Developing novel low complexity models using received in-phase and quadrature-phase samples for interference detection and classification in Wireless Sensor Network and GPS edge devices

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

doi:10.1016/j.adhoc.2021.102562

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…Thus, it is envisaged that the designed approach would not operate continuously and, instead, only operate on a certain duty cycle, or when called upon. To demonstrate how the features and approach identified in this paper can be used in other developments, the features and optimal models in this study were leveraged in a domain adaption transfer learning study to develop a novel WSN interference diagnostic framework in [48]. The work in [48] produced and tested various interference detection scenarios which on average achieved a model accuracy ≥ 98%.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…To demonstrate how the features and approach identified in this paper can be used in other developments, the features and optimal models in this study were leveraged in a domain adaption transfer learning study to develop a novel WSN interference diagnostic framework in [48]. The work in [48] produced and tested various interference detection scenarios which on average achieved a model accuracy ≥ 98%. These scenarios included interference detection, interference classification and legitimate node identification.…”

Section: Summary and Discussionmentioning

confidence: 99%

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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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Section: Summary and Discussionmentioning

confidence: 99%

Section: Summary and Discussionmentioning

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

Self Cite

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“…The methods for improving the wireless ecological security of IoT mainly include methods based on intrusion detection systems [15,16], based on machine learning [17,18], and SDR as a heterogeneous IoT network security tool [19,20]. By developing effective methods to identify attacks and threats, intrusion detection systems can be used to protect user data network security.…”

Section: Table1 the Most Common Wireless Vulnerabilitiesmentioning

confidence: 99%

Secure transmission of wireless energy-carrying communication systems for the Internet of Things

Zhou

Peng

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The Internet of Things, as an important part of important data aggregation, forwarding and control, is often subject to risks such as eavesdropping or data loss due to the huge amount of received data. Based on this, this paper introduces the GA-LM-BP algorithm, BP network, and LM-BP algorithm deep learning to optimize the data received by the Internet of Things, and selects the most suitable communication mode optimization algorithm. The experimental results show that the accuracy error of GA-LM-BP, BP and LM-BP algorithms shows a downward trend, from 0.029 to 0.011; the training time is reduced by 208 mins, and the training speed is increased to 74%, indicating that GA-LM-BP deep learning Excellent performance in the security and confidentiality of data transmission in the Internet of Things. In addition, we further analyzed GA-LM-BP from COP, SOP and STP to verify its reliability and safety.

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“…A study on interference effect on bit error rate and bit error locations has been done in [68] A spectral analysis framework for resource constrained edge devices is provided in [70]. PDF (Probability Density Function) based feature extraction is done for considered signal classes that are ZigBee (without interference) and ZigBee in presence of CW (Continuous wave) jamming signal, matched signal (deceptive jamming), thermal noise and WiFi signal.…”

Section: A Rssi and Iq Based Detection Techniquesmentioning

confidence: 99%

Coexistence Management in Wireless Networks-A Survey

Hasan

Khan

2022

IEEE Access

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The rapid proliferation of wireless networks poses a great challenge of effective coexistence management amongst a plethora of wireless communication protocol users that are co-located and contending for the ever scarce spectrum available. For effective spectrum utilization and optimum performance of existing wireless networks and for the realization of new wireless networks, coexistence management of the wireless spectrum is the key to ensure the performance of multiple wireless networks in close proximity. This paper provides a comprehensive review of state-of-the-art coexistence protocols in wireless networks. The paper not only discusses wireless interference detection techniques in detail but also provides various coping mechanisms to counter the interference.

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Developing novel low complexity models using received in-phase and quadrature-phase samples for interference detection and classification in Wireless Sensor Network and GPS edge devices

Cited by 7 publications

References 38 publications

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

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

Secure transmission of wireless energy-carrying communication systems for the Internet of Things

Coexistence Management in Wireless Networks-A Survey

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