Thursday, April 13, 2017

Leveraging IoT for Smarter Buildings

Imagine if one of your EOD (End of the day) task at work is collecting the ingredients of a pre-decided dinner recipe just outside your work place. Or picture coffee machines at work which remember your preferences and tell users when they need to be refilled. Too smart, too soon? Not exactly! These are the features of, what has been claimed and awarded as, the world’s most sustainable and smart office building called The Edge, with an “outstanding” rating and highest ever BREEAM (short for Building Research Establishment Environmental Assessment Methodology) score of 98.36%.

The way, ride sharing apps have connected our smartphones with a more convenient transportation (while simultaneously occupying a bigger wallet share!) similarly, buildings are one of the basic components which are swarming with opportunities. A staggering 40% of the world’s electricity is consumed by the 1-billion buildings on the earth’s surface, and they are responsible for 33% of the globe’s greenhouse gas emissions. This leads to the need for greener, smarter, and more intelligent solutions — The Smart Buildings.
Smart Buildings are said to be among the fastest growing commercial market under Internet of Things, along with Smart Home and Transport. According to Gartner estimates, by 2017, use cases in smart commercial buildings and transportation will be the main contributors, representing 58% of all IoT installed base in a Smart City. This article covers the business use cases and areas which show the impact of IoT technology in the Real Estate sector, or the Smart Buildings.
Buildings have evolved from simple to automated and now pave a way to smart connected buildings along with technology progression. IoT, by its basic definition, implies connected physical objects communicating data about their condition, position, or any other attributes over internet. For any business to derive value while leveraging IoT, it needs to focus on reducing the major cost forms to generate viable returns. Improvisation and reduction of the operational and maintenance costs, which form a huge portion of the Life-cycle cost of a building, are key attributes impacting the smart index of a building. Following details the important characteristics of a Smart Building:
  1. Optimization of the Building resources — As sensors track different building functions like motion, pressure, light, temperature, etc. an integrated platform can intelligently monitor and control them accordingly. This has led to a growing number of technology providers in the energy efficiency space like lighting, energy usage monitoring, smart plugs, occupant detection, HVAC monitoring and control, access control, water management, and others. According to the United States Environmental Protection Agency (EPA) about 35% of the total building operation costs is spent on energy, while a little over a third, is spent on lighting. An interesting example in this area being View Dynamic Glass, a Smart Glass player, whose tint can be modified automatically based on weather outside (solar heat coefficient) or through smartphones based on a user preference. The company has seen over 300 completed commercial installations and another 150 in progress of an extensive client portfolio since last 12 months and boasts of investors like GE, Khosla Ventures, DBL, etc.
  2. Automation — Reduced manual human intervention across various dimensions like building security, environment risk detection, structural health sensors, etc. not only aims to improve the user experience but can also identify grey spaces in savings through features like fault detection, proactive maintenance, energy savings, or interfacing with various applications in general. For instance PointGrab claims to use embedded edge analytics on a low-cost ARM-based processor for image sensors to identify and process occupant details while protecting privacy. Such technology can lay foundation to numerous upcoming use cases like staff planning, space utilization, etc. in a Smart Office or advanced analytics in retail chains.
  3. Personalization — A normal automated building might have a centralized feedback, however an interactive control flow makes a building smarter. Dynamic and self-learning control systems, preference-based operability providing occupants with smartphone based control over the end-user energy flow in workplaces, or personalized retail experiences like automatic check-in, marketing, payment, etc. are some opportunities which can be harnesses through Smart Buildings.
Hence, the real-time visibility of building operations and energy responsiveness are clearly the game changing way forward towards a smarter home, office, hospitals, and an overall smarter infrastructure in a Smart City ecosystem. However, there are few challenges which still need to be addressed before the wide-spread implementation of the technology to provide the desired commercial returns. These include:
  • Smart Building capabilities should match with the needs of existing facilities like existing legacy system controls or proprietary technology
  • Upfront cost needs to match with the life-cycle savings of the simple sub-systems like efficient light control. While implementing the smart solutions at The Edge in Amsterdam, Deloitte considered implementing the smart solutions with a return on ­investment of less than 10 years. Moreover, the recurring costs of a traditional and smart building might vary a lot for an end-user
  • The security issues associated with Smart Buildings — A simple break-in to the Building Automation System (BAS) can lead to not only software but physical vulnerabilities as well, as exposed by the IBM X-Force Ethical Hacking Team
  • Privacy concerns of an end user — The data being collected by continuous monitoring of a building brings out certain privacy concerns by the users. Hence smarter buildings need to come up with smarter regulations to address the same.
Real-time monitoring, sustainability analysis, energy consumption, and innumerable similar scenarios can further create multitude of business opportunities bringing forth a new disruptive frontier in both B2B and B2C space through real estate sector. Established players like Cisco, Intel, Honeywell, Huawei, Philips Electronics, and others have already realized the commercial importance this segment can bring with their own Smart Buildings solutions. Future lies in implementing the same to address the right problems, keep evolving the solution with enhanced underlying technology, and get back the expected overall returns in a long term.
Views expressed here are my own and do not represent that of my employer (Current or Previous)

Sunday, March 19, 2017

Deriving business value from the intersection of Edge IoT with Artificial Intelligence

According to The Economist Intelligence Unit IoT Business Index 2017 (Link), though 90% of business owners accept the evolving convergence of IoT in business, there are 2 major deterrents of wide-spread business implementation of IoT Technology:

1. High cost of required investment in IoT Infrastructure,
2. Security and privacy challenges

In order for a business to reap value from connected devices over internet, the crucial missing piece which needs to be harnessed is the right utilization of the humongous data from the ever increasing number of connected devices in terms of time sensitivity as well as bandwidth of the data being transferred. This is most likely to be possible when the following basic conditions, which reduces the cost and increases the return from an IoT implementation, are met:

