Content uploaded by Evangelos Markakis
Author content
All content in this area was uploaded by Evangelos Markakis on Mar 15, 2018
Content may be subject to copyright.
Content uploaded by Evangelos Markakis
Author content
All content in this area was uploaded by Evangelos Markakis on Mar 15, 2018
Content may be subject to copyright.
CLOUD 4ELE
34 IEEE CLOUD COMPU TING PUB LISHED BY T HE IEEE COMPU TER SOCIE TY 2325-60 95/16/$33.0 0 © 2016 IEEE
mbient assisted living (AAL) has grown in
popularity over the past few years among academic
communities,1 and several standards and platforms
have been produced2 (see the related work sidebar).
Interest in ambient intelligence (AmI) environments
as a way to support the elderly and individuals
with activity limitations has also been growing.3 The AAL European
Programme aims to foster the emergence of systems for aging well at
home, at work, and in the community, thus increasing quality of life
and reducing health and social care costs. Such systems can remotely
monitor health, well-being, and resource consumption. Observation of
this data leads to the creation of behavioral patterns, where any observed
behavioral deviation can be a preliminary indicator of a health issue.4
Cloud computing and the Internet of Things (IoT) are signicant
elements of AAL and the endeavor to produce a ubiquitous, efcient,
and cost-effective architecture that will assist targeted individuals to
become more independent and to effortlessly perform ever yday tasks
in their familiar environment. However, gathering all this information
into a remote, centralized authority where data is managed and can be
accessed by human actors raises security, ethical, social, cost, and user
experience issues.
An ambient
assisted-living
emergency system
exploits cloud and
fog computing, an
outdoor positioning
mechanism, and
emergency and
communication
protocols to locate
activity-challenged
individuals.
A Fog-Based
Emergency System
for Smart Enhanced
Living Environments
Yannis Nikoloudakis, Spyridon Panagiotakis, Evangelos Markakis, Evangelos
Pallis, and George Mastorakis
Technological Educational Institute of Crete
Constantinos X. Mavromoustakis
University of Nicosia
Ciprian Dobre
University Politechnica of Bucharest
NOVEMBER/DECEMBER 2016 IEEE CLOUD COMP UTING 35
Fog computing extends the cloud, shifting
resources, services, and data to the network edge.
It aims to avoid network bottlenecks, bring content
and computation closer to the user, reduce network
latency, and enhance system performance and user
experience. Furthermore, the fog empowers the IoT,
providing next-hop processing and thus alleviating
the network of massive dataow.
To address these issues, we present a virtualized,
decentralized approach that operates within a virtual
fog layer and uses the cloud in an assistive manner
to ensure resilient and robust operability. Services
formerly deployed in the cloud are seamlessly
deployed in a virtual fog layer using distributed IT
resources mined from fog devices participating in the
fog layer. All resources are pushed into a federated
pool, where they’re managed and provisioned by a
dynamic resource broker-manager service.
Fog-Based System Architecture
In our proposed distributed fog infrastructure, the
virtual fog layer facilitates a ubiquitous alerting
service for users in critical health conditions
requiring constant surveillance. The system
periodically calculates the user’s position and
determines if the individual is within the home’s
dened boundaries. A user who’s outside the
established geographical boundaries is classied
as unsafe. The system then recalculates the user’s
outdoor position and sends distress messages
containing various user information to the proper
authorities as well as any nearby volunteers able
to respond. Each user is equipped with a wearable
embedded device that interacts with the positioning
service, providing the system with the user’s real-
time location. Overall, we can dissect the system into
three basic virtual layers, as Figure 1 illustrates.
Cloud-Based Approach
A cloud infrastructure is at the top layer of the
proposed system. It operates in an assistive manner
as an extension of the fog layer, overseeing the
operations taking place in the fog and contributing
cloud resources as needed. An orchestration service
deployed in this layer tackles resource brokering and
managing. This way, the cloud assists any fog service
lacking sufcient resources, ensuring uninterrupted
operation of the system.
Fog-Based Approach
The classic fog computing paradigm is a dispersed
version of the cloud, where distributed dev ices
at the network ’s edge host certain serv ices to
minimize network latency and enhance the user
experience. In the proposed scenario, the fog is
implemented in a dispersed virtualized manner,
creating an abstraction of a cloud—not just
decentralizing resources and services, but shifting
and implementing the entire cloud functionality to
the network ’s edge, exploiting available resources
from diverse sources. All services that embody the
system are implemented within the fog.
