2016 IEEE First International Conference on Internet-of-Things Design and Implementation
Internet of Mobile Things: Mobility-Driven
Challenges, Designs and Implementations
Klara Nahrstedt, Hongyang Li, Phuong Nguyen, Siting Chang
Long Vu
University of Illinois Urbana-Champaign
{klara, hli52, pvnguye2, schang13}@illinois.edu
IBM T. J. Watson Research Center
lhvu@us.ibm.com
of security infrastructure the mobile device encounters when
moving among different locations, and what private information do service providers have about user using a mobile
device. Hence, when considering IoMT, mobility becomes a
first class object and one has to look at the IoMT separately
from IoT. It is important to note that mobility of devices
such as mobile phones and vehicles has been investigated
for many years [1]–[5], especially the design of individual
devices and their dealings with mobility and usage by users in
mobile environments. But what changes now is the increased
number of sensors per mobile device, the increased density of
mobile devices in users’ environments, and most importantly,
the increased interconnectivity and the increased reliance of
users on mobile devices, making mobile devices and their
interconnectivity an integral part of users’ daily routines and
smart environments.
The goal of this paper is to discuss the IoMT challenges,
and systems and protocols design and implementation, where
mobility impact on interconnected sensors in mobile devices is
the center of consideration. With mobility we get dynamism,
unpredictability, faults, hand-offs, disruptions when sensing,
communicating, analyzing data, and providing energy, security, and privacy-aware mobile services. To achieve the goal,
we will elaborate on IoMT challenges, design and implementation issues with respect to an integrated data cycle, starting
from sensory data collection, continuing with data forwarding
and delivery, to finishing with data analysis. The data cycle
operations will take into account the context issues as well
as the Internet access and connectivity to other cyber-physical
infrastructures. Over the data cycle operations, we will consider the cross-cutting energy, security and privacy properties.
In Fig. 1 we illustrate the interpreted data cycle impacted by
mobility, energy, security, and privacy considerations.
The contributions of the paper are (a) the characterization of
Internet of Mobile Things, their challenges and opportunities
to explore new problem spaces, (b) directions to solve these
challenges based on the related work and our own work, providing selective design methodologies and implementations,
and (c) principles that one needs to consider and adhere to in
order to have successful interactions among mobile things.
The paper is outlined as follows. In Section II, we outline
the mobility challenges and opportunities that we see when
considering mobile sensory data collection, exchange and
analysis, as well as the energy, security and privacy challenges
one has when things are moving. Section III discusses data
collection from mobile devices, the design methodology and
Abstract—Smart environments such as smart grid, smart
transportation, smart buildings are upon us because of major
advances in sensor, communication, cloud and other cyberphysical system technologies. The collective name for interconnected sensors, placed on “things” within fixed cyber-physical
infrastructures, is Internet of Things (IoT). IoT enables cities
and rural areas to become smarter and to offer new digital
services and functions to diverse groups of users. However, IoT
often represents interconnection of static things, which are builtin into the physical infrastructures of users’ homes, offices, roads
and other physical and critical infrastructures. In this paper, we
analyze things that are mobile, and explore the space of Internet
of Mobile Things (IoMT). Mobility of digital devices such as
phones and vehicles has been with us for some time, but as the
number of sensors in mobile devices increases, the density of
mobile devices increases, and users’ reliance on mobile devices
increases, mobile things become very much an integral fabric
of our smart environment. In this paper, our goal is to discuss
challenges, selective designs and implementations of IoMT. We
show the impact of mobility and the care we collectively have to
take when designing the next generation of smart environments
with mobile things in them.
I. I NTRODUCTION
Smart environments such as smart buildings, smart health,
smart grid, and smart transportation are upon us because of
the advances in sensor technologies, their communication, and
their interconnectivity to advanced cyber-physical infrastructures. The interconnected sensors, placed within fixed cyberphysical infrastructures, are collectively named Internet of
Things (IoT). IoT represents interconnected static things such
as smart meters in smart grid, smart sensors in advanced
water systems, RFID and motion sensors in smart buildings, or
traffic cameras at road intersections. But in addition to static
IoT, mobility of things is coming forward as mobile phones
and vehicles are equipped with more and more advanced
sensors. The mobile devices with their sensors are then able to
communicate with each other, with surrounding cyber-physical
infrastructures, and represent the Internet of Mobile Things
(IoMT).
The difference between IoT and IoMT is that when considering mobility of things, major changes occur in terms of
(a) context, e.g., where the mobile device is located, in what
hands it is now, (b) Internet access and connectivity, e.g., if
the mobile device is connected at all, and when connected
to what wireless or wired network, at what bandwidth level,
and with what security, (c) energy availability, e.g., where
can the mobile device charge again, how much energy does
the mobile app need, (d) security and privacy, e.g., what kind
978-1-4673-9948-7/16 $31.00 © 2016 IEEE
DOI 10.1109/IoTDI.2015.41
25
the context of Internet of Mobile Things (IoMT) thus become
highly challenging since: (1) the wireless communication
technology employed by these sensors is unreliable and errorprone, (2) continuous sensing requires a persistent supply of
energy and an extensive amount of data storage. On one hand,
designing a good data collection system requires extensive
knowledge in sensor selection, energy management, sensing
application implementation, and privacy management. On the
other hand, if data collection of mobile things is done properly
and effectively, it provides the fundamental building block for
the success of IoMT.
In the context of IoMT, we face several challenges in
the design and implementation of robust and efficient data
collection systems. These challenges include:
• Selecting the right set of sensors
• Managing energy usage of sensors
• Developing sensing applications
• Preserving privacy of collected data
• Understanding people mobility and context
First, selecting the right set of sensors among all device’s
sensors is critical. If too many sensors are used, device’s
storage may run out shortly and device’s power drains quickly.
