Eric Lyons

An end-user-responsive sensor network architecture for hazardous weather detection, prediction and response

We present an architecture for a class of systems that perform distributed, collaborative, adaptive sensing (DCAS) of the atmosphere. Since the goal of these DCAS systems is to sense the atmosphere when and where the user needs are greatest, end-users naturally play the central role in determining how system resources (sensor targeting, computation, communication) are deployed. We describe the meteorological command and control components that lie at the heart of our testbed DCAS system, and provide timing measurements of component execution times. We then present a utility-based framework that determines how multiple end-user preferences are combined with policy considerations into utility functions that are used to allocate system resources in a manner that dynamically optimizes overall system performance. We also discuss open challenges in the networking and control of such end-user-driven systems.

Closed-loop architecture for distributed collaborative adaptive sensing of the atmosphere: meteorological command and control

Distributed Collaborative Adaptive Sensing (DCAS) of the atmosphere is a new paradigm for detecting and predicting hazardous weather using a dense network of short-range, low-powered radars to sense the lowest few kilometres of the earths atmosphere. DCAS systems are collaborative in that the beams from multiple radars are actively coordinated in a sense-and-respond manner to achieve greater sensitivity, precision and resolution than possible with a single radar. DCAS systems are adaptive in that the radars and their associated computing and communications infrastructure are dynamically reconfigured in response to changing weather conditions and end-user needs. This paper describes an end-to-end DCAS architecture and evaluates the performance of the system in an operational testbed with actual weather events and end-user considerations driving the system. Our results demonstrate how the architecture is capable of real-time data processing, optimisation of radar control and sensing of the atmosphere in a manner that maximises end-user utility.

Distributed Collaborative Adaptive Sensor networks for remote sensing applications

Enabled by a dense network of Doppler weather radars with overlapping coverage, Distributed Collaborative Adaptive Sensing (DCAS) represents a new paradigm in remote sensing. Rather than each radar periodically sampling its surroundings with sit-and-spin volume coverage patterns as with todays NEXRAD weather radars, DCAS is an end-user driven approach that targets sensitivity when and where the needs of its end-users are greatest. The advantage is that by adaptively allocating sensitivity, higher quality measurements are possible due to the ability to dwell longer in volumes where echoes are weak, sample faster in volumes with rapidly evolving dynamics, and obtain multi-Doppler looks for high accuracy wind field retrieval. This paper describes the multiuser, multi-attribute utilities-based approach being used to coordinate the scanning activities of the weather radars in the first prototype DCAS system being fielded by the National Science Foundation sponsored Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA- ERC).

CloudCast: Cloud computing for short-term mobile weather forecasts

Since todays weather forecasts only cover large regions every few hours, their use in severe weather is limited. In this paper, we present CloudCast, an application that provides short-term weather forecasts depending on users current location. Since severe weather is rare, CloudCast leverages pay-as-you-go cloud platforms to eliminate dedicated computing infrastructure. CloudCast has two components: 1) an architecture linking weather radars to cloud resources, and 2) a Nowcasting algorithm for generating accurate short-term weather forecasts. We study CloudCasts design space, which requires significant data staging to the cloud. Our results indicate that serial transfers achieve tolerable throughput, while parallel transfers represent a bottleneck for real-time mobile Nowcasting. We also analyze forecast accuracy and show high accuracy for ten minutes in the future. Finally, we execute CloudCast live using an on-campus radar, and show that it delivers a 15-minute Nowcast to a mobile client in less than 2 minutes after data sampling started.

CloudCast: Cloud Computing for Short-Term Weather Forecasts

CloudCast provides clients with personalized short-term weather forecasts based on their current location using cloud services, generating accurate forecasts tens of minutes in the future for small areas. Results show that it takes less than 2 minutes from the start of data sampling to deliver a 15-minute forecast to a client.

Network capabilities of cloud services for a real time scientific application

Dedicating high-end servers for executing scientific applications that run intermittently, such as severe weather detection or generalized weather forecasting, wastes resources. While the Infrastructure-as-a-Service (IaaS) model used by todays cloud platforms is well-suited for the bursty computational demands of these applications, it is unclear if the network capabilities of todays cloud platforms are sufficient. In this paper, we analyze the networking capabilities of multiple commercial (Amazons EC2 and Rackspace) and research (GENICloud and ExoGENI cloud) platforms in the context of a Nowcasting application, a forecasting algorithm for highly accurate, near-term, e.g., 5-20 minutes, weather predictions. The application has both computational and network requirements. While it executes rarely, whenever severe weather approaches, it benefits from an IaaS model; However, since its results are time-critical, enough bandwidth must be available to transmit radar data to cloud platforms before it becomes stale. We conduct network capacity measurements between radar sites and cloud platforms throughout the country. Our results indicate that ExoGENI cloud performs the best for both serial and parallel data transfer with an average throughput of 110.22 Mbps and 17.2 Mbps, respectively. We also found that the cloud services perform better in the distributed data transfer case, where a subset of nodes transmit data in parallel to a cloud instance. Ultimately, we conclude that commercial and research clouds are capable of providing sufficient bandwidth for our real-time Nowcasting application.

Dense radar networks for low-flyer surveillance

The present inability to detect low-flying aircraft over international borders renders governments and citizens vulnerable to problems such as drug trafficking and illegal immigration. This paper describes an approach to comprehensive low-altitude surveillance based on networks of small radars being developed by the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere. We examine how low-cost networked radar technology might be applied to the public safety/security problem of detecting weather hazards while simultaneously supporting the border security mission of detecting and intercepting low-flying aircraft.

Meteorological Command & Control: Architecture and Performance Evaluation

IP1 is a prototype CASA radar sensor network located in southwestern Oklahoma whose goal is to detect severe weather in the lower part of the atmosphere. At the center of this systems control loop is its Meteorological Command and Control (MC&C). In this paper, we presented the overall control architecture for the IP1 network and highlight new features that have recently been added to the MC&C. We also present an analysis of the MC&C performance based on measurement data from a 5-day operation period. In addition, we introduce a distributed version of the MC&C.

NetRad: distributed, collaborative and adaptive sensing of the atmosphere calibration and initial benchmarks

We are currently building a NetRad prototype system to be deployed in southwestern Oklahoma, consisting of four mechanically scanned X-band radars atop small towers, and a central control site (later to be decentralized as the number of radars increases) known as the System Operations and Control Center (SOCC). The SOCC consists of a cluster of commodity processors and storage on which the Meteorological Command and Control (MC&C) components execute. NetRad radars are spaced approximately 30 km apart from each other and together scan an area of 80km x 80km and up to 3 km in height. In this paper, we overview the radar calibration process, as well as the initial benchmark execution times of the software modules we will demonstrate at DCOSS.

Adaptive wireless mesh networks

Large-scale wireless mesh networks, like the ones used as cellular back-haul, operate under circumstances, where individual links are affected by weather conditions. Reliability requirements in wireless mesh networks necessitate the ability to reconfigure the network in the face of changing environmental conditions. In this paper, we present a predictive routing protocol for wireless mesh networks, which operate at millimeter-wave bands with directional links, that uses in-network parameter prediction to make the network adaptive, as opposed to using meteorological weather information from external sources, such as weather radars. We validate our approach through simulations based on real-world weather events, observed through a network of weather radars, and comparisons with approaches that do not make use of predictions but may use the link quality as a parameter in routing decision making. Our results show that our link quality-based predictive approach can achieve throughput performance that is almost 8% better than a link quality-based routing algorithm that does not use prediction for the real weather scenario we use for our simulations.