Orchestration. The T-Nova initiative describes an
orchestration platform that dynamically manages
and optimizes network and IT resources.5,6 We
deploy an instance of that orchestration entity,
customized to meet the use case requirements,
within the cloud layer to facilitate the seamless
harvesting, managing, and provisioning of diverse
distributed fog resources. In addition to resource
SDN SDN
FogFog
Positioning
LoST
Profiling Profiling
Positioning
LoST
Service
logic
Service
logic
Fog nodes Fog nodes
Cloud
Orchestrator
FIGURE 1 . System architecture. The cloud orchestrates the virtual fog
layer’s resources and the services.
36 IEEE CLOUD COMPU TING WW W.COMPUTER.ORG/CLOUDCOMPUTING
CLOUD 4ELE
RELATED WORK IN AMBIENT ASSISTED LIVING
here has been significant academic and
commercial interest in creating platforms to
deliver ambient assisted-living (A AL) services. The
research mainly focuses on observing activities,
monitoring vitals, detecting danger, and alerting
relatives, doctors, or authorities.
Gilles Virone and Andrew Sixsmith discuss a
platform that extracts behavioral patterns from users’
daily activities.1 After processing and evaluating the
patterns, an intelligent algorithm provides activity
prediction, thus proactively alerting authorities
of possible danger or health decline of the target
user. 2 The Soprano project also employs pervasive
technologies (sensors, actuators, smart interfaces,
and artificial intelligence) to create a supportive
environment for older people living at home.3 This
user-driven platform tackles issues—such as social
isolation, security and safety, forgetfulness, mobility,
and quality of care—related to socially and activity-
challenged individuals confined to their homes.
Diego Lopez and his colleagues present a holistic,
aordable AAL platform that includes an AAL kit and
a centralized management service-provisioning
system.4 It employs an AAL store, where users can
dynamically install or uninstall AAL services—such as
smart TV interfaces, smart home applications, alerts,
and vitals monitoring—to their systems. Another
initiative, Orange Alerts, presents an infrastructure
that addresses individuals suering from dementia
or Alzheimer’s disease.5 The system facilitates a
set of services that monitor patients, build patient
profiles according to behavioral statistics, and track
patients’ geographical locations, and stores the data
in a server, where relatives, caregivers, and doctors
can access it. Lastly, the Saapho project presents an
infrastructure in which target users interact with the
system, configuring settings or initiating services using
an Android tablet.6 Environment (gas and temperature)
and health (glucose and heartrate) sensors provide
context about the user. Cloud middleware gathers
the data, detects abnormal behaviors, and predicts
possibly dangerous activities. Unfortunately, a human
actor must oversee the data and predictions.
Current AAL projects are implemented in a
centralized manner, deployed either in the cloud
or at dedicated server facilities. In addition, alerting
mechanisms are static, location agnostic, and don’t
use any standardized emergency protocols to
communicate with ocial responding authorities.
Our proposed system oers dynamic and
decentralized emergency management, deployed in
a virtual fog layer. It isn’t cloud dependent because it
operates at the edge of the network, utilizing only
network edge IT resources. The system’s alerting
mechanism employs a standardized emergency
communication protocol to alert the emergency
authorities geographically nearest to the user. The
system requires only an Internet connection. A cloud
infrastructure is a complementary service to the system,
since the system can operate without it. Yet, in cases
where the system requires additional resources,
the cloud will provide them, ensuring the system’s
uninterrupted operability.
References
1. G. Virone and A. Sixsmith, “Monitoring Activity
Patterns and Trends of Older Adults,” Proc. Ann.
Conf. IEEE Eng. Medicine and Biology Soc., 2008,
pp. 2071–2074.
2. G. Virone and A. Sixsmith, “Activity Prediction for
In-Home Activity Monitoring,” Proc. Conf. IET 4th
Int’l Intelligent Environments, 2008, pp. 1–4.
3. A. Sixsmith et al., “SOPRANO—An Ambient Assisted
Living System for Supporting Older People at
Hom e,” Ambient Assistive Health and Wellness
Management in the Heart of the City, LNCS 5597,
Springer, 2009, pp. 233–236.
4. D. López-De-Ipiña et al., “A Platform for a More
Widespread Adoption of AAL,” Aging Friendly
Technology for Health and Independence, LNCS
6159, 2010, pp. 250–253.
