When Lux first introduced a framework and taxonomy for approaching the industrial internet of things (IIoT), the focus was on tools and understanding the various parts of the solution stack. The Lux “IIoT Toolbox” provided clients and readers with a structured view of how to turn data from industrial assets, people, and environments into actionable insights to do things like improve operational efficiency, generate new services and revenue streams, and improve employee health and safety. This was an important and valuable baseline for understanding the IoT, and how to properly construct a solution stack.
However, understanding each of the elements in the IoT toolbox was only the first challenge. As clients and readers have started to figure out how to properly construct an IoT solution, the focus now shifts towards refining, optimizing, and future-proofing such a solution. The ensuing challenge here becomes effectively and efficiently managing all of the data generated by the IoT. For this, we propose a data-centric framework and taxonomy to help clients and readers understand the next layer of capabilities, processes, and approaches to properly managing the glut of data being generated within their respective deployments.
In no particular order, the seven core components to this data-centric framework include the following:
- Data creation (or data generation) references all of the activities related to capturing and contextualizing sensor data. It is more than just recording simple, discrete sensor readings. It is also determining how data is collected, choosing an appropriate sampling frequency, imposing boundaries and limits on the data to filter spurious events, and other activities like manual annotation to further contextualize the data sample.
- Data security references all of the activities related to isolating data to a discrete audience of authorized individuals, machines, applications, or processes.It is not just the deployment of software and systems such as firewalls, intrusion detection systems (IDS), and antivirus software to keep data isolated and protected. It is also establishing role-based data access, user authentication schema, data encryption, and the utilization of AI and machine learning to continuously monitor for network intrusion or unauthorized data access. It is also the ongoing remediation and patching of systems to ensure data and the surrounding environment remains protected from illicit activity. Many decisions must be made with regard to data security, and they will always appertain to the decisions made with regard to the other components of the data-centric framework.
- Data transmission references the connectivity and conveyance of data across the entire end-to-end IoT solution stack. It is more than just a singular event of transmitting data from sensor to end device. Data is often transmitted across multiple devices, among multiple locations, using multiple formats or standards, and using varying physical means. Data may be transmitted using short-range technologies, such as Bluetooth or Wi-Fi, or long-range technologies, such as LPWAN or cellular. Data transmission may also take place at varying intervals, and may only involve subsets or summaries of entire data sets. Decisions regarding what to send and how to send it are always unique to the specific end-to-end solution stack.
- Data cleanliness references the relative signal to noise ratio of the IoT device data being generated. It is a measure of how much useful information was collected, as a proportion of all data collected. Data often requires cleaning and validation prior to being stored and analyzed. Spurious sensor readings, instrumentation failure, transmission failure (leading to incomplete data sets), environmental interference, and other issues can “contaminate” data sets. “Cleaning” data to remove or repair these types of issues helps to ensure the general accuracy and completeness of IoT analytics and insights.
- Data analytics references all of the activities related to computational processing and understanding of IoT device data. It is the processes involved with examining data sets in order to draw conclusions about the information they contain. Analytics were traditionally administered by human data scientists, but artificial intelligence (AI) and machine learning have expanded the capabilities, speed, and breadth of analytical capabilities of most modern IoT analytics solutions.
- Data storage references all of the activities related to storing data across the entire end-to-end IoT solution stack. As with data transmission, data may be stored in multiple locations, in multiple formats, and on varying storage media. It is more than simply storing data in a particular digital or physical format. Data may need to be re-formatted in a particular schema (such as JSON or XML), or stored as a file, object or in a database. It may need to be aggregated, annotated, deduplicated, compressed, encrypted, or archived. Many decisions must be made with regard to data storage, and it often depends on other components of the data-centric framework, such as data analytics, transmission, and security.
- Data sharing references the imparting of data across separate IoT deployments. It may involve an IoT solution ingesting outside, third-party data to provide better internal analytics capabilities. In this scenario, outside data would be used for training of machine learning algorithms, and improving the accuracy of analytics and insights. Data sharing may also involve an IoT solution sending internal data to an outside, third-party entity to enhance collective analytics capabilities. Some platforms even enable the monetization and sale of internal data, which opens the possibility of generating new revenue streams.
We plan to explore each of these components in greater detail in upcoming journals. For each topic, we will discuss traditional approaches, key innovations, and some specific applications demonstrating the importance of each. Readers should continue to follow the discussion surrounding effectively and efficiently managing all of the data generated by their own respective IoTs.
