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Advertising in the IoT Era:
Vision and Challenges
Hidayet Aksu∗, Leonardo Babun∗, Mauro Conti†, Gabriele Tolomei†, and A. Selcuk Uluagac∗
∗Department of Electrical and Computer Engineering
Florida International University, Miami, FL, USA
Emails: haksu@fiu.edu;lbabu002@fiu.edu;suluagac@fiu.edu
†Department of Mathematics
University of Padua, Italy
Emails: conti@math.unipd.it;gtolomei@math.unipd.it
Abstract—The Internet of Things (IoT) extends the idea of
interconnecting computers to a plethora of different devices,
collectively referred to as smart devices. These are physical items
– i.e., “things” – such as wearable devices, home appliances, and
vehicles, enriched with computational and networking capabili-
ties. Due to the huge set of devices involved – and therefore, its
pervasiveness – IoT is a great platform to leverage for building
new applications and services or extending existing ones. In this
regard, expanding online advertising into the IoT realm is an
under-investigated yet promising research direction, especially
considering that traditional Internet advertising market is already
worth hundreds of billions of dollars.
In this paper, we first propose the architecture of an IoT
advertising platform inspired by the well-known business ecosys-
tem, which the traditional Internet advertising is based on.
Additionally, we discuss the key challenges to implement such
a platform with a special focus on issues related to architecture,
advertisement content delivery, security, and privacy of the users.
Keywords—IoT advertising, IoT advertising middleware, IoT ad
network, IoT publisher, Internet advertising, Online advertising
I. INTRODUCTION
The Web has gained so much importance in the market
economy during the last two decades because of the develop-
ment of new Internet-based business models. Among those,
online advertising is one of the most successful and prof-
itable. Generally speaking, online advertising – also referred
to as Internet advertising – leverages the Internet to deliver
promotional contents to end users. Already in 2011, revenues
coming from online advertising in the United States alone
surpassed those of cable television, and nearly exceeded those
of broadcast television [1]. Plus, worldwide investment in
Internet advertising have reached around 200 billion dollars
in 2016 [2] and are expected to get to 335 billion by 2020 [3].
Online advertising allows web content creators and service
providers – broadly referred to as publishers – to monetize yet
providing their business for free to end users. For example,
news websites or search engines can operate without charging
users as they get paid by advertisers who compete for buying
dedicated slots on those web pages to display ads [4]–[6].
The global spread of mobile devices has also been changing
the original target of online advertising [7], [8]. This is indeed
moving from showing traditional display advertisements (i.e.,
banners) on desktop computers to the so-called native adver-
tisements impressed within app streams of smartphones and
tablets [9]. More generally, Internet advertising business will
eventually extend to emerging pervasive and ubiquitous inter-
connected smart devices, which are collectively known as the
Internet of Things (IoT).
Enabling computational advertising in the IoT world is an
under-investigated research area; nonetheless, it possibly in-
cludes many interesting opportunities and challenges. Indeed,
IoT advertising would enhance traditional Internet advertising
by taking advantage of three key IoT features [8]: device diver-
sity,high connectivity, and scalability. IoT device diversity will
enable more complex advertising strategies that truly consider
context awareness. For example, a car driver could receive cus-
tomized ads from roadside digital advertisement panels based
on his habits (e.g., preferred stopping locations, hotels, and
restaurants). Furthermore, IoT high connectivity and scalability
will allow advertising to be performed in a really dynamic
environment as new smart devices are constantly joining or
leaving the IoT network. Finally, different from the traditional
web browser-based advertising where a limited number of user
interactions occur during the day, IoT advertising might count
on users interacting with the IoT environment almost 24 hours
a day.
The rest of this paper is organized as follows: Section II
motivates the idea of IoT advertising with a use case scenario.
Section III and IV articulate key background concepts. In Sec-
tion V, we propose our vision of an IoT advertising landscape;
in particular, we characterize the main entities involved as
well as the interactions between them. Section VI outlines the
key challenges to be addressed for successfully enabling IoT
advertising. Finally, we conclude in Section VII.
II. ANEX AM PL E OF A N IOT ADV ERT IS IN G SCENARIO:
IN-CAR ADV ERT IS IN G
Connected smart vehicles are one of the most dominant
trends of the IoT industry: automakers are indeed putting a
lot of effort to equip their vehicles with an increasing set of
computational sensors and devices.
