Report-Fog Based Emergency System For Smart Enhanced Living Environment

Dr. T. THIMMAIAH INSTITUTE OF TECHNOLOGY
A TECHNICAL SEMINAR REPORT
ON
“A Fog-Based Emergency System for Smart Enhanced
Living Environments”
Submitted in the partial fulfillment of the requirement
for the award of Degree of
Bachelor of Engineering
In
Computer Science and Engineering
Of
VISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELGAUM
By
M.KARTHIK
(1GV10CS022)
Under the guidance
Of
Mrs. Kavitha
Asst.Prof, Dept. of CSE
2016-2017
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING,
Dr. T. THIMMAIAH INSTITUTE OF TECHNOLOGY,
Kolar Gold Fields-563 120

Dr. T. THIMMAIAH INSTITUTE OF TECHNOLOGY
OORGAUM, K.G.F. – 563 120 (KARNATAKA)
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
CERTIFICATE
This is to certify that the Technical Seminar work entitled “A Fog-Based
Emergency System for Smart Enhanced Living Environments” is bonafide work
carried out by M.KARTHIK bearing register number 1GV10CS022 in partial
fulfillment for the award of degree of Bachelor of Engineering in Computer Science
and Engineering of Visvesvaraya Technological University, Belgaum during the year
2016-2017.
This report has been approved as it satisfies the academic requirements in
respect of Technical Seminar work prescribed for the Bachelor of Engineering
Degree.
___________________ ________________ _________________
Signature of Guide Signature of H.O.D Signature of Principal
(Mrs. Kavitha) (Mrs. Vinutha B.A) (Dr. Syed Ariff)

ACKNOWLDGEMENT
The successful completion of any task would be incomplete without
mentioning the people who made it possible, whose constant guidance and
encouragement crowned our effort with success.
I have great pleasure in expressing my deepest gratitude to Dr.T.Thimmaiah
Institute of Technology and Dr.Syed Ariff, Principal of Dr.TTIT for his support
and encouragement
I would like to thank Mrs.Vinutha B A, HOD, Dept.of Computer Science, for
her useful guidance and encouragement which were vital for this seminar
My deep and profound gratitude to my internal guide Mrs.Kavitha.N, Asst
Prof, Dept.of CSE and coordinator, Mrs.Tharadevi M, Asst Prof ,Dept. of CSE for
their keen interest, for being source of encouragement and timely suggestions during
the course of my seminar.
I thank all the teaching and non-teaching staff of the department, who has
helped me in completing the seminar.
Last but not least I would like to thank my parents for what I am today and
finally my friends who helped me in successful completion of my seminar.
M.KARTHIK
1GV10CS022

SYNOPSIS
Ambient assisted living (AAL) has grown in popularity over the past few
years among academic communities, and several standards and platforms have been
produced. Interest in ambient intelligence (AmI) environments as a way to support the
elderly and individuals with activity limitations has also been growing. 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.
Cloud computing and the Internet of Things (IoT) are significant elements of
AAL and the endeavor to produce a ubiquitous, efficient, and cost-effective
architecture that will assist targeted individuals to become more independent and to
effortlessly perform everyday 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.
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 dataflow. 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.

CONTENTS
Sl.No Chapter name Page no
1. Introduction 1
2. Fog Computing 7
3. Fog Based System Architecture 12
3.2 Fog Based Approach 14
3.3 Cloud Based Approach 14
4. System Overview And Working 15
4.1 Profiling service 15
4.2 Positioning service 16
4.3 Service logic 17
4.4 Location-to-service translation 17
4.5 Software-defined networking 17
4.6 Extreme Edge 18
5. Usecase Scenario 19
6. Performance Evaluation 24
7. Conclusion 25
Bibliography 26
Sl.No Chapter name Page no
1
2
3
4

