Funded by: The National Science Foundation
PI: Brian Helmuth
Co-PIs: Wenyuan Xu (University of South Carolina)
The intertidal zone is the region between the low and high tide lines along the coasts of the
world’s oceans. It serves as a key test bed for exploring the effects of global climate change on
species distributions and abundances. Recent studies show that the first signs of climate change
involve not only mortality but also changes in the growth and reproductive output of organisms.
Therefore, the ability to quantify spatial and temporal patterns of physical stressors in nature is
critical, in order to connect laboratory measurements of physiological tolerance to ecological
patterns in nature.
Many large-scale remote monitoring networks (buoys, satellites, weather stations) have been deployed to collect environmental data (e.g. air, water and surface temperature) across broad spatial scales for extended periods of time, with the goal of relating climate change to the present and future distributions of populations and species. While largescale measurements of these parameters are vital, they may not be sufficient for understanding and predicting the effects of weather and climate change on patterns of distribution and changes in biodiversity. This is because large-scale measurements of “habitat” are often very poor predictors of “the environment” as perceived by the organism. For example, air temperature is often a very poor indicator of animal body temperature, which is what drives physiological response.
We are designing, testing, and deploying a novel wireless “biomimetic” sensor network (WSN) that monitors environmental parameters most relevant to climate change impacts (temperature and pH) at temporal and spatial scales that are physiologically and ecologically relevant. Despite the wide varieties of WSNs that have been studied and deployed in various terrestrial environments (e.g., forests, deserts, swamps, volcanoes), monitoring the intertidal zone with WSN remains a challenge. Periodic changes in sea level due to tides cause the communication channel quality to oscillate and thus make the intertidal zone a highly unstable environment for radio communication. To survive in such an environment, we propose a channel-aware sensor network that can selfdiagnose its communication capability and adapt its network protocols accordingly, e.g., an adaptive MAC protocol has various transmission policies depending on the channel quality.
The goal of this project will (1) achieve a fundamental advance in understanding the ecological impacts of climate change by monitoring the small-scale, local conditions of ecologically important animals (mussels) in near-real time, and (2) achieve fundamental advances in channel-aware wireless sensor networks that can intelligently estimate the communication channel quality and select opportunities for uploading measurements to a data collection center in a highly unstable radio environment.