Compression-Aware Aggregation and Energy-Aware Routing in IoT–Fog-Enabled Forest Environment

Swaminathan, Srividhya; Sankaranarayanan, Suresh; Kozlov, S. A.; Rodrigues, Joel J. P. C.

doi:10.3390/s21134591

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…In this scenario, RPL faces very high packet loss and also frequent parent changes. The main challenge for RPL during interference is reliable packet delivery to the destined node (11) . This issue of interference has been widely studied, and many researchers have come up with cross-layer solutions to mitigate the problem of interference (12) .…”

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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“…A fault diagnosis system aims to improve the productivity of a process and reduce the costs by monitoring the manufacturing process and notifying it of any failures in advance [1][2][3][4][5][6]. Machining is one of the processing methods used in product manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

Park

Lee

Yeo

et al. 2022

Sensors

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Fault diagnosis systems are used to improve the productivity and reduce the costs of the manufacturing process. However, the feature variables in existing systems are extracted based on the classification performance of the final model, thereby limiting their applications to models with different conditions. This paper proposes an algorithm to improve the characteristics of feature variables by considering the cutting conditions. Regardless of the frequency band, the noise of the measurement data was reduced through an oversampling method, setting a window length through a cutter sampling frequency, and improving its sensitivity to shock signal. An experiment was subsequently performed to confirm the performance of the model. Using normal and wear tools on AI7075 and SM45C, the diagnosis accuracies were 97.1% and 95.6%, respectively, with a reduction of 85% and 83%, respectively, in the time required to develop a diagnosis model. Therefore, the proposed algorithm reduced the model computation time and developed a model with high accuracy by enhancing the characteristics of the feature variable. The results of this study can contribute significantly to the establishment of a high-precision monitoring system for various processing processes.

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“…Furthermore, sensor nodes consume more energy with increasing transmission range. To overcome the drawbacks in E-RPL, compressed data aggregation and energy-based RPL routing (CAA-ERPL) [9] was proposed. The aggregation scheme has been proposed for balancing the load, minimizing the delay, improving the scalability of the network, and reducing the power consumption.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment

Srividhya¹,

Sankaranarayanan²,

Kozlov³

et al. 2022

Computers, Materials &Amp; Continua

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Forested areas are extremely vulnerable to disasters leading to environmental destruction. Forest Fire is one among them which requires immediate attention. There are lot of works done by authors where Wireless Sensors and IoT have been used for forest fire monitoring. So, towards monitoring the forest fire and managing the energy efficiently in IoT, Energy Efficient Routing Protocol for Low power lossy networks (E-RPL) was developed. There were challenges about the scalability of the network resulting in a large end-to-end delay and less packet delivery which led to the development of Aggregator-based Energy Efficient RPL with Data Compression (CAA-ERPL). Though CAA-ERPL proved effective in terms of reduced packet delivery, less energy consumption, and increased packet delivery ratio for varying number of nodes, there is still challenge in the selection of aggregator which is based purely on probability percentage of nodes. There has been research work where fuzzy logic been employed for Mobile Ad-hoc Routing, RPL routing and cluster head selection in Wireless Sensor. There has been no work where fuzzy logic is employed for aggregator selection in Energy Efficient RPL. So accordingly, we here have proposed Fuzzy Based Aggregator selection in Energy-efficient RPL for region thereby forming DODAG for communicating to Fog/Edge. We here have developed fuzzy inference rules for selecting the aggregator based on strength which takes residual power, Node degree, and Expected Transmission Count (ETX) as input metrics. The Fuzzy Aggregator Energy Efficient RPL (FA-ERPL) based on fuzzy inference rules were analysed against E-RPL in terms of scalability (First and Half Node die), Energy Consumption, and aggregator node energy deviation. From the analysis, it was found that FA-ERPL performed better than E-RPL. These were simulated using MATLAB and results.

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Compression-Aware Aggregation and Energy-Aware Routing in IoT–Fog-Enabled Forest Environment

Cited by 6 publications

References 30 publications

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process

Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment

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