|By Harry Trott||
|April 17, 2016 02:00 PM EDT||
The biggest challenge to IOT is thus big data that requires an infrastructure that is capable of processing billions or more data points each second.
If you thought e-commerce revolutionized the retail industry over the past two decades, something much bigger is coming over the next ten years and that is the Internet of Things (IoT). By deploying sensors and network connectivity to everyday physical objects like cars, refrigerators, television and buildings, it is possible to remotely connect, manage and engage with these entities in ways that are not possible today.
Proponents of IoT describe a future where your car could interact with your air conditioner at home so that your house could be appropriately cooled by the time you reach home, or such devices could be automatically turned off each time you leave the house. While such examples are not far from truth, the true test for IoT shall come from policy makers.
The prospects for IoT in administration is so good that in countries like India and the UK, IoT is a matter of state policy today. However, it here that IoT is likely to meet its largest set of detractors. This is because IoT in state administration comes with huge stakes. Take the example of smart traffic management where Internet-enabled devices can monitor traffic movements across the city and can smartly administer lanes to ease congestion. Insufficient infrastructure or a badly executed IoT system could crumble under heavy load and could paralyze traffic movements across the city during peak times.
The biggest challenge to IoT is Big Data. The Internet of Things in administration could require an infrastructure that is capable of processing billions or more data points each second. Essentially, when IoT becomes main stream, we will be dealing with information that is much bigger than what the Big Data tools of today process on average. For mission-critical services like traffic management, this necessitates sophisticated data infrastructure that can handle the humongous volume of data thrown in by these large scale IoT systems.
Some experts argue that IoT and Big Data are essentially two sides of the same coin - like Big Data, the solution to a successful IoT implementation lies in storing, processing and extracting value from the data received. Protocols like Mosquitto and platforms like Hadoop and Hive do a good job in queuing and storing data while custom analytics tools will help process and derive meaningful information from this data. In both cases, however, the underlying infrastructure can make or break the system. A recent study published by CA Technologies found that among enterprise Big Data players, nearly 32% believe that their existing infrastructure is a major obstacle and not adequate to implement Big Data projects. It is not surprising then that close to half of the respondents believed their organizations needed major investments in infrastructure to implement their projects.
According to technology writer Arthur Cole, the solution to the infrastructural challenges facing IoT and the associated big data is not bigger infrastructure. Rather, it is in the design and implementation of smarter infrastructure that can process data in transit as well as optimize resources through prescriptive platform analytics and active copy analytics. Such an infrastructure could use machine learning algorithms to dynamically allocate the resources required to back up job processes, monitor services and produce output for the user.
This argument makes sense since focusing merely on larger infrastructure could create a bull-whip effect that could lead to exponential rise in infrastructural investment that could make IoT unviable. By focusing on smarter infrastructure that can possibly make use of current investments to process much larger volumes of data, we could be looking at larger adoption that could in effect lead to quicker mainstreaming of IoT. And that's a good thing for everyone in the ecosystem.
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