How do we make the “Smart Home” Smart?

How do we make the "Smart Home" Smart?

Without blinking, we today tend to call a lot of products and concepts smart. The light bulb that can be controlled through an app is smart, the dish washer that notifies you when it’s done is smart and the door lock that you can open through Bluetooth is also smart. But what’s so smart about them? We might find new ways of going about our daily lives which might be smart, but the products and their features are not smart, just connected. They provide accessibility (hopefully), but no intelligence.
Of course there are some exceptions that, at least to some extent, can be called smart. Soil sensors that tell us how to take care of our plants, robot vacuums that plans their cleaning efficiently and security systems using algorithms to determine who entered the door. But these are scarce and limited to specific tasks.

Then there are the smart home assistant like Alexa and Google Home that can be considered to have a certain level of artificial intelligence. But it’s usually for the functionality that is not related to the home, like answering questions and predicting when you want to go to work. In the home they are not much more than appealing remote controls.

How do we turn the Connected Home into the Smart Home?

So if we can agree on that today when we talk about the smart home, we’re actually referring to the connected home, how do we then make it into the real smart home?

Let’s first look at a few concrete examples of what could constitute a smart functionality in your home.

    • -Having the lights dim, music stop and tv starting to play the next episode of your current favorite tv series when you enter the living room after putting the kids to bed.
    • -Being notified that a service has been scheduled for your heating system as vibration data from the pump is showing a decline in performance.
    • -In preparation of you going to work, the car heater started 15 minutes ago and your smart speaker notifies you of a traffic situation on your normal route while the indoor AC lowers the temperature for the day.

All of these things require a combination of AI, interconnected devices and a lot of sensor data. They all “almost” exist today, but it will take surprisingly long before we can make use of the smart home as it’s sold in futuristic movies.

And here are a couple of reasons for that:

  • Lack of Integration - Predictions about your behavior in your home requires the correct data from several different devices. It’s not enough that your Philips Hue smart bulbs can be controlled by your Google Home. All the gathered data needs to be collected into one service with the necessary machine learning algorithms. This will first happen when everything is either standardized or there is a business case to provide this service. And hopefully that business case doesn’t revolve around selling your behavioral predictions to other companies.
  • The danger of inconvenient predictions - The only true prediction is that predictions will never be 100% true. While it might be acceptable that lights turn on in the living room when you didn’t intend to go there, or getting traffic information to work on your day off, it would probably not be acceptable having your coffee machine making morning coffee the day you wanted tea or unlocking the front door the time you just drove past your house. We can only dare predict things about people's behavior as long as a failure in the prediction is at the most causing a minor inconvenience. If a failure leads to a real inconvenience or distrust in the product, it will never be accepted by most consumers.
  • Lack of consumer value - The Smart Home market is still one of the markets that requires the highest focus on consumer value to progress past the enthusiasts into the mainstream market, but is instead doing the opposite. Products from major brands are still being connected because they can with little or no perceived real value for the consumer. And as long as companies doesn’t understand the importance of identifying the real value for consumers, we will never get into the much more complex predictive features of the smart home.
So what should we do?

Making the smart home intelligent in the same way that it’s portrayed in movies will unfortunately take time. It requires broader acceptance of smart home devices, more sensors, standardization and continued improvements with machine learning.

But in the meantime, there are still things you can do as an IoT solution provider to bring the smart home a few steps closer.

  • Focus on consumer value - never forget to always put consumer value first. A product with an advanced algorithm will still be perceived as dumb if it solves the wrong problem or no problem at all.
  • Understand advanced analytics - while machine learning & advanced analytics is definitely not for every product or use case, it’s important to understand its possibilities and potential on a high level. You don't have to know how to implement it, but you should be able to spot the opportunity when there is one.
  • Predictive maintenance - don’t forget the possibility of predictive maintenance. While this is a huge area within Industrial IoT it hasn’t found its way into consumer products yet. I have a connected dishwasher, but I’m pretty sure that it won’t tell me before it fails and it won’t help me find a local service technician. This is an opportunity that is available today for businesses to create products perceived as more intelligent and helpful.
  • Integrate and open products - While not making them intelligent in itself, ensuring products and data are open for integrations will allow consumers to link them with other devices making the overall solution, if not intelligent, at least “smarter”.