In contrast, if few sensors are used, the collected data might
not be sufficient for data analysis. Second, managing the
energy usage of sensors is highly important. In particular,
setting the right scanning period to collect sufficient sensing
data while preserving device’s energy plays a crucial role
in the design of data collection systems for IoMT. Third,
developing robust sensing applications significantly impacts
the quality of collected traces. Since a sensing application
is essentially a software program, it competes for device’s
resources and its performance may be heavily influenced by
other applications on the device. Implementing the sensing
application so that it runs transparently and resiliently to
collect prolonged data traces thus becomes fundamental for
data collection. Fourth, privacy of collected data needs to be
kept once the data collection system starts gathering sensing
traces. How to preserve data privacy while data is processed by
various components in such an universal and open environment
like IoMT is critical. Finally, people mobility and context need
to be well understood in the design of efficient data collection
systems. In Section III, we will discuss the above mentioned
challenges and selective design and implementation issues in
details.
Fig. 1: Illustration of the interpreted data cycle impacted by
mobility, energy, security, and privacy considerations.
implementation insights that we gained from our UIM system
and experiments. Section IV presents data analysis and exchange when mobile data are collected and discusses the usage
of mobile data analytics for user activity, people mobility
patterns and social relation detections. Within Section V, we
concentrate on one specific issue of energy management and
that is the placement of energy sources for IoMT, especially
in case of electric vehicles (EVs), which are our representative
entities of ”Mobile Things”. Security, especially, the real-time
authentication for EVs is discussed in Section VI, and we
conclude in Section VII with lessons learned.
II. C HALLENGES
Within the interpreted data cycle, mobility brings challenges
to the mobile sensory data collection, to the exchange of
data among mobile things and computing platforms, and to
the analysis of sensory data, i.e., what can the analysis help
us with. Cross-cutting concerns across the entire data cycle
are provisioning of energy for mobile things and security and
privacy protection.
A. Mobile Data Collection
B. Mobile Data Analytics
As sensing data are collected from mobile devices, they can
be transferred to a centralized server for storage and analysis.
Different from analyzing data of static sensor networks, the
analysis of data from mobile devices poses a number of
challenges that are centered around the mobility of devices:
• Mobility characterization: How to characterize the mobility of devices?
• Exploiting mobility models: How to leverage the mobility
models of IoMT devices to improve the effectiveness of
data analysis tasks.
Mobile phones and vehicles nowadays come equipped
with advanced sensing and communication capabilities. These
sensors can capture a wide range of information, including
physical, personal, and social contextual information that can
be used in data analysis and data management. However,
how to leverage and manage these sensors efficiently remains challenging since each of these sensors employs a
different technology with distinct tradeoffs in terms of energy
consumption, connectivity, and sensing capability [6]. More
importantly, the collected sensing traces are only useful if they
are clean, complete, and privacy-preserved. Data collection in
26
proper support of accounting and billing protocols, the vehicle
can buy electricity directly from another electric vehicle by
connecting their batteries via a charging cable.
An EV can give energy not only to another EV, but also
to the power grid, which is the so-called Vehicle-to-Grid
(V2G) technology and has been a major research area [9],
[10]. One major advantage of V2G is to smooth the load
by using a collection of EVs’ battery as emergency energy
source orchestrated by an aggregator [11], [12]. Essentially,
EVs can be viewed as mobile energy sources and part of the
IoMT ecosystem. They can be used to compliment the fixed
energy source placement design as we discuss in section V. In
the future there might be other devices in addition to electric
vehicles that can act as mobile energy sources, and we believe
the current research in V2G can bring valuable lessons to the
general energy exchange problem in IoMT.
We will study the challenges and selective
design/implementation issues in Section IV. Here, we
focus on summary of challenging issues for mobile data
analysis.
The challenges with mobility characterization include defining the right metrics (i.e., representation of mobility) and
analyzing the collected traces to characterize the mobility by
those metrics. This is non-trivial because the collected data
might be noisy and incomplete, and sometimes, lack important
context information, such as location (e.g., because location
sensor is turned off to save energy, or the device is indoor).
In addition, the characteristics should be able to capture the
realistic behaviors of people movements, which exhibit a high
degree of repetition [1].
With the mobility patterns learned from characterization,
the challenge becomes how to leverage those patterns to
improve data analysis tasks. This requires the ability to draw
the connection between the objective of each analysis task
with different mobility metrics, so that the appropriate metrics
are chosen as part of the analytical model for that task. For
example, for the task of sensor selection, to maximize the
sensing coverage, we would be interested in the mobility
metrics that represent group mobility. That is because devices
in the same group tend to produce highly overlapped data,
and thus low collective sensing coverage. In another example,
for the task of data forwarding in Delay Tolerance Networks,
we would be interested in the metrics that capture contact and
location regularity, since the regular contact between devices
at the same location is important to design an efficient data
forwarding protocol [7] [8].
D. Security and Privacy
As an essential feature of IoMT, the devices may move
and change their location. The device mobility brings unique
challenges to security and privacy for IoMT compared to
conventional IoT scenarios, including
• Recognizing and authenticating new devices.
• Adapting to different contexts and environments.
• Preserving location privacy.
Let us consider a typical IoT scenario of smart home appliances (TV, air-conditioner, thermometer, etc.). The smart
appliances generally do not move and obtain fixed location.
The appliance authentication needs to be configured only
once during the initial setup. From a networking point of
view, the smart home appliances constitute a static wireless
network with little node churns. Now let us compare this to
an IoMT scenario, where a user drives a smart vehicle on
the road that communicates wirelessly with other vehicles and
roadside units for collision avoidance, route suggestion, etc.
The vehicle needs to constantly authenticate other vehicles
as they meet on the road, which requires efficient real-time
authentication as opposed to one-time initial configuration.
As the vehicle moves to different areas, the environmental
context may vary, e.g., wireless interference may occur when
there are many other vehicles nearby communicating at the
same time. The communication protocol thus needs to adapt
to such changes in the context, whereas in the smart home
appliance scenario the context remains mostly unchanged.
The mobility of vehicles also brings location privacy into the
question. The communication and authentication protocol must
preserve the driver’s location privacy, e.g., PKI authentication
with vehicle’s long-term public key will allow anyone to infer
the trajectory of the vehicle by tracing the usage of the public
key.
C. Energy Management
Energy management for mobile devices is a critical issue
in order to accommodate the large amount of mobile things
as well as the various types of mobile things. Compared to
conventional energy management strategies, energy management for mobile things such as phones and electric vehicles
has several distinct features including
• Energy source placement
• Energy exchange
• Cross-device energy management and monitoring
We will study the first feature in more detail in Section V.
