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1. Communication about sensors and communication through sensors: localizing the Internet of Things in rural communities

   https://doi.org/10.1093/jcmc/zmad005  

   Butkowski, Chelsea; Chan, Ngai Keung; Berniker, Talia; Rodriguez, Alfredo; Schlather, Kenneth; Zhang, K. Max; Humphreys, Lee (August 2023, Journal of Computer-Mediated Communication)

   Özkul, Didem (Ed.)

   Abstract Internet of Things (IoT) sensor networks are an emerging technology at the center of the datafication and optimization of far-reaching environmental infrastructures—from “smart cities” to workplace efficiencies. However, this low-power, low-cost technology is also well suited to local deployments in rural communities, which are often overlooked by digital development initiatives. Therefore, we used a social construction of technology approach to study how various U.S.-based IoT stakeholders—including designers and advocates as well as citizen stakeholders—understand and value sensor network technologies. Through observational methods, in-depth interviews, and participatory design research in a rural Upstate New York municipality, we worked to design sensor networks with rural community members to generate data about and for community members to further local knowledge. We found that designing rural sensor networks requires stakeholders to navigate obstacles of communication about sensors and communication through sensors to facilitate secure, ethical, and localized sensing in rural communities. 

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2. Encoding Consistency: Optimizing Self-Driving Reliability With Real-Time Speed Data

   Fowler, W; Keahey, K; Esquivel-Morel, A (June 2024, Proceedings of the 4th Workshop on Flexible Resource and Application Management on the Edge)

   Self-driving cars can revolutionize transportation systems, o!ering the potential to significantly enhance efficiency while also addressing the critical issue of human fatalities on roadways. Hence, there is a need to investigate methods to enhance self-driving technologies through end-to-end learning techniques. In this paper, we investigate methodologies that integrate Convolutional Neural Networks (CNNs) to enhance self-driving consistency through real-time velocity and steering estimation. We extend an end-to-end state-of-the-art learning solution with real-time speed data as additional model input to refine reliability. Specifically, our work integrates an optical encoder sensor system to record car speed during training data collection, ensuring the throttle can be regulated during model inference. An end-to-end experimental testbed is deployed on the Chameleon cloud using CHI@Edge infrastructure to manage a 1:18 scaled car, equipped with a Raspberry Pi as its onboard computer. Finally, we provide guidance that facilitates reproducibility and highlight the challenges and limitations of supporting such experiments. 

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3. LOW-COST EFFICIENT WIRELESS INTELLIGENT SENSOR (LEWIS) DEPLOYMENT FOR COMMUNITY DRIVEN DECISION MAKING

   https://doi.org/10.12783/shm2023/36747  

   ALAMPALLI, SANDEEP; MALEK, KAVEH; MOHAMMADKHORASANI, ALI; MOREU, FERNANDO (September 2023, Destech Publications, Inc.)

   There have been several advances in Structural Health Monitoring (SHM) throughout the last two decades. Among these advances is that sensors and data acquisition have become smaller in size while wireless technologies have been making wireless communication and data accessing easier. These advances create cost effective sensing solutions for communities where flooding and wildfires put their members and infrastructure at risk. Therefore, with higher community involvement in understanding and utilizing new sensing technologies, there is more to be gained in preparing for and mitigating the effects of natural hazards. Low-cost easily deployable sensors will make sensor technology more popular and easier for communities to utilize and give them the ability to make decisions during natural hazards. LEWIS, a Low-cost Efficient Wireless Intelligent Sensor platform, is created by the Smart Management of Infrastructure Laboratory (SMILab) at the University of New Mexico (UNM) at Albuquerque for such a purpose: to give communities the ability to create innovative monitoring solutions, including combating climate change. This paper briefly discusses the LEWIS platform, their use for communities to combat natural hazards to make quick decisions to improve public safety, training and education components, and community (from student to industry professional) engagement efforts. 

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4. Self-Contained Electrical Conductivity Sensing Spikes for Monitoring of Levee Wetting and Drying Cycles

   https://doi.org/10.1115/DETC2025-169136  

   Morris, Sydney; Mokhtar, Ayman; Flemming, Malichi; Downey, Austin_R J; Chowdhury, Puja; Burnett, Matthew; Imran, Jasim; Khan, Sadik (August 2025, American Society of Mechanical Engineers)

   Abstract Levees are earthen structures built parallel to water bodies, such as rivers, to protect important infrastructure, assets, and people from flooding. They play a critical role in protecting lives, but their structural integrity is frequently threatened by lack of maintenance leading to moisture-induced degradation. Traditional levee monitoring systems can be expensive due to the need for extensive sensor networks across vast areas. To address this, there is a growing need for low-cost sensing solutions that can provide data without requiring dense sensor coverage. Direct measurements of subsurface parameters, such as soil saturation levels, are helpful for accurately assessing the structural health of levees, as these measurements provide real-time insights into the moisture conditions that can weaken soil strength and lead to potential failures. This study investigates the application of newly developed self-contained sensing spikes, equipped with electrical conductivity sensors that embed themselves 13 cm below the surface, designed to monitor wetting and drying cycles within levee soils. In a controlled and monitored 144-hour flume test, six spikes were strategically placed on a 0.45-meter-high sand embankment as water levels were raised and lowered to capture conductivity changes over time. Using the B-spline method, a map of conductivity was constructed, providing a graphical representation of moisture distribution across the levee. This non-linear mapping technique allows for accurate detection of moisture-driven faults with significantly fewer sensors, demonstrating that under-sampling can achieve reliable monitoring results. The embedded conductivity sensors enable continuous, low-cost monitoring of moisture dynamics directly at critical depths, offering a novel approach to detecting levee faults and predicting potential bank erosion. By linking conductivity data to fragility assessments, this method enhances the ability to forecast structural vulnerabilities, contributing to improved levee resilience and maintenance strategies under changing environmental conditions. 

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5. Advanced Microfluidic‐Based Wearable Electrochemical Sensors for Continuous Biochemical Monitoring

   https://doi.org/10.1002/aelm.202500010  

   Park, Sehyun; Kim, Seongyeop; Lee, Soojin; Tsukruk, Vladimir V; Park, SeungHyun; Lim, Hyo‐Ryoung (March 2025, Advanced Electronic Materials)

   Abstract Microfluidic‐based wearable electrochemical sensors represent a transformative approach to non‐invasive, real‐time health monitoring through continuous biochemical analysis of body fluids such as sweat, saliva, and interstitial fluid. These systems offer significant potential for personalized healthcare and disease management by enabling real‐time detection of key biomarkers. However, challenges remain in optimizing microfluidic channel design, ensuring consistent biofluid collection, balancing high‐resolution fabrication with scalability, integrating flexible biocompatible materials, and establishing standardized validation protocols. This review explores advancements in microfluidic design, fabrication techniques, and integrated electrochemical sensors that have improved sensitivity, selectivity, and durability. Conventional photolithography, 3D printing, and laser‐based fabrication methods are compared, highlighting their mechanisms, advantages, and trade‐offs in microfluidic channel production. The application section summarizes strategies to overcome variability in biofluid composition, sensor drift, and user adaptability through innovative solutions such as hybrid material integration, self‐powered systems, and AI‐assisted data analysis. By analyzing recent breakthroughs, this paper outlines critical pathways for expanding wearable sensor technologies and achieving seamless operation in diverse real‐world settings, paving the way for a new era of digital health. 

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