5. J. Wan et al., “Orange Alerts: Lessons from an
Outdoor Case Study,” Proc. 5th Int’l Conf. Pervasive
Computing Technologies for Healthcare Work,
2011, pp. 446–451.
6. J. Rivero-Espinosa et al., “SAAPHO: An AAL
Architecture to Provide Accessible and Usable
Active Aging Services for the Elderly,” Proc. ACM
SIGACCESS Accessibility and Computing, no. 107,
2013, pp. 17–24.
NOVEMBER/DECEMBER 2016 IEEE CLOUD COMP UTING 37
management, the orchestrator is responsible
for deploying virtual services that facilitate the
infrastructure’s intelligence.
Profiling service. A proling mechanism implemented
in the fog separates users into two categories:
volunteers and persons of interest. The service
maintains a non-SQL database of user proles
stored in the fog, and containing personal, health,
and positioning information. It also contains users’
current status as safe or unsafe. User proles are
dynamically updated by other services or authorities.
A user prole is a set of private information that
shouldn’t be accessed publicly. Yet, diverse groups
of actors must obtain pieces of that information
to be able to respond in an emergency situation as
effectively as possible. In the proposed use case
scenario, two general actors—volunteers and liable
authorities—must have access to that information.
The liable authority receiving the system’s rst distress
message must be granted access to the full personal
and medical information contained inside the user’s
prole. Volunteer responders, who w ill receive
complementary alert messages, require access only
to basic user information along with rst-response
instructions. To perform that task, the service creates
two different dynamic HTML5 pages containing the
appropriate information for each actor type.
Positioning service. A positioning ser vice periodica lly
obtains the user’s received signal strength indicator
(RSSI) between the embedded device and the in-
house 5G small-cell Wi-Fi interface. As long as
the service receives RSSI measurements from the
embedded device, the user remains classied as safe,
since the user is considered bounded within the Wi-
Fi radius of the indoor small cell. If the service stops
receiving RSSI measurements from the embedded
device, it sends an OUT message (meaning the user
is outside the home’s geographical boundaries) to
the proling service, which classies the user as
unsafe. Once a user is outside the small cell’s radius,
a cellular interface in the embedded device connects
to the outdoor cellular network and sends cellular
information of the positioning service’s adjacent
serving base stations (mobile network code, mobile
country code, location area code, cell ID, signal
strength, and so on). The service performs the
positioning task using an open geolocation API. In
addition, the positioning service informs the service
logic module, which updates the user’s location in
the user’s prole by probing the proling service.
Finally, the service logic module acquires the user’s
prole from the proling service and noties the
geographically nearest authority and possible nearby
rst responders by sending them an alert banner
containing information from the user’s prole and
geographical location, customized for each actor.
Service logic. In an emergency, rst-response time
is critical, owing to the mercurial state of mind
of vulnerable populations interacting with an
unknown and likely frightening environment. To
inform all possible responders of a given distress
situation, the ser vice rst acquires the URI of the
nearest public safety answering point (PSAP) by
triggering the location-to-service translation (LoST)
service. It then requests and retrieves the user’s full
prole, along with the list of the nearest volunteers,
from the proling service. After hav ing collected all
this information, it sends the nearest PSAP an alert
banner containing the user’s full prole and location.
To reduce rst-response time, the service also sends
all nearby volunteers an alert banner containing the
user’s limited prole and location, along with a set
of basic instructions on how to respond and attend
to the user in need. Lastly, it sends the limited user
prole, along with an interface-enabling signal, back
to the embedded device.
Location-to-service translation. The LoST ser vice
uses the LoST protocol7 to nd the geographically
nearest emergency response authority. As input, the
service receives the user’s location and it returns the
URI of the nearest PSAP.
Software-defined networking. The SDN inside
the virtual fog layer acts as a complementary
service for the orchestrator.8 It facilitates the
dynamic management and administration of the
network inside the fog layer, ensuring elasticity and
reliability. It provides services, such as capacity and
quality-of-service–specic links, and connectivity
management, such as creating virtual networks
required by the system.
Extreme Edge
Each user carries a discrete embedded device,
integrating various interfaces and providing the
system with a level of context awareness and
geographical information. A Wi-Fi interface connects
to an in-house small cell. The device periodically
collects and sends the measured RSSI to the
positioning ser vice, which determines whether
the user is inside or outside the small-cell radius
surrounding the user’s premises. Once the user is
found outside the Wi-Fi small-cell radius, a GSM
interface connects to the outdoor cellular network.
