>> Dimitrios Lymberolpoulos: So it's my great pleasure to welcome Maria Gorlatova to
Microsoft Research. She's a PhD student at Columbia University where she has been working
mainly on energy harvesting ultra-low-power wireless communication. So among her many
accomplishments, she was the recipient of the 2012 Google Anita Borg Fellowship in the US,
and she was also a Microsoft Research graduate student, Fellow final candidate for the year of
2011. So she's very familiar to the area. So I’m going to let you talk about your stuff.
>> Maria Gorlatova: Thank you Dimitrios. I'm here to talk about my work on the energy
harvesting active network tags that we've been doing at Columbia for the past five years. So
first, a little bit of my background. I am finishing up my PhD in electrical engineering. My
studies are funded by a Canadian version of [inaudible] Fellowship, as well as Columbia
University Fellowship. And before I started Columbia, I had, I got a Bachelor’s and a Master’s in
electrical engineering in Canada. My Masters was in wireless networks. It was in Wormhole
Attack Detections in Wireless Networks. And in between my Master’s and my PhD, I worked for
a couple of research labs, and as well as a diverse set of different things. I did an internship at
the Canadian patent offices as a patent examiner; I had a couple of software development
contracts, and during my PhD, and also did an internship at Disney Research in Zurich.
So I'm here to talk about my work on the energy harvesting active network tags project which is
a collaboration between five different labs at Columbia. And this is my lab. And we've been
working on this project since 2009. I am one of the founding members of it. It has really grown
over the years and evolved into various directions. So what I’m actually working on is creating
new types of devices that will be small flexible and in energetically self-reliant tags, so a tag
form factor, and is the harvest energy from them, they will be active tags. So they're able to
form multi-hop networks. And in this capacity, they will enable pervasive networks of
commonplace objects. This is what's commonly referred to as the Internet of Things. And what
is making them possible is the convergence in energy harvesting, so being able to get energy
from the environment and in ultra-low power communications. So there are some transceivers
that enable energy spending that is much lower than what is currently available. So the
emergence of these two domains is when making the tags a possibility at this stage.
In terms of technology space, they belong right between RFIDs and sensor networks. Unlike
RFIDs, they are active tags. They harvest energy from the environment, so they're able to form
a multihop networks compared to your traditional sensor networking however, they are much
smaller devices. It's a different form factor, as well as the image in that they will be mostly
exchanging ID information. So no transmitting video or anything like that, but just working as
tags. There is a lot of push in the industry in making these types of devices happen, but they do
not exist yet, and there are many research challenges that need to be addressed to make them
possible both on the device side, on the transceivers and the harvesters, as well as on all levels
of communications and networking.
And your typical application for these devices is what is envisioned for the Internet of Things, so
like getting objects in a warehouse or in home or keeping track of object configurations. One of
the applications that we typically describe to give us a sense of the different building blocks of it
is example of locating misplaced book in a library. Let's say that all your books in the library are
equipped with solar cells. They are harvesting energy from the environment, from the indoor
lights in the library, and they are communicating with each other within very short range.
They're sending these that are closely following the Dewey Decimal System, so the ideas of
books that are shelved close to each other are supposed to be similar. The books exchange
their ideas, and if one of them does not belong, if one of them is really different, then the books
themselves would have identified this misplacement, and the information about the
misplacement would be propagated through the network of books to the librarian. Yes, please.
>>: So it seems like a fundamental premise of this is that you need to do these short hops
because you have so little energy.
>> Maria Gorlatova: That's right.
>>: But if you are in a library where you can harvest lots of energy, there's clearly infrastructure
nearby because you're pouring photons down the lights. So the question is, is there a
fundamental reason why these little local hops are more practical than just having each book
talk to a infrastructure that can listen very carefully and can transmit very loudly. Like, the
infrastructure has this huge leverage, right? It can make a very loud transmission and it can
direct its listener>> Maria Gorlatova: Right, right, right. So essentially, we, in the library application, you can
indeed have readers that are right on the shelves. We envision this as more of a global
technology and use this just as an example. But yes, you're right.
>>: Let me ask you in a more pointed way.
>> Maria Gorlatova: Yeah.
>>: Can you help me understand the conditions in which you don't have infrastructure, like
how, as the network gets bigger and bigger, right, it has to get spatially really big before it gets
[inaudible]. I'm trying to imagine, you know, you have a containership, you’ve got thousands of
these things-
>> Maria Gorlatova: Right, right, right.
>>: But we can also afford [inaudible] really being transmitters.
>> Maria Gorlatova: Right, right, right. So some of the things that become possible here, for
example for the containership, is if you have the infrastructure, if you have just one reader
reading something, then your device can only report on itself. The only thing that you can do is
then see that the device is next to the reader. Here, you're talking about devices that are active
by themselves. So they can communicate with each other, for example, they can be deporting
to somebody on their interactions because they exchange information with each other. You
can figure out not just how close they are to the reader but how close they are to each other
and whether they stay close to each other at different times. That's the type of>>: So you’re saying that even if you don't use multihop to transmit information, you're using
measurement with your neighbors as the initial way to collect data.
