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1. An Environmental Data Collection for COVID-19 Pandemic Research

   https://doi.org/10.3390/data5030068  

   Liu, Qian; Liu, Wei; Sha, Dexuan; Kumar, Shubham; Chang, Emily; Arora, Vishakh; Lan, Hai; Li, Yun; Wang, Zifu; Zhang, Yadong; et al (September 2020, Data)

   null (Ed.)

   The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection. 

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2. Security and Privacy of Integrated Sensing and Communication Systems

   https://doi.org/10.1145/3733965.3733974  

   Zeng, Kai (June 2025, ACM)

   Integrated sensing and communication (ISAC) is considered an emerging technology for 6th-generation (6G) wireless and mobile networks. It is expected to enable a wide variety of vertical applications, ranging from unmanned aerial vehicles (UAVs) detection for critical infrastructure protection to physiological sensing for mobile healthcare. Despite its significant socioeconomic benefits, ISAC technology also raises unique challenges in system security and user privacy. Being aware of the security and privacy challenges, understanding the trade-off between security and communication performance, and exploring potential countermeasures in practical systems are critical to a wide adoption of this technology in various application scenarios. This talk will discuss various security and privacy threats in emerging ISAC systems with a focus on communication-centric ISAC systems, that is, using the cellular or WiFi infrastructure for sensing. We will then examine potential mechanisms to secure ISAC systems and protect user privacy at the physical and data layers under different sensing modes. At the wireless physical (PHY) layer, an ISAC system is subject to both passive and active attacks, such as unauthorized passive sensing, unauthorized active sensing, signal spoofing, and jamming. Potential countermeasures include wireless channel/radio frequency (RF) environment obfuscation, waveform randomization, anti-jamming communication, and spectrum/RF monitoring. At the data layer, user privacy could be compromised during data collection, sharing, storage, and usage. For sensing systems powered by artificial intelligence (AI), user privacy could also be compromised during the model training and inference stages. An attacker could falsify the sensing data to achieve a malicious goal. Potential countermeasures include the application of privacy enhancing technologies (PETs), such as data anonymization, differential privacy, homomorphic encryption, trusted execution, and data synthesis. 

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3. A Secure Cooperative Adaptive Cruise Control Design with Unknown Leader Dynamics Under False Data Injection Attacks

   https://doi.org/10.3390/computers14030084  

   Bonab, Parisa Ansari; Sargolzaei, Arman (March 2025, Computers)

   The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness. 

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4. Improving Sensing Coverage in Vehicular Crowdsensing Using Location Diversity

   https://doi.org/10.1109/ICCVE52871.2022.9742961  

   Chintakunta, Harish; Wang, Xin; Jaimes, Luis G (March 2022, Improving Sensing Coverage in Vehicular Crowdsensing Using Location Diversity)

   IEEE (Ed.)

   The massive use of vehicles as a primary means of transportation as well the increasing adoption of vehicles’ on-board sensors represents a unique opportunity for sensing and data collection. However, vehicles tend to cluster in specific regions such as highways and a few popular roads, making their utilization for data collection in isolated regions with low-density traffic difficult. We address this problem by proposing an incentive mechanism that encourages vehicles to deviate from their pre-planned trajectories to visit these isolated places. At the core of our proposal is the idea of compensation based on participants’ location diversity, which allows for rewarding vehicles in low-density traffic areas more than those located in high-density ones. We model this problem as a non-cooperative game in which participants are the vehicles and their new trajectories are their strategies. The output of this game is a new set of stable trajectories that maximize spatial coverage. Simulations show our approach outperforms the approach that doesn't take into account participants’ location diversity in terms of spatial coverage and road utilization. 

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5. Poster: Circa: Re-imagining Network Telemetry from an Approximation-First Perspective

   Srivastava, Milind; Sekar, Vyas (August 2024, ACM SIGCOMM Posters)

   Telemetry systems are widely used to collect data from distributed endpoints, analyze data in conjunction to gain valuable insights, and store data for historical analytics. These systems consist of four stages (Figure 1): collection, transmission, analysis, and storage. Collectors at the endpoint collect various types of data, which is then transmitted to a central server for analysis. This data is used for multiple downstream tasks, such as dashboard monitoring and anomaly detection. Finally, this data is stored in long-term storage to aid retrospective analytics and debugging. 

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