38 IEEE CLOUD COMPU TING WW W.COMPUTER.ORG/CLOUDCOMPUTING
CLOUD 4ELE
The device collects information about the serving
base stations and sends it to the positioning service
over a data connection (General Packet Radio
Service/2G/3G/4G/5G) so the service can determine
the user’s outdoor geographical location.
To achieve faster response time, after receiving
the enabling signal from the service logic module,
the device employs a Bluetooth 4.0 (Bluetooth low-
energy, or BLE) interface to use as a beacon. The
interface, using the Google Eddystone open protocol
(https://github.com/google/eddystone/ blob/master/
protocol-specication.md), broadcasts a distress
signal containing the user’s limited prole, which
includes the user’s current medical condition and
contact information (telephone number, email,
Skype contact, and so on) of authorities responsible
for the user. Figure 2 shows the architecture of the
embedded device.
Use Case Scenario
We divide our use case scenario into two phases.
In the rst phase, the user is within the household
boundaries and classied as safe, as depicted in
Figure 3. An embedded device, carried by the
user and connected to the indoor 5G small cell,
continuously measures the RSSI and sends it to the
fog positioning service, which is thus assured that
the user is bounded within the small cell’s radius.
Once the user leaves the household premises, thus
exiting the small cell’s radius, the service stops
receiving RSSIs from the embedded device. After
a predened time period, the positioning ser vice
noties the service logic, which classies the user
as unsafe.
The second phase deals with the user stepping
out of the small cell’s radius, thus becoming unsafe.
Once in an outdoor environment, the embedded
device connects to a cellular network and starts
collecting information about the adjacent serving
base stations, using a data connection to send
the information back to the positioning service.
It repeats this task periodically. The positioning
service acquires the user’s current position using an
open geolocation API, and then triggers the service
logic module, which, in turn, locates and informs
the authorities responsible for the user by invoking
a LoST service, providing them with the user’s full
prole and geographical location (Figure 4a). In
addition, the service logic module acquires a list of
the nearest volunteer responders from the proling
service (Figure 4b), and provides them with a brief
user prole, a set of rst-response instructions,
and the user’s geographical location (Figure 4c).
Finally, the service logic directs the embedded
device to employ a BLE interface and the open
Google Eddystone beacon protocol to broadcast a
distress message with basic user information and
a set of rst-response instructions to any person
passing by. Once found, the user is classied as safe
by the authority in charge of the situation or the
system administrator. Figure 5 illustrates the second
phase, and Figure 6 shows the sequence in which
the services are deployed and interacted with each
other, along with the messages they exchange.
Experimental Results
To demonstrate the system’s functionality and
efciency, we dened several experiments to validate
the basic use case scenario where a user drifts away
from the predened safety radius. The service logic
classies the user as unsafe and acquires the URI
of the geographically closest PSAP by invoking the
LoST server, and consequently collects the contact
Wi-Fi Cellular
B
LE
O
peratin
g
s
y
ste
m
Services
Embedded device
FIGURE 2. Block view of embedded device
architecture layers.
Fog node
Small cell
S
m
a
ll-
ce
l
l
r
adius
FIGURE 3. Overview of the system within the radius of the small cell. The
user is indoors and classified as safe.
NOVEMBER/DECEMBER 2016 IEEE CLOUD COMP UTING 39
information of the geographically closest volunteers
by probing the proling service. To emulate real-life
conditions, we deployed the system components in
different cloud servers (Amazon and Okeanos).
We measured three values during the execution
of this experimental scenario (see Table 1). The
rst value is the time needed for the service to
acquire the PSAP URI from the moment the user
is classied as unsafe. The second value is the time
needed to acquire the list of nearby volunteers after
receiving the PSAP URI. The third value is the
total time needed for the system to collect all the
information needed.
By observing the experimental results, we infer
that the system can identify users wandering off
a predened radius and notify the nearest liable
authority, along with any possible nearby volunteers,
in approximately ve seconds. The response time
can uctuate slightly due to network abnormalities,
depending on the system components’ point of
presence. Still, our system offers a solution to a
problem that would otherwise require days to resolve.
ur future work will focus on adding
telemedicine functionalities to the proposed
system, providing health measurements such as
pulse, blood oxygen level, airow (breathing), body
temperature, glucose level, and muscle activity to
enhance the patient context and help the system
evolve to predict dangerous activities or health
decline.