>> Maria Gorlatova: That's just one of the points, yes. But also in unconstrained environments
if you have, for example, devices that are on you that are wearable devices and that you're not
necessarily close to the reader at all times, that's another scenario where not having to rely on
infrastructure is very useful.
>>: Well, but in that case, you still have to be close to some other devices, right? In other
words, if the network is getting sparse enough, you're not close to enough other devices, you're
still disconnected. Okay.
>> Maria Gorlatova: No, no, no. Right, right, right. Any other questions?
>>: There's a bunch of work from Intel in Washington on the wisps.
>> Maria Gorlatova: Yes, yes, yes. Wisps are still communicating [inaudible]. So they harvest
energy from the environment and they, it allows them to improve their communication rates,
improve their distance and reach, but they still communicate into the reader. They're not in
envisioned to form networks and to talk to each other.
>>: In the library example, would it work, the wisps?
>> Maria Gorlatova: That's [inaudible]. No, so wisps are>>: [inaudible] not be able to find the missing book or [inaudible]?
>> Maria Gorlatova: You would, there is a work that explores, actually as a side note, finding a
misplaced book in the library is surprisingly an important thing for the librarians to do. So
there's actually work out there that explores a mobile robot, like [inaudible] robot that you
send around and you have books equipped with RFIDs doing that. It's one of the options.
>>: [inaudible]?
>> Maria Gorlatova: Yeah. Anything else? Any other questions? Okay. So the presented, the
overall idea of this work in 2009 in the Challenges Paper [inaudible], and since then we’ve been
going towards enabling this vision of these devices. So I'll first talk about, a little bit about the
enabling technologies for them, the harvesting and the ultra-low-power communications. So
this isn't my work, but this is the work of the labs that we work very closely with. And then I'll
talk about my contributions to the project which are in characterizing the energy availability for
these devices, developing a testbed for the future devices that incorporate the some of the
hardware that the groups developed, and then developing energy harvesting adaptive
communications and networking algorithms for the devices.
So the first enabling technology for them is energy harvesting. And for devices that we
envision, the most promising sources are light and motion. What's interesting to note here is
that we are talking about harvesting the energy of indoor lights rather than sunlight, and there
is a factor of 1000 difference in terms of energy availability. For collecting this energy for light,
these are solar cells, and the difference from your conventional solar cells is that [inaudible]
solar cells that can be made flexible and integrated with different objects are becoming
available. And this is an example of a flexible solar cell that was developed by a group we work
with. You need some form of an energy for these devices, and you can use rechargeable
batteries. Here's an example of a flexible battery that's being developed. Also, using capacitors
is becoming possible for them. However, batteries and capacitors have different chemical
properties, so they actually call for different mathematical models, and I'll get back to that
point later on. Any questions about the harvesting? No?
The second enabling area is ultra-low-power communications. And specifically, we are
exploring ultra-wideband impulse radio communications. The idea there is that rather than
generating a long [inaudible], you're placing on the air in very short pulses, nanosecond
[inaudible] pulses, and as a result, your energy consumption is greatly reduced because you no
longer have to have your transmitter running for the whole time. You can [inaudible] cycle it
very heavily in between the pulses. As a result, this enables your energy spending that is much
less than what is conventional. And here's an example of the chips that we developed as part
of this project. And this is the board that incorporates them that we’ve built into our testbed.
One interesting thing to note about this technology is that here, the energy to receive a bit is
actually much higher and to manage and transmit a bit, and the reason for that is that bit
transmission here is very cheap. To send a nanosecond duration pulse, you have to, you just
turn on the oscillator and put the very short pulse on the air and that is it. Detection, you have
to run much more complicated circuitry. And thus, you have this paradigm shift that calls for
redesign of many of the legacy protocols that have been developed under the assumption that
transmission is a product you need to minimize. That transmission is the expensive part. Any
questions?
>>: So is there a reason that transmission is relatively inexpensive? I'm trying to figure out
what [inaudible]. Is it, you send the zero by not send anything [inaudible] particular timeslot?
>> Maria Gorlatova: Actually, so, no. The way that you send the [inaudible] is zero, the
undulations cannot be used here is, for one you send two pulses, for zero you sent a pulse and
a no pulse.
>>: So is the reason that the receiving is expensive because you have to, you don't know when
the transmission is going to occur? Or can you actually synchronize them [inaudible]>> Maria Gorlatova: Right, right, right. The reason why receiving as expensive is receiving isn't
particularly expensive. Receiving is still relatively cheap. The trade-off happens because
transmission is extraordinarily cheap. Those devices, we talk about short range
communications, so these devices do not spend all of power putting pulses on the air, in
generating these pulses that travel a very short and travel for a short distance is very cheap. It's
much cheaper than what is conventional. So that's a basic result for the trade-off.
>>: So where's the energy getting consumed on the receive side? Is it amplifying that very
weak signal?
>> Maria Gorlatova: On the receive side it's more about listening for the pulses.
>>: Okay. [inaudible] if you knew when the pulse was going to be, would that will that bring
down the receive cost?
>> Maria Gorlatova: You would have to synchronize them very tightly. So this is not>>: [inaudible].