With millions of smart vehicles going around – each one
carrying possibly multiple passengers – automobiles are no
longer just mechanical machines used by people to move from
point A to point B; rather, they are mobile, interconnected,
and complex nodes constituting a dynamic and distributed
computing system. This opens up new opportunities for devel-
opers who can leverage such an environment to build novel
arXiv:1802.04102v1 [cs.CY] 31 Jan 2018
application and services. In particular, smart vehicles – in
fact, passengers traveling on board of those – may become
interesting “targets” for advertisers who want to sponsor their
businesses.
Assume a family of three is traveling in their smart car;
their plan is to drive to a seaside destination a few hours away
from their home and spend the weekend there. To do so, they
rely on the GPS navigation system embedded in their car. Bob
is actually driving the car; he is a forty-five years old medical
doctor and he likes Cuban food. Alice – Bob’s wife – is forty
and an architect. She is really passionate about fashion design
and shopping. Sitting in the back of the car, Charlie – their
son – is a technology-enthusiast teenager who is listening to
his favorite indie rock music from his smartphone. Suppose
there exists a mechanism for profiling passengers traveling
on the same smart vehicle, either explicitly or implicitly. In
other words, we assume the smart car can keep track of each
passenger’s profile. Such a profile needs to be built only from
data which the user agrees to share with the surrounding IoT
environment.
Suppose these travelers are about to cross a city where
an iconic summer music festival takes place. Interestingly, an
emerging rock band is going to perform on stage the same
evening. Festival promoters have already advertised that event
through analog (e.g., newspapers and small billboards) and
digital (e.g., the city’s website) channels. However, they would
also like to take advantage of an IoT ad network to send more
targeted and dynamic sponsored messages, namely to reach
out to possibly interested people who happen to be around,
such as Charlie.
Assume Charlie gets an advertisement on the music app
installed on his smartphone, and he convinces his parents to
stop to attend the concert. Other similar advertising messages
might be delivered to Alice and Bob as well. For example,
Alice could be suggested to visit the city’s shopping mall on
her dedicated portion of the car’s head-up display. Further-
more, the eye-tracking sensors installed in the car could detect
that Bob is getting tired, as he has been driving for too long.
Therefore, Bob might be prompted with the coordinates of the
best local Cuban cafe on the GPS along with a voice message
suggesting to have a coffee there.
We propose an IoT advertising platform that behaves as an
intermediary (i.e., a broker) between advertisers (the festival
promoters), end-users (Alice, Bob, and Charlie), and possibly
publishers, the same way well-known ad networks do in
the context of Internet advertising. Note though that in IoT,
several entities can play the role of “publisher”, which is
not limited to a single web resource provider, but it may
be a composite entity with several IoT devices. As such, the
automaker, as well as any other device embedded in the car or
dynamically linked to it, may act as publisher. Providing the
IoT ad network can gather information from smart vehicles and
passengers traveling around a specific geographic area, that
information can be further matched against a set of candidate
advertisements, which in turn are conveyed to the right target.
Note that triggering of ad requests is somewhat transparent to
the end user, i.e., we do not conjure any explicit publisher-
subscriber mechanism between end users and advertisers. On
the other hand, users must have control over their data, which
in turn may be used by the IoT ad network for targeting.
Figure 1 depicts the scenario above, where Alice, Bob, and
Charlie all receive their targeted advertising messages. The
IoT ad network is responsible for choosing the most relevant
advertisements and it delivers them through one or more IoT
devices that are either embedded in the car (e.g., the head-up
display and the GPS) or temporarily joined to the car (e.g., the
passengers’ smartphones).
Fig. 1. Targeted ads triggered by the IoT environment (e.g., a smart car
traveling close by a smart city) are delivered to end users on IoT devices via
an intermediate IoT ad network.
We claim that IoT represents a huge opportunity for mar-
keters who may want to leverage the IoT ecosystem to increase
their targeted audience. Indeed, although online advertising
is already a multibillion-dollar market, we believe one of its
limitations is that it is essentially based on the activities users
perform on the web. Instead, IoT advertising will overcome
this limitation by bringing advertisement messages to users
interacting with the IoT environment (which is potentially
much larger than the web).
