Turn Off Your Radios ! Environmental Monitoring Using Power-Constrained Sensor Agents

CNAS (Collaborative Network for Atmospheric Sensing) is an agent-based, power-aware sensor network for ground-level atmospheric monitoring. In many multi-agent applications, reducing message transmission is a primary objective. In CNAS, however, it’s not the cost of sending messages, but when messages can be sent that is the driving communication constraint. CNAS agents must have their radios turned off most of the time, as even listening consumes significant power. Working in such collaborative isolation changes the character of agent interaction, as agents must have their radios turned on when others are sending messages to them. CNAS requires agent policies that can intelligently meet operational requirements while communicating only during intermittent, mutually established, communication windows. In this paper, we describe the CNAS agents and their hardware and blackboard-system software architectures. We also relate experiences and lessons learned from a field deployment of CNAS at the 2006 PATRIOT Exercise held last July at Fort McCoy, Wisconsin, and we discuss the upcoming CNAS deployment in conjunction with the Talisman Saber Combined Exercise to be conducted May–July 2007 in Australia. We conclude with an overview of current CNAS research that is exploring the addition of a rollable solar panel to each sensor agent that allows its battery reserves to grow (up to full capacity) when sunlight is available. Replenishable power reserves can support unlimited operational lifetimes, but activity decisions become more complex as each agent now must consider how much additional power may become available and when. 1. ATMOSPHERIC MONITORING The U.S. Air Force is interested in sensor networks for ground-level environmental monitoring, as detailed knowledge of local atmospheric conditions increases air drop precision and all-weather landing safety. Such networks also The UMass portion of this work is supported by the AFRL “Advanced Computing Architecture” program, under contract FA8750-05-1-0039. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views contained in this paper are the authors.’ ∗Doug Holzhauer is now retired and teaching at SUNYIT. Pronounced “see-nas.” ATSN-07 Honolulu, Hawaii USA have application to detecting forest fires, monitoring their changing status, and informing firefighters of changing conditions that affect their strategy and safety. Similarly, detailed knowledge of local atmospheric conditions is important in managing responses to airborne hazardous materials (hazmat) incidents and in determining prudent evacuation areas and routes. Low-level atmospheric phenomena are characteristically complex with changing spatial gradients. Because of this complexity, mathematical models based on a small number of observations can not accurately quantify important local environmental variations. At present, weather-based mission decisions use predictions made by the Air Force Weather Agency using complex large-scale models such as Mesoscale Model 5.1 (MM5). Using the new Weather Research & Forecasting (WRF) model, which incorporates individual observations into MM5, can increase the prediction accuracy. Currently most observations fed into MM5/WRF are acquired by satellites or by land based radar. Naturally, the closer the direct observations used as inputs to MM5/WRF are to the region of interest, the more accurate the predictions for that region will be. Even when used in combination, these large-area sensors can exhibit serious limitations when the area of interest is located in a remote isolated region. Cloud cover can mask the lower elevation weather parameters, and the curvature of the earth quickly restricts ground radars from observing lower portions of the troposphere. In the case of mountainous terrain, large geographical changes over small distances can prevent even the best models from accurately determining local weather conditions [3]. Large, battery-powered, ad hoc sensor networks can provide the high-accuracy environmental data needed in these application settings. Work by both DARPA and AFRL’s Sensors Directorate is leading to sensor nodes that are sufficiently rugged that they can be air dropped into regions of interest and that are able to selectively control their battery-power expenditures to provide monitoring services over extended time periods. Self-organizing, air-dropped sensor networks will enable the collection of detailed envihttp://www.mmm.ucar.edu/mm5/ http://www.wrf-model.org/ The current tactical weather station used by the Air Force, the AN-TMQ-53, cannot be air dropped because of its cost and packaging. ronmental data from regions that were previously closed to ground-level monitoring. Air-dropping atmospheric monitoring nodes introduces additional issues. Normally when a weather station is positioned, meteorologists use their understanding of geography and meteorology to optimize the location for weather measurements. Precise placement is not possible, when sensor nodes are air dropped. (However, research and development into maneuverable air-drop delivery systems are underway.) For the time being, though, even a marginal location for an air-dropped weather station can not be assured. To compensate, additional sensors may be deployed and their observations weighted as to quality. Environmental monitoring networks may also include many different types of sensors, and individual sensor capabilities may need to be dynamically adjusted (in terms of what aspects of the environment are sensed, the precision, power, and usage frequency of sensing, and the amount of local processing done by each sensor node before transmitting information). Information processing in the network may require the integration/fusing of information coming from heterogeneous and geographically distant sensors. Additionally, sensor usage and parameters may need to be adjusted in realtime as the network tracks phenomena moving through the environment and as the power and communication resources available to the sensor nodes change. Battery-powered sensor nodes need to spend their limited power wisely in collectively performing their best in achieving overall sensornetwork goals. In addition to this real-time, operational agility, the design of the sensor network should allow the software approaches and algorithms of nodes to be changed, improved, and extended throughout the operational lifetime of the network. We should expect from the outset that new and improved components and software techniques will be developed over time and added to the system. The underlying design of the sensor network should be able to adapt to such new capabilities and be able to manage their use effectively.