>> Maria Gorlatova: Right, right, right.
>>: [inaudible] wrap my head around it. You could more practically do it, perhaps. But if you
could organize them very tightly, would that do it?
>> Maria Gorlatova: It would help. It would help, yes.
>>: So the issue is you’re [inaudible] process or cycles, listening and trying to process the
received pulses. [inaudible] signal [inaudible].
>> Maria Gorlatova: Right, right, right.
>>: So what is the [inaudible]? Do you have the [inaudible], I mean intuitively, [inaudible].
>> Maria Gorlatova: Yes.
>>: [inaudible].
>> Maria Gorlatova: Yes.
>>: What are the major components of power [inaudible] receiving this transmission? Why is
it, what is spending this power?
>> Maria Gorlatova: It's listening to the meter. That's exactly what it is. You run some sort of
an energy detector that has to be run for a certain time.
>>: [inaudible] much higher than that of the [inaudible].
>> Maria Gorlatova: That's right.
>>: So do you have breakdown of how much time you spend for listening and how much for
[inaudible]?
>> Maria Gorlatova: This is, I think most of this work is still fairly exploratory. So I know they're
looking at it. I don't have a sense of it. I know there are still challenges on the device side, in
addition to passive listening, there's also the side of how quickly you take and turn the device
on and off. That's one of the challenges that they’re solving on the circuits level.
>>: So are you involved in [inaudible]?
>> Maria Gorlatova: No. Right, right, right. So this is an enabling technology for our overall
devices. This is, the chips are being developed by another group at Columbia. And we are
building them into our testbed. There is, as part of this work, another PhD student is looking at
mac[phonetic] level. My development for these devices but it's notI want to mention how the enabling technologies, low level technologies, they're bringing
about many challenges in communications and networking. Because of this trade-off in
crossroad to transmit and receive, your communication decisions are tightly dependent
between the devices. Because of energy harvesting, your energy now comes from your
environment. So it is not a constant supply that you can count on. Rather it is a dynamic
source that changes over time, that changes in space. Moreover, in the environment that we
consider, this energy is fairly restricted. So you now have to deal with the tight dependency of
decisions and dynamic availability in a very tight, tightly constrained budget. So it brings about
many issues in communications and networking. Any questions? No.
One of the complicating things here is that the sources of energy for these devices, for energy
harvesting, are not entirely understood which brings about my first area of contributions that is
characterizing the energy availability for these devices. When we originally started this work,
we wanted to have our devices harvested energy of the indoor light. And we very quickly
realized that there was almost no information about how much energy the devices would
actually harvest. There were some spots measurements and rule of thumb estimates, but there
were no traces and there was no analysis of the availability. So one of the first areas of
contributions of my PhD was in, actually getting a sense of that, of characterizing that. So what
I did is put together an indoor light energy measurement campaign where we took sensors that
measured the energy of the light and we put them on [inaudible] data position boards, created
a monitoring system, and deployed them in a number of indoor locations at Columbia
University. We let them be for a while. We have traces for over a year for certain locations.
And this is an example of the light energy trace that was reported in a particular office over four
days.
As part of this work, we've done some measurements with mobile devices as well. So this is an
example of a trace that was recorded as a student was working around New York City's Times
Square at night time. Based on this study, we obtained insights, but we also got a number of
traces that we can use as energy feeds for simulators and emulators. The traces are publicly
available. People actively don't load them. And we've been using them to validate the
performance of different algorithms, as well as we’ve now created a system that allows us to
replicate the traces in our testbed. If any questions? Yep.
>>: So the first one is, they’re in a particular location and don't move at all?
>> Maria Gorlatova: Yes, yes, yes. So we deployed a setup by a windowsill in a particular office.
>>: Okay. So they didn't even move around within the office.
>> Maria Gorlatova: No, no, no. Right. Yes, yes, yes. Right. We deployed about 10 of these
setups in different offices in the conference room. Got the sense of>>: So you have completely static and very dynamic. You don’t>> Maria Gorlatova: Yes, yes, yes.
>>: For example, like the library book, which would be not moving around a lot, but move
around>> Maria Gorlatova: Right, right, right.
>>: Part of [inaudible] look like intensity, or does it say how much intensity [inaudible]? Does it
have any spectral information?
>> Maria Gorlatova: I'll get to that question later. No, it doesn't, but I’ll get to it shortly. This
is, the data here is very simple. It's a radiance [inaudible]. That's all there is. But traces are
publicly available. There was another group that did similar measurements after us. They
validated much of, many of our findings. So that was good. One of the things that the traces
allowed us to do was to look at the energy budgets that our devices would have for themselves.
And under certain assumptions, we're assuming that relatively small device, relatively small tag,
and its efficiency, as when you would imagine for an organic solar cell, a flexible solar cell, and
under the energy spending assumption’s consistent with the communication technologies that
we imagined. These are the data rates that our devices will be able to maintain in your typical
environment. Note that these are continuous data rates, so this is what the device would be
able to maintain throughout today when harvesting energy for only a certain fraction of it. And
the data that's here is not particularly great, but that at the same time, you're talking about
tags, so this is just sufficient for tags to have some sort of, to still communicate in network. We
looked at, any questions?
