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1. Deep Learning Dataset Generation for Physical Layer Authentication in Wireless Sensor Networks (WSN)

   Dentremont, Christopher; Liu, Hong (May 2024, 2024 International Wireless Communications and Mobile Computing (IWCMC))

   Structural Health Monitoring (SHM) uses wireless sensor network (WSN) to monitor a civil construction’s conditions remotely and constantly for its sustainable usage. Security in WSN for SHM is essential to safeguard critical transportation infrastructure such as bridges. While WSN offers cost-effective solutions for Bridge SHM, its wireless nature expands attack surfaces, making security a significant concern. Despite progress in addressing security issues in WSN for Bridge SHM, challenges persist in device authentication due to the unique placement of sensor nodes and their resource constraints, particularly in energy conservation requirements to extend the system’s lifetime. To overcome these limitations, this paper proposes an innovative authentication scheme with deep learning at the physical layer. Our approach steers away from conventional device authentication methods: no challenge-response protocol with heavy communication overhead and no cryptography of intensive computation. Instead, we use radio frequency (RF) fingerprinting to authenticate sensor nodes. Deep learning is chosen for its ability to discover patterns in large datasets without manual feature engineering. We model our scheme on IEEE 802.11ah, Wi-Fi HaLow of long-range communication and low-power consumption for machine-to-machine (M2M) applications. Simulations and experiments using universal software radio peripheral (USRP) demonstrate the effectiveness of the proposed scheme. By integrating security into Cyber-Physical System/the Internet-of-Things (CPS/IoT) design of WSN for Bridge SHM, our work contributes to critical infrastructure protection. 

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2. Defense against Black Hole Attacks in Wireless Sensor Network with Anomaly Report Cycling

   Vieira, Marcel; Liu, Hong (May 2024, 2024 International Wireless Communications and Mobile Computing (IWCMC))

   Wireless Sensor Network (WSN) becomes the dominate last-mile connection to cyber-physical systems and Internet-of-Things. However, WSN opens new attack surfaces such as black holes, where sensing information gets lost during relay towards base stations. Current defense mechanisms against black hole attacks require substantial energy consumption, reducing the system's lifetime. This paper proposes a novel approach to detect and recover from black hole attacks using an improved version of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is an energy-efficient routing protocol for groups of battery-operated sensor nodes in hierarchy. A round of selection for cluster heads is scheduled in a set time. We propose to improve LEACH with Anomaly Report Cycling (ARC-LEACH), tradeoff between security strength and energy cost. ARC-LEACH absorbs an attack when it occurs by rotating cluster heads to reestablish communication and then sending a message from the base station to coordinate all nodes against the malicious nodes. ARC-LEACH actively blocks malicious nodes while leveraging the resilience of LEACH for stronger resistance to blackhole attacks. ARC-LEACH can provide more defense capability when under attack from multiple malicious nodes that would otherwise be defenseless by LEACH, with only minor increase in energy consumption. 

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3. Wireless Sensor Node System to Monitor Pig Activities for Behavior Classification

   Cheung, Brandon; Xu, Yuezhong; Ha, Dong Sam (August 2023, IEEE International Midwest Symposium on Circuits and Systems)

   Wireless sensor nodes (WSNs) are useful to monitor animals remotely and continuously. The proposed WSN aims to monitor pig activities, and it consists of a 3-axis accelerometer, a 3-axis gyroscope, and a microcontroller with embedded BLE (Bluetooth Low Energy) radio. The WSN was designed and prototyped with a custom PCB and used to collect data from pigs in field for about 131 hours, and the collected data was processed to classify pig behaviors with machine learning models. The sampling rate of the sensors is 10 samples per second. The proposed WSN dissipates 6.29 mW, on average, and the peak power dissipation is 41.01 mW during transmission of the sensed data. The WSN is estimated to operate for about three weeks with a coin cell battery CR2477. 

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4. Actively managed battery degradation of wireless sensors for structural health monitoring

   https://doi.org/10.1117/12.2658497  

   Nishat, Tahsin Afroz; Jeong, Jong-Hyun; Jo, Hongki; Zhou, Qiang; Liu, Jian (April 2023, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems)

   Su, Zhongqing; Limongelli, Maria Pina; Glisic, Branko (Ed.)

   The battery-powered wireless sensor network (WSN) is a promising solution for structural health monitoring (SHM) applications because of its low cost and easy installation capability. However, the long-term WSN operation suffers from various concerns related to uneven battery degradation of wireless sensors, associated battery management, and replacement requirement, and ensuring desired quality of service (QoS) of the WSN in practice. The battery life is one of the biggest limiting factors for long-term WSN operation. Considering the costly maintenance trips for battery replacement, a lack of effective battery degradation management at the system level can lead to a failure in WSN operation. Moreover, the QoS needs to be ensured under various practical uncertainties. Optimal selection with a maximal number of nodes in WSN under uncertainties is a critical task to ensure the desired QoS. This study proposes a reinforcement learning (RL) based framework for active control of the battery degradation at the WSN system level with the aim of the battery group replacement while extending the service life and ensuring the QoS of WSN. A comprehensive simulation environment was developed in a real-life WSN setup, i.e. WSN for a cable-stayed bridge SHM, considering various practical uncertainties. The RL agent was trained under a developed RL environment to learn optimal nodes and duty cycles, meanwhile managing battery health at the network level. In this study, a mode shape-based quality index is proposed for the demonstration. The training and test results showed the prominence of the proposed framework in achieving effective battery health management of the WSN for SHM. 

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5. Cybersecurity Analysis of an IEEE 802.15.4 based Wireless Sensor Network for Smart Grid Power Monitoring on a Naval Vessel

   Rivas Rey, Xaime; Halpin, Thomas; Hadgekar, Shantanu; Miu, Karen; Dandekar, Kapil R. (June 2018, ASNE Technology Systems and Ships (TSS))

   Most sensor networks on a naval vessel are wired directly to the control unit, and this includes the Power System. This paper demonstrates how an IEEE 802.15.4 based Wireless Sensor Network (WSN) could be used to have an easy to deploy, flexible and affordable Smart Grid Power System monitoring structure. In published literature, it has been qualitatively proven that a WSN can work on a ship, despite its more complex Radio Frequency (RF) environment. This work quantifies this, showing the achievable levels of Packet Error Rate under different levels of Signal to Interference and Noise Ratio, proving that it could be used instead of a wired channel. Another important aspect studied was the cybersecurity implications of using a wireless network versus a wired one. The effects of delayed, missing and faked power measurements were also done, along with a discussion of what could be done to detect and mitigate these effects. 

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