In many areas of Earth science, including climate change research and operational oceanography, there is a need for near real-time integration of data from heterogeneous and spatially distributed sensors, in particular in-situ and space- based sensors. The data integration, as provided by a smart sensor web, enables numerous improvements, namely, 1) adaptive sampling for more efficient use of expensive space-based and in situ sensing assets, 2) higher fidelity information gathering from data sources through integration of complementary data sets, and 3) improved sensor calibration.

Our ocean-observing smart sensor web presented herein is composed of both mobile and fixed underwater in-situ ocean sensing assets and Earth Observing System (EOS) satellite sensors providing larger-scale sensing. An acoustic communications network forms a critical link in the web, facilitating adaptive sampling and calibration. We report on the development of various elements of the smart sensor web, including (a) a cable-connected mooring system with a profiler under real-time control with inductive battery charging; (b) a glider with integrated acoustic communications and broadband receiving capability; (c) an integrated acoustic navigation and communication network; (d) satellite sensor elements; and (e) a predictive model via the Regional Ocean Modeling System (ROMS) interacting with satellite sensor control.

We consider the problem of feature based automatic classification of single and multitone signals. Our objective is to extend existing blind demodulation techniques to multitone waveforms such as MIL-STD-188-110B (Appendix B) and OFDM, developing a capability to identify signal types based on short data records, and maintaining robustness to channel effects. In this paper, we report on the first phase of our approach, namely, building a coarse classifier for a range of single tone and multitone signals. Among the features considered by the coarse classifier are those based on trigonometric moments and higher order statistics of the instantaneous frequencies of the received signal. No a priori information is assumed on the part of the received signal. The received signal of interest has not been previously observed; it is not part of a library of known signals; and no automated classifier has been built for it. Extensive simulation results based on real world signals are presented demonstrating the feasibility of the above features for automatic classification purposes of single and multitone signals.

The impact of bottom sediment type in relation to acoustic communications via orthogonal frequency division multiplexing (OFDM) is shown via experimental results and simulation. Experimental data from Lake Washington, Seattle with a "silty clay" bottom show that the multipath delay spread is longer at 250 m than at 4 km. This results in better OFDM performance at the longer range. Similar results are shown via simulation using a channel model developed from Bellhop, a Gaussian Ray tracing tool [M. Porter, "Bellhop Gaussian beam/finite element beam code," Available: http://oalib.hlsresearch.com/Rays/index.html (2007)]. Through simulation, results are also shown under similar conditions to the experiment but with varying bottom type. The results show that the performance of OFDM signaling is dependent on the bottom type as well as specific source/receiver geometry.

In many areas of Earth science, including climate change research, there is a need for near real-time integration of data from heterogeneous and spatially distributed sensors, in particular in-situ and space- based sensors. The data integration, as provided by a smart sensor web, enables numerous improvements, namely, 1) adaptive sampling for more efficient use of expensive space-based sensing assets, 2) higher fidelity information gathering from data sources through integration of complementary data sets, and 3) improved sensor calibration.

The specific purpose of the smart sensor web development presented here is to provide for adaptive sampling and calibration of space-based data via in-situ data. Our ocean-observing smart sensor web presented herein is composed of both mobile and fixed underwater in-situ ocean sensing assets and Earth Observing System (EOS) satellite sensors providing larger-scale sensing. An acoustic communications network forms a critical link in the web between the in-situ and space-based sensors and facilitates adaptive sampling and calibration.

After an overview of primary design challenges, we report on the development of various elements of the smart sensor web. These include (a) a cable-connected mooring system with a profiler under real-time control with inductive battery charging; (b) a glider with integrated acoustic communications and broadband receiving capability; (c) satellite sensor elements; (d) an integrated acoustic navigation and communication network; and (e) a predictive model via the Regional Ocean Modeling System (ROMS). Results from field experiments, including an upcoming one in Monterey Bay (October 2008) using live data from NASA's EO-1 mission in a semi closed-loop system, together with ocean models from ROMS, are described. Plans for future adaptive sampling demonstrations using the smart sensor web are also presented.

In the coming decade, the autonomous coordinated utilization of space, atmospheric, surface, and ocean assets, sensor webs, and data will assume more importance, as systems become more complex and tightly integrated, and as the need to know our environment with ever greater accuracy and precision becomes more acute. We have begun to address this issue with a prototype virtual ocean observatory that includes present and future NASA satellite missions (Jason-2 and QuikSCAT; and SWOT [swath altimetry] and XOVWM [ocean vector winds], respectively); atmosphere and ocean models (WRF/LAPS and ROMS, respectively); and in-situ sensors and platforms (underwater gliders).

In our prototype system, the goal is to develop the architecture and implementation of the necessary software modules (e.g., automated data fusion/assimilation, and automated planning technology) to achieve adaptive in-situ sampling through feedback from space-based-assets (in this case via the SWOT simulator) thereby contributing to the orbit design during the first, experimental phase (~6-9 months) of the SWOT mission. This work is one step in the process of infusing technology into the development pipeline.

We address several inter-related aspects of underwater network design within the context of a cross-layer approach. We first highlight the impact of key characteristics of the acoustic propagation medium on the choice of link layer parameters; in turn, the consequences of these choices on design of a suitable MAC protocol and its performance are investigated.

Specifically, the paper makes contributions on the following fronts: a) Based on accepted acoustic channel models, the pointto- point (link) capacity is numerically calculated, quantifying sensitivities to factors such as the sound speed profile, power spectral density of the (colored) additive background noise and the impact of boundary (surface) conditions for the acoustic channel; b) It provides an analysis of the Micromodem-like linklayer based on FH-FSK modulation; and finally c) it undertakes performance evaluation of a simple MAC protocol based on ALOHA with Random Backoff, that is shown to be particularly suitable for small underwater networks.

This paper discusses a methodology for predicting underwater acoustic communications performance using high fidelity acoustic time series simulation and acoustic modem processing emulation. Multiple source/receiver combinations can be simultaneously simulated, so that aspects of a complete underwater network can be studied. Here, the fundamental modeling and emulation capability will be described, with examples of the propagation modeling, time series simulation, and modem processing over multiple realizations of example communications channels. The results show the dependence of source and receiver location in the water column with respect to the sound speed profile on communications performance. The utility of such simulations for ad hoc network design in the presence of moving communications nodes will be discussed.

Much of the cost and effort of new ocean observatories will be in the infrastructure that directly supports sensors, such as moorings and mobile platforms, which in turn connect to a "backbone" infrastructure. Four elements of this sensor network infrastructure are in various stages of development, presented here: (1) a cable-connected mooring system with a profiler under real-time control with inductive battery charging; (2) a glider with integrated acoustic communications and broadband receiving capability; (3) an integrated acoustic navigation and communication network with tomography on various scales; and (4) a satellite uplink and feedback system. We also present initial results from field experiments, as well as from studies on communication performance of the underwater sensor network system under development.