While the real intelligent home might still be far away, let's do what we can today to make it at least feel smarter. Make sure there is real value for the consumer and that the products are perceived as helpful. Don't connect a product for the sake of connecting it, do it because you believe you can solve a real problem for the consumer. Take you time to challenge your use cases and test them on real consumers.

More information:

Partner and Senior Advisor Mikael Rönde,, +46 (0)70 88 66 794

Positioning technologies currently applied across industries:

Global Navigational Satellite System: Outdoor positioning requires line-of-sight to satellites, e.g. GPS: the tracking device calculates its position from 4 satellites’ timing signals then transmits to receiving network
–    via local data network, e.g. wifi, proprietary Wide Area Network
–    via public/global data network, e.g. 3G/4G

Active RFID: A local wireless positioning infrastructure built on premises indoor or outdoor calculates the position based on Time of Flight from emitted signal & ID from the tracking device to at least 3 receivers or when passing through a portal. The network is operating in frequency areas such as 2.4 GHz WiFi, 868 MHz, 3.7 GHz (UWB – Ultra Wide Band), the former integrating with existing data network, the latter promising an impressive 0.3 m accuracy. Tracking devices are battery powered.

Passive RFID: Proximity tracking devices are passive tags detected and identified by a reader within close range. Example: Price tags with built-in RFID will set off an alarm if leaving the store. Numerous proprietary systems are on the market. NFC (Near Field Communications) signifies a system where the reader performs the identification by almost touching the tag.

Beacons: Bluetooth Low Energy (BLE) signals sent from a fixed position to a mobile device, which then roughly calculates its proximity based on the fading of the signal strength. For robotic vacuum cleaners an infrared light beacon can be used to guide the vehicle towards the charging station.

Dead Reckoning: Measure via incremental counting of driving wheels’ rotation and steering wheel’s angle. Small variations in sizes of wheel or slip of the surface may introduce an accumulated error, hence this method is often combined with other systems for obtaining an exact re-positioning reset.

Scan and draw map: Laser beam reflections are measured and used for calculating the perimeter of a room and objects. Used for instance when positioning fork-lifts in storage facilities.

Visual recognition: The most advanced degree of vision is required in fully autonomous vehicles using Laser/Radar (Lidar) for recognition of all kinds of object and obstructions. A much simpler method can be used for calculating a position indoor tracking printed 2D barcodes placed at regular intervals in a matrix across the ceiling. An upwards facing camera identifies each pattern and the skewed projection of the viewed angle.

Inertia: A relative movement detection likewise classical gyroscopes in aircrafts now miniaturised to be contained on a chip. From a known starting position and velocity this method measures acceleration as well as rotation in all 3 dimensions which describes any change in movement.

Magnetic field: a digital compass (on chip) can identify the orientation provided no other magnetic signals are causing distortion.

Mix and Improve: Multiple of the listed technologies supplement each other, well-proven or novel, each contributing to precision and robustness of the system. Set a fixpoint via portals or a visual reference to reset dead reckoning & relative movement; supplement satellite signal with known fixpoint: “real time kinematics” refines GPS accuracy to mere centimetres; combine Dead Reckoning and visual recognition of 2D barcodes in the ceiling.

LoRaWAN: A low power wide area network with wide reach. An open standard that runs at unlicensed frequencies, where you establish a network with gateways.

Sigfox: A low power wide area network reminiscent of LoRa. Offered in Denmark by IoT Danmark, which operates the nationwide network that integrates seamlessly to other national Sigfox networks in the world.

NFC: Used especially for wireless cash payments.

Zigbee: Used especially for home automation in smart homes, for example. lighting control.

NB-IoT: Telecommunications companies’ IoT standard. A low-frequency version of the LTE network.

2-3-4G Network: Millions of devices are connected to a small SIM card, which runs primarily over 2G, but also 3G and 4G.

Wifi: The most established standard, especially used for short-range networks, for example. in production facilities.

