Elastic Sensor Networks: Towards Attention-Based Information Management in Large-Scale Sensor Networks

该项目将由伦敦帝国理工学院安全科学与技术研究所共同资助，旨在为大规模传感器网络（LSSN）的信息管理开发一种新的理论框架和相关计算模型。这种网络的应用正在引起学术界和工业界的广泛关注，从家庭监测到工业传感，包括环境和栖息地监测、安全、交通控制和医疗保健。管理这类网络的一个关键挑战是，随着所监测的信息量的增加，如何避免信息过载。第二个关键挑战是如何在资源和实时约束下有效地最大化所收集信息的价值。解决这两个挑战需要发展有效和高效的方法来组织信息收集和信息处理，重点只分析与用户需求有关的信息。我们在本提案中的关键假设是，人类的信息处理，特别是人类进化良好的注意力机制，与传感器网络中的信息处理之间的类比，将导致lssn开发出新颖而高效的信息管理策略。这种类比将使我们能够有效地利用本地和全局信息之间的关系，避免应用程序中的信息过载，并最大限度地减少网络中不必要的资源消耗（处理和通信）。我们对在传感器网络中开发和使用类似注意力的机制感兴趣，是因为它可以很容易地映射到一个简洁而健壮的贝叶斯公式。这样的表述将使我们和其他研究人员能够推断出该方法的正确性，也可以推断出该方法的扩展和潜在的改进。因此，我们在该项目中的工作侧重于解决理论和实践层面的一些关键挑战，包括将标准贝叶斯概率框架扩展和应用于LSSN设置，为此类网络开发弹性资源分配模型的基础，并支持我们的实现扩展到大规模网络实现的分散方法。除了发展理论基础之外，我们的工作还将包括使用模拟和真实传感器硬件开发分布式LSSN信息管理系统的功能原型。对我们的方法进行评估将使用来自两个应用领域的案例研究：多模态安全监测和城市污染监测。评估将与伦敦帝国理工学院安全科学与技术研究所（ISST）和交通研究中心（CTS）的最终用户以及三所国际机构（罗格斯大学、哈佛大学和莫纳什大学）的合作者密切合作进行。评估将基于真实和模拟数据集，以比较所提出方法与传统方法和竞争方法的功效和效率。

This project, which will be co-funded by the Institute of Security Science and Technology at Imperial College London, aims to develop a novel theoretical framework and associated computational model for information management in Large-scale Sensor Networks (LSSN). Applications of such networks are drawing wide attention from both academia and industry ranging from home monitoring to industry sensing, including environment and habitat monitoring, security, traffic control and health care. A key challenge in managing such networks is that of avoiding information overload as the amount of information monitored increases. A second key challenge is how to effectively maximize the value of collected information under resource and real-time constraints. Addressing both challenges requires developing effective and efficient methods for organizing information collection and information processing that focus on analyzing only information relevant to the user needs.Our key hypothesis in this proposal is that an analogy between information processing by humans, in particular their well-evolved human attention mechanism, and information processing in sensor networks would lead to the development of novel and highly effective information management strategies for LSSNs. This analogy would enable us to exploit effectively the relationship between local and global information, avoid information overload in the application and also minimize unnecessary resource consumption (processing and communication) in the network. Our interest in developing and using an attention-like mechanism in sensor networks is driven by the fact that it could be mapped easily to a concise and robust Bayesian formulation. Such a formulation would enable us and other researchers to reason about the correctness of the approach and also to reason about its extensions and potential improvements beyond this project.Our work in this project thus focuses on addressing a number of key challenges both at the theoretical and practical levels, including the extension and application a standard Bayesian probabilistic framework to the LSSN setting, developing the foundations for an elastic resource allocation model for such networks and supporting a decentralized approach for our implementation that scales to large scale networks implementations.In addition to developing the theoretical foundations, our work will also include developing functional prototypes of a distributed LSSN information management system using both simulations and real sensor hardware. The evaluation of our methods will proceed using case studies from two application areas: multi-modality security monitoring and urban pollution monitoring. The evaluation will be conducted in close collaboration with end users in the Institute of Security Science and Technology (ISST) and the Cenre of Transportation Studies (CTS) at Imperial College London as well as with collaborators in three international institutions (Rutgers University, Harvard University and Monash University). The evaluation will be based on real and simulated data sets to compare the efficacy and efficiency of the proposed approach against traditional and competing methods.