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In the early days of M2M/IoT, you might recalled that one of the earliest examples of M2M is connecting the Coke vending machine. Later, another popular example is to connect the fridge which tells you when to replenish your Milk and order automatically. Nowadays, almost in all (or nearly) IoT conversations, “Smart Parking” application will be the main topic as an example for a smart city application. One of the main reason because traffic congestion in cities have reached a state where citizens got so frustrated and tired. Productivity can be simply dropped because of the time wasted on the road. Carbon monoxide has been the main contributor to air pollution in cities. And studies have shown that 30% of the traffic congestion is being caused by drivers trying to find parking spaces. If we are able to solve parking, we might be able to solve the traffic congestion and thus reduce the carbon in the environment.

We have some good experience in developing a smart outdoor parking trial with different kinds of sensors. Below are some of the challenges and lessons that we faced during the trial.

1. Quality of Sensors — We have tested three kinds of sensors. One made from China which is cheap but low quality and another from Netherlands (more expensive but higher quality). The third sensor we developed in-house used an Arduino as the controller. The ones that we developed in-house are simple and easily programmable with a rechargeable battery and a small solar panel. We placed at the back (end) of the parking bay which will detect the car’s presence. Another sensor (from China) is screwed on the tarmac. The other sensor (one from Netherland) is embedded into the parking bay by making a hole in the tarmac. It is recommended to get a high quality sensors to get accurate reading and reliability. You might regret getting cheap sensors because you might end up with high maintenance costs in the future.
2. Coverage — Depending on the location of your gateway that collect the parking data, it can be quite challenging. You have to ensure the sensor signals are not being block by any obstacles. Otherwise, you need more gateways to provide good car park coverage.
3. Location of Sensors — As mentioned in (2), placement of the sensors underneath the cars must not be block.
4. Battery Life — Parking sensors battery normally have a life-span of about 3–5 years. We are not able to test to that extend but the low quality sensors doesn’t last that long. Another thing to note is the frequency of sending the data. Thus, it’s important if we are able to program the sensors easily.
5. Management of Sensors — It’s easy to mange less than 10 parking sensors or one parking area. But how do you manage several parking areas in a big city? It can be big challenge managing several thousands sensors outdoor. In our case, we developed an IOT Network Management System that is able to monitor the health of each individual sensors including its data activity and battery level.
6. Application — We have developed simple app to view the availability of the parking bays, its battery level for both desktop or mobile version. Depending on the objectives of the smart parking project — you can also develop a parking utilisation report or analytics for the parking management of city municipal. To make it more convenience for the public, the parking availability and its direction should be display on a bigger signboard.
7. Culture and Attitude — It’s always a question whether the public is able to accept this new way of parking. Can we make a parking reservation or mobile payment? Are we able to discipline ourselves not to park illegally?

However, we believed that the reliability and accuracy of the data from the sensors are the utmost important in smart parking application. Once the public trust the new smart parking system, they will definitely adopt it naturally. Otherwise, whoever operates the smart parking will face many complaints everyday. Do you think our country is ready to adopt this?

[Original article — “Seven (7) Key Lessons for Smart Parking Deployment” published in IoT World]

The complexity of different owners of data sources will be a big stumbling block if we can’t find the right business model of sharing the data. This will defeat the purpose of IoT when its main suppose is to fuse different data sources. It’s like building several cellular base stations (towers) at the same location which provides similar coverage to the users. It’s such a waste of investment when we can’t actually reuse the same cellular tower. The same goes with IoT. Can the same sensor provides service to different stakeholders with different usage?

Who wants to send different groups of people to the tops of Himalayas mountains to get same sample of data? Who wanted to send spend billions of dollars to Mars to get the same photos? Who wanted to send different Astronauts to International Space Station and doing the same experiments? It’s a no brainer that we shouldn’t waste our money for doing the same thing. In fact, we have to find ways how to recoup back the investment by monetising the rare data that has been collected.

However, there are several issues and concerns that need to be address first:-

1. Privacy and authenticity of data — there is a possibility that the data might reveal weakness of a system, open to security breach or fake data which results in wrong insights
2. Determine the right value for the data — how much people are willing to pay that kind of data?
3. What kind of data is worth sharing — Capturing the wrong sets of data will results in unnecessary investment
4. How much detail of the data needs to be captured — since the infrastructure has been built (sensors, gateways, connectivity, towers, etc), we shouldn’t waste capturing only a single data point. Why can’t we use the same infrastructure to deploy different sensors too?

Internet of Things are quite similar to the current Internet — Millions or Billions of data sets are now sitting around in the World Wide Web whereby many of that same data is being used many times by different people in different ways. The question is — why can’t IoT do the same thing?

[Original article -”IoT — Build Once, Use Many Times” published at IOT World]

Original article — “How to Self-Quantify a Network Using Internet of Things (IoT)” at IoT World Blog.

