SixthSense focuses on applying RFID to an enterprise setting, such as a corporate office or university department. An enterprise setting is different from the supply chain scenario in one fundamental way: the central role of people. Unlike in a supply chain, an enterprise setting involves rich interaction amongst people, between people and objects, and between people and workspaces. For instance, people own objects such as books, cell phones, and laptops, which they often carry around and sometimes mis-
place. SixthSense provides a platform for tracking and inferring such interactions, and then exposing these to the higher layers via APIs that enable useful applications and services to be built. Thus SixthSense raises the level of abstraction for applications in this domain beyond tag-level events, akin to how RFID stacks such as Microsoft’s BizTalk do so in the supply chain context. In short, SixthSense represents a form of mobile computing applied to non-computing entities.
SixthSense assumes a setting where most people (or rather their employee badges) and objects are tagged with passive RFID tags, and the coverage of RFID readers spans much of the workspace. However, we do not assume that this tagging is always catalogued systematically. Indeed, many objects present in a workplace may not even belong to the enterprise (e.g., a user’s personal mobile phone). Even if all objects (and people) were cataloged, this would be a manual process prone to errors and furthermore would require updating each time a new object is added or an object needs to be retagged because of the deterioration of its old tag . Therefore, a key goal of SixthSense is to make all inferences automatically, without requiring any human input.
SixthSense incorporates algorithms that start with a mass of undifferentiated tags and automatically infer a range of information based on an accumulation of observations. Sixth-Sense is able to automatically differentiate between people tags and object tags, learn the identities of people, infer the ownership of objects by people, learn the nature different zones in a workspace and perform other such inferences. Mobility of people and objects is key to the inference performed by Sixth-Sense. For example, tags attached to people are more likely to move, with less dependence on other tags, than tags attached to objects. Likewise, the owner of an object is likely to be the person who carries it around the most.
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