We intend to further expand the boundaries
of the virtual fog toward the extreme edge
of the network, enabling diverse connected
devices (cellphones, tablets, wearables, smart
appliances, and so on) to participate in the virtual
infrastructure, not only as end devices providing
context or requesting serv ices, but as contributors to
the infrastructure’s federated IT resource pool.
The proposed system can play a signicant
role in the AAL European Programme and the
endeavor to elevate quality of life and participation
for certain groups, such as the elderly. Nevertheless,
the adoption of such a system raises numerous
implementation and coordination issues and
challenges. The system’s functionality relies on the
LoST and geolocation services, whose performance
and robustness must be guaranteed. The former
should be provided by a national authority, and the
latter by an eligible application provider such as
Google. Additionally, international humanitarian
organizations, such as the Red Cross, could provide
volunteers trained for emergency situations.
References
1. H. Sun et al., “Promises and Challenges of
Ambient Assisted Living Systems,” Proc. 6th Int’l
Conf. Information Technology: New Generations,
{
“first name” : “John”,
“last name” : “Doe”,
“email” : :j.doe@gmail.com”,
“age” : 76,
“
photo” : “http://85.223.98.99/users/images/john_doe.png”,
“current_position” : {
“lat” : 35.353233,
“long” : 24.482689
},
“volunteers” : [
“Esteban Pena”,
“Jorge Sherman”,
“Anna Hines”
],
“volunteer_radius” : 5000,
“first_response_info” : “User suffers from dementia
and must be approached with extreme caution”
}
(a)
(b)
(c)
FIGURE 4. Alert system: (a) full profile banner for the public safety
answering point (PSAP); (b) profiling service response; and (c) limited
profile provided to volunteer responders.
Table 1. Experimental results
Minimum
(seconds)
Maximum
(seconds)
Average
(seconds)
Response containing nearest
public safety answering point
(PSAP)
1.554 2.192 1.609
Response containing nearest
volunteers
2.010 2.650 2.650
Total handling time 3.564 4.842 4.259
40 IEEE CLOUD COMPU TING WW W.COMPUTER.ORG/CLOUDCOMPUTING
CLOUD 4ELE
2009, pp. 1201–1207.
2. F. Overgaard Hansen, “Ambient Assisted Living
Healthcare Frameworks, Platforms, Standards,
and Quality Attributes,” Sensors (Basel), vol. 14,
no. 3, 2014, pp. 4312–4341.
3. L. Burzagli, L. Di Fonzo, and P.L. Emiliani,
“Serv ices and Applications in an A mbient Assisted
Living (AAL) Environment Design of Smart
Environments: The Present Situation,” Universal
Access in Human-Computer Interaction, LNCS
8515, Springer, 2014, pp. 475–482.
4. C. Tunca et al., “Multimodal Wireless Sensor
Network-Based Ambient Assisted Living in Real
Homes with Multiple Residents,” Sensors (Basel),
vol. 14, no. 6, 2014, pp. 9692–9719.
5. G. Xilouris et al., “T-NOVA: A Marketplace for
Virtualized Network Functions,” Proc. European
Conf. Networks and Comm. (EuCNC 14), 2014,
pp. 1–5.
6. G. Xilouris et al., “T-NOVA: Network Functions
as-a-Service over Virtualized Infrastructures,”
Proc. IEEE Conf. Network Function Virtu alization
and Software Dened Network (NFV-SDN 15),
2016, pp. 13–14.
7. T. Hardie et al., LoST: A Location-to-Service
Translation Protocol, IETF RFC 5222, 2008;
www.rfc-editor.org/rfc/rfc5222.t xt.
8. B.A.A. Nunes et al., “A Survey of Software-
Dened Networking: Past, Present, and Future
of Programmable Networks,” IEEE Comm.
Surveys and Tutorials, vol. 16, no. 3, 2014, pp.
1617–1634 .
YANNIS NIKOLOUDAKIS is a graduate student
in the Informatics Engineering Department at the
Technological Educational Institute of Crete and
an intern at Pasiphae Research Lab. His research
interests include cloud computing, fog computing,
the Internet of Things, and software development.
Contact him at g.nikoloudakis@pasiphae.eu.