This month’s iPhone X launch comes a full decade after the iPhone’s original debut and harkens back to its first release in 2007. Like the original, the X is priced much higher ($999 for 64GB and $1,149 for the 256 GB model) than the average phone available today; the first iPhone was originally priced at $399, while most phones at the time were $199. The release of X is also similar to the original, with its limited availability due to manufacturing constraints and its certain role as a status symbol. Continue reading
Singapore’s Changi airport recently announced that it will be implementing the use of AR-enabled Vuzix M300 smart glasses for its ground handling crew members. The cameras on the smart glasses scan a QR code on cargo and baggage containers and display relevant information such as weight, loading sequence, and allocated position within the aircraft. The smart glasses will also allow for easier audio communication between crew members, as well as streamline the work of the control center, who will have full access to the video feed of each pair of smart glasses. Continue reading
Over the years, numerous analytical and empirical methods have been developed to prevent machine failure, reduce costs, and increase production capacity. In the present age, companies like M2M Data Corporation and Senseye started out by developing physical models of machines based on data collected from their customers’ facilities. The data consists of parameters such as pressure, speed of motors, particle concentration in lubricants, acoustic data, temperature in the friction generating part of the equipment, and machine unique data sheets. For example, each pump has its unique “pump curves” that correlate pump rotation speed with discharge pressure and flow. A deviation of these above parameters is used to flag the plant personnel of possible machine issues, and could also suggest a specific malfunction. Continue reading
We’re just beginning to get our heads around Artificial Intelligence, but the machines are already making their next move: creativity. While we still think of imagination as an innately human capability, advances in computing power are making arts as diverse as architecture, music, movies, and material design into easily-accessible, programmable spaces. In some areas, machines have already surpassed human originality and quality – as rated by other humans – and more fields are likely to fall. Continue reading
Google Glass is back. Last week, X, a subsidiary of Google parent Alphabet, announced a revival of its most embarrassing wearable mishap with a new focus on the enterprise market. In the past couple years, Google Glass Enterprise Edition (EE) has been silently tested in pilot programs with companies such as GE, DHL, Boeing, Volkswagen, and Sutter Health. After last week’s announcement of Glass EE, the wearable device will now be more widely available via a network of partners. As of now, there are no further plans to bring back the original consumer edition. Continue reading
Earlier this week, healthcare IT firm Change Healthcare became the newest member of the Hashed Health Blockchain Consortium, a distributed ledger consortium whose goal is to advance the use of blockchain protocols in healthcare. The expansion of this group and the quest for the establishment of standards for implementation of blockchain in healthcare are not surprising – the last year has witnessed a sharp uptick in developers looking to bring blockchain to the industry. However, while the number of companies starting to apply blockchain – a distributed ledger technology that claims to offer several benefits over traditional databases, such as improved trustworthiness and automated smart contracts – to healthcare is growing, and while there certainly is a lot of hype surrounding this activity, there still remains confusion on the specific challenges these companies are looking to tackle and on the value they promise to deliver. In the table below, we synthesize currently sought-after use cases for blockchain in health, outline tech developers’ claims, and highlight players in each solution category.
We have expressed previously that predictive maintenance (PdM) is one of the most promising areas of the industrial internet of things (IIoT) and that there are a number of startups developing innovative sensor-based and/or software solutions. To identify where these technology developers are innovating, we took a look at the patent activity on PdM. During the past 20 years, there have been approximately 4,400 patents focused on PdM applications, and companies are filing more patent applications every year to differentiate themselves and protect their models, algorithms, or hardware-based solutions. In our “Predictive Maintenance: The Art of Uptime” report, we mentioned that innovations in connected sensing technologies and analytics are driving better operations, enabling users to gather and process real-time data on machine health to decrease downtime. For example, a study related to the oil and gas (O&G) industry revealed that performing PdM using a data-driven approach with sensors experienced 36% less unplanned downtime. There is still significant space for companies to decrease their unplanned downtime, and one method to do that is by adding more data streams from sensors. Therefore, we filtered our search to focus only on developments in PdM using sensing technologies, and we can see in Figure 1 that the IP space has been increasingly more active since 2014. Nonetheless, the patents aiming to protect sensor-based PdM are roughly 30% of the total amount of IP seeking protection for PdM applications.
Several days ago, Rolls-Royce announced a brand-new “Airline Aircraft Availability Center.” According to a press release from the company, this facility is aimed at ensuring “every aircraft it powers departs and lands on time, every time.” The company monitors about 4,500 jet engines powering commercial airliners, which operate for a combined 14 million hours per year. The company said its new center will employ data analytics to optimize operations and plan and manage maintenance activities. Unsurprisingly, its SVP of Civil Aerospace Services said, “We are entering a new era of digital connectivity and new services technology which allows us to greatly expand the type of services we can offer….” Rolls-Royce also disclosed plans to make this availability center a hub for innovation, deploying new technologies such as “remote surgery” engineering tools; it is aiming to be using robots within the next five years. Continue reading
On June 16, Amazon announced it intends to buy U.S. grocery retailer Whole Foods for $13.7 billion. Whole Foods is a relatively young (founded in 1980), small chain with about 450 stores in mostly affluent neighborhoods (as well as nine in the U.K.), and focuses on organic, healthy, and relatively expensive products – earning it the nickname “Whole Paycheck.” In comparison, grocery giants like Albertsons and Kroger were founded in 1939 and 1883 respectively, and have more than 2,000 stores each. For its part, Amazon’s foray into grocery, Fresh, has not had much impact on the $700 billion industry. Still, this move is absolutely a big deal, and one that will reshape not only retail (news of the deal destroyed nearly $40 billion in market value of competing retailers) but also consumer packaged goods (CPG) categories like food, personal care, and home care, and impact industries beyond. We discussed five strategic implications with retail technology expert Jim Crawford of RED-LAB to ascertain what will happen now, what’s next for these industries, and where this positions Amazon to go in the future: Continue reading