  > Limited human intervention
  > Limited hardware

This is when the concept of Edge processing comes into picture. Edge processing (or Fog Computing, as Cisco originally coined the term) is the concept which in simple terms uses data from unlimited sources before it looses significance as it brings processing at the edge of the network or ‘on-the-device’. It is not a new idea as reducing the dependency over the cloud and internet has been understood since the importance of cloud infrastructure was understood in real scenarios and would be an important piece in commercializing the connected devices in the future. Example: India’s leading ride sharing operator Ola launched its Ola Offline App in the late 2016. With consistently increasing number of connected devices, one can only think of the ease edge processing can bring to an IoT solution both it terms of the RoI of an IoT investment as well as Security. This means devices to turn and act ‘intelligent’ on their own and hence the intersection of artificial intelligence.

USE CASES which see a huge amount of data generated by IoT / mobile devices can definitely use the high precision machine learning / deep learning (ML/DL) platform as it can tackle some of the major issues related to data like: Latency, Bandwidth, Security of the data, as well as the Cost incurred in transmitting it, apart from several others. Some of the use cases which can be thought of are:

1. Time sensitive decision making in connected devices — Decision making in autonomous vehicles where information processing needs to be done in micro/milli seconds and it can’t afford the transmission lag and latency issues

2. When enormous data should be processed before being transmitted like monitoring plants in a vast farmland, where only the information about withering or infected plants needs to be transmitted based on certain sensor parameters

3. Security is of concern in automated industrial plants or surveillance cameras where local processing and intelligence of huge amount of recorded data is of high priority

4. Smart Home applications which need reduced human intervention like (taking inspiration of Amazon Echo here) replenishing the empty food bottles and cans, or switching between devices might also use edge analytics use case

A CHALLENGE seen in the intersection IoT with AI can be the fact that high precision ML/DL platform usually require higher processing and costlier hardware. The use cases listed above and similar upcoming use cases might depend more on how the technology solves the the size and cost of processing in the edge devices.

RECENT EXAMPLES: NVIDIA’s Jetson TX2 launched in March 2017 claims to be the 1st supercomputer on a module, a processor to bring AI to the Edge. Another example being that of Qualcomm’s deep learning SDK for devices called Zeroth Machine Intelligence Engine SDK which enables running of independent OEM’s neural network models on its Snapdragon 820 devices such as smart phones, security cameras, automobiles and drones, without any dependency to the cloud.

This post is inspired from my quora answer which first appeared here

Tuesday, February 28, 2017

The Indian On-Demand Economy

In last 2 years, as an independent earning millennial with hardly any financial liabilities, there has been a major shift in my spending patterns. Having succumbed to what once started as discounts, and majorly to convenience, on-demand services occupy a major share of my wallet. It's not just me, ride-sharing apps have seen an enormous 290% growth in 2016 (mobile downloads) which is driven by the rapid rate this trend has been adapted by Indian millennials and reflects in their spending patterns. However, as this growth matures, and discounts start to slow down, now comes the real test for the shared service providers on the sustainability of their model.

Renting is not a new concept and at a local scale, it has always been there for students or the young working on-the-move population's convenience. However, to actually make it a habit which makes people spend which leads to 'money-making disruption' is a major challenge.

Logic seldom supports trends and this has been true for some time in a family-oriented culture of ours. This very fact can actually be a bad news for businesses in India trying to make moolah based on the instant gratification spending of this generation. The fixed asset ownership model still gets a preference over renting it out, even though the overall cost for the both comes to be the same, as per a recent UBS analysis. However, even keeping the costs same as the ownership model is a challenge of its own and needs to be tackled without the mere cash-burning model, as can be learnt from the e-commerce market in India

Sunday, August 21, 2016

What did you learn from failure?

I have shifted job since I last posted here, in the blog. Instead, I have been posting a lot on Instagram, tweeting a lot, retweeting a lot. I have even been writing on Medium now! I have been doing many things lately, instead of writing here :| This post is to put an end to this "jinx", though it was first published here.

Disclaimer: Theory ahead!

A teacher is of no use if you are not ready to inculcate the lessons being taught.

If taken in the right spirit, it is a blessing which lets you know “timely” that this is not for you and you should - move on. Channelize your efforts in the right direction. This applies to any kind of failure, be it in a career, in an exam, in a relationship, even in your own long worshiped thoughts or in whatever area you face the failure. For instance, a failure in an exam took me closer to a career more suitable to me. What looked like a disaster a few years back took me to something, which at present, I enjoy more at this moment. Something similar can be said for say, failure in a relationship which makes you realize your self-worth and takes you in a direction of what really suits you the best.

Mind you, I am not talking about giving up here, which is an easier option. This is when you give your heart and soul to your aim but all you get back in return is a door slammed in your face. It is then, that this failure redirects you to a path more suited to you. It reinforces the law of averages. In a way, it gives you time to re-evaluate and reboot yourself. Kind of, the market correction in the stock markets :) Looks bad at first but if the fundamentals are correct it helps you in the long run. The difficult part here, of course, is to take the lesson from this difficult teacher in the right spirit.

Keynes quotes that “In the long run we are all dead”. Similarly, history has proved time and again that on a time scale, failure is a very relative term. If Soichiro Honda wasn’t turned down for a job at Toyota, there wouldn’t have been the Honda we know. From J K Rowling to Charlie Chaplin. More google search can add to this list.

Life’s too short and there are too many beautiful things around, which you can ever know / visit / do in your life. Let the failure re-direct you to a better path awaiting you :)