Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management

Alsukayti, Ibrahim S.

doi:10.3390/electronics12183834

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…The vast majority of the works in the RPL put together IoT network centered concerning the Objective Function based Directing. The setting mindful goal capability-based steering likewise acted in an RPL-based IoT network (17) . In any case, none of the works have focused on the information setting (i.e., Touchy and Non-Delicate information) based steering in the RPL organization (18) .…”

Section: Introductionmentioning

confidence: 99%

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Shashidhar,

Thanuja,

Kunabeva

2024

IJST

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Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for LowPower and Lossy Networks), Optimization, Routing, Multimedia, Healthcare

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Section: Introductionmentioning

confidence: 99%

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Shashidhar,

Thanuja,

Kunabeva

2024

IJST

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Monitoring and enhancing the co-operation of IoT network rhrough scheduling function based punishment reward strategy

Sangi,

Li,

Anamalamudi

et al. 2024

PLoS ONE

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The Internet of Things (IoT) has revolutionized the connectivity of physical devices, leading to an exponential increase in multimedia wireless traffic and creating substantial demand for radio spectrum. Given the inherent scarcity of available spectrum, Cognitive Radio (CR)-assisted IoT emerges as a promising solution to optimize spectrum utilization through cooperation between cognitive and IoT nodes. Unlicensed IoT nodes can opportunistically access licensed spectrum bands without causing interference to licensed users. However, energy constraints may lead to reduced cooperation from IoT nodes during the search for vacant channels, as they aim to conserve battery life. To address this issue, we propose a Punishment-reward-based Cooperative Sensing and Data Forwarding (PR-CSDF) approach for IoT data transmission. Our method involves two key steps: (1) distributing sensing tasks among IoT nodes and (2) enhancing cooperation through a reward and punishment strategy. Evaluation results demonstrate that both secondary users (SUs) and IoT nodes achieve significant utility gains with the proposed mechanism, providing strong incentives for cooperative behaviour.

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Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management

Cited by 2 publications

References 27 publications

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Monitoring and enhancing the co-operation of IoT network rhrough scheduling function based punishment reward strategy

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