Investigating Supervised Machine Learning Techniques for Channel Identification in Wireless Sensor Networks

O'Mahony, George D.; Harris, Philip J.; Murphy, Colin C.

doi:10.1109/issc49989.2020.9180209

Cited by 5 publications

(9 citation statements)

References 10 publications

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“…Among the solutions developed against various problems of WSNs, the accurate detection and prediction of channel events play a crucial part. O'Mahony et al [15] proposed a method of analyzing the channel characteristics of WSNs using Support Vector Machine (SVM) and Random Forest (RF)-based Classification model (SMC). SVM and RF are the ML approaches used in the work to understand the nature of real-time wireless channel qualities.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Rajasoundaran

Stanislaus

Ramasamy³

et al. 2023

Energies

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Wireless Underground Sensor Networks (WUGSNs) transmit data collected from underground objects such as water substances, oil substances, soil contents, and others. In addition, the underground sensor nodes transmit the data to the surface nodes regarding underground irregularities, earthquake, landslides, military border surveillance, and other issues. The channel difficulties of WUGSNs create uncertain communication barriers. Recent research works have proposed different types of channel assessment techniques and security approaches. Moreover, the existing techniques are inadequate to learn the real-time channel attributes in order to build reactive data transmission models. The proposed system implements Deep Learning-based Multi-Channel Learning and Protection Model (DMCAP) using the optimal set of channel attribute classification techniques. The proposed model uses Multi-Channel Ensemble Model, Ensemble Multi-Layer Perceptron (EMLP) Classifiers, Nonlinear Channel Regression models and Nonlinear Entropy Analysis Model, and Ensemble Nonlinear Support Vector Machine (ENLSVM) for evaluating the channel conditions. Additionally, Variable Generative Adversarial Network (VGAN) engine makes the intrusion detection routines under distributed environment. According to the proposed principles, WUGSN channels are classified based on the characteristics such as underground acoustic channels, underground to surface channels and surface to ground station channels. On the classified channel behaviors, EMLP and ENLSVM are operated to extract the Signal to Noise Interference Ratio (SNIR) and channel entropy distortions of multiple channels. Furthermore, the nonlinear regression model was trained for understanding and predicting the link (channel behaviors). The proposed DMCAP has extreme difficulty finding the differences of impacts due to channel issues and malicious attacks. In this regard, the VGAN-Intrusion Detection System (VGAN-IDS) model was configured in the sensor nodes to monitor the channel instabilities against malicious nodes. Thus, the proposed system deeply analyzes multi-channel attribute qualities to improve throughput in uncertain WUGSN. The testbed was created for classified channel parameters (acoustic and air) with uncertain network parameters; the uncertainties of testbed are considered as link failures, noise distortions, interference, node failures, and number of retransmissions. Consequently, the experimental results show that DMCAP attains 10% to 15% of better performance than existing systems through better throughput, minimum retransmission rate, minimum delay, and minimum energy consumption rate. The existing techniques such as Support Vector Machine (SVM) and Random Forest (RF)-based Classification (SMC), Optimal Energy-Efficient Transmission (OETN), and channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) are identified as suitable for the proposed experiments.

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Section: Related Workmentioning

confidence: 99%

“…The generic nonlinear channel regression model is determined as shown in Equation (15). Generally, SNIR determined at the channel is nonlinear in nature.…”

Section: Snir Distribution and Analysis Using Multi-channel Nonlinear...mentioning

confidence: 99%

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Rajasoundaran

Stanislaus

Ramasamy³

et al. 2023

Energies

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“…Therefore, ML technology provides a good model for reducing the cost of some areas of security. Anomaly detection, for example, provided excellent results against all types of malicious activity, and in the process of packet analysis [64,66,74], tracking and protection against DoS [20,21,67,[75][76][77][78]. The processes of raising the availability of networks, error detection [23][24][25] and traffic congestion [17][18][19] are also based on the ML approach.…”

Section: Why Is Machine Learning Needed In Wsn Security?mentioning

confidence: 99%

“…The Random Forest classifier was chosen because it comprises a huge number of individual decision trees that work together as an ensemble. Next, the same authors in [74] investigated the performance of Random Forest and SVM classifiers on WSN channel identification. The authors extracted data features from the samples received in I and Q and then collected other data from wired devices (without interfering).…”

Section: Ml-based Wsn Diversified Securitymentioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

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Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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“…The feature engineering in this study initially leveraged previous features extracted from simulated ZigBee data in [20], which focused on identifying error-free ZigBee signals from interference injected ZigBee instances. The wired approach developed in [11] provided additional evidence that a live practical implementation had promise. As per these previous cases, features are entirely based on received I/Q samples and initially extracted from the calculated PDF and statistical analysis of the I/Q data.…”

Section: Feature Engineeringmentioning

confidence: 99%

Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

O'Mahony

McCarthy

Harris³

et al. 2021

IoT

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Classifying fluctuating operating wireless environments can be crucial for successfully delivering authentic and confidential packets and for identifying legitimate signals. This study utilizes raw in-phase (I) and quadrature-phase (Q) samples, exclusively, to develop a low-order statistical feature set for wireless signal classification. Edge devices making decentralized decisions from I/Q sample analysis is beneficial. Implementing appropriate security and transmitting mechanisms, reducing retransmissions and increasing energy efficiency are examples. Wireless sensor networks (WSNs) and their Internet of Things (IoT) utilization emphasize the significance of this time series classification problem. Here, I/Q samples of typical WSN and industrial, scientific and medical band transmissions are collected in a live operating environment. Analog Pluto software-defined radios and Raspberry Pi devices are utilized to achieve a low-cost yet high-performance testbed. Features are extracted from Matlab-based statistical analysis of the I/Q samples across time, frequency (fast Fourier transform) and space (probability density function). Noise, ZigBee, continuous wave jamming, WiFi and Bluetooth signal data are examined. Supervised machine learning approaches, including support vector machines, Random Forest, XGBoost, k nearest neighbors and a deep neural network (DNN), evaluate the developed feature set. The optimal approach is determined as an XGBoost/SVM classifier. This classifier achieves similar accuracy and generalization results, on unseen data, to the DNN, but for a fraction of time and computation requirements. Compared to existing approaches, this study’s principal contribution is the developed low-order feature set that achieves signal classification without prior network knowledge or channel assumptions and is validated in a real-world wireless operating environment. The feature set can extend the development of resource-constrained edge devices as it is widely deployable due to only requiring received I/Q samples and these features are warranted as IoT devices become widely used in various modern applications.

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Investigating Supervised Machine Learning Techniques for Channel Identification in Wireless Sensor Networks

Cited by 5 publications

References 10 publications

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

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