Distributed Successive Approximation Coding using Broadcast Advantage: The Two-Encoder Case

Traditional distributed source coding rarely considers the possible link between separate encoders. However, the broadcast nature of wireless communication in sensor networks provides a free gossip mechanism which can be used to simplify encoding/decoding and reduce transmission power. Using this broadcast advantage, we present a new two-encoder scheme which imitates the ping-pong game and has a successive approximation structure. For the quadratic Gaussian case, we prove that this scheme is successively refinable on the {sum-rate, distortion pair} surface, which is characterized by the rate-distortion region of the distributed two-encoder source coding. A potential energy saving over conventional distributed coding is also illustrated. This ping-pong distributed coding idea can be extended to the multiple encoder case and provides the theoretical foundation for a new class of distributed image coding method in wireless scenarios.Comment: In Proceedings of the 48th Annual Allerton Conference on Communication, Control and Computing, University of Illinois, Monticello, IL, September 29 - October 1, 201

Top-down Constraints on Regional Nitrous Oxide and Methane Emissions within the U.S. Corn Belt

University of Minnesota Ph.D. dissertation.April 2018. Major: Land and Atmospheric Science. Advisor: Timothy Griffis. 1 computer file (PDF); v, 129 pages.Nitrous oxide (N2O) and methane (CH4) are important long-lived greenhouse gases. However, evidence has been found that CH4 and N2O emissions from the US Corn Belt are much larger than budgeted for in the most up-to-date emission inventories, implying that one or more sources in bottom-up approaches are underestimated, resulting in poorly constrained source partitioning. Therefore, this dissertation sought to: 1) quantify importance of direct versus indirect N2O emissions, and explore their seasonality and regional budgets; 2) assess the retrospective and future N2O emissions at fine spatiotemporal scales to propose mitigation priorities for the Corn Belt; 3) partition methane emissions into natural (e.g. wetlands) and anthropogenic (e.g. livestock, waste, and natural gas) sources, and explore their temporal variability. We compared in-situ measurements of N2O from the University of Minnesota tall tower site with a time-inverted transport model and a scale factor Bayesian inverse (SFBI) method. Our analysis suggested an upward adjustment of the emission factor of indirect sources by 1.9 to 4.6 times relative to the IPCC inventory. Further, indirect emissions were predicted to exhibit a trend of 0.36 nmol N2O m-2 s-1 per year in an Eularian modeling study, using the Weather Research and Forecasting Chemistry (WRF-Chem) model with implementation of the CLM45-BGC-CROP land surface scheme under RCP8.5 scenario. With respect to CH4, our analysis revealed that the anthropogenic source (7.8 ±1.6 Tg CH4 yr-1) was 1.5 times greater than accounted for in the EPA inventory. Most prominently livestock and oil/gas sources were underestimated by 1.8- and 1.3-fold, respectively. In contrast, the temporal variability of total CH4 emissions was dominated by wetlands with peak emissions occurring in August. Our findings suggest that indirect N2O and anthropogenic CH4 emissions within the Corn Belt Region need to be upward-adjusted in inventory updates. Further, our model predictions indicate large N2O mitigation potential in the lower Midwest region

African rice cultivation linked to rising methane

Africa has been identified as a major driver of the current rise in atmospheric methane, and this has been attributed to emissions from wetlands and livestock. Here we show that rapidly increasing rice cultivation is another important source, and estimate that it accounts for 7% of the current global rise in methane emissions. Continued rice expansion to feed a rapidly growing population should be considered in climate change mitigation goals.Comment: 7 pages and 2 figure

Event-Driven Video Coding for Outdoor Wireless Monitoring Cameras

Reducing communication cost is crucial for outdoor wireless monitoring cameras which are constrained by limited energy budgets. From event detection point of view, traditional video coding schemes such as H.264 are inefficient as they ignore the "meaning" of video content and thus waste many bits to convey irrelevant information. To take advantage of the powerful computing resource on cameras, we propose a novel event-driven video coding scheme. Unlike previous approach that attempts to find anomalous image frame with potential events, we propose to detect salient regions in each image and transmit the image fragments marked with saliency to the receiver. This scheme rarely drops an event as it transmits all image fragments with potential events, and also requires no training procedure. The experimental results show that it performs substantially better than conventional video coding schemes for outdoor monitoring task

Share Risk and Energy: Sampling and Communication Strategies for Multi-Camera Wireless Monitoring Networks

In the context of environmental monitoring, outdoor wireless cameras are vulnerable to natural hazards. To benefit from the inexpensive imaging sensors, we introduce a multi-camera monitoring system to share the physical risk. With multiple cameras focusing at a common scenery of interest, we propose an interleaved sampling strategy to minimize per-camera consumption by distributing sampling tasks among cameras. To overcome the uncertainties in the sensor network, we propose a robust adaptive synchronization scheme to build optimal sampling configuration by exploiting the broadcast nature of wireless communication. The theory as well as simulation results verify the fast convergence and robustness of the algorithm. Under the interleaved sampling configuration, we propose three video coding methods to compress correlated video streams from disjoint cameras, namely, distributed/independent/joint coding schemes. The energy profiling on a two-camera system shows that independent and joint coding perform substantially better. The comparison between two-camera and single-camera system shows 30%-50% per-camera consumption reduction. On top of these, we point out that MIMO technology can be potentially utilized to push the communication consumption even lower

Sensorcam: An Energy-Efficient Smart Wireless Camera for Environmental Monitoring

Reducing energy cost is crucial for energy-constrained smart wireless cameras. Existing platforms impose two main challenges: First, most commercial smart phones have a closed platform, which makes it impossible to manage low-level circuits. Since the sampling frequency is moderate in environmental monitoring context, any improper power management in idle period will incur significant energy leak. Secondly, low-end cameras tailored for wireless sensor networks usually have limited processing power or communication range, and thus are not capable of outdoor monitoring task under low data rate. To tackle these issues, we develop Sensorcam, a long-range, smart wireless camera running a Linux-base open system. Through better power management in idle period and the "intelligence" of the camera itself, we demonstrate an energy-efficient wireless monitoring system in a real deployment