III. HOW INTERNET ADVERTISING WORKS TODAY
The general idea behind Internet advertising is to allow web
content publishers to monetize by reserving some predefined
slots on their web pages to display ads. On the other hand,
advertisers compete for taking those slots and are keen on
paying publishers in exchange for that. Actually, publishers
often rely on third-party entities – called ad networks – which
free them from running their own ad servers; ad networks
decide on behalf of publishers which ads should be placed
in which slots, when, and to whom. Furthermore, advertisers
partner with several ad networks to optimize their return on
investment for their ad campaigns. Finally, ad networks charge
advertisers for serving their ads according to a specific ad
pricing model, e.g., cost per mille impressions (CPM) or cost
per click (CPC), and share a fraction of this revenue with the
publishers where those ads are impressed [8].
At the heart of online advertising, there is a real-time
auction process. This runs within an ad exchange to populate
an ad slot with an ad creative1. For each ad request, there
are multiple competing advertisers bidding for that ad slot.
And, before any ad is served, publishers and advertisers outline
a number of ad serving requirements, such as budget, when
the ad should be displayed as well as targeting information.
1An ad creative is the actual advertisement message (e.g., text and
image) impressed on the slot.
In particular, targeted advertising allows to deliver sponsored
contents that are more likely tailored to each user’s profile,
which is either explicitly collected (e.g., through the set of user
queries submitted to the search engine in the case of sponsored
search) or implicitly derived (e.g., from user’s browsing history
in the case of native advertising) [10], [11]. The auction
process uses all those requirements to match up each ad request
with the “best” ad creative so as to maximize profit for the
publisher.
Figure 2 shows the high-level architecture of current online
advertising systems. Although the actual architecture can be
more complex than the figure, the main entities involved are:
the user who typically sits behind a web browser or a mobile
app; the publisher (i.e., a service provider) who exposes some
“service” to the user (e.g., a web content provider like cnn.com
or a web search engine like Google or Yahoo); the advertiser
who wants to promote its products and possibly attract new
customers by leveraging the user base of the publisher; the
ad network that participates in the ad exchange and acts as
intermediary between the publisher and the advertiser.
Fig. 2. High-level architecture of traditional online advertising.
The workflow is as follows:
•The user accesses a service exposed by the publisher,
e.g., using HTTP GET (1).
•The publisher responses with the “core” con-
tent/service originally requested (2).
•The publisher also asks its partner ad network to fetch
ads which best match user’s profile, and are eventually
shown to the user within the same content delivered
before (3).
•The ad network uses profile information during the
real-time auction which takes place on the ad ex-
change to select advertisements that are expected to
generate the highest revenue (4).
•The ad network instructs the publisher on how to tell
the user how to fetch the selected ad (5–6).
•Finally, the user requests (7) and retrieves (8) the
actual ad to be displayed.
As it turns out from the description above, there is a clear
event which activates an ad request, i.e., the user accessing a
resource exposed by a web publisher. Conversely, in the IoT
world that triggering event might be less explicit (i.e., the user
interacting with IoT devices). Nevertheless, in Section V, we
discuss how the scheme described above can be adapted to the
context of future IoT advertising.
IV. IOT KE Y FEATURES
The IoT stack is normally described as a four-layer infras-
tructure. The first layer defines how the smart physical world
(e.g., networked-enabled devices, devices embedded with sen-
sors) interact with the physical world. The second layer is
in charge of providing the necessary connectivity between
devices and the Internet. Further, a third layer incorporates data
aggregation and other preliminary data processing. Finally,
the fourth layer is in charge of feeding the control centers
and providing IoT cloud-based services [12]. In general, IoT
bounds a cooperative relationship among computing systems,
devices, and users with these layers.
Connectivity: A crucial element in IoT is the high con-
nectivity required among devices, servers, and/or service con-
trol centers. Indeed, high-speed connectivity is necessary in
order to cope with real-time applications and the level of
cooperation expected from IoT devices. Currently, IoT con-
nectivity is guaranteed by traditional network protocols and
technologies like WiFi, Bluetooth Smart, and Device-to-Device
(D2D) communications. IEEE and the IETF are designing new
communications protocols specifically devised for IoT [13].