Section III includes an extensive study on energy management
issues in mobile phones when performing data collection,
therefore, in the rest of this section, we will focus on discussing the energy exchange challenges.
One critical challenge of energy management is to allow
direct energy exchange between different devices of different
users. Today it is commonly seen in airports that one charges
their smartphone via a USB cable connected to their laptop. As
both devices belong to the same user, there is no accounting or
billing issues involved. However, with each user having access
to multiple IoMT devices with different battery storage, it is
likely that one sells energy directly to another in a device-todevice manner. Imagine the case where an electric vehicle runs
out of battery and there is no charging station nearby. With
III. M OBILE DATA C OLLECTION
In this section, we first discuss the design methodology in
the implementation of data collection systems. We then present
our implementation of a data collection system named UIM
27
that collected movement traces from cell phone users for six
months at the University of Illinois campus.
Wifi
Scanner
Bluetooth
Scanner
A. Design Methodology
Local storage
of Collected
Trace
How to leverage a variety of sensors to collect high quality
sensing traces remains highly difficult to achieve. A collected
trace is only useful if knowledge or patterns can be extracted
from it. That is, the trace must be collected for an extended
period of time so that data analysts can find and explain
patterns derived from it. In this section, we present the design
methodology of data collection systems as follows:
a) Sensor Selection: Selecting right sensors for the sensing task is the most critical factor [2], [13], [14]. A good
sensing system should include sensors that complement each
other in terms of collected data forms and formats. For example, a scanning system on the phone may not include a WiFi
scanner and a GPS coordinate scanner since they both only
provide location information. Further, we need to understand
the tradeoff between the quality of the sensing traces one
sensor collects and the amount of energy it consumes. Some
sensors capture high quality sensing data but might consume
too much energy for a prolonged sensing task. As a result,
we may have to use sensors that provide less quality data in
order to capture longer sensing traces. Although vehicles might
be equipped with large storage, sensor selection still impacts
the use of storage space. For example, continuous use of the
camera to record video and photos on the road may fill up the
vehicle’s storage space quickly.
b) Energy Management: Once sensors are selected, the
next step is to decide how frequently each sensor collects its
sensing data [15]. This is crucial since it directly impacts (1)
the quality of collected traces and (2) energy usage of the
devices. A typical mobile phone without sensing applications
may need to be recharged every two or three days. In order
to obtain prolonged non-broken traces, phone carriers must
remember to recharge their phones. We have learned from our
real deployment, if a phone carrier does not use the phone
as her daily phone, she would likely forget to recharge it.
To make the phones usable for carriers, we need to ensure
that sensing applications do not unreasonably drain the phones
with short scanning periods. On the other hand, if we set
too long scanning periods, the collected sensing data may
not provide the needed granularity of information to extract
adequate resource usage or people movement. At the first
glance, energy consumption of sensors is not an issue with
vehicles. However, since a large number of sensors is running
continuously, a well-defined energy management scheme for
sensors can significantly save cars power and gas.
c) Sensing Software Development: There are two challenges in developing sensing software on phones and vehicles.
First, collecting sensing data on mobile phones and vehicles
is a “best-effort” task and we always have to be prepared for
the worst case scenario, i.e., implemented sensing applications
may fail due to unanticipated reasons. A sensing application
essentially is a software, which coexists and competes with
other applications on the phones or vehicles for resources, and
A Google
Android
Phone
Status
Reporter
Database
Server
HTTP connection
over Wifi networks
Fig. 2: UIM data collection system
thus it may crash or halt at anytime. If we bundle all sensing
applications of all sensors into one single sensing application,
and one of sensing components crashes or one sensor fails,
then it is likely that sensing applications fail altogether. If this
happens, no sensing data is collected. So, sensing applications
should be decoupled. Second, cell phone users or drivers install
many applications on their phones and vehicles. For example,
gaming and entertaining applications are favorites on phones
and cars. A well-designed sensing application should incur
little interference on other applications and should not interrupt
the usage of users. In other words, a sensing application
must: (1) start by itself whenever the device reboots for
any reasons (robustness) (i.e., a phone may reboot or the
dashboard in a car may reboot after a software update), (2)
run in the background and not display messages on the graphic
user interface (transparency), (3) keep running even if other
applications halt or crash (resilience).
d) Privacy: In the context of IoMT, privacy issue may
arise because of the widespread use of mobile phones and
vehicles since identities and locations of mobile phones and
vehicles are associated with their human owners. Phones and
vehicles become entities to uniquely identify their owners and
their locations. As a result, identity theft becomes a major issue
and identity mismatch may cause significant consequences.
e) Mobility: The mobility patterns and characteristics
of cell phone users or vehicles can significantly impact data
collection. For example, a shorter scanning period can be set to
collect sensing traces if the vehicles move faster. Meanwhile,
a longer scanning period can be set if the phone user performs
stationary activities [16]. More importantly, since the mobility
of cell phone users and vehicles is usually not known in
advance, data collection in the context of IoMT is very opportunistic. That is, regardless how the sensors are selected and
energy is managed, the amount of collected data depends fully
on people mobility. So, the knowledge of people movement
behavior can be useful in the design of data collection systems.
In the next section, we present an implementation of a
mobile data collection system on Google Android phones.
B. Implementation of Data Collection System
In this section, we present our implementation of a data
collection system on Google Android phones named UIM,
which stands for University of Illinois Movement. UIM addresses several challenges presented in previous section. As
discussed above, the first step is to choose the sensors and we
28
triggers the booter, which starts the Bluetooth inquirer and
the Bluetooth receiver. The inquirer and receiver work in an
asynchronous fashion in which the inquirer uses a request
timer to periodically (i.e., every 60(s)) issue a Bluetooth
scanning request to the phone OS. After sending the request,
the inquirer makes the phone discoverable by other experiment
phones (so that experiment phones can scan each other), goes
to sleep, and wakes up for the next request when the request
timer expires. The receiver, on the other hand, sleeps and is
only waken up whenever a Bluetooth scan is returned by the
phone OS and ready for collection. Upon receiving a Bluetooth
scan that includes a set of MACs of Bluetooth-enabled devices
in proximity of the experiment phone, the receiver writes the
Bluetooth scan and a timestamp to a log file, and then goes
to sleep. To conserve phone energy, the inquirer is configured
to only issue scanning requests from 7AM of a day to 1AM
of the next day. As a result, we can collect most of people
movement while saving phone energy.
