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Building the NoT Graph
A Graph Theoretical Approach to the NoT Architecture George F. Hurlburt firstname.lastname@example.org 301-481-6657 NIST Special Publication 800-183 defines the concept of the "Network of Things" (NoT). Just as the Internet is made up of loosely federated subnets, the NIST NoT premise holds that the Internet of Things (IoT) is also made up of subnets known as NoTs. Each NoT contains certain primitives, which include: sensors and their mobile platforms, agents for sensor clustering and weighting, aggregators to combine sensor data, decision triggers to fire actuators or create response to sensory input, communications to move data and electronic utilities to further manipulate raw NoT data. NIST 800-183 defines rules which govern each primitive and the relationships between the primitives. These rules compose a rudimentary NoT ontology. The NoT environment is far from static. For example, sensors can be mobile and hence in and out of communications range. Significantly, NoT primitives can come and go at will. Most importantly, sensors detect changing conditions in the NoT environment which, in turn, effect NoT activity in response to certain stimuli. Thus, NoT architecture cannot be viewed an entity with any fixed design. Rather, the NoT design architecture must be dynamic, connective, and adaptive as it continually changes over time. The NoT architectural dynamics are best viewed as a directed graph where nodes (primitives) come and go. Nodes can have one or many arcs (relationships) with other nodes as they morph. Likewise, data flows across the arcs and influence nodal behavior based upon embedded NoT rules and varying environmental conditions. This is further compounded by the notion that NoTs can federate and such federation further compounds NoT dynamics. Just as software, which when federated, tends to decouple cause from effect, NoTs, when federated, will tend to gain increasingly more complex behaviors. The graph database is well suited for depicting the dynamics of such complex adaptive activity. The notion of a dynamic NoT architecture, however, starts with a straightforward listing of Not components and their attributes. Largely drawn from specification documents, these data are easily captured and persisted in a relational database environment. To that end, the use of WordPress and MySql permits a rudimentary web-based NoT data capture capability which does not require an advanced degree to populate. More subjective data, such as contextually based reliability, integrity and security assessments are reduced to numeric weighting scales. Through SQL conversion, the NoT rules are enforced and converted to a language suitable to analysis via a graph database using an API to transfer selected NoT subgraph data from the MySql persistent store. The popular NEO4J permits graph representation of the NoT, albeit there are other alternatives which permit better semantic relationship building using Resource Definition Format (RDF). This becomes important as the rigor of the NoT ontology will ultimately determine the fidelity of the resulting NoT design model. Future work entails fleshing-out the demonstration application, introducing real-time data streams, formalizing the ontological framework, and introducing applied graph metric algorithms for more thorough and quantitative graph analysis capabilities.
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George Hurlburt (Primary Presenter,Author), STEMCorp, email@example.com;
George Hurlburt is chief scientist at STEMCorp, a nonprofit that works to further economic development via adoption of network science and to advance autonomous technologies as useful tools for human use. He is engaged in dynamic graph-based Internet of Things architecture. Hurlburt is on the editorial board of IT Professional and is a member of the board of governors of the Southern Maryland Higher Education Center.