Fog Based Emergency System
1. INTRODUCTION
Ambient Assisted Living (AAL) systems have a huge potential to meet the
personal healthcare challenges and involve citizens in their healthcare through
Information and Communication technologies (ICT). The AAL systems provide an
ecosystem of medical sensors, computers, wireless networks and software
applications for healthcare monitoring. The primary goal of AAL solutions is to
extend the time which elderly people can live independently in their preferred
environment using ICT technologies for personal healthcare . Presently, there is a
huge demand for AAL systems, applications and devices for personal health
monitoring and telehealth services . Moreover, personal health monitoring is setting a
trend with increased empowerment of citizens in healthcare, stimulated by the
growing awareness and understanding of healthcare concepts and systems, i.e.,
electronic health medical records, health monitoring systems, and mobile health
applications.
The AAL systems are also used for telehealth and telemedicine facilities for
providing remote healthcare services to the citizens. According to a report by
InMedica, telehealth is projected to reach 1.8 million patients worldwide by 2017 for
monitoring the post-acute and ambulatory patients . AAL systems consist of medical
sensors, wireless sensor and actuator networks (WSANs), computer hardware,
computer-networks, software applications, and databases, which are interconnected to
exchange data and provide services in an Ambient Assisted environment. Medical
Sensors and actuators are connected with the AAL applications and home gateways
for sending medical data to the health monitoring systems. The sensors rely on
WSANs for connecting with home gateways and healthcare applications . The home
gateways, also known as smart home gateways, often use a wireless router that
provides connectivity to enable multiple applications for real-time health monitoring
through home networks . Many of the available sensors used for monitoring blood
sugar, blood pressure, and pulse-rate are capable of sending vital signs to the health
monitoring systems, so that a caregiver or physician can monitor the patients
remotely. Moreover, due to increasing availability of portable, wireless medical
devices and wide access to data networks the usage of medical devices is
Dept. of CSE, Dr TTIT, KGF 1 2016-17

Introduction
continuously growing. According to a recent research report, ―Medical devices
purchased by consumers used to self-monitor health conditions will account for more
than 80% of wireless devices in 2016. The proportion of wireless devices used in
managed telehealth programs is predicted to increase from 5% in 2011 to 20% in
2016 . Another report cites, ―The number of home health monitoring devices in use‖
with embedded cellular connectivity increased from 420,000 in 2010 to about 570,000
in 2011, and is expected to hit 2.47 million in 2016. The figures imply that the
demand for healthcare devices and ambient assisted living systems is increasing to
involve citizens‘ in personal healthcare, support independent living and economize
the healthcare expenses.
In order to produce systems ensuring high-quality-of-service, it is important to
consider different aspects of AAL systems to achieve interoperability, usability,
security, and accuracy, which are essential requirements of AAL systems. However,
the available systems do not consider all aspects of AAL systems having
personalized, adaptive, and anticipatory requirements. To identify the essential
aspects of AAL systems, we conducted a state-of-the art survey of the literature,
which is presented in this paper. We also found other reviews of Ambient Assisted
Living systems addressing AAL aspects in general. For example, a recent review by
Rashidi et al. presents a general technical survey of ambient assisted living platforms,
systems, algorithms and standards [16]. A short review by Iliev et al. provides a high-
level survey of current Ambient Assisted Living systems targeting smart homes,
middleware technologies and standards for elderly people [17]. A more distinct
review by Antonino et al. specifically focuses on architecture-based quality attributes
of AAL platforms and evaluates existing frameworks for reliability, security,
maintainability, efficiency, and safety properties [18]. In our review, we present the
latest research findings and technology advancements of the related AAL systems
aspects addressed in these reviews. In addition, we review more aspects of AAL
systems, which are also important but not covered in existing reviews.