3) Collected Sensing Traces: Table I summarizes major
statistics of the sensing traces collected by the UIM system.
Specifically, from March 2010 to August 2010, we conducted
three rounds of experiments with 123 participants at the
University of Illinois campus. Our participants included grads,
undergrads, faculties, and staffs. The first experiment lasted
19 days, the second was 38 days, and the third was 85 days.
The number of scanned WiFi MACs and Bluetooth MACs of
the third experiment were fewer than the second experiment
(although the third experiment was much longer) since the
third experiment was conducted during the summer break with
fewer classes and students on campus. Our traces were the
most detailed traces collected in the university campus [20].
choose to implement a WiFi scanner and a Bluetooth scanner,
since WiFi scans can be used to infer location while Bluetooth
scans can be used to infer social contact. These two pieces
of information can be used to understand people movement
behavior. Due to space limitation, we present the overview of
our system here, detailed discussions and results can be found
in our previous papers [17]–[20]. Figure 2 shows our system
architecture, which has a WiFi scanner, a Bluetooth scanner, a
database server for sensing data storage, and a Status Reporter
for sensing status update. In following sections, we present the
WiFi scanner and the Bluetooth scanner in detail.
1) WiFi scanner: The WiFi scanner has three decoupled
components: a booter, a WiFi inquirer, and a WiFi receiver.
Each component runs as a separate process and interacts with
each other via the message passing mechanism within the
Google Android phone operating system (OS). WiFi scanner
runs as a background service, anytime the phone restarts, the
phone OS triggers the booter, which starts the WiFi inquirer
and the WiFi receiver. This design achieves robustness since
anytime the phone reboots, the WiFi scanner can start its
scanning work automatically. The inquirer and the receiver
work in an asynchronous fashion in which the inquirer uses
a request timer to periodically (i.e., every 30 minutes) issue a
WiFi scanning request to the phone OS. After sending the
request, the inquirer goes to sleep, and wakes up for the
next request when the request timer expires. On the other
hand, the receiver always sleeps and is only waken up by
the phone OS whenever the WiFi scans are available for
collection. Upon receiving a WiFi scan that includes a set of
MACs of WiFi access points in proximity of the experiment
phone, the receiver writes the WiFi scan and a timestamp to
a log file, and then goes to sleep. Our design also allows the
receiver to opportunistically receive WiFi scans, which result
from other usages of WiFi connectivity, since each time the
WiFi connection is initiated, a WiFi scan is performed by the
phone OS. Note that keeping WiFi connection up and issuing
WiFi scanning requests is much more energy-consuming than
receiving WiFi scans. In order to conserve phone battery, we
configure the WiFi inquirer so that it only issues scanning
requests from 7AM of a day to 1AM of the next day. As a
result, we can collect most of people movement while saving
phone energy.
There are two reasons the WiFi scanning period is set
to 30(min). First, our scanning system was deployed at a
university campus where people usually stay in one location
inside buildings for a long period (e.g., a class session is
usually 50 minutes). Second, a higher WiFi scanning period
may drain the phones quickly and make them unusable as
daily phones for phone carriers.
2) Bluetooth scanner: The Bluetooth scanner has three
decoupled components: a booter, a Bluetooth inquirer, and
a Bluetooth receiver. Each component runs as a separate
process and interacts with each other via the message passing
mechanism within the Google Android phone OS. Similar to
the WiFi scanner, the Bluetooth scanner is implemented as a
background service. When the phone restarts, the phone OS
Overall Characteristics
Number of phones (participants)
28
Length of experiment (day)
19
Bluetooth Scanning Period (s)
60
WiFi Scanning Period (m)
30
Number of Scanned BT MACs
8508
Number of Scanned WiFi MACs 7004
79
38
60
20
17080
29324
16
85
60
30
7360
6822
TABLE I: Overall characteristics of our collected traces
4) Impact of Energy Usage on Amount of Collected Data:
In this section, we investigate the impact of energy usage on
data collection. Specifically, we study changes in the number
of collected WiFi MACs when we vary the scanning period (or
scanning frequency) of the WiFi scanner. We have a participant
carry the phone with an implemented WiFi scanner for a
week (or seven days) and the scanning frequency is set to
5 minutes. After one week, we receive the dataset D5 of
the 5 minute scanning frequency. This is the largest dataset
and then we create smaller datasets with longer “artificial”
scanning frequencies of 15, 30, 60, 120 minutes from D5 .
For example, the dataset D15 of the scanning frequency of 15
minutes is created by taking the ith , (i + 3)th , and (i + 6)th ,
and so on scanning records of D5 , since records in D15 is
29
100
Wifi scan frequency = 120(m)
Wifi scan frequency = 60(m)
Wifi scan frequency = 30(m)
Wifi scan frequency = 15(m)
Wifi scan frequency = 5(m)
15
Correct Location Assignement (%)
Ratio of Unique Scanned Wifi MACs
20
10
5
2
3
4
Day Index
5
6
90
85
80
75
70
65
User 1
User 2
User 3
User 4
60
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0
1
95
7
Similarity threshold
Fig. 3: Impact of Scanning Period on Collected Data
Fig. 4: Results on inferring location by clustering WiFi records
15 minutes apart while records in D5 is five minutes apart.
The dataset D120 is the smallest dataset since it is derived
from the longest frequency. We use D120 as the baseline and
k
for each day (of seven days), we calculate the ratios of DD300
,
with k ∈ {5, 15, 30, 60, 120} and plot these ratios in Figure 3.