>>: This is assuming some amount of energy storage as well, right?
>> Maria Gorlatova: Yes. This is assuming relative, a battery that allows you to absorb all the
variations that have to do with it, yes.
>>: Do you have a characterization of what that storage capacity has to be in order to achieve
this?
>> Maria Gorlatova: Right. So one of the things that this allowed us to do is to get a sense of
the size of a battery that those devices would need.
>>: Okay. We'll get to that?
>> Maria Gorlatova: It's, essentially the devices are, what this adds up to is about to the order
of a joule per day of energy for your devices. This is the total energy that they get throughout
the day.
>>: And is the day the level of variance that you have to deal with? Or is it-
>> Maria Gorlatova: I'll get to some of that in the algorithmic variations. But yes. So essentially
your battery, even if you want to absorb all the variations, your battery will be relatively small.
And if we looked at, based on this data, we looked at some finer properties of the device, the
light [inaudible] availability, such as how predictable it is, what are the correlations between
different locations, whether you can take some factors into account in improving that
prediction. So we presented this at Infocom a few years back, and this is about [inaudible]
transactions in mobile computing. However, the hardware work, hardware group that we
interacted with, they love our traces, but they find them insufficient because of those spectral
component of it. So what we've been doing with them is looking at a way of getting the level of
energy as well as the spectral component. So we are currently finishing up some of that work
for a Ubicom submission, actually. Any questions?
Some, I want to very briefly mention our ongoing work that we are just now finalizing on
characterizing energy of motion for these devices. So there's currently no idea of how much
energy is, a motion energy harvesting device would gather. Again, there is, there are some rule
of thumb estimates, but there is no full characterization and no traces available. Motion energy
harvesters are more difficult to model than solar cells, so you have to model them as a second
order mass-spring system and you cannot directly record the power. Instead you record
acceleration, and then you process it to obtain the power. So here's an example of an
acceleration trace that we collected for 11 hours. And here's the example of the corresponding
power that would be harvested by the harvester. And under certain assumptions, again, we are
talking about relatively small devices, small size, small weight, the data reads that your devices
would then be able to maintain, they are on the order of what you would be able to maintain in
a dimmer office. So this is, it establishes feasibility, at the same time, your energy budget is
fairly restricted. And again, we've collected traces and we've been playing with the traces to
evaluate some of the energy harvesting adopted policies that we've been working on. Any
questions?
My second area of contributions is in designing and developing their prototype testbeds for our
future devices. So in the future, the EnHANTs will be small and flexible tags. And they will be
harvesting energy and communicating with each other using ultra-low power, ultra-wideband
technology. And in the future, we will have a network, a testbed that is relatively large that
demonstrates algorithms on all the levels of communication stack and that actually has the
Internet of Things applications running on it. Currently, our prototypes, they do not yet have
the form factor we envisioned. They are currently much larger, but they already harvesting the
energy from the indoor lights. Storing this energy in them battery, spending it as we want, so
they are energy harvesting adaptive algorithms running on all levels of it. They’re already
communicating with each other using this ultra-wideband technology. To the best of our
knowledge, this is only the known multihop network that is running on top of the ultra-
wideband transceivers at this stage. And our testbed right now, it’s also, it's not a large-scale
testbed that we envision in the future. However, it already has the components that enable
you to be testing energy harvesting adaptive devices. In particular, one of the latest features
that we are very proud of is a first of its kind software-based light control system. So this is a
system that allows you to expose every prototype to the exact level of energy that you want.
And you can do it again and again and again. So this allows you to feed a particular energy
trace to the system and record as the hardware in the system responds to it.
So this is the example of feeding some of our energy traces to the system. This is the energy
levels harvested by a particular prototype in the system. And the variations here are, they
correspond to different runs of the test. So this actually shows you training different runs. And
the level of energy harvested, the different runs, is remarkably consistent. It's in the order of
two percent. So I'm about to present this test shortly at Infocom. Any questions? Questions
about the features?
To get the testbed to the state, we've been doing a lot of iterations on the development. We
actually started with a system that was completely based on commercial hardware. And we
were phasing out the commercial components and bringing in our own technology. In the first
phase, we put a solar cell [inaudible] system that was not harvesting the energy, but it was
letting the system be aware of it, so it allowed us to start developing some the algorithms
ahead of time. Then we added actual harvesting. And then we phased out, the commercial
communication chips and put in our custom transceivers. And then after that, we've added our
own solar cells. The light control system and multihop functionality. Yes.
>>: So [inaudible] on your slide, which are the components [inaudible]?
>> Maria Gorlatova: Right. The algorithmic side is mine, from the design to the specifications
writing, to a lot of the code, to a lot of testing. All of this is mine done with some help of some
students. I’ll get to that. The side where the algorithms are interacting with energy harvesting
hardware, that is also mine. That is, it requires a lot of very diligent work in actually learning
how to deal with hardware and to jointly design the interface there. We went for a few rounds
of iteration of that interface design. That testbed side is nearly all mine. The light control
system is joint work with the hardware group, but my ideas, my traces, and a lot of hands-on
stuff as well. Interface design, all of this interfacing, so that's all my work.
