CT - ISG: ROME: Robust Measurement in Sensor Networks

CT - ISG：ROME：传感器网络中的稳健测量

• 批准号: 0831186
• 负责人: Yanyong Zhang
• 金额: $ 40万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0831186&HistoricalAwards=false
• 关键词: CT; ISG; ROME; Robust; Measurement

Regardless of whether sensors are used just for observations or as the basis for actual responses, the trustworthiness of sensor data becomes critical when important decisions are based upon these data. Unlike traditional networked systems, sensor networks also take measurements of physical phenomena. Traditional information assurance, which focuses on the integrity of data, does not cover all of the sources of errors that might arise in sensing data. In fact, sensor data can be corrupted at the environmental level, whether through a natural loss of calibration or through a deliberate perturbation of the measurement environment by an adversary. These sources of errors, which affect the process of measurement (PoM), are unique to sensor networks and cannot be addressed through the usual network-centric methods. Hence, to complement traditional information assurance services, defense mechanisms are needed to protect the sensor network from PoM errors. Only when a trust wrapper surrounds sensor measurements should applications make decisions or take actions with important implications. The proposed research consists of two aspects: corruption research and assurance research. For the former, the team will conduct a thorough threat analysis by cataloging the set of PoM errors that may be introduced for a variety of different sensors. For the latter, the team will augment the existing programming stack for sensor networks by developing a suite of measurement assurance tools. Developing methods that give sensor networks a natural immunity to PoM errors will allow sensor applications to have a greater chance for success and wider spread use.

无论传感器是否仅用于观察或作为实际响应的基础，当重要决策基于这些数据时，传感器数据的可信度变得至关重要。与传统的网络系统不同，传感器网络还可以测量物理现象。传统的信息保证侧重于数据的完整性，并不能涵盖传感数据中可能出现的所有错误来源。事实上，传感器数据可能在环境层面上被破坏，无论是通过校准的自然损失还是通过对手对测量环境的故意扰动。这些影响测量过程（PoM）的误差源是传感器网络所特有的，无法通过通常的以网络为中心的方法来解决。因此，为了补充传统的信息保障服务，需要防御机制来保护传感器网络免受PoM错误的影响。只有当信任包装器包围传感器测量时，应用程序才能做出具有重要意义的决策或采取行动。本文的研究内容包括腐败研究和保证研究两个方面。对于前者，该团队将通过编目可能为各种不同传感器引入的PoM错误集来进行彻底的威胁分析。对于后者，该团队将通过开发一套测量保证工具来增强传感器网络的现有编程堆栈。 开发使传感器网络对PoM错误具有天然免疫力的方法将使传感器应用有更大的成功机会和更广泛的使用。