Abstract Refraction networking is a next-generation censorship circumvention approach that locates proxy functionality in the network itself, at participating ISPs or other network operators. Following years of research and development and a brief pilot, we established the world’s first production deployment of a Refraction Networking system. Our deployment uses a highperformance implementation of the TapDance protocol and is enabled as a transport in the popular circumvention app Psiphon. It uses TapDance stations at four physical uplink locations of a mid-sized ISP, Merit Network, with an aggregate bandwidth of 140 Gbps. By the end of 2019, our system was enabled as a transport option in 559,000 installations of Psiphon, and it served upwards of 33,000 unique users per month. This paper reports on our experience building the deployment and operating it for the first year. We describe how we overcame engineering challenges, present detailed performance metrics, and analyze how our system has responded to dynamic censor behavior. Finally, we review lessons learned from operating this unique artifact and discuss prospects for further scaling Refraction Networking to meet the needs of censored users.

Refraction Networking (formerly known as "Decoy Routing") has emerged as a promising next-generation approach for circumventing Internet censorship. Rather than trying to hide individual circumvention proxy servers from censors, proxy functionality is implemented in the core of the network, at cooperating ISPs in friendly countries. Any connection that traverses these ISPs could be a conduit for the free flow of information, so censors cannot easily block access without also blocking many legitimate sites. While one Refraction scheme, TapDance, has recently been deployed at ISP-scale, it suffers from several problems: a limited number of "decoy" sites in realistic deployments, high technical complexity, and undesirable tradeoffs between performance and observability by the censor. These challenges may impede broader deployment and ultimately allow censors to block such techniques. We present Conjure, an improved Refraction Networking approach that overcomes these limitations by leveraging unused address space at deploying ISPs. Instead of using real websites as the decoy destinations for proxy connections, our scheme connects to IP addresses where no web server exists leveraging proxy functionality from the core of the network. These phantom hosts are difficult for a censor to distinguish from real ones, but can be used by clients as proxies. We define the Conjure protocol, analyze its security, and evaluate a prototype using an ISP testbed. Our results suggest that Conjure can be harder to block than TapDance, is simpler to maintain and deploy, and offers substantially better network performance.

This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the `right' places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.

Current feature tracking frameworks in sensor networks exploit advantages of either mobility, where mobile sensors can provide micro scale information of a small sensing area or numerical models that can provide macro scale information about the environment but not both. With the continual development of sensor networks, mobility becomes an important feature to integrate next generation sensing systems. In addition, recent advances in environmental modeling also allow us to better understand basic behavior of the environment. In order to further improve existing sensing systems, we need a new framework that can take advantages of existing fixed sensor networks, mobile sensors and numerical models. We develop CoTrack, a Collaborative Tracking framework, that allows mobile sensors to cooperate with fixed sensors and numerical models to accurately track dynamic features in an environment. The key innovation in CoTrack is the incorporation of numerical models at different scales and sensor measurements to guide mobile sensors for tracking. The framework includes three components: a macro model for large-scale estimation, a micro model for locale estimation of specific features based on sensor measurements, and an adaptive sampling scheme that guides mobile sensors to accurately track dynamic features. We apply our framework to track salinity intrusion in the Columbia River estuary in Oregon, United States. Our framework is fast and can reduce tracking error by more than half compared to existing data assimilation and state-of-the-art numerical models.

The Columbia River (CoRie) Observation Network includes an extensive array of fixed stations monitoring the Columbia River estuary and nearby coastal ocean. At each station, variable combinations of in-situ sensors measure one or more physical properties of water or atmosphere. Using a multi-scale data assimilation model, the CORIE modeling system integrates models and field controls to produce a simulation of 3D circulation, in a region centered in the estuary and plume. The CORIE data assimilation framework [1] combines observational data with numerical data models to produce an estimated system state for the physical process. To augment the fixed observational network, additional data is collected during periodical cruises of a mobile sensor station. Because these cruises are expensive and rare, an important goal for scientists is to sample data at points that most reduce the uncertainty of the data assimilation model. This is challenging, since the estuary environment is very dynamic, and therefore the optimal cruise path cannot be determined in advance. The goal of our system is to move the mobile station as the data assimilation proceeds in order to maximally reduce the uncertainty in the data assimilation process.