CDI-TYPE I: Biologically Relevant Sensor Networks for Climate Change Studies in Intertidal Ecosystems

潮间带是指世界沿岸低潮线和高潮线之间的区域。海洋是探索全球气候变化对物种分布和丰度影响的关键试验台。最近的研究表明，气候变化的最初迹象不仅涉及死亡率，还涉及生物生长和繁殖产量的变化。因此，量化自然界物理压力源的时空模式的能力至关重要，以便将生理耐受性的实验室测量与自然界的生态模式联系起来。已经部署了许多大型远程监测网络（浮标、卫星、气象站），以便在长时间内跨大空间尺度收集环境数据（例如空气、水和地表温度），目的是将气候变化与种群和物种的当前和未来分布联系起来。虽然对这些参数的大规模测量是至关重要的，但它们可能不足以理解和预测天气和气候变化对分布模式和生物多样性变化的影响，因为对“生境”的大规模测量往往不能很好地预测生物体所感知的“环境”。例如，空气温度通常是动物体温的一个很差的指标，而动物体温是驱动生理反应的因素。我们建议设计和部署一个无线“仿生”传感器网络（WSN），以监测与气候变化影响最相关的环境参数——温度和pH值——在生理和生态相关的时空尺度上。尽管在各种陆地环境（如森林、沙漠、沼泽、火山）中研究和部署了各种wsn，但监测潮间带仍然具有挑战性。由于潮汐引起的海平面周期性变化导致通信信道质量振荡，从而使潮间带成为无线电通信的高度不稳定环境。为了在这样的环境中生存，我们提出了一种通道感知传感器网络，它可以自诊断其通信能力并相应地调整其网络协议，例如，自适应MAC协议根据信道质量具有各种传输策略。除了研究气候变化对社会的广泛影响外，我们还提出了一个由教师、本科生和研究生组成的综合计划。这些团队将合作进行研究，并将根据我们的研究结果为当地K-12教室制作基于探究和标准的教育材料，强调网络技术在研究自然世界，特别是天气和气候影响方面的应用。最终目标是研究对温度和pH值的生理反应，以及天气和气候的生态影响。该提案将(1)通过使用无线传感器网络近实时地监测具有重要生态意义的动物（贻贝）的小规模、局部状况，在了解气候变化的生态影响方面取得根本性进展，并为全球关键潮间带地点（如保护区、贝类养殖场）建立“早期预警系统”机制；(2)在信道感知无线传感器网络方面取得根本性进展，该网络可以在高度不稳定的无线电环境中智能地估计通信信道质量，并选择将测量结果上传到数据收集中心的机会。除了监测感兴趣的环境参数外，网络还将感知直接影响通信信道状况的属性并实现信道感知。

The intertidal zone - the region between the low and high tide lines along the coasts of the world?s oceans - 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 large-scale 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, 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 propose to design and deploy a 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 remains challenging. 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 self-diagnose its communication capability and adapt its network protocols accordingly, e.g., an adaptive MAC protocol has various transmission policies depending on the channel quality.In addition to the broader implications of our study for climate change to society, we propose an integrative plan to form teams of teachers, undergraduate and graduate students. These teams will collaboratively conduct research, and will produce inquiry- and, standards-based educational materials for local K-12 classrooms based on our study results, emphasizing the applications of cyber-enabled technology for studying the natural world, and the effects of weather and climate in particular.The ultimate goal is to study physiological responses to temperature and pH, and the ecological effects of weather and climate. This proposal 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 using wireless sensor networks, and produce a mechanism for creating an "early warning system" for key intertidal sites (e.g., reserves, shellfish farms) around the world, (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. In addition to monitoring the environmental parameters of interest, the network will sense properties that directly affect the communication channel condition and achieve channel-awareness.