Conceptually, the promise of the Internet of Things is almost halcyon. Its billions of sensors are all connected, continuously transmitting data to support tailored, cost-saving measures maximizing revenues in applications as diverse as smart cities, smart price tags, and predictive maintenance in the Industrial Internet.
Practically, the data management necessities of capitalizing on this promise by the outset of the next decade are daunting. The vast majority of these datasets are unstructured or semi-structured. The data modeling challenges of rectifying their schema for integration are considerable. The low latency action required to benefit from their data implies machine intelligence largely elusive to today’s organizations.
JSON-LD redresses these issues to simplify the data management rigors of the IoT. This data interchange format is adaptive enough to incorporate any schema into pliant data models for structured, unstructured, and semi-structured data. Its linked data methodology is machine readable, which is essential to inter-machine communication and authenticating end point device data. It swiftly integrates IoT data with other data sources for informed, time-sensitive action.
Adopting JSON-LD for IoT initiatives delivers these advantages at scale, enabling organizations in any vertical to harness the distributed power of continuously generated big data.
Schema on Read
The chief difficulty of grappling with the IoT’s expedient semi-structured and unstructured data is the data modeling demands, especially when integrating this data with other sources. The JSON format is flexible enough to accommodate any schema on demand, eradicating the laborious modeling efforts of incorporating such data in relational settings. Regardless of its schema or initial format, any data type can be quickly converted to JSON and ingested in semantic graphs with standardized data models.
The cardinal advantage of leveraging JSON-LD is these JSON objects can be linked to any other data object in the semantic graph, thereby expediting the integration and aggregation of IoT data alongside other enterprise data. For instance, when deploying image recognition systems to identify customers or purchasing habits in real-time retail deployments, JSON-LD accelerates the integration of this data with previous purchase trends for intelligent graph analytics supporting real-time marketing opportunities like individualized discounts and advertising to customers. JSON-LD’s rapid integration of sensor data provides similar boons across verticals.
The self-describing, linked data approach upon which JSON-LD is founded excels at the low latent action resulting from machine to machine communication in the IoT. The nucleus of the linked data methodology—semantic statements and their unique Uniform Resource Identifiers (URIs)—are read and understood by machines. This characteristic aids many of the IoT use cases requiring machine intelligence; by transmitting IoT data via the JSON-LD format organizations can maximize this boon. Smart cities provide particularly compelling examples of the machine intelligence fortified by this expression of semantic technology.
Smart lights and streetlamps can turn off when sensors indicate there’s no traffic present to conserve energy and reduce overall costs; machine learning can accurately predict the right times to do so or when to reactivate them. In other use cases, road sensors can determine high traffic rates and dynamically adjust the pricing for parking options in the vicinity to increase revenues. Using JSON-LD to transmit the roads’ sensor data to systems for cognitive computing analytics can determine where and how much to adjust the pricing, then automatically implement it at specific municipal lots based on availability. This data interchange format enables all machines to understand the same data to automate this intelligent action.
JSON-LD also improves IoT deployments by simplifying the verification of the ID of the various connected devices. Since the crux of the linked data approach is the unique identifier ascribed to each datum, each sensor’s emission would have its own unique URI when formatted with JSON-LD. Moreover, the standards used to harmonize linked data could also describe the sensor by type (such as a pressure sensor in the manufacturing or oil and gas industries) and describe exactly what that sensor type entails with rich taxonomies.
By manipulating the semantic statements or triples for these URIs, organizations could create an additional layer of security making it almost impossible to duplicate this information by fraudulent devices attempting to infiltrate an organization’s network. Subsequently, the devices in these IoT deployments would be duly authenticated, helping to fortify the vulnerability issues conventionally plaguing this aspect of data management.
Making the IoT Work
Several of the most intractable issues surrounding IoT deployments are resolved by JSON-LD. This format is flexible enough to rectify differences in schema and produce timely data integrations for low latent action. It’s also machine readable, assisting in the timeliness and communication between devices in this distributed computing environment. Its identifiers are also useful for authenticating devices onto networks to buttress security. JSON-LD is a fundamental enabler of the data management necessary for monetizing the IoT.
About the Author: Jans Aasman is Ph.D. psychologist and expert in Cognitive Science and CEO of Franz.com, an innovator in Artificial Intelligence and provider of Semantic Graph Databases and Analytics. As both a scientist and CEO, Aasman continues to work in the areas of Artificial Intelligence and Semantic Databases with organizations such as Montefiore Medical Center, Blue Cross/Blue Shield, Siemens, Merck, Pfizer, Wells Fargo, BAE Systems as well as US and Foreign governments.
Edited by Ken Briodagh
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