Performance improvement of Routing Protocol for Low‐Power and Lossy Networks protocol in an Internet of Things‐based smart retail system

Nazaralipoorsoomali, Mohammad; Asghari, Parvaneh; Javadi, Hamid Haj Seyyed

doi:10.1002/dac.5166

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…WSN middleware exists in many aspects of computer science, including artificial intelligence, computer systems and networks, security, database systems, human–computer interaction, vision and graphics, numerical analysis, programming languages, software engineering, bioinformatics, and the theory of computing [ 39 ]. WSNs are also employed in applications related to urban [ 40 ] and rural [ 41 ] projects, outdoor [ 42 ] and indoor [ 34 , 43 ] tracking, environmental monitoring [ 44 ], disaster monitoring [ 45 ], water quality [ 46 ], habitat monitoring [ 47 ], traffic monitoring [ 48 ], earthquake detection [ 49 ], volcano eruption [ 50 ], agriculture [ 51 ], weather forecasting [ 52 ], smart water management [ 53 ], smart cities [ 54 ], smart health [ 55 ], smart energy management [ 56 ], smart traffic management [ 57 ], smart retail [ 58 ], smart security [ 59 ], smart waste management [ 60 ], smart education [ 61 ], and smart homes [ 62 ].…”

Section: Related Workmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

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This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design’s innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis’ residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

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Section: Related Workmentioning

confidence: 99%

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Gazis,

Katsiri

2024

Sensors

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“…An alternative performance evaluation of RPL protocol done by Nazaralipoorsoomali et al. [14] with three routing metrics MRHOF ETX, MRHOF RE and OF0. This work shows a slight performance improvement based on the node placement.…”

Section: Related Workmentioning

confidence: 99%

ETXRE: Energy and delay efficient routing metric for RPL protocol and wireless sensor networks

Nait Abbou,

Manner

2023

IET Wireless Sensor Systems

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Internet of Things is an emerging paradigm based on interconnecting physical and virtual objects with each other and to the Internet. Most connected things fall into the category of constrained devices, with restricted resources (processing power, memory, and energy). These low‐power and lossy networks (LLNs) are known for their instability, high loss rates and low data rates, which makes routing one of the most challenging problems in low‐cost communications. A routing protocol for low‐power and lossy networks (RPL) is a proactive dynamic routing protocol based on IPv6. This protocol defines an objective function (OF) that utilises a set of metrics to select the best possible path to the destination. Minimum rank hysteresis objective function (MRHOF) and objective function zero (OF0) are the most basic OFs, where the first one selects the path to the sink based on the expected transmission count (ETX) metric, and OF0 is based on the hop count (HC). These two metrics prioritise either brute performance (i.e. ETX) or simplicity (i.e. HC). Therefore, using a single metric with an OF can either limit the performance or have an inefficient impact on load management and energy consumption. To overcome these challenges, a routing metric based on MRHOF OF which takes into consideration the link‐based routing metric (i.e. ETX) and node‐based metric (i.e. remaining energy) for route selection is provided. Expected transmission count remaining energy (ETXRE) is evaluated through 36 scenarios with different parameters. Preliminary results show that ETXRE outperforms ETX and RE in terms of end‐to‐end delay by an average of at least 17%, packet delay by 13% and consumes 10% less energy.

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“…An IoT network is formed by the connecting of sensing nodes (2) . Routing protocol for low power and lossy network (RPL) acts as an efficient platform for the routing operation of the IPv6 nodes (4) . Meanwhile, RPL suffers from certain drawbacks, which include QoS parameter degradation and energy distribution.…”

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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Performance improvement of Routing Protocol for Low‐Power and Lossy Networks protocol in an Internet of Things‐based smart retail system

Cited by 9 publications

References 24 publications

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

ETXRE: Energy and delay efficient routing metric for RPL protocol and wireless sensor networks

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

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