The importance of trust in a Layered Sensing construct

The technological advances of the information age have created both an opportunity and a challenge for military decision makers. At first glance, the variety of data gathered by a multitude of traditional and non-traditional Intelligence, Surveillance, and Reconnaissance (ISR) sensors should allow decision makers to better understand their environment and the threat. However, a number of factors tend to erode this advantage. Incompatible data formats, bandwidth limitations, sensor persistence and revisit rates, and multi-level security issues are but a few of the technical challenges that preclude end users from being able to take full advantage of the sensor information. Further, as the amount of sensor data increases, real challenges emerge in identifying the most salient pieces of information, fusing that with other relevant sensor modalities, and rapidly moving this information in a presentation format that does not overwhelm the decision maker. It was this problem space of improving information understanding to increase the situational awareness of decision makers that spawned the Layered Sensing vision. Layered Sensing has grown from an Air Force Research Laboratory (AFRL) Sensors Directorate initiative into an AFRL effort that seeks to bring fused, multi-source, multi-spectral, multi-dimensional (e.g. cultural, environmental, etc.) information to decision makers consistent with their needs, regardless of their location. The end goal of Layered Sensing is to improve the situational awareness of decision makers by providing them with tailored sensor information. In essence, Layered Sensing is a Sensing Oriented Architecture where the customer can provide or request information tailored to their needs in order to facilitate situational awareness. The benefits of Layered Sensing extend beyond military application. For example, the Department of Homeland Security is one federal agency that could potentially face many of the same information challenge in operations- - as diverse as providing a common operating picture during a post-Katrina type reconstitution scenario or in fusing multi-source and multi-mode data (e.g. HUMINT, electronic intercepts, etc) in order to track or capture a suspected terrorist. Regardless of who implements a Layered Sensing architecture, the critical attribute of “trust” will have to be addressed - trusted information, trusted communication and trusted sensors. Trust is the sine qua non for any Layered Sensing implementation as it is difficult, if not impossible, to build situational awareness upon inaccurate, misleading, or maliciously modified information. The challenge of instilling and measuring trust in a system has many components. At the sensor node the issue of trust is one of ensuring the sensor works as intended and has not been compromised or co-opted. Between sensors, communications paths must be reliable, secure, and robust enough to recover from equipment degradation or malfunctions. Ideally, the local and global architecture should be autonomic with the ability to optimize sensor and communication performance. Finally, there needs to be a means to measure and portray the overall architecture trust picture status to those who are using it. The use of commercial off-the-shelf equipment, while cost effective, introduces its own trust issues due to uncontrollable supply chains. Relying solely on components from trusted sources is not feasible either economically or practically. As Layered Sensing moves from local architectures supporting an urban scenario to a global scenario the issue of complexity will arise. Seamlessly integrating a large number of spatially and temporally distributed heterogeneous sensors will require effective means to authenticate the new arrivals. Dynamic bandwidth allocation to account for optimal data transmission will also be important. Encompassing all of these trust issues is a way to measure and portray a “trust picture” to use