Technological advances in wireless sensor networks enabling diverse Internet of Things applications

Yoo, Seong-eun; Cheng, Albert M. K.; Chong, Pohkit; López, Tomás Sánchez; Kim, Taejoon

doi:10.1177/1550147718763220

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…The article 15 devoted to the model of IoT interactions inspired us to create scenarios and requirements for application and generalization to embedded systems. Vibration monitoring using low-priced microcontrollers 16 is described by Atmel processor and another article 17 summarizes wireless communications in networks working with IoT modules. Related articles often deal with network architecture, protocols, and frameworks and describes limitations of traditional network architectures, 11 but the aim of our research is to develop software architecture.…”

Section: Related Workmentioning

confidence: 99%

Measurement and data processing from Internet of Things modules by dual-core application using ESP32 board

Foltýnek

Babiuch

Suranek

2019

Measurement and Control

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The contribution describes the application of an Internet of Things device with an ESP32 microcontroller with the dual-core implementation of data processing and wireless communication. The first leading idea of using low-end hardware and software optimization to create software architecture in order to make the most of this hardware and reduce the cost of more expensive hardware solutions was tested. Our goal in architectural design was achieved. We created the basis of a general framework and defined its use scenarios, and then implemented these scenarios using components. This article describes the progressive development of our embedded vibration measurement testing system, on which we tested the individual software components for our framework. We created an application for our components using ESP32 processor cores, which divided the responsibility of these cores for the components of our framework.

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Section: Related Workmentioning

confidence: 99%

Measurement and data processing from Internet of Things modules by dual-core application using ESP32 board

Foltýnek

Babiuch

Suranek

2019

Measurement and Control

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“…Nevertheless, the process of collecting and analyzing data in these networks presents significant difficulties due to the large volume of data produced by the sensors. In recent years, academia has researched a range of methodologies aimed at enhancing efficiency of the data collection stage [1]. The limited memory capacity, energy, and computational capabilities of sensor nodes restrict their ability to handle massive amounts of generated data.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

Saadallah,

Alabady

2024

JTIT

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This paper introduces a method that combines the K-means clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system's effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches.

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“…A wireless sensor network (WSN) is a collection of sensor nodes distributed within a particular area to observe some physical conditions and gather the observation at a base station (BS). 1 The nodes are typically battery-operated, and they have inherent limitations in computation and communication capacities. In most monitoring applications, it is often pricey and unattainable to interchange the node’s batteries since the networks are formed in harsh regions with hundreds to thousands of nodes.…”

Section: Introductionmentioning

confidence: 99%

A lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks

Agbulu

Kumar

Juliet

2020

International Journal of Distributed Sensor Networks

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Despite unique energy-saving dispositions of cluster-based routing protocols, clustered wireless sensor networks with static sinks typically have problems of unbalanced energy consumptions, as the cluster head nodes around the sink are typically loaded with traffic from upper levels of clusters. This results in reduced lifetimes of the nodes and deterioration of other crucial performances. Meanwhile, it has been inferred from current literature that dedicated relay cooperation in cluster-based wireless sensor networks guarantees longer lifetime of the nodes and more improved performance. Therefore, to attain further enhanced performance among the current schemes, a lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks is proposed in this article. The proposed lifetime-enhancing cooperative data gathering and relaying algorithm shares the nodes into clusters using a hybrid K-means clustering algorithm that combines K-means clustering and Huffman coding algorithms. It makes full use of dedicated relay cooperative multi-hop communication with network coding mechanisms to achieve reduced data propagation cost from the various cluster sections to the central base station. The relay node selection is framed as a NP-hard problem, with regard to communication distances and residual energy metrics. Furthermore, to resolve the problem, a gradient descent algorithm is proposed. Simulation results endorse the proposed scheme to outperform related schemes in terms of latency, lifetime, and energy consumption and delivery rates.

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Technological advances in wireless sensor networks enabling diverse Internet of Things applications

Cited by 6 publications

References 7 publications

Measurement and data processing from Internet of Things modules by dual-core application using ESP32 board

Measurement and data processing from Internet of Things modules by dual-core application using ESP32 board

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

A lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks

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