Recently, I have posted the concept of Shadow Network that intelligently sense and adapts its network characteristics to its surroundings. I began to realise that this concept is quite similar to the Quantified-Self whereby sensors are being used to collect data regarding the individual and track his health, productivity, social life and habits. With the information gathered via these Quantified-Self tools, the person will know a lot better about himself and thus can change his lifestyle habit to be healthier and productive.

It’s quite similar to the Shadow Network; it will use the surrounding sensors i.e. smartphones or dedicated sensors to collect data and later make a decision by itself how best to serve the users connected to it.

That’s why I termed Shadow Network as a “Self-Quantified Network“

Now, imagine a network is just like a “human” trying to make himself more “healthy”, “Sociable”, “good mood”, “more productive”, “enjoy hobbies”, “enjoy the travels” — And what if the network is also capable of improving itself by having “better coverage”, “providing good QoS’, “give better capacity”, “understand the situation”, etc?

The question is — where does it learn from? Previously, network learns from its network traffic condition which either collects its “throughput”, “latency”, “error-rate”, etc. But with the era of Internet of Things (IOT), sensors are now available everywhere. What if these sensors provide that extra information to the network? The context information can be in many forms, such as:-

1. Spatial — Location, Speed, Orientation
2. Temporal — Time and Duration
3. Environmental — Temperature, Light, Noise Level
4. User Characterisation — Mobility Pattern, Social interactions
5. Device Resource Availability — Battery Power, Computational, Storage, etc.

And now — just imagine if you can harness the data and turn into knowledge that will allow the network to be re-configurable (we termed as Self-Reconfigurable Network). It will transform the network to be more intelligent and adapts to its surroundings and give a better service to the users attached to it.

We called this — Shadow Network.

Original article published — “Sensing-as-a-Service” — New IoT Cloud Service is in Town” at IoT World.

As we moved our sensor data to the IOT Cloud, we can manipulate the data for useful applications. But many people are sceptical about storing their data in the Cloud. Before we go further in arguing on the topic of privacy, let’s take a look at the four main categories of sensor data ownership:

1. Personal and Households — These are all personal items, such as mobile phones, wrist watches, spectacles, laptops, soft drinks, food items and household items, such as televisions, cameras, microwaves, washing machines, etc
2. Private — Business organisation, has the right to take the decision whether to publish the sensors attached to those items to the cloud or not.
3. Public — Infrastructure such as bridges, roads, parks, etc. All the sensors deployed by the government will be published in the cloud depending on government policies.
4. Commercial Sensor Data Providers — Business entities who use and manage sensors by themselves by keeping ownership. They earn by publishing the sensors and sensor data they own through sensor publishers.

Once we can categorise the ownership of the data, we will be able to use them to become “IOT-as-a-Service” or “sensing-as-a-service” (check the concept in the Slideshare here) meaning that we can provide a free or chargeable service to any person who wishes to view or manipulate the data

The benefits of “IOT-as-a-Service” are as follows:

1. Harnessing the creativity of application developers
2. Built-in cloud computing — “Pay-per-Use.”
3. Participatory sensing — Rapid deployment via “crowd-sensing” method
4. Sharing and reusing — “free and paid.”
5. Reduction of data acquisition cost — “sustainable business model
6. Collect data previously unavailable — “Assist the scientific community or survey activities.”

It’s also possible to combine different multiple sensor data sources and create many innovative applications such as combining between parking sensor data and public transport data, between environmental data and flood detection data, between river quality data and fertility of soil data. An IOT service provider can provide such services and thus, it will certainly open up many opportunities to generate new ways of monitoring and managing our valuable assets. You might want to continue the M2M and IOT Communication LinkedIn Group discussion that I have posted here — “Are there any IOT Cloud Service Providers?” and “What do you think about the idea of “Sensing-as-a-Service”?”

[Article originally published at LinkedIn — “Six (6) Benefits of IOT-as-a-Service“]

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1. Internet of Things — Will It Fly?
2. Sensing-as-a-Service — An IOT Cloud Service
3. 4 Reasons Why Internet of Things (IOT) Adoption Will Not be as Slow as IPv6?
4. Using Smartphone as the “Sensing Device” for Crowdsourcing Applications
5. Internet of Things (IOT) Target market — Consumer (B2C) or Enterprise (B2B)?
6. 4 Main Challenges to Become an IOT Service Provider
7. How to Self Quantify A Network Using Internet of Things (IoT)?
8. The Early Adopters of Internet of Things (IoT)
9. How to Create Knowledge from Raw Data Using Internet of Things (IoT)
10. Commercial IOT Sensor Provider
11. Can IOT Answer These Questions?
12. Avoiding Malaysian Floods Using IOT
13. The Importance of IOT Data Sets in Malaysia
14. Five (5) Reasons Why “Things” (IoT) Require Their Own Facebook
15. Will IoT Make You Lazy?
16. Four (4) Compelling Reasons to Adopt IoT

