Artificial Intelligence (AI) and Machine Learning (ML) are amongst the emerging trends in Business and Marketing. Yet, a lot of this cleverness is located in the Cloud. Read: in big server parks with high-end processing capabilities. In a not too distant future, several applications will enter our lives that require an increased amount of Intelligence and Computation being implemented closer to the user. Be it for reasons of speed, energy-efficiency or privacy. Think about self-driving vehicles who have to respond more quickly than the time it takes to send data up and down to the cloud. Or privacy-sensitive tasks as voice analysis and face- or fingerprint recognition, for which legal or user constraints might keep you from sending data over the air. In a way, this leads to the question we engineers are now facing: ‘how to get a server rack in your back pocket’? Read More
Tag Archives: IoT
Machine Learning Advances and Edge Computing Redefining IoT
Smart, connected products are changing the face of competition. That was the thesis of a formative 2014 article in Harvard Business Review that highlighted the transformative potential of information technology integrated into an array of products.
In the past five years, however, the seemingly straightforward words of “smart” and “connected” have become more enigmatic and, arguably, more loaded terms. And the meaning of those two terms has steadily evolved and continues to change. Five to ten years ago, a “smart” product was one with embedded sensors, processors and software. These days, to qualify as “smart,” a device needs to take advantage of some form of basic machine learning at a minimum. Read More
The 7 levels of the Internet of Things
Is it possible to build a secure, collaborative platform required to support IoT’s solutions orientation? Cisco believes it is, and outlined a first step in this direction at the event with announcement of the IoT Reference Model. Read More
Making The Internet Of Things (IoT) More Intelligent With AI
According to IoT Analytics, there are over 17 Billion connected devices in the world as of 2018, with over 7 Billion of these “internet of things” (IoT) devices. The Internet of Things is the collection of those various sensors, devices, and other technologies that aren’t meant to directly interact with consumers, like phones or computers. Rather, IoT devices help provide information, control, and analytics to connect a world of hardware devices to each other and the greater internet. With the advent of cheap sensors and low cost connectivity, IoT devices are proliferating. Read More
IT/OT convergence: Addressing the perception gaps
IoT can be viewed as an ideal opportunity to bridge the worlds of information technology (IT) and operational technology (OT). IoT projects over the last several years have focused on device onboarding and getting data aggregated into the cloud…usually an IT-managed cloud. Data access and analytical insight from that data has been parsed out from IT as a service. The “IT as a service” model is not well positioned to take advantage of the new opportunities presented by edge compute devices and the analytical capabilities in those devices transacted at origination of the data.
Historically, IT and OT data did not intersect. The departmental silos were hardened by “division-of-responsibility” charters that ratified barriers to working together. But today’s ability to deploy analytics at the edge raises the need for the integration of the IT and OT worlds. Read More
Information Exposure From Consumer IoT Devices
Internet of Things (IoT) devices are increasingly found in every-day homes, providing useful functionality for devices such as TVs,smart speakers, and video doorbells. Along with their benefits come potential privacy risks, since these devices can communicate information about their users to other parties over the Internet. However,understanding these risks in depth and at scale is difficult due to heterogeneity in devices’ user interfaces, protocols, and functionality.
In this work, we conduct a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK. Through a total of 34,586 rigorous automated and manual con-trolled experiments, we characterize information exposure in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, what are the IoT-device interactions that can be inferred from such content, and whether there are unexpected exposures of private and/or sensitive information (e.g., video surreptitiously transmitted by a recording device). We highlight regional differences between these results, potentially due to different privacy regulations in the US and UK. Last, we compare our controlled experiments with data gathered from an insitu user study comprising 36 participants. Read More
How Fog Computing is changing the BigData paradigm for IoT device?
The new era of BigData and advances in technology have made significant transitions towards the high functionality of IoT devices. The popularity of IoT devices has led to more easier methods for BigData collection, analysis, and distribution at a rapid rate. According to a report by Statista, by 2020, there will be 30 billion IoT devices worldwide, with this number set to exceed over 75 billion by 2025, Statistically, also, BigData accumulation over IoT devices and networks is clearly visible and to solve this problem, various computing methods are already popular. There are methods like quantum computing, cloud computing, edge/fog computing.
Though Quantum computing has a bright prospect, it has a long way to go, meanwhile, cloud computing is already a popular analytic method among developers and data scientists. In 2014, a new method, ‘fogging’ was first termed at Cisco. Fogging is better known as edge computing/fog computing. Big data analytics tools like Hadoop helps in reducing the cost of storage. This further increases the efficiency of the business. Read More
Predicting Failures from Sensor Data using AI/ML — Part 2
This is Part 2 of the blog post series and continuation of the original post, Predicting Failures from Sensor Data using AI/ML — Part 1.
Sensor data takes time-based maintenance to the next level. Part 1 explored hard-disk failure detection with H2O.ai’s Driverless AI. Read More
Predicting Failures from Sensor Data using AI/ML— Part 1
Whether it’s healthcare, manufacturing or anything that we depend on either personal or in business, Prevention of a problem is always known to be better than cure!
Classic prevention techniques involve time-based checks to see how things are progressing, positively or negatively. Time-based checks are based on statistical measures like ‘how likely’ things would go wrong based on historical data. It works really well for the most part:
— Taking your vehicle to service on regular intervals (X miles or Ymonths) as recommended by your manufacturer, can reduce the odds of something failing unexpectedly.
— Doing yearly physical checkups with your Doctor can prevent any developing adverse condition.
— <name your favorite use case>
The total cost of disruption in business continuity or personal health care, is generally orders more than what is paid for periodic checks! Read More
How to define, describe & think about IoT
There is no doubt that IoT is here to stay, mainly in areas such as manufacturing and transportation where there are clear benefits to connecting devices such as energy efficiency. Industrial IoT (IIoT), in particular, is being increasingly adopted as early adopters have successfully used the technology to remove inefficiencies, prevent errors, and optimize yields with real-time adjustments. …
Here is one model to think about how IoT projects can grow in complexity, and correspondingly, value: the IoT Extensibility Framework, taken from The Amazon Way on IoT. This describes four increasingly complex levels of IoT projects.
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