Deep Learning-Based Multiparametric Predictions for IoT

The dynamism and successive changes in the distribution of nodes are among the important characteristics of wireless sensor networks (WSNs). To adapt with those changes, network designers frequently have to configure multiple parameters for each layer on WSN architecture (i.e physical, medium access control, network, and application layer). This tuning has an important impact on the network performances (e.g packet loss, energy efficiency, throughput, network lifetime, etc). However, finding the optimal configuration is the main challenge. Deep learning (DL) based on neural network layers can be used to extract patterns from high-dimensional data provided by sensor nodes. In this paper, we survey the most recent DL approaches which aim to predict WSN performances by finding the pattern on the network parameters (such as transmission power level, MAC protocol type, spectrum availability, congestion points, etc.). Moreover, we classify the studied articles by considering the targeted network layer or cross-layer. This paper can be considered as a starting point for researchers to review the recent DL applications on the optimization of WSNs performances based on multiple network parameters.

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“…The trade-off between PDR and signal-to-noise ratio (SNR) is studied in [32]. An MLP approach is adapted to predict both metrics.…”

Section: The Joint Effect Of the Parameters From Multiple Layersmentioning

confidence: 99%

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

et al. 2022

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“…The idea of this experiment was to carry out foundation work to stem data-driven research aiming to facilitate and improve adaptive QoS control in WSNs driven IoT. In our previous endeavors [39]- [41] we have used the single-hop performance data [38] for statistical analysis and predictions. However, considering the limitations, we extended the idea to a broader perspective of performance by including more important metrics: i.e., throughput (THP), and detailed energy consumption (EC) in addition to packet delivery ratio (PDR).…”

Section: Experiments Parametersmentioning

confidence: 99%

Towards Data-Driven Control of QoS in IoT: Unleashing the Potential of Diversified Datasets

et al. 2021

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Cognition is of paramount importance in modern communication systems for this brings the potential for adaptiveness and self-fine-tuning for dynamic reconfigurability. To achieve this feat, two primary tasks are to identify the influential configurable parameters and availability of comprehensive datasets representative of the real-world scenarios rather than simulated ones. For this article, an extensive dataset covering diverse settings of wireless sensor networks (WSNs) driven internet of things (IoT) is collected. It covers broad variations of 10 pre-configured communication parameters as well as some runtime information. In addition to legacy parameters (e.g., transmission power, and packet size, etc.), we also used two different medium access control protocols (i.e., carrier sense multiple access (CSMA) and time-slotted channel hopping (TSCH)), and routing metrics (i.e., objective function 0 (OF0), minimum rank with hysteresis (MRH), MRH with expected transmission count (ETX 2 )). Important quality of service (QoS) metrics like packet delivery ratio, throughput, and energy consumption against all combinations of the communication parameters are measured and recorded. A thorough statistical analysis is carried out to identify the correlations among the communication parameters and QoS metrics. The results lay the foundation for the design of a data-driven framework for predictive QoS control in the IoT.

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“…Despite this, the results indicate that signal distortion can be decreased and enhanced greatly. SNR and PDR are predicted using a Neural Network (NN) in the publication [7]. Even when trained with only 10% of the data, the NN can predict signal-to-intervention noise ratio (SNR) and Packet Delivery Ratio (PDR) with up to 96 and 98 percent accuracy, respectively, using an actual dataset.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Chiwariro,

Muduva,

Makate

2024

IJRASET

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Smart cities are comprised of intelligent objects that can collectively and automatically improve living standards, preserve lives, and function as a sustainable environment. Drones or Unmanned aerial vehicles (UAV), robotics, cognitive computing, and the Internet of Things (IoT) are mandatory to enhance the intellectual ability of smart cities by enhancing connectivity, energy efficiency, and Quality of Signal (QoS). Consequently, the integration of drones with IoT plays a crucial part in enabling a vast array of smart-city applications. Drones are undeniably the technology of the future. They glide in the air, keeping an eye on things in their metropolis. They do not need human control or operation. They capture data based on visuals and sounds by employing a variety of sensors, webcams, and mics, and then transfer it to a gateway for processing and retrieving information. Drones will let us gather vast volumes of data for processing, while also enhancing the intelligence of smart cities. The drone's signal is vulnerable to absorption, refraction, diffraction, and attenuation. Therefore, it is essential to predict the signal from the drone. This study presents an intelligent method using Deep Learning to predict the signal strength for enhancing network connectivity and delivering the desired QoS of IoTs and drone integration. This enables effective data transmission, boosts QoS for end-users/devices, and reduces data transmission power consumption.

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Deep Learning-Based Multiparametric Predictions for IoT

Cited by 9 publications

References 28 publications

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

Performances prediction in Wireless Sensor Networks: A survey on Deep learning based-approaches

Towards Data-Driven Control of QoS in IoT: Unleashing the Potential of Diversified Datasets

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

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