About the Author

Dr. Mazlan is the co-founder and CEO of FAVORIOT Sdn Bhd. He is ranked among Top 20 Global Thought Leaders on the Internet of Things (February 2019) by Thinkers360, Top 50 Most Impactful Smart Cities Leaders by World CSR Congress 2017, ranked Top 10 in IoT Top 100 Influencers by Postscapes 2017 & 2018, ranked №20th Thought Leader in IOT by 2014 Onalytics Report — “The Internet of Things — Top 100 Thought Leaders” and UTM Alumni Industry Personality 2016. He is currently one of Global Vision Board Member (2017). Before Favoriot, he spent 2.5 years as CEO of REDtone IOT and 8 years in MIMOS Berhad as Senior Director Wireless Communications Cluster. He also spent 13 years in CELCOM (mobile operator), handling many senior management positions. Prior to Celcom, he spent 10 years as an Assoc. Professor at Universiti Teknologi Malaysia. He is currently the Adjunct Professor at Universiti Sultan Zainal Abidin (UniSZA) and Universiti Malaysia (UniMY). He was also the Adjunct Professor for UTM from 2008 to 2013 and UTHM (2004–2005, 2013–2016). Dr.Mazlan is a frequent speaker at many major & established IOT, Smart Cities and telco conferences locally and globally. He sits in Industry Advisory Panel (IAP) for several local universities. He graduated from Universiti Teknologi Malaysia with a BEE (1984), University of Essex (UK) with MSc. in Telematics (1986) and Universiti Teknologi Malaysia with a Ph.D. in Telecommunications (1993). He also received an Honorary Doctorate in Electrical Engineering from UTHM (2017).

You can reach him on LinkedIn or Twitter. Check all his presentation slides HERE.

You can download Dr. Mazlan’s photo from HERE.

Original article — “Who Are The Early Adopters of Internet of Things (IoT)?” published at IoT World

If you noticed that most IoT companies came from startups such as the ones that introduced their early products through Kickstarter crowdfunding. Only recently, we can see bigger companies like Apple, Samsung, Google are developing IoT solutions. But who are their customers? I believed the customers are the early adopters.

But who are these early adopters? Early adopters are a larger group at 13.5 percent of the population. Early adopters are well-known opinion leaders. Because of their leadership, early adopters command attention and respect, and they function as cross-pollinators. Like bees fertilize flowers by bringing pollen from one flower to another, early adopters spread new ideas from innovators to others. IoT requires these opinion leaders to spur the industry.

IoT startups are the innovators — they are the evangelists and the believers. They will take whatever risks to be the leaders in this new business. The more reasons we can also see the early customers are also the ones that wanted to be the first industry mover that adopts such solutions even though they know the IoT solutions might not be 100% perfect. They are willing to take the risk of an 80% matured product. That’s the price if one wants to become an early adopter.

Begin by bringing on board the opinion leaders — the adventurous innovators who spearhead new practices — and the early adopters, who are not far behind. Encourage the opinion leaders to mobilize the majority of staff — the early and late majorities — to understand and adopt the new practice or set of practices.

The early adopters are known as the revolutionist, evangelist, futurist and visionary. Bring them to the IoT industry and we will see IoT become a reality. Nurture the startups because they are poised to shape the future of the Internet of Things.

Gartner predicts that by 2017, 50 percent of IoT solutions (typically a product combined with a service) will originate in startups that are less than three years old. — Source

About the Author

Dr. Mazlan is the co-founder and CEO of FAVORIOT Sdn Bhd. He is ranked among Top 20 Global Thought Leaders on the Internet of Things (February 2019) by Thinkers360, Top 50 Most Impactful Smart Cities Leaders by World CSR Congress 2017, ranked Top 10 in IoT Top 100 Influencers by Postscapes 2017 & 2018, ranked №20th Thought Leader in IOT by 2014 Onalytics Report — “The Internet of Things — Top 100 Thought Leaders” and UTM Alumni Industry Personality 2016. He is currently one of Global Vision Board Member (2017). Before Favoriot, he spent 2.5 years as CEO of REDtone IOT and 8 years in MIMOS Berhad as Senior Director Wireless Communications Cluster. He also spent 13 years in CELCOM (mobile operator), handling many senior management positions. Prior to Celcom, he spent 10 years as an Assoc. Professor at Universiti Teknologi Malaysia. He is currently the Adjunct Professor at Universiti Sultan Zainal Abidin (UniSZA) and Universiti Malaysia (UniMY). He was also the Adjunct Professor for UTM from 2008 to 2013 and UTHM (2004–2005, 2013–2016). Dr.Mazlan is a frequent speaker at many major & established IOT, Smart Cities and telco conferences locally and globally. He sits in Industry Advisory Panel (IAP) for several local universities. He graduated from Universiti Teknologi Malaysia with a BEE (1984), University of Essex (UK) with MSc. in Telematics (1986) and Universiti Teknologi Malaysia with a Ph.D. in Telecommunications (1993). He also received an Honorary Doctorate in Electrical Engineering from UTHM (2017).

You can reach him on LinkedIn or Twitter. Check all his presentation slides HERE.

You can download Dr. Mazlan’s photo from HERE.