Today you’re going to learn how IoT Objects can be connected to build up a Human-Centered
Decentralized SioT Network which provides best-fit recommendations to the end-user.
Hey my name is Daniel and I will present you together with Panos and Dimitris DANOS today
Let's build up the scenario first. Imagine you had a stressful day and want to relax
in the evening and watch a movie. Because you aren’t a movie expert you need some
help to find a good one for you.
You start for looking in a platform like Netflix where you get some recommendations, but this
platform limits the provided content. You can also get information about new movies
through newsletters or advertisements, but do you share their opinion? Last, you can
ask around which cost a lot of time. Are there some better ways to find a good movie?
Imagine, you have an IoT TV. Is there a way to find a good movie for you by asking other
IoT TVs? And hopefully without slowing the network down because your request generates
a lot of network traffic? Let’s try to solve this in the area of SIOT, that promises a
more human-centered point of view and seamless interaction of humans and smart things within
a social framework.
Imagine instead of having only a smart TV, you have a TV which digitally represents you,
like a Digital Me or an Object that can find the best information for you?
But bevor we started, let me show to you our motivations, why we created DANOS: If you
have Netflix, Netflix owns your Data and learned patterns and you cannot use it on Disney Plus.
So how we build up a single profile from you on your device to increase the data privacy?
How can a Digital Me of you have also the same behavior as you in the real world to
be human-centered? How can architecture be scalable with billions of IoT devices?
Based on the motivation, we build the Framework DANOS, which is a combination of our Names,
Daniel and Panos. Dimitris joined later.
Now, let me transform you into a Digital Me running on an IoT TV which has the task to
find a good movie for his human. First, you need to search and find a location in the
IoT Network, where the probability is the highest to find good answers. Second, you
want to establish relations with the most similar ones, that you can thirdly ask them
about good movies to build up recommendations.
But how is your anatomy as a Digital Me? You inherit attributes from your human, like age,
gender and so one, but also the personality traits.
And you don’t have only these attributes, you gain a human-centered behavior based on
the Big 5 personality traits. Every combination of personality characteristics my generate
a number of behaviors. For example, the Approaching behavior may relate to an extrovert, so you
crawl for friends more time than others regarding your human, which has also more friends on
Facebook because he is strong extroverted too.
Ok, know you have the characteristics of your human, but you have also an task, an intent
to find a good movie for your human. So you know preferences and the rating history of
your human what he liked and don’t like. This is context-related, so the preferences
and User Feedback can differ if you are running on a fridge and your user asks you for the
best beer.
But don’t talk the whole time about you because your human is waiting. Let’s start
to find the location where other similar Digital MEs are.
So you are entering the DANOS Cyberworld, a virtual location management framework where
other Digital Mes are registered. The DANOS cyberworld is divided into areas where each
one represents an intent. And Areas are divided into cells, which are clusters to reduce the
communication load to find the most similar ones. You ask a schedule manager, which generates
a traveling rout for you that you can register on the most promising cells to crawl for new
friends.
Usual clusters will be generated through a global knowledge or a godlike perspective
about each dataset. But in the concept of DANOS cells, a cell is completely detached
from the surrounding cells and area to be scalable and have better data privacy because
your data will only be shared in the cell where you are registered.
Ok Ok, but how does it work to generate the cell cluster? A cell has a memory of profile
information from previous successful generated movie recommendations. This memory will be
accumulated to a single proxy which will be used to calculate the similarity between your
profile and the cells to find the best one for you.
After you get the address from the schedule manager for the best cells, you register in
the cell and ask the cell for new friendships.
The cell’s relationship manager calculates the similarity between you and all other profiles.
And you will go into a friendship with the most similar other Digital MEs, which means
that you will share your direct communication channels with them, to ask them later for
a recommendation.
But wait, there was a Human-Centered Behaviour introduced at the beginning. Because you have
the personality traits from your human, you will manipulate the similarity metric in a
human-centered way before you decide to go into a friendship. For example, you are risk-avoiding,
this means you only go into a friendship if the similarity is really really strong.
Here you see the formulas for the different Behaviors which manipulate the similarity
metric.
Now it is time to ask your new friends for the rating history to build up the recommendations
You are getting the rating history from your friends.
And you already know the similarity metric from the relationship manager
So it is really easy to build up the recommendations for your user by using the similarity metric
and the rating history from your friends.
Now you can provide your calculated recommendations to your human. And if he is happy about it,
the profile information will be feedback to the memory of the cell where it will use in
the calculation of the proxy that other objects can make use out of it.
Let's see how the evaluation looks like.
For visualizing the process and evaluate the algorithms, we have built a UI to show how
the Digital MEs are traveling. In the center you see a map, the map is divided into squares
which are the cells, the clusters. Each Dot represents a Digital Me and you can see how
they are traveling over time. And if I click on the heat map on the right top, I get some
insights about the data in the cluster through the cards below. Over time I can see, how
these clusters are specialized more over time. On the left side, you see general information
about the Digital MEs.
Let us observe the traveling behavior with intelligent traveling and without. Intelligent
traveling means that we are using the proxy of the cells to find the right cells. With
intelligent traveling, you see that the Schedule manager will send you back to the same cells
over time. If you see another digital Me which is in some parts similar to you but not overall,
you having some overlapping cells at some point, but the traveling route is different.
And without the intelligent traveling, it is how expected, really random.
Let's take a look more into the details. For this simulation, we simulate 2000 Digital
MEs and run one simulation with intelligent traveling and one without. The Memory size
of a single cell is 30 and we are using 400 Cells. Each Simulation will stop after 240000
Object activations. On the left, you see with intelligent traveling
and right without. The Dots are the similarity Metric for the established relationships.
You can see that the density is much higher on the left side, which means stronger cluster.
Also, the left site generates 56 % fewer friendships which reduce the network load. The quality
of the relationships is 5.8 % higher because only the most similar Digital Mes come together.
To observe the performance more. We created 1000 Digital MEs and for each one, we created
an exact Clone. Now we want to see how long does it takes till each one has found his
clone. With intelligent traveling, we are 110 times faster and generated fewer resources.
Just looking back to the motivation, here you can see some conclusions and added values.
We have managed to adopt a human-centered behavior to the objects increasing the quality
of the recommendations and maintaining the privacy of the user data among other benefits.
Thank you and have a nice day