Introduction
1.1 Ambient Assisted Living
There has been significant academic and commercial interest in creating
platforms to deliver ambient assisted-living (AAL) services. The research mainly
focuses on observing activities, monitoring vitals, detecting danger, and alerting
relatives, doctors, or authorities.
Ambient assisted living (AAL) has grown in popularity over the past few years
among academic communities, 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. 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.
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 official responding authorities.
System modelling and implementation of AAL systems is mostly led by the
conceptual frameworks, architectures, and open solutions. There is substantial
research in this direction; however, we will mention only some of the major
frameworks and architectures for brevity. Tazari et al. present a reference model for
AAL as the UniversAAL platform for large-scale integration of different AAL
systems and solutions. Their objective is to build a consensus among the AAL
community and consolidate their efforts to produce technically feasible and
economically affordable standardized AAL systems. The main conceptual
components of the proposed domain-specific models in the UniversAAL platform
consists of AAL services, network artefacts, AAL spaces, and AAL platforms, which
lead the development of AAL systems. Schmidt et al. believe that AAL systems are

Introduction
too diverse and cannot be provided as commercial-of-the-shelf (COTS) solutions . To
cope with the diversity challenges of AAL systems, they propose an open middleware
OpenAAL to enable easy implementation and configuration through situation-
dependent and context-aware personalized AAL services. The middleware platform is
built on the OSGi architecture and Business Process Execution Language (BPEL)
used for deployment of loosely-coupled platform services and ontologies-based
information to capture sensor and situation inputs in ambient environment. OpenALL
was preceded by the SOPRANO project with similar objectives to support
independent living and social participation empowering AAL systems with sensors,
actuators, smart interfaces and artificial intelligent . In the automatic smart home
framework of the U-Health project, Agoulmine et al. have proposed four main
conceptual layers, i.e., sensors & actuators, home communication network (HCN),
automatic decision-making system (ADMS) and safety & healthcare services, as the
most important elements of smart homes used for personal healthcare monitoring
[64]. Also, the AmiVital interaction framework architecture by Jiménez et al. exhibits
a relationship among functional and technological service. Besides, it also provides
components for context management, knowledge management and device
connectivity. The architectural layers of the Hydra middleware by Eisenhauer et al.
define the conceptual levels of ambient-intelligence systems as physical (Zigbee,
Bluetooth, WLAN), OS (Windows, Linux, TinyOS), Hydra middleware (for
applications and devices) and application (workflow, user interfaces, configuration
and business logic) . Their middleware provides security to the applications and
devices, which are connecting through Hydra. Similarly, the OpenCare framework by
Wagner et al. extends the conceptual architecture in four logical tiers of home,
mobile, central, and public to provide a complete pervasive and connected
infrastructure for healthcare monitoring . The OpenCare framework is implemented as
the Sekoia platform used for personal healthcare monitoring through locally-installed
healthcare applications and tele health services.
Our proposed system offers 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

Introduction
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.
Figure 1.1 Ambient Assisted Living
1.2 Cloud Computing
Cloud computing and the Internet of Things (IoT) are significant elements of
AAL and the endeavor to produce a ubiquitous, efficient, and cost-effective
architecture that will assist targeted individuals to become more independent and to
effortlessly perform everyday 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.

Introduction
1.3 Fog Computing
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 dataflow.
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.

2. Fog Computing
Fog computing is a term for placing some of transactions and resources at the
edge of the cloud, rather than establishing channels for cloud storage and utilization.
Fog computing reduces the need for bandwidth by not sending every bit of
information over cloud channels, and instead aggregating it at certain access points.
By using this kind of distributed strategy, we can lower costs and improve
efficiencies.
The term fog computing is also referred to as “edge computing,” which
essentially means that rather than hosting and working from a centralized cloud, fog
systems operate on network ends.
That concentration means that data can be processed locally in smart devices
rather than being sent to the cloud for processing. Fog computing is one approach to
dealing with the Internet of Things (IoT).
Figure 2.1 Internet of Things