The x-axis of the figure is the index of day in the one week
k
experiment and the y-axis is the ratio DD300
. This figure shows a
clear tradeoff between the scanning frequency and the amount
of collected sensing data. A shorter scanning period will
collect bigger traces. In other words, if more energy is used for
scanning, we will collect larger traces. So, the scanning period
should be set carefully so that phones can perform prolonged
experiment while collecting acceptable traces.
interactions and contact patterns, which can be a key factor
in social science studies and the design of efficient message
forwarding schemes for mobile networks [7]. Given the stay
duration at locations, we can estimate the arrival and departure
time at locations, which is essential for many applications such
as traffic monitoring, social network analysis, urban planning.
Characteristics of contact group formed by co-located people,
if available, could be used to enrich collected sensing data
(e.g., inferring missing contacts), or infer new knowledge for
sensor selection.
2) Implementation of Mobility Characterization on UIM
Traces: In this section, we first present our implementation
on acquiring location information from non-spatial data, and
then, describe our findings on mobility characterization for
in-door environments.
In terms of location profiling, from the collected traces
of Bluetooth and WiFi scans, we infer location information
by clustering the WiFi records (i.e., records of WiFi access
point SSID detected by mobile devices overtime) [17], [18].
Specifically, we define location as a unique set of WiFi access
points, or a WiFi record. Since the WiFi scan results from
different devices are not always consistent, even at the same
location, we first construct a similarity graph of WiFi scan
records (the more overlap between two records, the more
similar they are) and then perform clustering over the records.
Each cluster in the final result represents a physical location.
Figure 4 presents the accuracy of our results on inferring
locations by clustering WiFi scan records for different users
using different similarity thresholds.
In terms of mobility characterization, by analyzing collected
traces of Bluetooth contacts and aggregating them over time,
we are able to identify distinctive contact patterns of users
[14]. Figure 5a shows the first contact pattern in which people
usually have a considerably higher number of contacts during
the weekdays than the weekends. This is the most common
contact pattern found in our sensing traces since most people
perform the casual routines at work for the weekdays when
they make contacts with many more people. In contrast,
Figure 5b shows an opposite (and less popular) contact pattern
in which people make more contacts during the weekends than
the weekdays.
In terms of stay duration and location regularity, our analysis
IV. M OBILE DATA A NALYSIS
In this section, we present our design methodology and implementations of mobility characterization, and our approach
to leverage mobility to improve mobile data analysis tasks.
A. Mobility Characterization
1) Design Methodology: The main challenges in mobility
characterization are to define the right metrics and to analyze
the collected traces towards mobility characterization by those
metrics.
In terms of the metrics, we focus on the contextual information that help define the movement of mobile device users,
including temporal, spatial, and social context. By capturing
those contexts, we will be able to answer questions about the
mobility patterns of mobile devices: Where does the device
move over time?, What are the most regular visit locations?,
or Which devices it interacts with most frequently? Among
those information, location is the required information that a
characterization study must obtain, since knowledge of where
people visit is fundamental to obtain movement patterns. In
case location information is not available (e.g., GPS sensor
is turned off to save energy, or when the device is indoor),
we need to infer the locations of devices based on other nonspatial information, or location profiling.
In terms of mobility characterization, we believe a good
characterization study should be able to capture contact patterns, stay duration at a location, and grouping behavior of
people movement. Knowledge of who a person meets at a
certain time (i.e., contact) allows us to understand the social
30
70
60
60
40
30
20
50
40
30
20
10
0
0
TUE
WED THU
FRI
Day of Week
SAT
MON
TUE
WED THU
FRI
Day of Week
SAT
Number of Participants
20
15
10
5
0
5
6
Fig. 6: Location regularity by different frequency thresholds.
[14] helps answer the question: Do people visit locations
regularly for their daily activities? A location is regular if the
number of people at that location during a time period exceeds
a certain regularity threshold. Figure 6 shows the regularity of
locations with time period of 6 hours and different regularity
threshold. The results show that most people have at least two
regular locations. This is consistent with the results of previous
work, which have shown that people spend most of their time
at a few places, such as their home and work locations [1] [3].
For contact group behavior, our results (Figure 7) show
that people rarely form large contact groups during their daily
activities (i.e., 90% of groups have the size of 6 or smaller).
This should take into account that the collected data [20] are
from a university campus. Algorithms in multi-casting, content
distribution, or DTNs could benefit greatly from these results.
B. Exploiting Mobility Models
1) Design Methodology: The results of mobility characterization help us improve the performance of various data
100
90
CDF of Cluster size (%)
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60
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CDF of Cluster size
0
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Time Interval (minute)
120
(b) Impact of time
interval.
analysis tasks.
First, to improve the completeness of collected data, we
leverage the contact patterns and group mobility behaviors to
recover the contacts that have been missing from the collected
traces [14] (i.e., due to sensing errors or limited number of
sensing devices).
Second, with more complete traces and the understanding
of people mobility, we are able to predict people’s future
movements [17], [18].
Third, our understanding of mobility patterns helps us to
improve sensor selection to maximize the sensing coverage
[21], and improve message delivery in mobile peer-to-peer
networks [8].
2) Implementation: Usage of Mobility Models:
a) Predicting missing contacts: To improve the completeness of collected traces, we leverage contact patterns,
location information of devices, and the observed contacts to
build binary classifiers (using Support Vector Machine (SVM))
and to classify whether a contact between two devices exists
or not. The results (Figure 8) show that the model that uses
mobility patterns (i.e., SVM-CL) outperforms the model that
only uses the contact statistics (i.e., SVM-C) for different
sensing intervals and different number of sensing devices.
b) Predictive models of future movement: We exploit the
regular patterns of people movement learned from mobility
characterization to predict the future movements [17], [18].