CATM1: A low power wide area network, especially used in the United States.

Glaze IoT Cloud Project Process

Beacon Tower is Glaze’s Industrial IoT Cloud Platform that can act as either a stepping stone (Platform-as-a-Service, PaaS) or as an out-of-the-box solution (Software-as-a-Service, SaaS) for collection of IoT-data.

Beacon Tower resides in Microsoft Azure and is designed as a customisable and cost-effective IIoT Cloud Platform that helps simplify deploying, managing, operating, and capturing insights from internet-of things (IoT)-enabled devices. Our customers have the full ownership of their data.

When running it as a PaaS we utilise the design and can run it on our customers’ Azure tenant and customise it fully to their requirements.

Beacon Tower connects to all sensors, PLC, DCS, SCADA, ERP, Historians and MES to gain maximum automation flexibility and ​prevent vendor lock-in.

For more information visit or read the PDF.

Edge Computing Categories and Questions

o Sensors
o Internet connectivity
o Battery consumption
o Field Gateway
o Communication protocols (HTTP, AMQP, MQTT, Gateway)
o Format of the telegrams sent to the cloud (JSON, Avro, etc.)

o Number of devices & number of signals
o Amount of data to transfer per day
– Event-based or batched or mix
– Transfer rate (every second, minute, hour)
o Device timestamps
– Synchronized timestamps with cloud or not
– Local buffering on device, late and/or repeated data
o Any time-critical notifications / alarms
– Latency expectations for non-time critical data
– Alarms generated by device and/or by cloud platform
o Cloud-to-device messages & commands
o Analytics
– Results from time-series data / Streaming analytics
– Analytics workflows on data, machine learning etc.
– Edge analytics / intelligence

Cost expectations:
o Retention periods (for reporting purposes)
o Aggregation of data, possibilities for cost saving

External integrations:
o Reference data / online data

Administration, rights and access:
o Requirements for multi-tenancy (segregated owners)
o Owners/tenants and operators/technicians
o Administrating access to data, auditing use
o API management, consumption of data, 3rd party integrators

o KPI measurements for device
o KPI measurements for cloud platform
o Requirements on operators and SLA’s

User-interfaces and functions:
o Operators/technicians
o Customers/end-users

Glaze Business Innovation and Development Framework (BIDF)

1. Strategy

Creating an IoT Strategy that aligns with the existing company strategy and/or points out any discrepancies that needs to be addressed. The IoT Strategy should pinpoint type of IoT opportunities that should be sought and how they can support the Company delivering on their overall strategies.

2. Ideation

The Ideation phase is an innovative and creative phase where we identify the IoT opportunities within the company. This is done by using existing assets, industry expertise, industry analysis, strategy and IoT expertise to find opportunities for IoT endeavors. This is done in an structured but open-minded and creative setting.

3. Refinement

In Refinement the opportunities are detailed, prioritized and evaluated in a series of steps with the goal of finding a short list of initiatives the company want to pursue. These steps takes strategy, competence, risk level, customer maturity etc into account during prioritization.

4. Valuation

The short list of opportunities are detailed even further and business cases are created for each of them. This will lead to a decision which opportunity to pursue further.

Moving on from the Business Innovation phases to Development activities we focus on taking the minimum possible risk of building the wrong solution by using agile development practices.

5. Exploration

Proof of Concepts carried out in this phase in order to map out technology as well as user-oriented risks. This also refines the budget and thus valuation and business case. Also giving valuable input to baseline system architecture and eco system involvement.

6. Planning

Moving to Planning phase, the most promising business case has been selected and now it is time to plan the Minimal Viable Product (MVP), in terms of timeline, resources and detailed design.

7. Foundation

Implementing the baseline architecture, toolchains and most critical points of the project.

8. Development

Full MVP is developed using these three principles: Start small, don’t over-engineer; Agile software development – late changes welcomed; Continuous delivery – every change is immediately visible.

9. Operations

Operations in an IoT-project is more than just keeping the product alive. It is life-long updates and continous sharpening of features and business model, meaning new ideas are fed back in the Innovation and Development Framework.

Heat map example on a typical business case: