An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Energy Management <s> The wireless sensor network "macroscope" offers the potential to advance science by enabling dense temporal and spatial monitoring of large physical volumes. This paper presents a case study of a wireless sensor network that recorded 44 days in the life of a 70-meter tall redwood tree, at a density of every 5 minutes in time and every 2 meters in space. Each node measured air temperature, relative humidity, and photosynthetically active solar radiation. The network captured a detailed picture of the complex spatial variation and temporal dynamics of the microclimate surrounding a coastal redwood tree. This paper describes the deployed network and then employs a multi-dimensional analysis methodology to reveal trends and gradients in this large and previously-unobtainable dataset. An analysis of system performance data is then performed, suggesting lessons for future deployments. <s> BIB001 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Energy Management <s> Author(s): Richard Guy; Ben Greenstein; John Hicks; Rahul Kapur; Nithya Ramanathan; Tom Schoellhammer; Thanos Stathopoulos; Karen Weeks; Kevin Chang; Lew Girod; Deborah Estrin | Abstract: The Extensible Sensing System (ESS) has been in use for several years in a variety of sensor network deployments. It is a key component of a collection of tools that together are a nearly complete, end-to-end, sensor-to-user facility for deploying and managing a sensor network. This paper provides the context and architectural overview of ESS, along with selected deployment details and a series of lessons learned. Lesson areas include connectivity, interactivity, energy vs. robustness, vertical integration, and real-time visibility. The current version of ESS reflects changes from these lessons; further, new tools are in development that complement ESS. <s> BIB002
Energy is one of the most difficult challenges in developing a remote embedded acoustic system. Much of the prior work in embedded sensing has focused on low-rate sensing of environmental parameters, such as temperature and humidity BIB001 BIB002 . While low-rate sensors can conserve energy by sleeping much of the time, this problem is much more challenging in acoustic sensing. In addition, the much higher data rates associated with acoustic sampling typically require more processing, storage, and network resources, in general leading to higher energy requirements.