So one of the things that is I think evident here is that this is a lot of work by many different
people done over a span of several years. A lot of this work was actually done by junior
students as part of student projects. I mentored over, more than 25 students. These are just
some examples of projects I supervised or co-supervised. This is the breakdown of projects by
layer of discipline and by the level of students. This is just some photos from earlier this week
from our lab as we are preparing the system for our next demo. After having done over a few
iterations of this, having computed several phases of it, we realized that our experience with
building, bringing together the work of different students like this is actually fairly unique. The
experience students get from this project is also unique. These are typically interdisciplinary
projects; they are typically on the boundary of different domain areas. So they get some very
interesting experience from it. The experience of students working towards real deadlines and
being actually accountable for what they produce is also new to my new students. So we've
put together a small assessment of this work as a learning experience.
I initiated this survey where we just asked students what they thought, what they learned, what
they liked, what they disliked. And there’s some quotes from the students. Students usually
love it. They love this project. It's fun, it's ongoing, lots of people, lots of things happening.
One of the things that they noted is that working on this project has improved their ability to
function on multidisciplinary teams more than anything they have ever done. And as areas of
improvement, they suggested further collaboration between the groups, as well as better
knowledge transfer, better ways of introducing them to the project, as well as better ways of
transferring their work to the next students. So we just summarized this for a teaching
conference, for a conference on innovation and computer science [inaudible]. Any questions?
The last big area of my contributions is in developing energy harvesting adaptive
communications and networking algorithms for these devices. Recall that it's very complicated.
The energy spending is tightly dependent and the energy is dynamic. In general, within the
space of energy harvesting adaptive algorithms, you have many different things that still
constitute that space. As I already mentioned, whether you have a battery or a capacitor is an
important factor in your, for your algorithm design. Some of the other important factors are:
what type of energy your harvesting, what is your ratio of energy availability to your storage
capacity, within all of these can vary by many orders of magnitude, what proper size you're
considering is also important, whether you work on a node or link or whole network, and
whether you're making the decision for the next little bit or for the next hour or for the next
day, that's also important.
One of the most important things, however, is what type of energy you're relying on. And
within that space, you can have many different cases. Your energy can be periodic and
predictable. So this is an example of a trace that was recorded in Las Vegas, Nevada over four
different days. And some of the researchers, including researchers who are now with this
group, have been working in that space. In other cases, your energy is time independent and
kind of periodic, but not as predictable. So this is an example of a trace that was recorded in
one of our offices. In other cases, your energy can be modeled by a stochastic process. And a
lot of more theoretical people develop algorithms under those assumptions.
And then finally, in certain cases, your energy is so variable that you don't want to be modeling
it at all. And this is an example of a trace that was recorded when the device was carried both
indoors and outdoors. Orders of, and not the log scale here, on this axis. So, any questions?
What is common to these different energy models however, is that the energy sources are
highly dynamic in all of these cases. And in this work, but we're focusing on tracking
applications. So we want to make sure that our devices can communicate at all times. We
don't want them to communicate here and be silent during the night. So what we are actually
looking at are ways of allocating this dynamic energy in a uniform way with respect to time.
This has some parallels with economics, namely with economic consumptions moving; and the
idea there is that humans have different levels of income during different times in their lives,
but they want to be maintaining a more or less stable level of life throughout. So this has
certain parallels with that. Questions?
>>: Do you focus exclusively on these tracking applications? Or>> Maria Gorlatova: Right. For the time being, we are focusing on the case where your devices
want to communicate at all times. So in theory work, that’s what they call saturated model.
They want to communicate at all times, and all communication is equally important.
>>: [inaudible] for example [inaudible] library would find a case that [inaudible]?
>> Maria Gorlatova: Right. So in the library or book finding case, what you want them to do is
you want your books to be maintaining relationship with each other at a continuous rate
throughout the time. So that applies there.
>>: Why?
>> Maria Gorlatova: Because you want them to be exchanging their ideas in the more or less
constant way with respect to time. Make sense?
>>: Suppose on the find the book>> Maria Gorlatova: Right.
>>: Once an hour or something. I mean, why does it have to be [inaudible]?
>> Maria Gorlatova: Right, right, right. The idea there is that it's easier for them to stay
connected then to form connections as necessitated. So we envision that they would be
forming a network that is relatively stable at all times. There are reasons for other types of
applications, for sure in the library application for example, you may argue that you don't want
to be doing it throughout the night. You want them to only be doing this during the day
anyhow. But it's a good way of looking at this problem in general. And definitely other ideas
can be introduced later on. One of the important things here is what some researchers are
trying to do is essentially match per timeslot energy spending with energy harvesting for a
node, which is fine under certain assumptions and certain scenarios. However, in the more
general scenario, what happens then is it works fine if you have just one node more or less, but
once you start trying to network them, to bring up many devices together, then if this is the
mindset that you have, you end up with a real mess plus a very small number of nodes. So this
is, the insights of this is actually useful for that type of case. Make sense?
>>: Is the reason it's a mess because of the energy available to one node at any given moment
is not the same as>> Maria Gorlatova: That's right, that's right, that's right.
