoT applications. These findings highlight the effectiveness of combining fuzzy logic with routing protocols to address key IoT challenges, paving the way for further innovation in this area.

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Copyright © 2025 Anjali Gujar, Ruchi Kulshrestha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.