Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities

Data sensing and gathering is an essential task for various information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffer from limited sensing coverage. On the other hand, data can also be gathered dynamically through crowdsensing contributed by voluntary users but suffer from its unreliability and the lack of incentives for users' contributions. In this paper, we explore an integrated paradigm called "hybrid sensing" that harnesses both IoT-sensing and crowdsensing in a complementary manner. In hybrid sensing, users are incentivized to provide sensing data not covered by IoT sensors and provide crowdsourced feedback to assist in calibrating IoT-sensing. Their contributions will be rewarded with credits that can be redeemed to retrieve synthesized information from the hybrid system. In this paper, we develop a hybrid sensing system that supports explicit user privacy -- IoT sensors are obscured physically to prevent capturing private user data, and users interact with a crowdsensing server via a privacy-preserving protocol to preserve their anonymity. A key application of our system is smart parking, by which users can inquire and find the available parking spaces in outdoor parking lots. We implemented our hybrid sensing system for smart parking and conducted extensive empirical evaluations. Finally, our hybrid sensing system can be potentially applied to other information-driven services in smart cities.Comment: To appear in ACM Transactions on Internet of Thing

DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space

Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images. Herein, we look at this problem from a different viewpoint and propose to model the quality degradation in perceptual space from a statistical distribution perspective. As such, the quality is measured based upon the Wasserstein distance in the deep feature domain. More specifically, the 1DWasserstein distance at each stage of the pre-trained VGG network is measured, based on which the final quality score is performed. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination caused by various types of distortions and presents an advanced quality prediction capability. Extensive experiments and theoretical analysis show the superiority of the proposed DeepWSD in terms of both quality prediction and optimization.Comment: ACM Multimedia 2022 accepted thesi

Environmental Controls on Multi-Scale Dynamics of Net Carbon Dioxide Exchange From an Alpine Peatland on the Eastern Qinghai-Tibet Plateau

Peatlands are characterized by their large carbon storage capacity and play an essential role in the global carbon cycle. However, the future of the carbon stored in peatland ecosystems under a changing climate remains unclear. In this study, based on the eddy covariance technique, we investigated the net ecosystem CO2 exchange (NEE) and its controlling factors of the Hongyuan peatland, which is a part of the Ruoergai peatland on the eastern Qinghai-Tibet Plateau (QTP). Our results show that the Hongyuan alpine peatland was a CO2 sink with an annual NEE of -226.61 and -185.35 g C m(-2) in 2014 and 2015, respectively. While, the non-growing season NEE was 53.35 and 75.08 g C m(-2) in 2014 and 2015, suggesting that non-growing seasons carbon emissions should not be neglected. Clear diurnal variation in NEE was observed during the observation period, with the maximum CO2 uptake appearing at 12:30 (Beijing time, UTC+8). The Q(10) value of the non-growing season in 2014 and 2015 was significantly higher than that in the growing season, which suggested that the CO2 flux in the non-growing season was more sensitive to warming than that in the growing season. We investigated the multi-scale temporal variations in NEE during the growing season using wavelet analysis. On daily timescales, photosynthetically active radiation was the primary driver of NEE. Seasonal variation in NEE was mainly driven by soil temperature. The amount of precipitation was more responsible for annual variation of NEE. The increasing number of precipitation event was associated with increasing annual carbon uptake. This study highlights the need for continuous eddy covariance measurements and time series analysis approaches to deepen our understanding of the temporal variability in NEE and multi-scale correlation between NEE and environmental factors

Middle Jurassic ooidal ironstones (southern Tibet): Formation processes and implications for the paleoceanography of eastern Neo-Tethys

