Readwrite autonomous car landscape
Many businesses still use general-purpose architecture to manage their IoT data. We explore a few popular IoT use cases through this lens and detail the major factors to take into consideration when building out IoT infrastructure. The ability to capture, preserve, access, and transform becomes critical to get the best use out of IoT data. Our recent article highlights the top 10 IoT use cases, from autonomous vehicles to virtual reality. Such data could also create more personalized and proactive treatments, especially as telehealth and remote healthcare continue to progress. This IoT data can be transformed into daily, monthly, and yearly trends that identify opportunities to improve health habits using data-informed decisions.
Medical Devices and Wearablesīy 2022, over 1 billion wearable devices are predicted to be in use around the world – generating data to track sleep patterns, measure daily movements, and identify nutrition and blood oxygen levels. In addition, IoT data creates value in personalized infotainment and in-vehicle services that improves the passenger experience.
#Readwrite autonomous car landscape driver#
That data is used to inform real-time driving decisions using technologies such as 3D-mapping, advanced driver assistance systems (ADAS), over-the-air (OTA) updates, and vehicle-to-everything (V2X) communication. These large-scale IoT devices are loaded with sensors, cameras, LIDAR, radar, and other devices generating data – estimated to reach 2TB+ per day. Here are a couple examples of this data generation –> transformation –> value creation approach. Thus, data architecture need to go beyond generation and transformation to find ways to create business value from data. Infrastructure is critical in our digital world because data must be stored and analyzed quickly, efficiently, and securely. Now that we’ve discussed the journey of IoT data, let’s talk about data infrastructure. 5G could offer a potential solution by using millimeter wave (mmWave) bands between 20-100 GHz to create “data superhighways” for latency and bandwidth sensitive innovations. The key is to reduce network latencies and increase throughput between these layers (cloud-to-edge, edge-to-endpoints) for data-intensive use cases. Finally, we reach the endpoints, where data is generated by connected machines, smart devices, and wearables. Then, we migrate to the edge, where data is often cached in distributed, edge servers for real-time applications such as autonomous vehicles, cloud gaming, manufacturing robotics, and 4K/8K video streaming. These could include genomic research, batch analytics, predictive modeling, and supply chain optimization. We start in the cloud, where high-capacity drives – now reaching 20TB – store massive amounts of data (including from IoT devices) for big data use cases. Cloud, Edge, Endpoints – The IoT Data Journey But first, let’s define the IoT data journey. In this blog post, I’ll discuss getting the most value out of IoT data and moving from general-purpose to purpose-built data storage. A robust data architecture is critical to properly capture, preserve, access, and transform this data in its journey – not just in cloud data centers, but at edge servers and endpoints.
Connected machines, fueled by the Industry 4.0 transition and wearables, are projected to contribute an increasing amount of IoT data. Now, we focus on the rapidly evolving Internet of Things (IoT) landscape – with an estimated 41.6 billion connected devices generating 79.4 zettabytes (ZB) of data in the year 2025. In a recent blog post, we talked about how the current digital environment is accelerating connected technology adoption and innovation.