Data aggregation protocols for WSN and IoT applications – A comprehensive survey

Begum, Beneyaz A.; Nandury, Satyanarayana V.

doi:10.1016/j.jksuci.2023.01.008

Cited by 43 publications

(17 citation statements)

References 128 publications

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“…• Data aggregation: Data aggregation in IoT networks involves gateway nodes strategically collecting and consolidating data from multiple sensor nodes to optimize network efficiency and resource utilization [88]. This process minimizes the amount of data transmitted, improving resource utilization and reducing congestion in scenarios with many sensor nodes [89].…”

Section: Gateway Node Configurationmentioning

confidence: 99%

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Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Uddin,

Koo

2024

Applied Sciences

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This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations through real-time remote patient monitoring.

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Section: Gateway Node Configurationmentioning

confidence: 99%

“…The complexity of aggregating heterogeneous data from diverse biosensors requires standardized formats and protocols to ensure interoperability. Integrating data seamlessly while maintaining accuracy and reliability is a fundamental challenge given the variations in sensor types, data rates, and formats[88].…”

mentioning

confidence: 99%

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Uddin,

Koo

2024

Applied Sciences

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“…A wireless sensor network (WSN) [1,2], which is made up of different kinds of sensors with limited resources, is an important part of monitoring an environment and sending important data to a designated node, also called a sink, through different communication protocols. These data are then relayed to a base station for meticulous analysis and processing, catered to the specific demands of contemporary applications.…”

Section: Introductionmentioning

confidence: 99%

“…The presence of irrelevant features and network communication disturbances further exacerbate this challenge, leading to increased resource expenditure and diminished detection rates. Therefore, the nuanced selection of features has emerged as a vital aspect of machine learning designed to accurately encapsulate object properties while eliminating redundant data [2].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security

Krishnasamy,

Alotaibi,

Alehaideb

et al. 2023

Sensors

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In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F3S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.

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“…As a result of their useful properties [3], such as low consumption of energy, high reliability, as well as simple deployment, WSNs are being used in a variety of applications, including medical and monitoring equipment. WSNs have a shorter lifespan as a result of constraints such as constrained connectivity, memory, processing capabilities, and compute capability [4]. Due to the fact that many sensor devices run on batteries, it is crucial to find ways to conserve electricity.…”

Section: Introductionmentioning

confidence: 99%

Adaptive traffic aware clustering and routing model for wireless sensor networks

Veeregowdanadoddi Chandraiah,

Ramalingappa

2023

IJEECS

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<span>Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.</span>

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Data aggregation protocols for WSN and IoT applications – A comprehensive survey

Cited by 43 publications

References 128 publications

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security

Adaptive traffic aware clustering and routing model for wireless sensor networks

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