These protocols (i.e., IEEE 802.15.4e, 6LoWPAN, LoRa) are
intended to homogenize the IoT low-energy communication
environment among the huge IoT device diversity.
Resource availability: This defines the amount of comput-
ing resources available to implement IoT services. In general,
IoT devices can be categorized into two groups: resource-rich,
with faster CPUs and higher memory availability and resource-
limited devices, with limited memory and low-performance
CPUs. Note that the way IoT devices interact with users (e.g.,
display availability, user-input enabled devices, etc.) depends
on the available resources [14].
Power consumption: The nature of IoT applications im-
poses several power constraints on the devices. In general,
IoT devices are meant to be remotely monitored, autonomous,
wearable, and/or with high mobility. These characteristics
define the specific power restrictions for every application.
Complexity and Scalability: Today, IoT devices can be
found in several user-oriented (e.g., smart home, wearables
devices) and industrial (e.g., smart grid, healthcare IoT) ap-
plications. The different IoT architectures need to be scalable
to handle the constant flow of new devices and the always-
increasing set of new services and applications.
V. A VISION FOR AN IOT ADV ERT IS IN G LANDSCAPE
The ultimate aim of IoT is to provide new applications
and services by taking advantage of the IoT features discussed
above. Different from the simplistic approach of utilizing
traditional legacy sensors combined with decision entities,
the high connectivity and intelligence present in IoT along
with the possibility of continuous scalability, allow building a
wide pool of applications based on users’ generated IoT-data.
Among those, expanding the traditional Internet advertising
marketplace is one of the most promising.
To enable the IoT advertisement vision, we introduce our
model of an IoT advertising architecture (Figure 3). Despite
this is clearly inspired by the Internet advertising architecture
(Figure 2), IoT advertising has its own peculiarities, and
therefore, deserves a dedicated infrastructure to be successful.
Our IoT advertising model consists of three layers, each
one composed of several entities: the bottom layer (IoT Physi-
cal Layer) contains physical IoT devices; the middle layer (IoT
Advertising Middleware) coincides with the IoT Advertising
Coordinator, which allows physical IoT devices to interface
with the upper layer (IoT Advertising Ecosystem), and in
particular with the IoT Publisher.
In the remaining of this section, we discuss the role and
Fig. 3. The proposed IoT advertising model consists of three layers: IoT
Physical Layer,IoT Advertising Middleware, and IoT Advertising Ecosystem.
characteristics of each entity separately.
A. IoT Advertiser
This represents an entity which would like to take advan-
tage of IoT to advertise its own products/services such as the
music festival promoters in the use case discussed above. It
is expected to interact with other actors of the advertising
ecosystem in the same way web advertisers do on traditional
Internet advertising. Due to the high diversity of devices
involved, the IoT advertiser needs to conceive and design its
campaign for heterogeneous targets, i.e., newer ad formats,
which are not necessarily visual (e.g., acoustic messages), as
opposed to traditional banners displayed on web browsers or
mobile apps. Moreover, targeting criteria may go beyond just
user’s demographics and/or geolocation; in fact, the contextual
environment will play a crucial role in the ad matching phase.
B. IoT Ad Network and IoT Ad Exchange
The IoT ad network, in combination with the IoT ad
exchange will be responsible for matching the most profitable
ads with target IoT publishers on behalf of both the publisher
and the advertiser. This can be achieved in the same way
as traditional ad networks interact with ad exchanges for
Internet advertising, i.e., through real-time auctions. Moreover,
differently from Internet advertising where those auctions
are triggered by the user requesting a resource from a web
publisher, in IoT such events can be extremely blurry as the
user keeps constantly interacting with her surrounding IoT
environment. That means IoT ad networks and ad exchanges
may need to operate at an even larger scale and higher rate.
C. IoT Publisher
The role of IoT publisher is not limited to a web resource
provider anymore. An IoT publisher can rather be thought
of an ensemble of IoT devices, which collectively cooperate
to implement and expose to the user multiple functionalities,
as well as to deliver advertisements. For instance, the smart
vehicle introduced in our use case is a possible example of
an IoT publisher. The smart vehicle is indeed composed of
several embedded IoT atomic devices (e.g., the GPS, the tire
controller, the sound system), each one implementing its own
communication standard and exposing a specific functionality
through its own user interface. In addition, many other IoT
devices can dynamically and temporarily join the smart vehicle
(e.g., the smartphones of car passengers).
