This is part two of a previous post about building and operating a Big Data SaaS for Home Area Network devices during my 5-year tenure with EcoFactor. Simply put, our main goal was to add “smarts” to residential heating and cooling systems (i.e. heaters and air conditioners, a.k.a. HVAC) via ordinary thermostats. That focus led to a superficial perception by some people that we’re a smart thermostat device company. In actuality, we have always been a software service, virtually agnostic to both hardware and communications protocol. It’s more of an IoT version of the “Intel Inside” business model.
Challenges from all fronts
Like building any startup company, there was a wide spectrum of challenges confronting us which is what this post is going to talk about. Funding environment was pretty hellish as we started just shortly before the financial crisis in 2007-2008. And failure of some high-profile solar companies in subsequent years certainly didn’t help make the once hyped cleantech a favorable sector for investors.
The ever-growing fierce competition for software engineering talent was and has been a big challenge for pretty much every startup in the Silicon Valley. On the technology front, production-grade open-source Big Data technologies weren’t there, leading to the need for a lot of internal R&D effort by individual companies, which in turn requires domain experts in both development and operations who were scarce endangered species back then, thus completing the vicious infinite loop that starts with the hiring difficulty.
On the operational front, there was a long list of processes that need to be carefully established and managed – from user acquisition, on-boarding, device installer training, scheduling coordination for on-site device installation, technical support for installers, to customer service. To get into the details of how all that was done warrants writing a book. In charge of product and marketing, Scott Hublou who is also a co-founder of the company owned the “horrendous” list.
Many of the items in the list are correlated. For instance, getting HVAC technicians to create a HAN network and pair up thermostats with the HAN gateway during an on-site installation not only required a custom-built software tool with a well-thoughtout workflow and easy UI, but also thorough training and a knowledgeable support team to back them up for ad-hoc troubleshooting.
Back to the engineering side of the world, a key piece in operations is the technology infrastructure that needs to cope with future business growth. That includes systems hosting, network and data architecture, server clusters for distributed computing, load balancing systems, fail-over and monitoring mechanism, firewalls, etc. As a startup company, we started with something simple but expandable to conserve cash, and scaled up as quickly as necessary. That’s also a practical approach from the design point of view to avoid over-engineering.
State of WPAN
On hardware, applicable HAN communications protocol and HAN device hardware were far from ready for mass deployment at the time when we started exploring in that space. That’s a non-trivial challenge for anybody who wants to get into the very space. On the other hand, if done right it represents an opportunity for one to pioneer in a relatively new arena.
ZigBee, an IEEE 802.15.4 standard WPAN (Wireless Personal Area Network) protocol, was our selected communications protocol for scaled deployment. While it’s a robust protocol compared with others such as Z-Wave, its specifications was still undergoing changes and few real-world implementations had ever exploited its full features.
The protocol comes with a few predefined application profiles including Energy Efficiency and Home Automation profiles. Part of our core business is about translating HVAC operations data via thermostats into actionable business intelligence, hence ability to acquire key attributes from these devices is crucial. We quickly discovered that some attributes as basic as HVAC state were missing in certain application profiles and we had to not only utilize multiple profiles but also extend to using custom attributes in ZCL (ZigBee Cluster Library).
Working with technology partners
Working with hardware technology partners does present some other challenges. HAN device firmware and embedded software development is a totally different beast from SaaS/server application development. Python on Linux is a prominent embedded software platform. While that’s also a popular combo for server software development, the two worlds share little resemblance. Building a system that bridges the two worlds takes learning and collaborative effort from both camps.
Some of our HAN device partners were quick to realize the significance of the need to back their gateway devices with a scalable PaaS infrastructure and invest significant effort in M2M (Machine-to-Machine) through acquisition and internal development. But coming from a hardware background, there was inevitably a non-trivial learning curve for our hardware partners to get it right in areas such as software service scalability. Leveraging our internal scalable SaaS development experience and our partners’ embedded software engineering expertise, we managed to put together the best ingredients from both worlds into the cooperative work.
OTA firmware update
OTA (Over-the-Air) firmware update generally refers to wireless firmware update. Our devices run on a WPAN protocol and the firmware is OTA-able. It’s probably one of the operations that create the most anxiety, as an update failure may result in “bricking” the devices in volume, leading to the worst user experience. A bricked thermostat that results in an inoperable HVAC (i.e. heater / air conditioner) would be the last thing the home occupant wants to deal with on a 105F Summer day, or worse, a potentially life-threatening hazard on a 10F Winter night.
This critical task is all about making sure the entire update procedure is foolproof from end to end. The important thing is to go through lots of rehearsals in advance. In addition, the capability of rollback of firmware version is as critical as the forward-update so to undo the update should unforeseen issues arise post-update. Startups typically work at a cut-throat pace that it’s tempting to circumvent pre-production tests whenever possible. But this is one of those operations that even a minor compromise of stringent tests could mean end of business.
Pull vs Push
The around-the-clock time series data acquisition from a growing volume of primitive HAN devices is a capacity-intensive requirement. Understanding that it was going to be a temporary method for smaller-scale deployments, we started out using a simplistic pull model to mechanically acquire data from the HAN gateway devices. These devices gather data serially from their associated thermostat devices, making a single trip to a gateway-connected thermostat device cost a few seconds to tens of seconds. To come up with a data acquisition method that could scale, we needed something that is at least an order of magnitude faster.
With larger-scale deployments in the pipeline, we didn’t waste any time and worked collaboratively with all involved parties early on to build a scalable solution. We went back to the drawing board to scrutinize the various data communication methods that are supported by the WPAN specifications and laid out a few architectural changes. First, we switched the data acquisition model from pull to push. Such change affected not only data communications within our internal SaaS applications but the end-to-end data flow spanning across our partners’ PaaS systems.
One of the key changes was to come up with standards compliant methods that minimize necessary data retrievals via unexploited features such as attribute grouping and differential reporting under the push model. Attribute grouping allows selected attributes to be bundled as a single packet for delivery instead of spitting individual attributes serially in multiple deliveries. Differential reporting helps minimize necessary data deliveries by triggering data transfer only when at least one of the selected attributes has changed. All that means lots of extra work for everybody in the short term, but in exchange for a scalable solution in the long run.
Collaborative work pays off
The challenges mentioned above wouldn’t be resolvable hadn’t there been a team of cross-functional group technologists working diligently and creatively to make it happen. Performance was boosted by orders of magnitude after implementing the new data acquisition method. More importantly, the collective work in some way set a standard for large-scale data acquisition from SaaS-managed HAN devices. It was an invaluable experience being a part of the endeavor.