Particularly, we train three supervised machine learning based
predictive models using Naive Bayesian technique, including
location predictor, stay duration predictor, and contact predictor. We then evaluate these predictors with three different
datasets. The experiment results (Figure 9) show that our
predictors perform well and provide accurate prediction on
location (i.e., Figure 9a), stay duration (i.e., Figure 9b), and
social contacts (i.e., Figure 9c).
c) Sensing devices selection: From the insights of the
contact group and location popularity characteristics, we implement a context-aware sensing devices selections [21] to
maximize collective sensing coverage in opportunistic mobile
social networks. Our results (Figure 10) show that, by leveraging spatio-temporal contexts from the observed mobile data
traces, our approach (denoted as HCONTEXT) is able to assign the sensing tasks to the group of devices to achieve better
collective sensing coverage, compared with other optimization
approaches (i.e., GREEDY for greedy coverage selection, and
RANDOM for random selection of sensing devices). Details
25
2
3
4
Number of Regular Locations
40
Fig. 8: Predicting missing contacts.
support threshold=0.6, timeslot=2(h)
support threshold=0.6, timeslot=4(h)
support threshold=0.6, timeslot=6(h)
support threshold=0.6, timeslot=8(h)
1
60
20
(a) Impact of number of
sensors.
Fig. 5: Distinctive contact patterns.
0
40
10
30
Percentage of Sensing Nodes (%)
SUN
(a) Fewer contacts at the week- (b) More contacts at the weekend.
end.
30
60
0
SUN
SVM-C
SVM-CL
80
20
10
MON
100
SVM-C
SVM-CL
80
Prediction Accuracy (%)
50
100
Prediction Accuracy (%)
Number of Distinct Contacts
Number of Distinct Contacts
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20
Cluster size
Fig. 7: Contact groups.
31
k=1
k=2
k=3
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20
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40
20
Duration Prediction
0
10 20 30 40 50 60 70 80 90 100
Location Prediction Accuracy (%)
(a) Location prediction.
CDF - Percentage of Nodes (%)
CDF - Percentage of Nodes (%)
CDF - Percentage of Nodes (%)
100
10 20 30 40 50 60 70 80 90 100
Duration Prediction Accuracy (%)
100
80
k=1
k=3
k=5
k=7
60
40
20
0
10 20 30 40 50 60 70 80 90 100
Contact Prediction Accuracy (%)
(b) Location stay duration prediction.
(c) Contact prediction.
Fig. 9: Future movements prediction results.
V. E NERGY M ANAGEMENT
1
Since IoMT devices are powered by electricity, the first
challenge of energy management for IoMT is to deliver energy
to the device. One crucial problem of energy delivery is the
energy source placement. For example, today users might
experience difficulty in finding energy sources in an airport to
charge their smartphones. As users use more and more IoMT
devices that need charging, the placement of energy sources
has increased impact on the usability of mobile devices.
The most notable mobile devices that can be affected by
the placement of energy sources are electric vehicles (EVs).
Compared to conventional combustion engine vehicles, electric vehicles have much shorter range, and typically require
daily charging. In this section, we focus on discussing the
energy source placement issues for electric vehicles.
Coverage
0.8
0.6
0.4
0.2
RANDOM
GREEDY
HCONTEXT
0
434000
438000
442000
446000
Starting time
Fig. 10: Sensing coverage comparison
1
Message Overhead
AVG Number of Messages
AVG Successful Delivery Ratio
20
0.8
0.6
0.4
0.2
0
A. Charging Facilities
15
Charging station is one type of widely adopted charging
facilities for electric vehicles. Charging stations charge electric
vehicles similarly to gas stations in the way that vehicles come
into the stations to get served and stay for a certain amount
of time to get their batteries charged to a satisfactory level.
However, major concerns about charging stations are electrocution and charging stations becoming frozen on vehicles in
extreme weather [24]. This facilitates the development of static
and dynamic wireless charging pads. And the placement of
charging stations and wireless charging pads has an important
impact on both the driver’s convenience and the traffic flow.
Since the wireless charging pads are not widely deployed
yet, despite the major concerns of charging stations, charging
stations are the most widely adopted charging facilities on the
road network for electric vehicles. The charging stations must
be placed not too far away from each other to ensure good
coverage, and not too close to each other to avoid causing
traffic congestion in case many electric vehicles choose to
charge in the same area. The charging stations are placed
normally at intersections of road networks or points of interests
in cities. These locations are typically modeled as nodes of a
network graph. When planning charging stations, the design
issues and considerations are
10
5
0
3R
Prophet
Epidemic
(a) Average Successful Delivery Ratio
3R
Prophet
Epidemic
(b) Average Message
Overhead
Fig. 11: Comparison of 3R with Epidemic and Prophet routing
protocols.
of our approach can be found in [21].
d) Content Distribution: Mobility characteristics and
models, learned from characterization studies, can be used to
improve message delivery in mobile peer-to-peer networks [8]
[22] [7] [23]. Specifically, we first analyze UIM traces and then
design an efficient routing protocol named 3R that exploits
regular contact patterns found in the mobility traces for
message delivery. Figure 11 compares 3R and two other state
of the art message forwarding protocols, named Prophet [7]
and Epidemic [22]. Our evaluation shows that 3R outperforms
Prophet in successful message delivery ratio while minimizing
message overhead. Details of our protocol design can be found
in [8].
•
32
Determining the locations for placing charging stations.
•
•
Determining the number of charging servers at each
charging station. Note that charging servers are the access
handles for electric vehicles to connect to the energy
source. Charging servers work similarly to the refueling
pumps at gas stations in the way that each electric vehicle
needs to obtain a charging server to get charged. And
each charging station could have several charging servers
in order to minimize the waiting time of electric vehicles
by charging multiple electric vehicles at the same time.
The workload of charging stations also needs to be taken
consideration and a load balancing strategy is needed in
order to balance the energy load across the power grid.
TABLE II: Maximum flow charged under various situations
# pad
3
2
1
0
•
•
•
# station
0
1
2
3
Configuration
L4, L5, L10
L4, L5, N1
N1, N2, L5
N1, N2, N4
Volume (veh/hr)
3515.1
2309.1
2726.9
1692.2
Determining which road links to equip with the dynamic
wireless charging pads.
Deciding how many charging pads should be placed on
a certain road link.
Determining when to turn on and off the charging pads.