D. IoT Advertising Coordinator
The role of IoT advertising coordinator is twofold: On
the one hand, it allows bottom-layer IoT devices to expose
themselves as a single IoT publisher entity to the upper-layer
advertising ecosystem. On the other hand, it is responsible
for dispatching and delivering advertisements coming from
advertising ecosystem down to physical IoT devices, and in
turn, to the end user. To achieve both those capabilities,
the IoT advertising coordinator makes use of several sub-
components. Among those, we focus on three of them: (i)
IoT Aggregator,(ii) IoT Profiler, and (iii) IoT Ad Dispatcher.
Those are responsible for:
•Unifying different communication standards utilized
in a vast variety of IoT devices, so they can all respond
to the specific advertisement needs.
•Providing a cross-platform that will translate IoT-
customer interaction into usable data for real-time
effective advertisement (i.e., collecting meaningful
metadata or “profiles”, which can, in turn, be exploited
during ad matching at the layer above).
•Managing the actual delivery of advertisements to the
target IoT device, and therefore to the user, according
to specific supported ad formats.
More specifically, the ability of the IoT advertising coordinator
to take advantage of IoT devices and user identification via
digital fingerprinting will open the door to new advertising
strategies. These might consider the following key aspects:
•User profile: IoT advertising will vary based on the
actual recipient (age range, gender, known user behav-
ior) so we can have ads anticipating the user’s needs
not based on what he/she browses, but based on what
he/she is and what he/she does.
•Context awareness: IoT advertising will adapt to new
contexts, that is, the advertisement strategy will also
focus on the location, time, and the type of activity the
user is performing (e.g., a regular traveler can receive
ads based on the most visited restaurants and hotels
during lunchtime).
•Services/Features: IoT advertising ecosystem can
make use of an unlimited number of features to know
more about the user (e.g., most visited locations,
driving mode, behavioral characteristics). That will
translate into a new set of services from the IoT ad-
vertising landscape (e.g., announcing upcoming events
with better price deals, lower car insurance due to the
driver record directly derived from the smart car, etc.).
•Security/Privacy: User security and privacy protection
will impact the new IoT advertising model in two
different ways. First, the coordinator needs to be
transparent to the implementation of traditional (or any
new) IoT security mechanisms. Second, these security
mechanisms will inevitably limit the amount and type
of data that can be extracted from IoT devices and
will scarce the quality of the user’s digital fingerprint.
•Device capabilities: The coordinator may have to
deal with devices supporting a broader spectrum of
advertising formats by themselves (e.g., smartwatches
have full display capabilities and adequate computing
resources). Conversely, other devices would either
accept only custom, resource-friendly ad formats (e.g.,
acoustic messages sent to smart speakers) or rely on
other devices with more capabilities (e.g., the smart
lighting system may use the client application running
on the smartphone to interact with the user). In this
regard, the ad dispatcher will have a crucial role in de-
ciding what specific types of ads to generate/integrate
from/to the different devices and how those ads can
be delivered to the user.
Furthermore, the sensors present in smart devices and
interacting with users will play a major role in profiling what
the user does (e.g., presence sensor can report when the user
leaves the house) and the specific context of such activities
(e.g., Saturday night). These constitute key elements for a more
effective advertising (e.g., restaurants and nightclubs). Even-
tually, to be fully effective in a fast-changing and very limited
power-consuming IoT world, the amount of data required to
characterize users needs to be minimized while coping with
the demand imposed by the proposed IoT advertising model.
In this context, the IoT advertising coordinator will “translate”
data flow from/to IoT devices into a common language and,
more importantly, it will adapt IoT requirements to the well-
known Internet advertising model to enable the new IoT
advertising ecosystem. Finally, timing and geographical dis-
tribution of sensors will influence the effectiveness of the IoT
Advertising Coordinator by (1) effectively using user location
and IoT device availability to deliver the most appropriated ad
(e.g., take advantage of the presence of electronic road signals
to show ads to drivers) and (2) timely deliver the right apps
(e.g., nearby preferred restaurants at lunchtime).