>>: Because they all were going up and down at the same time. If you’re just varying the lights
in the room, which you don’t do, of course. You were doing that, then it would be okay, right?
We all have energy at the same time. We all communicate. We don't have energy, so that we
communicate.
>> Maria Gorlatova: Yes, yes, yes. But in your typical room environment what happens is some
parts of the room and get light, get sunlight and others don't, so the availability of light to
different devices, it changes, let me see. Okay. So this is your typical case of light in a room.
This is a device that is on a shelf and this is a device that is close to the window. So you can see
that the light of availability levels, they don't necessarily correspond. And this is just one
example. So if you're trying to allocate this energy in a uniform way, and to do that, we're using
some frameworks that are fairly well established, but they’re typically used for allocating
resources between different nodes. Well, we are using them to allocate the resources between
different timeslots for the same node. The two frameworks that we use are a Utility
Maximization Framework. We assign the Utility to your allocation per timeslot; Utility has a
certain property, and it maximizes your evenness of allocation. Another framework that we use
is a Lexicographic Maximization. Again, these are fairly well known, but used for node to node
allocation, typically.
>>: So how do you define [inaudible] Utility?
>> Maria Gorlatova: Utility here, you can use, what is important for you is that Utility, its
function is concave. If Utility function is concave, that forces that evenness of allocation.
>>: [inaudible] whether a function is concave or not is more the [inaudible] of the function,
right? [inaudible] function to give some information that you really want to minimize or
optimize the-
>> Maria Gorlatova: Right, right, right.
>>: [inaudible] the information>> Maria Gorlatova: Right, right, right. So we are either trying to maximize and even out
energy, the energy allocate per slot, so this is Utility. We are assuming that Utility one energy is
concave. Or it's the data when we work with links. So the model that we are using is a
[inaudible]. It's fairly standard, with one exception. The time is slotted. Your energy storage
has a certain capacity and a certain level, certain resolution as well, and there's a rate of energy
spending and the rate of energy harvesting, and between the different time floats, your energy
[inaudible] state evolves according to this equation. This is fairly standard, aside from the
energy harvested parameter.
If your device is a battery, then the amount of energy you’re harvesting depends on the energy
that is available in the environment. So we call it the linear energy storage, and that’s what’s
typically examined. However, if your energy storage device is a capacitor, then the amount of
energy you can get into a capacitor is a function of the amount of energy already in the
capacitor. So this is what we call a nonlinear energy storage model, and our algorithms allow
for that. And, as I mentioned, we focused on small scenarios, so we are either optimizing the
energy spending per timeslot which can then be used as an input for communication
parameters. And as well as it is a building block, the stable allocation is a building block for
anything that you want to do on the higher layers.
We also consider links. We are looking at links in areas allows us to look at the cases where
dependency of the communication decisions, and it allows us to get insights into policies for
more global scenarios. In this case, we are optimizing data rate, data rate allocations directly.
And this is under the energy constraints as well as the communication constraints. We
considered algorithms mostly in the space of predictable energy profiles and where we can
model energy by some sort of stochastic model, and mostly simple stochastic models. We
considered different things. Here I'm only going to touch upon these ones. So for the
predictable energy profile model, we defined optimization problems using the frameworks that
I discussed earlier, and we developed algorithms that would allow you to allocate this energy in
the uniform continuous way for the most general case, including battery or capacitor. For the
case where you're only working with batteries, we developed algorithms that are more
straightforward and have a lower complexity. And then finally, if your storage is both a battery
and it's relatively large, then your solution is obvious. And we developed, our contribution
there was to show, as you mentioned before about the battery sizes, our contribution there
was to demonstrate algorithmically and mathematically where that happens.
We are also considered some more simple policies for this energy allocation. Some of these
policies have been proposed before. We got some algorithmic performance guarantees for
them, but also we evaluated them with our traces, the traces that we have collected, as well as
in the testbed that we have developed. For the case of a link, again we define optimization
problems, and developed algorithms for solving them. What we also considered, however, is
this more practical scenario where instead of nodes optimizing directly, their data rates for all
times. The nodes first allocate their energy, and then they develop, jointly solve, jointly decide
on the data rates. So the energy allocation isn't dependent, but they dated rates are jointly
decided. This is what's actually implicitly used in some of the papers. We looked at a few
variants of it that depend on what nodes choose to do for their energy allocation, whether
they're allocating it optimally or whether they are using something simplified for it. We've
demonstrated scenarios where some of these more simple policies are optimal, where this
approach is the optimal one to take. And we've also extensively evaluated this work, again,
with our traces and with, in the testbed that we've developed.
>>: What exactly is the policy? Are you optimizing [inaudible] policies? What is the policy?
>> Maria Gorlatova: A policy here is your decision on how much, the rate of your
communication to your neighbor for every timeslot. So your timeslots are, let's say half an hour
during the day, and you're deciding at what rate you and your neighbor will be communicating
over the next hour.
>>: For all of them? [inaudible] policy that every time use the policy to figure out what to do?
