Identifying Distinct Features based on Received Samples for Interference Detection in Wireless Sensor Network Edge Devices

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

doi:10.1109/wts48268.2020.9198724

Cited by 6 publications

(10 citation statements)

References 13 publications

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“…ZigBee, IEEE 802.15.4, is the most widespread technology used for WSNs [ 51 ]. It is the current de facto standard, as almost all existing commercial and research sensors are operated using ZigBee transceivers [ 52 ]. It has been proven that ZigBee is one of the best candidates for WSN design due to its excellent performance on connectivity and power consumption [ 53 ].…”

Section: Results and Discussionmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

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Section: Results and Discussionmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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“…While in [19], deep learning and I/Q samples are used once again for radio device identification (fingerprinting) by learning unchanging hardware-based characteristics of individual transmitters. Finally, in [20], Matlab simulations showed how I/Q samples have promise in detecting interference in WSNs. The majority of the literature using raw I/Q samples focuses on transceiver or directional identification, which proves the value of raw I/Q samples and opens up new areas for investigation.…”

Section: Related Workmentioning

confidence: 99%

“…The commercial signals are typical ISM RF band transmissions and include WiFi both with (IEEE802.11ac and IEEE802.11n) and without internet access (IEEE802.11b and IEEE802.11g), Bluetooth, where the advertising channels are targeted, and DIGI XBee ZigBee nodes. SDR sources produce Python3 generated CW and ZigBee signals, that are based on previous Matlab simulations [20], where the Matlab code has been translated to Python3. In Figure 2, the Python3-based ZigBee Pluto SDR transmissions are confirmed to be comparable to commercially transmitted ZigBee signals.…”

Section: Experimental Data Collectionmentioning

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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“…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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Identifying Distinct Features based on Received Samples for Interference Detection in Wireless Sensor Network Edge Devices

Cited by 6 publications

References 13 publications

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

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

Coexistence Management in Wireless Networks-A Survey

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