B. Wireless Charging
C. Implementation of Energy Source Placement Model
As described in the previous subsection, wireless charging
pads are another type of charging facilities for electric vehicles. Electric vehicles can get charged by parking over a static
wireless charging pad. They can also get charged dynamically
by driving over the wireless charging pads. Dynamic charging,
using wireless charging pads, has been studied in recent
years [25]–[29]. In dynamic charging of electric vehicles, the
magnetic induction between the charging pads installed under
the road and the receiving coils attached to the EV’s battery
automatically charges the EV as it moves over the charging
pads. Each charging pad is typically short (e.g., 30-50 cm),
and a charging section of several kilometers consists of a
series of charging pads placed close to each other (e.g., 50 cm
away). The advances of dynamic charging allow EV’s battery
to be charged while moving over wireless charging pads. But
the advances of dynamic charging also complicate the energy
source placement problem.
The placement of charging pads depends on the models of
electric vehicles. Different models share various features such
as battery capacity, max charging rate and miles of charging
per hour. And these features lead to different maximum driving
distances, required charging times, etc. It also heavily depends
on the traffic flow densities on the road network. Traffic
flow density is defined as the number of electric vehicles per
unit length of the road link. Placing charging pads along the
road links that have heavy traffic flows helps to maximize
the amount of electric vehicles traveling on the roads. We
also need to consider the impact of the traffic flow patterns.
For example, for traffic flows during peak hours, turning the
charging pads on during the peak hours while keeping them
off for the rest of the day may help saving energy compared
to turning the pads on for the whole day.
The placement of static wireless charging pads is similar
to the placement of charging stations which is discussed in
Section V-A. Static wireless charging pads are normally placed
at points of interests and each service area (i.e. charging spot)
includes several charging pads to support charging multiple
electric vehicles at the same time. However, dynamic wireless
charging pads are placed under the roadbed and require that
EVs travel a certain distance to get charged. The design issues
for dynamic wireless charging pads include
In this subsection we describe our initial design and simulation towards solving the source placement for charging stations
and wireless dynamic charging pads for electric vehicles.
The design in [30] focuses on placement of both charging
stations and charging pads. Several assumptions are made to
simplify the implementation:
•
•
•
We assume that charging pads are always turned on.
Every road link is treated as one entity and is not further
segmented into smaller sections. It means that a road link
gets charging pads or it gets none.
We assume that after traveling on the road links which
have charging pads, vehicles will be charged to full
battery status.1
When considering allocating charging stations, we adopt the
Flow Refueling Location Model (FRLM) proposed in [31].
FRLM is a flow-based location-allocation model which aims
at finding optimal locations for refueling stations.
The placement of energy charging stations and wireless pads
for electric vehicles problem is formulated as an optimization
problem and we simulate the model on a 9-node center-formed
sample network as depicted in Figure 12 of which the feature
of traffic flows is similar to the city Berlin in Germany. It
means that the center node/district attracts and generates the
largest amount of traffic flows that travel between the center
node and its surrounding nodes.
We test our allocation model given different charging facilities and the evaluation results are shown in Table II. For
example, when allocating 2 charging stations and 1 road link
with charging pad, the maximum amount of traffic flows we
are able to charge is 2309.1 (veh/hr). The evaluation results
indicate that placing charging pads on 3 road links doubles the
amount of vehicles being charged in comparison to placing 3
charging stations only. The bold lines in Figure 12 indicate the
selected links for placing charging pads. The selected links are
the links that have the top amount of traffic flows of the road
network and assigning charging pads to these links helps to
satisfy the charging needs of these traffic flows first.
1 This assumption requires very long road links, which is not very realistic.
In our future work we will investigate pratial charging with energy source
placement.
33
stores to receive price information, share multimedia content
with other vehicles, or communicate with pedestrians carrying
IoMT-enabled devices for collision avoidance. Apparently vehicles, pedestrians, and streetside stores have different mobility
patterns, and an authentication framework for IoMT is needed
that is able to authenticate different types of devices with
different mobility patterns.
Let us consider an example from vehicule-to-vehicle (V2V)
communication, where vehicles periodically broadcast their
own speed and location to help avoid collision with other
vehicles. When a vehicle receives a message containing the
speed and location information, it must verify that the message
is indeed generated by another vehicle nearby, instead of a
fake message generated by an attacker trying to mislead the
driver into changing its speed. In this V2V authentication
scenario, the challenge is that a vehicle needs to constantly
authenticate new vehicles, and depending on the speed of the
traffic flow, the contact time with a new vehicle is small and
the authentication must be completed within milliseconds.
Another challenge for authentication in IoMT scenario is
that the mobile device may need to authenticate with other
mobile devices very frequently. Recall the dynamic charging
scenario for electric vehicles (EVs) introduced in Section V-A.
When the EV is moving at high speed (e.g., 70 mph), it
encounters and authenticates with a new charging pad around
every 20 milliseconds, and the authentication must complete
within the first few of the 20 milliseconds so that the rest
can be used to charge the EV’s battery. This makes it very
challenging to design the authentication protocol.
Fig. 12: Sample network with 9 nodes, where nodes (e.g.,
N 1, N 2) represent road intersections, and lines (e.g., L1, L2)
represent road links. Bold lines indicate that 3 links are
selected to place dynamic wireless charging pads.
VI. S ECURITY
AND
P RIVACY
In Section II-D, we have briefly discussed the security and
privacy challenges brought by the concept of IoMT. In this
section we use authentication as an example, discuss various
challenges of authentication for IoMT devices, and review a
recent authentication protocol for dynamic charging of electric
vehicles to illustrate the impact of mobility on protocol design
and implementation.
B. Design of Portunes+ Authentication Protocol
We have designed a solution for the dynamic charging
authentication problem [38]. Our authentication design, called
Portunes, adopts a key-predistribution approach where session
keys are generated and pre-distributed to charging pads prior
to the actual authentication. We have further improved our
design and proposed Portunes+ by including an implicit authentication protocol that allows the charging pads to share
authentication results with each other and effectively reduce
the required authentication frequency [39].
Portunes+ involves three different entities: the Charging
Service Provider (CSP) that the EV subscribes to, the Pad
Owner (PO) that operates the dynamic charging section, and
the EV. The intuition behind dividing the authentication into
two phases also comes from an observation of the mobility
pattern of vehicles: statistics have shown that most vehicles
(except for trucks) are parked during the night (e.g., between
1 am and 6 am). From the authentication protocol’s point of
view, when the EV is parked, it is idle in that it will not
interact with the charging pads under the road to charge its
battery. This idle period thus provides an opportunity where
cryptographic operations can be performed in preparation for
future use.