SPYRIDON PANAGIOTAKIS is an assistant
professor in the Department of Informatics
Engineering at the Technological Educational
Institute of Crete and head of the group for Sensor
Networks and Telematics. His research interests
include mobile multimedia, communications and
"cellTowers":
"cellId": 21532831,
"locationAreaCode": 2862,
"mobileCountryCode": 214,
"mobileNetworkCode": 7
"cellTowers":
"cellId": 21532950,
"locationAreaCode": 2862,
"mobileCountryCode": 214,
"mobileNetworkCode": 7
"cellTowers":
"cellId": 21532840,
"locationAreaCode": 2862,
"mobileCountryCode": 214,
"mobileNetworkCode": 7
Small cell
radius
Help
Alert LoST Positioning
FIGURE 5. Overview of the system outside the radius of the small cell. The user is outside of the household boundaries and thus
classified as unsafe.
NOVEMBER/DECEMBER 2016 IEEE CLOUD COMP UTING 41
networking, Internet of Things, pervasive computing,
sensor networks, Web engineering, and informatics in
education. Panagiotakis has a PhD in communication
systems from the Department of Informatics and
Telecommunications at the University of Athens.
Contact him at spanag@teicrete.gr.
EVANGELOS MARKAKIS is a senior research
associate at the Technological Educational Institute
of Crete and the technical manager of the Horizon
2020 DRS-19-2014 Emynos Project. His research
interests include fog networking, P2P applications,
and next-generation networks. Markakis has a PhD
in communication systems from the University of the
Aegean He’s a member of the IEEE Communications
Society. Contact him at markakis@pasiphae.eu.
EVANGELOS PALLIS is an associate professor in
the Department of Informatics Engineering at the
Technological Educational Institute of Crete and
acting director of the Research and Development
of Telecommunications Systems Laboratory. His
research interests include wireless broadband and
mobile networks and network management. Pallis has
a PhD in telecommunications from the University of
East London. Contact him at pallis@pasiphae.eu.
GEORGE MASTORAKIS is an associate professor
in the Department of Applied Informatics and
Multimedia at the Technological Educational Institute
Positioning
service
LoST
service
Service
logic
Profiling
service
Embedded
device
RSSI
Estimate position
loop
User indoor
User IN
loop
User outdoor
Cellular info
Estimate position
Nearby
volunteers
Nearby
PSAP
User is OUT
Persons
passing by
User location
User location
PSAP Request full
user profile
Full user profile
User full profile URL and location
Request limitied
profile
Limited user
profile
User limited profile URL and location
Enable BLE beacon / limited profile URL
Alert message / limited user profile URL
User IN
User is OUT
User location
FIGURE 6. Sequence diagram describing the interaction between the system entities
42 IEEE CLOUD COMPU TING WW W.COMPUTER.ORG/CLOUDCOMPUTING
CLOUD 4ELE
of Crete and a research associate in the Research
and Development of Telecommunications Systems
Laboratory at the Center for Technological Research of
Crete, Greece. His research interests include cognitive
radio networks, mobile cloud computing, networking
trafc analysis, radio resource management, and
energy-efcient networks. Mastorakis has a PhD in
telecommunications from University of the Aegean,
Greece. Contact him at gmastorakis@staf f.teicrete.gr.
CONSTANTINOS X. MAVROMOUSTAKIS is an
associate professor in the Department of Computer
Science at the University of Nicosia, Cyprus, where
he also leads the Mobile Systems Lab. His research
interests include the design and implementation of
hybrid wireless testbed environments and mobile peer-
to-peer systems, Internet of Things congurations
and smart applications, high-performance cloud
and mobile cloud computing systems, modeling and
simulation of mobile computing environments, and
protocol development and deployment for large-scale
heterogeneous networks and green mobility-based
protocols. Mavromoustakis has a PhD in informatics
from Aristotle University of Thessaloniki, Greece.
Contact him at mavromoustakis.c@unic.ac.cy.
CIPRIAN DOBRE is a professor at the University
Politechnica of Bucharest. His research interests include
large-scale distributed systems concerning monitoring,
high-speed networking, grid application development,
evaluation using modeling and simulation, mobile
applications, and smart technologies to reduce urban
congestion and air pollution, and context-aware
applications. Dobre has a PhD in computer science
from the University Politechnica of Bucharest. Contact
him at ciprian.dobre@cs.pub.ro.
Read your subscriptions through
the myCS publications portal at
http://mycs.computer.org.