VI. CHALLENGES OF IOT ADVE RTI SI NG
In this section, we analyze the possible key challenges of
IoT advertising.
A. Architectural Challenges
From the IoT advertising perspective, the current IoT
architecture (see Section IV) has several challenges that need
to be addressed. IoT device heterogeneity will add an extra
burden to the IoT advertising coordinator. The coordinator
would need to deal with different memory, CPU, energy, and
sensor availability and capabilities, so the right advertising
strategy is chosen for every device and user while keeping
the required efficiency and reliability of services. Moreover,
IoT can be configured in several different network topologies,
which require the use of different network metrics to charac-
terize the IoT traffic and to successfully identify devices and
users.
B. Ad Content Delivery Challenges
Content delivery in IoT advertising involves three different
scopes: user profile, user location-activity, and device capabil-
ities. Content delivery challenges will defy the capacity of the
IoT devices to cope with the requirements of the proposed IoT
advertising scheme in two main aspects:
1) Quality and quantity of available user data: Different
levels of data obtained from the user will create
user-based digital signatures (i.e., user profile) with
different quality levels. Also, different permission
policies can impact negatively on the quality of users’
activity/location tracking processes.
2) Device capabilities: In cases where IoT device coop-
eration is not possible, the delivery of the advertise-
ment content to the user will be exclusively defined
by the device capacity. For instance, the amount of
advertisement content that the user can get from
devices with visual capabilities is expected to be
higher.
C. Security and Privacy Challenges
Integrating IoT into the traditional advertising model poses
security challenges for customers, advertisers, and publishers.
Some of the security challenges that need to be overcome are
the following:
•Due to the high diversity of devices and communi-
cation protocols in IoT, there exists a perpetual need
for monitoring and detecting new vulnerabilities and
attacks in a constantly changing environment.
•Sensitive user data needs to be protected not only from
outsiders, but also from malicious corporations that
can misuse it.
•Users are not always aware of security risks and a lot
of effort needs to be done on the educational side.
•Current and new communication protocols incorporate
state-of-the-art protection mechanisms, but, in most
cases, security is optional and these protocols are
insecure in default mode.
•The high level of interconnection in the IoT opens
creates more opportunities for malware and worms to
spread over the network.
•Advertisements should not become intrusive for user
privacy nor disrupt the user experience of the sur-
rounding IoT environment.
Traditionally, Internet advertising has compromised user pri-
vacy by tracking people’s browsing habits. IoT advertising
would go further by tracking user behavior based on day-to-
day activities. Here, dataveillance becomes more valuable con-
sidering that IoT user data is much more diverse if compared
with regular web browsing data.
D. Fragmentation of IoT
Currently, there is not a single inter-operable framework
that integrates all IoT devices and services. In fact, despite
the efforts to design dedicated protocols for IoT [13], the
current IoT ecosystem offers several options for developers
to write smart apps using a variety of different programming
architectures (e.g. SmartThings, OpenHAB, and Apple Home
Kit). Also, multiple combinations of standards and protocols
are possible (e.g., Communications: IPv4/IPv6, RPL, 6Low-
PAN, Data: MOTT, CoAP, AMPQ, Websocket; Device Man-
agement: TR-069, OMA-DM; Transport: Wifi, Bluetooth, LP-
WAN, NFC; Device Discovery: Physical Web, mDNS, DNS-
SD; Device Identification: EPC, uCode, URIs). The proposed
IoT advertisement middleware should be able to adapt and
convert the current fragmentation of the IoT world into a
common language to enable IoT advertising.
E. IoT Data Flow
Data flow in IoT highly depends on the programming
architecture. There are few cases where smart apps run on
specific IoT devices or hubs; however, most of the IoT apps
are cloud-based [15]. Smart apps obtain information from the
smart devices (sensors) and send data to the cloud to execute
the app logic. External web programming tools like IFTTT and
Node-RED can also be integrated into the IoT architecture
to connect, control, and request information from different
devices. The integration of these third-party applications can
also represent a challenge to the proposed IoT advertising
model. On the other hand, such integration would simplify
the overhead caused by the current IoT fragmentation.
VII. CONCLUSIONS
Internet advertising market is worth hundreds of billions
of dollars and is one of the fastest growing online businesses.