The major facies changes documented in shallow-marine sediments of the northern Indian passive margin of Neo-Tethys throughout the Jurassic, from widespread platform carbonates in the Early Jurassic to organic-rich black shales in the Late Jurassic, imply a substantial turnover in oceanic conditions. All along the Tethys (Tibetan) Himalaya, from the Zanskar Range to southern Tibet, a peculiar interval characterized by ooidal ironstones of Dingjie Formation (Ferruginous Oolite Formation, FOF) marks the base of the organic-rich Spiti Shale. This laterally-extensive ooidal ironstone interval is a fundamental testimony of the mechanisms that led to major paleoceanographic changes that occurred in the eastern Neo-Tethys during the Middle Jurassic. In this article, we illustrate in detail the petrology, mineralogy, and geochemistry of ooidal ironstones and the major element contents of the entire Lanongla section. The FOF is characterized by significantly high contents of Fe2O3 (56.80% ± 9.07%, n = 7) and P2O5 (1.72% ± 1.19%, n = 7). In contrast, the Fe2O3 and P2O5 contents average 3.58% and 0.15% in the overlain carbonates of Lanongla Fm., and 5.55% and 0.16% in the overlying Spiti Shale. The ooidal ironstones are mainly composed of iron ooids with a few quartz grains and bioclasts cemented by sparry calcite. The iron ooids consist of concentric dark layers of francolite (carbonate fluorapatite), hence enriched in Ca, P, and F, and bright layers of chamosite, enriched in Fe, Si, Al, and Mg. Precipitation of francolite ensued from oversaturation of phosphorous ascribed to intensified upwelling, high biogenous productivity, and degradation of organic matter, whereas the formation of chamosite reflects enhanced continental weathering and erosion leading to increased Fe input to the ocean during transgressive stages characterized by low sedimentation rate and scarce oxygenation at the seafloor. Modern upwelling zones in outer shelf or slope areas perform similar geochemical characteristics to those as observed in this study. Under the Mesozoic greenhouse background, fluctuating redox conditions induced the alternate growth of francolite under anoxic conditions and of chamosite under suboxic conditions. Ooids were thus formed on the seafloor during continued resuspension and vertical oscillations of the chemocline rather than from interstitial waters after burial. The mineralogy of iron ooids indicates mainly reducing conditions in the water column, suggesting that extensive upwelling along the continental margin of eastern Neo-Tethys contributed significantly to the transition from carbonate deposits to organic-rich black shales during the Jurassic, as testified by the transition from well-oxygenated in Lanongla Fm. To a reduceing condition in Spiti Shale indicated by the Mn/Al ratios compared to PAAS

In-Use Emissions From Heavy-Duty Off-Road Equipment and On-Road Vehicles

This dissertation provides investigation and evaluation of new engine technologies and aftertreatment systems on reducing emissions of critical pollutants on in-use heavy-duty vehicles or off-road equipment under real-world operation conditions. Real-world driving emissions have become a key factor to understand or identify high-emitting events under real-world driving conditions.This dissertation evaluated emissions from in-use heavy-duty on-road vehicles, off-road equipment under a variety of different conditions. This dissertation characterized NOx and PM emissions for 10 pieces of Tier 4 final construction equipment including 3 excavators, 3 wheel loaders, 2 crawler tractors and 2 backhoe/loaders. The duty cycles included a pre-defined combined sequence of a cold-start phase, trenching, backfilling, travelling, and idling. The information obtained in this study provides a more accurate dataset for emissions inventory development, and for designing or optimizing emissions models such as NONROAD or OFFROAD, which are currently utilized for estimating off-road emissions. The dissertation also discussed gaseous and particulate emissions from a fleet of 14 heavy-duty vehicles. The test matrix includes vehicles from vocations including transit buses, school buses, refuse trucks, delivery trucks, and goods movement trucks fueled with a combination of alternative fuels, conventional and alternative diesel fuels. This thesis evaluated the impact, issues, improvement, and benefits of the current technologies for heavy-duty vehicles. This thesis also measured and characterized NOx emissions from five heavy-duty diesel and natural gas goods movement vehicles with different engine technologies under real-world conditions. All five vehicles were tested on-road under four pre-defined goods movement routes in SCAB, representing grocery distribution, port-drayage operation, and highway driving with and without elevation change. NOx emissions were measured using a mobile emissions laboratory. Understanding emissions from ultra-low NOx CNG vehicles is important as CNG vehicles/engines are capable of meeting more stringent emission standards. This dissertation in detail evaluated and characterized two near-zero NOx stoichiometric ultra-low natural gas engines in different vocations. This demonstration of this engine technology was done in a goods movement vehicle and a yard tractor. Both vocations represent a major source of NOx emissions and other pollutants within the heavy-duty vehicle population