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1. 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 2018)

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

   Rey, Xaime Rivas; Halpin, Thomas J.; Hadgekar, Shantanu; Miu, Karen; Dandekar, Kapil R. (January 2018, Naval engineers journal)

   Most sensor networks on a naval vessel are wired directly to the control unit,[1, 16] 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 studied, along with a discussion of what could be done to detect and mitigate them. 

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3. Development and Implementation of S-MAC based RPL Protocol to Increase Energy Efficiency for the TelosB

   https://doi.org/10.1109/UEMCON51285.2020.9298077  

   Skaggs-Schellenberg, Russell; Wright, Daniel; Wang, Nan (October 2020, IEEE UEMCON 2021)

   null (Ed.)

   It is vital to consider the energy usage of motes when designing a Wireless Sensor Network (WSN). Protocols can be altered to their application to enhance a system's performance. This project modifies the Routing Protocol for Low-Power and Lossy Networks (RPL) protocol using a S-MAC algorithm to increase its energy efficiency. The project began with the application and the focus of the WSN. The proposed protocol was developed within the Cooja simulator, then implemented on TelosB motes using the Contiki-NG operating system. Lastly, the WSN was tested with the proposed system and compared against its original counterpart. In conclusion it was found that the proposed method provides a significant increase in energy efficiency, extending the life of a WSN. 

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4. 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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5. Origin: Enabling On-Device Intelligence for Human Activity Recognition Using Energy Harvesting Wireless Sensor Networks

   https://doi.org/10.23919/DATE51398.2021.9474017  

   Mishra, Cyan Subhra; Sampson, Jack; Kandemir, Mahmut Taylan; Narayanan, Vijaykrishnan (February 2021, 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE))

   null (Ed.)

   There is an increasing demand for performing machine learning tasks, such as human activity recognition (HAR) on emerging ultra-low-power internet of things (IoT) platforms. Recent works show substantial efficiency boosts from performing inference tasks directly on the IoT nodes rather than merely transmitting raw sensor data. However, the computation and power demands of deep neural network (DNN) based inference pose significant challenges when executed on the nodes of an energy-harvesting wireless sensor network (EH-WSN). Moreover, managing inferences requiring responses from multiple energy-harvesting nodes imposes challenges at the system level in addition to the constraints at each node. This paper presents a novel scheduling policy along with an adaptive ensemble learner to efficiently perform HAR on a distributed energy-harvesting body area network. Our proposed policy, Origin, strategically ensures efficient and accurate individual inference execution at each sensor node by using a novel activity-aware scheduling approach. It also leverages the continuous nature of human activity when coordinating and aggregating results from all the sensor nodes to improve final classification accuracy. Further, Origin proposes an adaptive ensemble learner to personalize the optimizations based on each individual user. Experimental results using two different HAR data-sets show Origin, while running on harvested energy, to be at least 2.5% more accurate than a classical battery-powered energy aware HAR classifier continuously operating at the same average power. 

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