Smart spaces are the inevitable evolution of workplaces in the mining and resources industry. While it will become comprehensively pervasive through the workplace, they are personalised to individuals, and embody how workplaces will ultimately harness and interact with vast datasets and technology. While the universe was created by several fundamental forces, the smart space is formed by the collision of accelerating technologies leveraging from one another and peaking at the same time. In fact, for the mining and resources industry, it’s hard to see any other future without it.
A smart space is simply a space such as an office which is personalised to be an immersive and interactive experience using technology and automation. This may not seem like much, but it heralds a quantum leap in technology integration.
Take the below diagram for example. There are four core elements to a successful smart space. Individually they are useful, nonetheless quite benign. But together, they integrate a staggering number of technologies and drive safety and productivity. Let’s see how these fundamental elements would work in a simple real-world example.
Alex, who is wearing a smart band around her wrist that records her vital signs with every heartbeat, leaves her workstation for a coffee in the office kitchen. While waiting patiently for the coffee machine to aid her desire of a caffeine hit, the smart band has recorded over 500 records of Alex’s vital signs into a time series database which have been routinely analysed over the last 5 minutes. It turns out Alex’s heart rate has experienced periods of sudden fluctuations while subsiding over a short period of time. Unknown to Alex, such patterns can be indicators of cardiac arrest. A smart surface facing Alex on the kitchen counter, pings as it determines that Alex is close by though the detection of a proximity sensor in her smart band and a message appears on the surface via a chatbot.
“Are you exercising?” Alex can speak to her surface. In fact, it can read any of Alex’s primary biometrics.
Alex replies verbally with a “no.”
The chatbot responds by asking Alex to take an ECG (electrocardiogram) through her smart band. It takes 1 minute to perform the analysis by grounding her fingertips on the smart band. Her ECG data has once again been recorded and analysed from a time series database. The smart surface displays the critical results as a graph, diagnosis, and actionable step. In this case, a cardiac event is imminent and immediate medical treatment is required.
In this example, the sensors in Alex’s smart wearable transmitted data that could be analysed in real-time, from which Alex could interact with personally through smart technology. The quiet achiever in this example is the Internet of Things. It offers the means for data to exchange between technologies via the internet. It’s how real-time data analysis is achieved, and why results can be interpreted quickly.
You may be asking yourself, what on earth is a smart band, a chatbot, a time series database, or biometrics. And why do they matter? While, they are all completely different technologies, they also necessary pieces of the smart system jigsaw.
Biometrics are critical. In fact, a personalised smart space experience would not work without them. Biometrics can be physical characteristics such as speech, retinal patterns, or fingerprints which are used to digitally identify and grant individuals access to locations, systems, devices, or data. Our bodies are capable providing so much recordable digital data that it’s mind boggling, and we do this not only though biometrics, but our vital signs. Vital signs are clinical measurements such as temperature, respiration rate, blood pressure that can be used to determine our basic state of health. Vital signs can be monitored though smart bands which harness specific sensors enabling the transmission of data, which means that an individual’s health can be monitored in real time. In a working environment such as mining where health and safety is critical, wearable technology gives a distinct advantage.
Behind its somewhat disconcertingly simple name, a chat bot is an artificial intelligence (AI) that can simulate a conversation with an individual through mobile messaging, apps, or direct voice recognition. Within a smart space environment, they are only activated on a smart surface when an individual is in close proximity or verified through biometric readings. It can answer questions and make suggestion based on real-time data.
Last but not least, a time series database is typically a single table of records in which sensor data is commonly stored. A times series database representing vital signs may look something like the table below.
Pretty boring huh? But to get an understanding of how much data a time series database processes, let’s use Alex’s workplace as an example. 70 individuals work on her floor with a combined average heart rate of 80 bpm. A time series database is transmitted approximately 5,250 records a minute. Every day a time series database would receive 7,560,000 vital sign records. While vitals are recorded every heartbeat, each minute the time series data is subjected to a variety of analytical processes, machine learning algorithms, and AI, to determine whether there are any dramatic changes in patterns or danger to life that may warrant intervention.
A smart space doesn’t need to be an office. In fact, in mining, vastly different versions of the office exist. A smart space for instance is an open cut or underground mine, a processing plant, or an exploration drill site. They vary in architecture and purpose. In the mining and resources industry, it is not uncommon for a person to inhabit more than one environment, such as in the illustration below. An employee can access services, data, and systems specific to individual requirements and security within any workplace.
While we have focussed heavily on analysis of real time vital signs, it turns out that machines are not that different from human beings. While a human may exhibit vital signs, machines do as well. For instance, when coordinating fleet management, location is important. Tracking vehicles is similar to vital signs in time series data base. However instead of human characteristics, it reads something called an NMEA output from a GPS receiver which consists of time, location, number of satellites and various other data. If 12 trucks are being tracked, a signal is sent to a time series database every 2 seconds from which the data can be analysed and, in some cases, displayed in real time as a tool for viewing the location of vehicles in the fleet.
This goes for information derived from any machinery from which sensors provide information to a central database in real-time. Rather than health or locational, it may be capturing mechanical instances of data such as revolutions per minute, angle, temperature, wear, or metal fatigue from any machine on a mine site that requires monitoring.
All data ends up somewhere to be processed and analysed. Locational data, biometric and health data, mechanical data, SCADA data, or fleet management (for example) - it is data that can potentially be creating hundreds of millions of records per day which can be analysed in real time and shared through business intelligence (BI) across the company. BI helps to make sense of large and complex datasets by amplifying the ability to present information in various ways which are relatable to employees such as graphs, tables, maps, or images though smart devices.
Let’s look at how a smart space might work for different employees of the mining industry.
Proximity sensors detect that the MD has stepped inside an elevator from the carpark. A smart surface (visual screen) begins to display automated graphs and tables. Before the MD steps out onto the eleventh floor, they have seen yesterday’s daily processing plant reports and throughput, along with monthly yields in comparison to budgets and forecasts. An exploration manager is retrieving milk from the fridge, prompting a visual device to display a target generation map derived through real time drilling results and machine learning. A plant operator stops 3 times in four hours to interact with a strategic smart surface location within the plant to log downtime incidents.
Its potential is so staggering, that the extent of data representation and accessibility is only bound through our limitations as human beings to understanding how to present it.
With every upside, however, exists a downside. Incorporating new technology challenges traditional perception.
The smart space presents some real doozies too.
For starters, we must accept that our own body is like a mobile password within a smart space. Rather that typing it into a keyboard, we use biometrics to access smart devices, systems, and data. Our bio-signs will correspondingly be intrinsically linked to health and safety of a smart space environment. Human beings are a walking talking linkage in the smart space setting.
Secondly, the lines between a traditional cubical or workspace will blur, while pervading all physical spaces within an organisation. This conjures another perceptual challenge. By incorporating Software-as-a-Service (SaaS) with smart spaces, anywhere is a workspace. Traditional personal work computers and software are accessible anywhere where access is granted through our biometric passwords.
Finally, and most importantly, how do we feel regarding our work environments being so personalised? Will workplaces become a data driven dystopia, bombarding information – kind of like Facebook but without the adverts? Is personal bio data secure, and how ethical is it to use?
While the ethics can be argued, and the winners and losers can be also argued, it is inevitable that smart spaces will become the natural evolution of the workplace in the resources industry. Like any industry, the mining sector looks to drive process, increase productivity, and do it safely. Smart spaces embody the convergence of emerging technologies, while championing a productive and safe environment that a successful mining company ought to strive towards.
Besides, why are we collecting all this data if we don’t use it?