In Fig. 13 we illustrate the major steps in Portunes+. In
the key pre-distribution phase, the CSPs generate the key sets
and send them to the POs, which in turn disseminate the key
A. Challenges for Authentication of IoMT devices
Authenticating the identity of devices is by no means a new
problem in security research. Consider an IoT scenario, where
the doorbell has a camera that connects to the living room TV
through the in-house WiFi router. Since neither the doorbell
nor the TV are likely to move, the wireless connection and
the authentication between the doorbell and the TV only need
to be configured once during the initial setup. What makes
mobile device authentication a new challenge is the fact that
(i) the device changes its location as the user moves; and (ii) it
constantly meets new other IoMT-enabled devices, as opposed
to having a fixed list of neighbors which it has learned during
its pre-configuration.
While authentication in vehicular network has been extensively studied in the research community [32]–[37], we
want to emphasize that they constitute only a small subset of
applications of IoMT. In particular, most V2V authentication
approaches focus on authenticating the safety message that
has a fixed content (location and speed) and fixed broadcast
frequency (every 100 milliseconds) according to the IEEE
802.11p standard, and most Vehicle-to-Infrastructure (V2I)
authentication solutions focus on authentication between vehicles and roadside units. It is easy to imagine an IoMT
scenario that goes beyond the current focus of V2V and V2I
authentication: the vehicle may communicate with street-side
34
Time (ms)
16
10
1
0.1
0.01
100
Fig. 13: Overview of Portunes+ authentication
ECDSA (verification)
ECDSA (generation)
Portunes+ (generation)
Portunes+ (verification)
200
300
400
500
600
700
800
900
Message size [Bytes]
Fig. 14: Generation and verification time of authentication
message using Portunes+ and ECDSA vs. message size. Error
bars indicate 95% confidence intervals. The evaluation is done
on Raspberry Pi 2 Model B.
sets to each charging pad. In the authentication step, the CSPs
allocate keys and pseudonyms to EVs before they enter the
charging section, and the EVs authenticate with each charging
pad encountered using the assigned key. The true identity
of the EV is not revealed to the charging pads during the
authentication. Since the session key assigned to the EV has
already been pre-distributed to the charging pads, the EV can
immediately start using the assigned session key for mutual
authentication with the charging pads without additional key
negotiation.
First, in data collection, mobility of things such as phones
and vehicles impacts several important relations: (a) relation
between number of sensors used during data collection, energy
usage, and storage usage, i.e., if number of sensors goes up
in a mobile device, energy usage goes up and data collection requires more storage space; (b) relation between data
collection, energy management and data analysis, i.e., if one
collects more data, more energy is being spent, but also better
data analysis can be done learning more detailed patterns such
as mobility patterns, usage patterns, social context patterns,
and other patterns of mobile devices; (c) relation between
data collection and privacy; i.e., if one collects private data,
one needs to provide privacy-preserving algorithms for mobile
devices; (d) relation between data collection and data quality,
i.e., since mobile data collection is opportunistic, how much
data one collects (duration and frequency of data collection)
impacts the data quality implicitly.
Second, during data analysis and delivery, one has to pay
attention to the following issues: (a) relation between data
selection for analysis and mobility, i.e., mobility will impact
the data selection for analysis since if one chooses data that is
erronous during high mobility speeds, analysis will be highly
erronous as well. (b) relation between data analysis, security,
privacy and energy; i.e., the accuracy of analysis and the
level of security/privacy very much depend on the energy
availability on a mobile phone and vice versa.
Third, in our energy management considerations, two
lessons learned came out: (a) strong assumptions on the
mobility of devices make the modeling and analysis of energy
placement easier, but one needs to relax the strong assumptions
to encompass more realistic scenarios; (b) relation between
energy placement, mobility patterns, and data collection and
analysis is of importance since if one understands mobility
patterns of devices, one can better design placement of energy
sources, and in order to gain accurate mobility patterns, one
needs to do data collection and analysis from devices to get
C. Implementation and Results
We have implemented Portunes+ in C++ using Crypto++
5.6.2 library, and evaluated its performance on Raspberry
Pi 2 Model B platform. Raspberry Pi is a portable general
computing platform featuring a 900 MHz Quad-core CPU and
1 GB RAM, and costs $35 (USD) at the time of writing.
We choose to evaluate Portunes+ on Raspberry Pi because
we think future EVs will be equipped with equivalent or
better computational resources. As shown in Fig. 14, the authentication message generation and verification of Portunes+
are orders of magnitude faster compared to Elliptic Curve
Digital Signature Algorithm (ECDSA) currently suggested by
the IEEE 802.11p standard.
One important lesson we learned is that the mobility pattern of the IoMT devices in question plays an important
role in designing the protocol. The reason why Portunes+
outperforms ECDSA in real-time authentication speed is that
Portunes+ utilizes the idle period during which the EV is
parked to perform computationally intensive operations, and
only uses lightweight cryptographic operation during real-time
authentication. This two-phase design choice comes from the
observation on the vehicle’s mobility pattern, i.e., most sedan
vehicles are parked during the night.
VII. C ONCLUSION
In this paper, we have discussed Internet of Mobile Things
with variety of themes ranging from data collection, analysis,
exchange to energy management and security and privacy of
mobile things. In each of these themes, one can draw many
lessons learned. We want to highlight some of them.
35
their individual locations and energy usage.
Fourth, security is very much impacted by the mobility
since (a) mobility means adaptation and adaptation means
design of security protocols that accept continuous changes
in key management, exchange of credentials, authentication
algorithms and other parts of the security and privacy frameworks; (b) relation between data analysis and security will
impact predictions in mobility patterns which again can yield
better key management and stronger authentication via a twofactor authentication.
In summary, even though research around Internet of Mobile
Things may happen along the above discussed themes separately, it is very important to stress that all of these themes,
i.e., data collection, analysis, exchange, energy management,
and security and privacy, must be considered together. Only
an integrated approach towards IoMT will be successful if we
want to see Internet of Mobile Things developed and deployed
broadly.
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