Nevertheless, it is still restricted to web browser-based and,
more recently, mobile in-app contexts.
The Internet of Things (IoT) will open up a novel, large-
scale, pervasive digital advertising landscape; in other words, a
new IoT advertising marketplace that takes advantage of a huge
collection of smart devices, such as wearables, home appli-
ances, vehicles, and many other connected digital instruments,
which end users constantly interact with in their daily lives.
In this paper, we introduce the architecture of an IoT
advertising platform and its enabling components. We also
discuss possible key challenges to implement such a platform
with a special focus on issues related to advertisement delivery,
security, and privacy of the user.
To the best of our knowledge, this is the first work
defining the IoT advertising and discussing possible enabling
solutions for it. We expect our work will impact both upcoming
researches on this topic, and the development of new products
at scale in the industry.
ACKNOWLEDGMENT
This work is partially supported by the US National
Science Foundation (Awards: NSF-CAREER-CNS-1453647,
1663051). Mauro Conti is supported by a Marie Curie Fel-
lowship funded by the European Commission (agreement
PCIG11-GA-2012-321980). This work is also partially sup-
ported by the EU TagItSmart! Project (agreement H2020-
ICT30-2015-688061), the EU-India REACH Project (agree-
ment ICI+/2014/342-896), by the project CNR-MOST/Taiwan
2016-17 “Verifiable Data Structure Streaming”, the grant n.
2017-166478 (3696) from Cisco University Research Program
Fund and Silicon Valley Community Foundation, and by the
grant ”Scalable IoT Management and Key security aspects in
5G systems” from Intel. The views in this document are of the
authors, not of the funding agencies.
ADDITIONAL NOTE
Authors are listed in alphabetical order and each one of them
equally contributed to this work.
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Hidayet Aksu received his Ph.D. de-
gree from Bilkent University, in Depart-
ment of Computer Engineering. He is
currently a Postdoctoral Associate in ECE
Department of Florida International Uni-
versity. Before that, he conducted research
as visiting scholar at IBM T.J.Watson Re-
search Center, USA, for one year. His
research interests include security for cyber-physical systems,
internet of things, IoT security, security analytics, social net-
works, big data analytics, distributed computing, wireless ad
hoc and sensor networks, and p2p networks.
Leonardo Babun is currently a PhD
student and Research Assistant in the De-
partment of Electrical and Computer En-
gineering at Florida International Univer-
sity, as a member of the Cyber-Physical
Systems Security Lab (CSL). He pre-
viously completed his M.S. in Electri-
cal Engineering from the Department of
Electrical and Computer Engineering at
Florida International University in 2015.
His research interests are focused on Cy-
ber Physical Systems (CPS) and Internet of Things (IoT)
security and privacy.
Mauro Conti is Associate Professor
at the University of Padua, Italy. He ob-
tained his PhD from Sapienza University
of Rome, Italy, in 2009. After his PhD, he
was a Post-Doc Researcher at VU Amster-
dam, The Netherlands. He has been Vis-
iting Researcher at GMU, UCLA, UCI,
FIU, and TU Darmstadt. He has been
awarded with a European Marie Curie
Fellowship, and with a German DAAD
Fellowship. He is Senior Member of the
IEEE.
Gabriele Tolomei is Assistant Pro-
fessor at the University of Padua, Italy.
Before, he was a Research Scientist at Ya-
hoo Research in London, UK. He received
his Ph.D. in Computer Science from Ca’
Foscari University of Venice, Italy in
2011. His research interests are: Web
Search, Machine Learning, and Compu-
tational Advertising. He authored around
30 papers on topmost international journals and conferences,
and 4 US patents. He is PC member of many IEEE and ACM
conferences.
Selcuk Uluagac leads the Cyber-
Physical Systems Security Lab at Florida
International University, focusing on se-
curity and privacy of Internet of Things
and Cyber-Physical Systems. He has a
Ph.D. and M.S. from Georgia Institute
of Technology, and M.S. from Carnegie
Mellon University. In 2015, he received
the US National Science Foundation CA-
REER award and US Air Force Office
of Sponsored Researchs Summer Faculty
Fellowship, and in 2016, Summer Faculty Fellowship from
University of Padova, Italy.