>> Maria Gorlatova: Yes. That's right, that's right, that's right. So some of the more simple
policies are online in the sense that they are trying to directly match spending to harvesting in
any given timeslot.
>>: So I have a question. So in the previous slide you said the dynamic programming
algorithms [inaudible] policy, so usually when you're trying to optimize these policies in a
planned setting, [inaudible] you come up with a program. The standard solutions in a dynamic
program.
>> Maria Gorlatova: Yes. That's right.
>>: Is what you’re doing the same thing or we need something else? So in order to [inaudible].
>> Maria Gorlatova: Right, right, right. So the approach here is, the framework here, this is
very well known. What we're doing differently is we are looking at this over time. They are
only looking at how you would be allocating things for different nodes. So your constraints are
then your>>: [inaudible]. I mean you’re talking about one policy.
>> Maria Gorlatova: Yes.
>>: The policy, once you figure it out, then the same policy, you use in any timeslot, right? Are
you only change the policy?
>> Maria Gorlatova: No, no, no. The policy’s fixed. Yes.
>>: So the planning setting, that's also the same setting where you [inaudible] optimal policy
[inaudible] which dynamic programming>> Maria Gorlatova: Right, right, right.
>>: [inaudible].
>> Maria Gorlatova: No, no, no. So what happens here is that you are allocating different
things to different timeslots. So it's not that I would solve for a timeslot, what you're saying
and then run that same allocation throughout. My allocation actually changes throughout the
day that I calculated once offline and I calculate it with taking time into consideration as well.
>>: So your policy actually has time as one of its parameters?
>> Maria Gorlatova: Yes.
>>: [inaudible]? This is actually the date.
>> Maria Gorlatova: Right, right right. So actually here's some examples of allocations.
>>: Okay.
>> Maria Gorlatova: This is your predictable profile that you will, you calculate here, right? So
you calculate [inaudible]. What you actually spend in different timeslots, it's per timeslot. So
the factor of your decision here, for this case is, it's when I'm at a particular time I will be
spending at a particular rate. And this isn't what’s commonly done in metric scenarios. There,
it’s that if I’m, the time isn't considered.
>>: So it's an open loop? You've designed the policy and just execute it or>> Maria Gorlatova: Right, right, right. So this is for the case where your energy is fully
predictable. So you decide once and you spend according to it and that's what happens. The
extensions of it should really consider more practical cases of where you would be adapting as
you go along. The issue is that this is fairly complicated even for small scenarios and even those
simplifying assumptions. So, yeah.
>>: It’s not just your energy availability is predictable, but also you're on low is predictable,
right?
>> Maria Gorlatova: Right, right, right. So we are currently considering the case where you
want to communicate at all times and you want to, you assume that at all times you have
something to communicate, yes. There is a lot of, so let me just get to the next point a little bit.
So the other case where we consider is where your energy is stochastic. So for this case, for the
case where energy is stochastic, researchers are currently coming up with different policies that
consider many different parameters. Again, that's already very complicated and also it's not
exactly, for many cases this isn't exactly a practical assumption. So the space of coming up with
algorithms for the case where your energy is partially predictable, is one of the subjects of
future work and it's currently more or less open area, actually. Not too many people are
working on it.
So for the case where the energy is stochastic, you no longer, you're still trying to spend your
resources smoothly with respect to time, but you'll no longer have to take time into
consideration making your decisions. You can come up with a policy once and then run it, base
your decision on the level of the battery or a capacitor on the level of storage. So this is an
example of how you would be spending for a particular case, energy in different timeslots
depending on where you are in your battery. And for this case, we also came up with a way of
calculating this policy. And we considered also many policies that are much more simple. This
is extraordinarily complicated even for the case where you are making an assumption that you
are your energy is very nice stochastically. Some simple policies, such as just spending energy
in the linear fashion increasing with respect to where you are in the battery, some of these
policies actually work fairly well; and we've evaluated them extensively in simulations and in
testbeds implementations; and with this framework we actually have, be able to get the
optimal policy and have some compared to the simple nodes. Any questions?
>>: So do you have results to show [inaudible]?
>> Maria Gorlatova: They are, I can show them, we can talk about this later on>>: There's a [inaudible] how do evaluate minimization [inaudible]? Do you take your real data
and play them back and try to see what happens? What is your baseline? How they do get>> Maria Gorlatova: Right, right, right.
>>: [inaudible].
>> Maria Gorlatova: Right, right, right. The contribution of this work is in, not so much in your
performance metrics as in coming up with a way of modeling this and in the coming up of the
way of approaching this problem. So the contribution here isn't so much that we are better
than somebody according to some criteria, the contribution is that we can now, for example,
we are able to come up with policies that work for capacitors, for example. And that's, nobody
has done that before.
>>: [inaudible] correspond to having more nodes, more energy per node? Or even correspond
to being able to [inaudible] communication rates over a large period of time [inaudible]
approach, right? Because>> Maria Gorlatova: Right, right, right.
>>: [inaudible] we use metric at the end, right?
>> Maria Gorlatova: Right, right, right. No, we looked at different metrics. We looked at Utility
throughput on times. I didn't include it here, but we have some results.
>>: Okay.
