Five Trends in IoT and Edge Computing to Track in 2019
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The document discusses 5 trends in IoT and edge computing to track in 2019: 1) The growth of multi-locational hybrid data architectures to store data locally at the edge as well as in the cloud; 2) Increased convergence of facial analysis and machine learning, such as using cameras to detect driver fatigue; 3) The emergence of data marketplaces as enterprises purchase IoT data streams; 4) The convergence of IoT and blockchain to provide data provenance and immutability; 5) The rise of autonomous edge devices with local storage, ML models, and the ability to take actions without cloud latency.
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Five Trends in IoT and Edge Computing to Track in 2019
- 1. FIVE TRENDS IN IoT and Edge Computing TO TRACK IN 2019
- 2. Based on the industry use case, the storage may be on-premises or on the cloud or even have a hybrid model. However, in the last couple of years, the edge is no longer just a data-generation medium. The challenges around network latency and the need for immediate real-time insights have pushed enterprises to evolve the edge to be smarter. To enable this, in some use cases in specific verticals, there is a new need to store data locally at the edge as well. This leads to the evolution of a new model of multi-locational hybrid data architectures. GROWTH OF MULTI-LOCATIONAL DATA STORAGE
- 3. Over the last couple of years, we have seen limited success with video cameras capturing and understanding human sentiment using facial analysis for use cases across retail for customer engagement. For example: The camera inside a truck watching the driver’s actions and facial movements. It can quickly detect fatigue on the driver’s face and alert them immediately. Another requirement was to understand how many times the driver is taking their eyes off the road and is distracted with the radio. The idea is to increase driver safety and prevent loss of life or property in their fleet. INCREASED CONVERGENCE OF FACIAL ANALYSIS AND MACHINE LEARNING
- 4. With the hyper-growth in data production potentially reaching up to 163 zettabytes in just a few years, and with IoT and streaming data being a major contributor to that, every enterprise is sitting on goldmines of data. As vertical data ecosystems emerge as has been evident in how insurance companies tap into connected cars for driving history of its subscribers. The need for refining and building more sophisticated and more reliable ML models will warrant the need for more data. So, naturally, enterprises will start purchasing data – even subscribe to IoT data streams directly. EMERGENCE OF DATA MARKETPLACES
- 5. One of the key elements of streaming data that we tend to overlook is the importance of data provenance and lineage tracking. We build our businesses amidst so many compliance laws and regulations and it is only fair that we keep track of the data – its origin, the personas that handle it, the varying values through the data chain etc. With trust and data immutability being critical requirements in this model, blockchain will be an inherent part of this architecture. CONVERGENCE OF IOT AND BLOCKCHAIN IS INEVITABLE
- 6. As the realization dawns on us that device autonomy is already a reality in a few verticals today, we should start preparing for accepting the autonomous edge for many more use cases across automotive, healthcare, retail, manufacturing etc. When the use case cannot afford the cost of latency for the sake of detailed analysis, the edge has to become smarter, self-reliant, has access to local storage, has a set of rules (and a light-weight rules engine too maybe), has ML models to score the data and even has the capability to fire off a set of actions – potentially involving other robotic devices. RISE OF THE AUTONOMOUS EDGE
- 7. T H A N K Y O U ! www.blog.tyronesystems.com www.facebook.com/tyronesystems www.tyronesystems.com