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IoT Basics: What is Predictive Maintenance?
JAKOB SCHREINER | April 14, 2019
oT Now is the leading global magazine covering machine-to-machine communications (M2M), embedded devices, connected consumer devices, smart grids & metering, and the Internet of Things.
Article | April 9, 2020
So you’re building an IoT solution and you’re ready to select your connectivity approach. Should you use Bluetooth? WiFi? LoRa? Cellular? Satellite? As I’ve explored in a previous post, the connectivity approach you choose ultimately comes down to the specific needs of your use case. Some use cases favor mobility and bandwidth, and power consumption doesn’t matter as much. Other use cases favor extensive battery life and broad coverage, and bandwidth doesn’t matter as much. In this post, I argue that for Enterprise IoT solutions, you shouldn’t use WiFi regardless of the use case. To build and implement a successful IoT solution, your connectivity needs to be reliable and consistent. When there’s an issue that needs troubleshooting, knowing that certain components of your IoT solution are reliable and consistent enables you to narrow your focus and address issues more effectively. There are many challenges in IoT, many of which stem from operational challenges and from having thousands of devices out in the real world where they’re subject to harsh, ever-changing environments.
To paraphrase a well-known saying, the journey to a complete smart city begins with a single building. No matter the size of the city, the extent of the technology or the most helpful use cases, a prospective smart city can integrate into — or branch off of — initiatives pushed forward by a smart building or campus. And when there is an increasing demand for these types of solutions, large corporations have the opportunity to improve corporate and social governance practices, as well as stand out in their community by championing more connected technologies.
As industries invest in the next generation of technology, the internet of things is at the top of the list. Once a novelty best known in residential settings, IoT technology has become mainstream in business and industry. Some even predict that having a Google Home or Amazon Echo in the conference room will become the norm. Now, you will find connected devices in Amazon’s warehouse robotics system Kiva, Airbus’ Factory of the Future and the augmented reality used by Caterpillar in its Cat Connect system. The number of IoT devices in use by 2025 could reach 22 billion, according to an IoT Analytics report. They will transform manufacturing, warehouse and logistics, health care, agriculture, shipping, energy and aviation. IoT security can’t be done as an afterthought; it must be integrated into operations from Day One because connected devices and systems use cryptographic keys that transmit data. Cybercriminals can gain access through these keys, allowing them to infect devices and systems with malware, steal data or shut down systems. Sometimes, the vulnerability has gone undetected for months.
As development teams race to build out AI tools, it is becoming increasingly common to train algorithms on edge devices. Federated learning, a subset of distributed machine learning, is a relatively new approach that allows companies to improve their AI tools without explicitly accessing raw user data. Conceived by Google in 2017, federated learning is a decentralized learning model through which algorithms are trained on edge devices. In regard to Google’s “on-device machine learning” approach, the search giant pushed their predictive text algorithm to Android devices, aggregated the data and sent a summary of the new knowledge back to a central server. To protect the integrity of the user data, this data was either delivered via homomorphic encryption or differential privacy, which is the practice of adding noise to the data in order to obfuscate the results.
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