>> Maria Gorlatova: So in the modeling work, we mostly considered cases of nodes and links,
and we focused on smaller scenarios. We also, for our testbed work, we've implemented some
behavior that is on a more global scale. We implemented some behavior that is for networks.
So these are just heuristics. And I want to very briefly just to show what we have in mind.
So what we have running in the testbed, for example, is nodes are sending information to the
coordinator, the coordinator assigns to them the data rates to use, and they are transmitting
their ideas back; and the data adaptation can be done using different formats online and offline different things, and this is available and this is running. Another scenario that is also, we
have implemented in a testbed is a scenario where your nodes not just adapt their transmission
rates based on their environmental energy, but also adapt a network apology. So depending on
the situation, you can choose what, whether to forward information on this G[phonetic] or
along this G[phonetic]. And this is what we have running.
So future work for this is further development of the testbeds, integration of more hardware,
integration of different harvesters, different batteries, as well as different transceivers,
implementation of other algorithms and extensive use of the testbeds for algorithm [inaudible].
We've started on some of that, but some of our testbed features are really very new, so this is
active ongoing work. Development of energy harvesting adaptive algorithms is a very big
space. So some of the different subspaces that we have yet to consider is, for example, the
case where your energy isn't entirely predictable, but it's partially predictable, predicable
[inaudible], so that you can be developing, there's lots of work to that. As well as extending
developed models and developed algorithms to the network settings. So far, we've considered
simple scenarios, but there's a lot of work to be done on that. Any questions?
So in summary, on this project we are developing a new class of ultra-low-power devices that
are going to be in between RFIDs and sensor networks. They are enabling technologies for
these devices are energy harvesting and ultra-low-power communications. And the
applications are in the domain of the Internet of Things. And what we are doing on this project
is characterizing the energy availability for these devices. I've worked on energy of light and
motion, and we've obtained insights that, first of its kind study insights that weren't ever
available before. I'm also working on designing and developing the prototypes and the
testbeds for the prototypes. And my other area of work is in designing and developing the
energy harvesting adaptive algorithms for these devices. So just some publications, and I would
first of all thank you for your attention. I would like to acknowledge the support that I had, a
lot of students that have done projects as part of this project, this is here; my two close
collaborators that have worked with me on the mathematical aspects of some of this, as well as
PhD students that work on different aspects of the testbed development. Thank you.
Questions?
>>: So if it takes off, what do you think would be the first application Internet of Things might
[inaudible]. If you had to speculate [inaudible].
>> Maria Gorlatova: Um.
>>: I know it's hard, but>> Maria Gorlatova: Right, right, right. So there's a business push for this in general. Business
really wants this because they are imagining the amount of data that you will get and the
amount of information that you can do with that data. I personally think that logistics is one
where there is greatest business interest. At the same time, I think the true application of
looking for things at your home is, really speaks the people, so that could be something that
you see you soon. One of the interesting, oh, another interesting thing, so I was with Disney
Research for a semester as an intern, and I think toys are a very good area of this as well
because then you have enough, you have a lot to play with. And also a lot of, this is the space
where you can both have the finances and the needs and as well as not be dealing with a lot of
constraints that other fields deal with. So maybe>>: [inaudible] intense cost pressures in toys, though.
>> Maria Gorlatova: True.
>>: You probably know [inaudible], right?
>> Maria Gorlatova: We are hoping in the future will be extraordinarily cheap, but yes. Right,
right, right.
>>: But even in logistics, even the [inaudible] history has had a tough time breaking even.
>> Maria Gorlatova: Right, right, right.
>>: It seems hard. So cost be one of the issues.
>> Maria Gorlatova: True, true, true.
>>: Yeah, exactly.
>>: [inaudible] policy the disruptions that come into this. Do you anticipate a new kind of solar
panel [inaudible] helping out or a new kind of battery that will change the game in here, or>> Maria Gorlatova: You mean the game>>: Of pushing this to the next level.
>> Maria Gorlatova: Right. What I think this is happening right now is if you talk to people who
actually make these devices there is, a lot of work is being done. There are a lot of batteries,
there are a lot of solar cells; if you go to one of their conferences, the amount of stuff they have
going on is absolutely staggering. I think communications is one aspect that isn’t fully resolved.
You really need to drive your communications down. And that is both on the transceiver, the
transmitter side as well as on the networking side. So in terms of solar cells and batteries, they
are becoming available by dozens, flexible solar cells, you can also already buy by now. But on
the communications there's still a lot of work to be done. Yeah. So basically, I think this is right
now at the stage where you have to give people enough, you have to allow people to work with
the budgets that are made possible by the source, essentially. And I don't think we currently do
a good job of that. I can show the slide with, so bear with me. It’s one of the first ones that
demonstrate some of the ideas that industry had about going towards this vision. So industry
wants indoor light energy harvesting. This is a, there's a huge push for that. However, the
harvesters, the energy budgets that you deal with our extraordinarily small still in these
environments. So on top of this, you can't run Zigbee or Bluetooth on it. And this is what think
some people I think don't understand exactly. So you have to be developing other approaches,
and that's I think one of the big possibilities here. Any other questions?
>>: Thank you.