Fog Based Emergency System Fog
Computing
Fog Computing extends the cloud computing paradigm to the edge of the network
to address applications and services that do not fit the paradigm of the cloud
including:
• Applications that require very low and predictable latency
• Geographically distributed applications
• Fast mobile applications
• Large-scale distributed control systems (smart grid, connected rail, smart
traffic light systems).
Applications of Fog Computing
Tech giants Cisco and IBM are the driving forces behind fog computing, and
link their concept to the emerging Internet of Things (IoT). Today there might be
hundreds of connected devices in an office or data center, but in just a few years that
number could balloon to thousands or tens of thousands, all connected and
communicating. Fog computing advocates say leveraging these devices is a more
efficient way to transfer data.
Most of the buzz around fog has a direct correlation with the emergence of
the Internet of Things (IoT). The fact that everything from cars to thermostats are
gaining web intelligence means that direct user-end computing and communication
may soon be more important than
ever :
• Connected cars: Fog computing is ideal for Connected Vehicles (CV)
because real-time interactions will make communications between cars, access
points and traffic lights as safe and efficient as possible.
• Smart grids: Fog computing allows fast, machine-to-machine (M2M)
handshakes and human to machine interactions (HMI), which would work in
cooperation with the cloud.
• Smart cities: Fog computing would be able to obtain sensor data on all levels,
and integrate all the mutually independent network entities within.

Computing
• Health care:The cloud computing market for healthcare is expected to reach
$5.4 billion by 2017, and fog computing would allow this on a more localized
level.
Fog Computing is about taking decisions as close to the data as possible. Hadoop
and other Big Data solutions have started the trend to bring processing close to where
the data is and not the other way around. Now Fog Computing is about doing the
same on a global scale. You want decisions to be taken as close to where the data is
generated and stop it from reaching global networks. Only valuable data should be
travelling on global networks.
Fog Computing is best done via machine learning models that get trained on a
fraction of the data on the Cloud. After a model is considered adequate then the model
gets pushed to the devices. Having a Decision Tree or some Fuzzy Logic or even a
Deep Belief Network run locally on a device to take a decision is lots cheaper than
setting up an infrastructure in the Cloud that needs to deal with raw data from millions
of devices. So there are economical advantages to use Fog Computing. What is
needed are easy to use solutions to train models and send them to highly optimized
and low resource intensive execution engines that can be easily embedded in devices,
mobile phones and smart hubs/gateways.
Future of Fog Computing
Fog computing can really be thought of as a way of providing services more
immediately, but also as a way of bypassing the wider internet, whose speeds are
largely dependent on carriers.
To avoid network bottlenecks, Google and Facebook are among several companies
looking into establishing alternate means of Internet access such as balloons and
drones. But smaller organizations could be able to create a fog out of whatever
devices are currently around to establish closer and quicker connections to compute
resources.
Cisco, which has invested $1 billion to be a pioneer in building next-
generation “Internet of Everything” services, is specifically seeking out new ways to
incorporate fog computing into service delivery.There will certainly still be a place

Computing
for more centralized and aggregated cloud computing, but it seems that as sensors
move into more things and data grows at an enormous rate, a new approach to hosting
the applications will be needed.Fog computing, which could inventively utilize
existing devices, could be the right approach to hosting an important new set of
applications.
Figure 2.2 Future of Fog Computing
Dept. of CSE, Dr TTIT, KGF 10 2016-
17

Computing
However, the movement to the edge does not diminish the importance of the
center. On the contrary, it means that the data center needs to be a stronger nucleus for
expanding computing architecture than it ever has before. InformationWeek
contributor Kevin Casey recently wrote that the cloud hasn’t actually diminished
server sales, as one might otherwise expect. Hybrid computing models, big data and
the Internet of Things have all contributed to server requirements that may be shifting,
but aren’t really abating as some experts had predicted.
The IoT is a relevant bridge to some of the biggest issues dividing the cloud
and the fog – bandwidth, which could lead to a hybrid fog-cloud model, as
organizations seek to balance their enterprise-grade data center needs with support for
increasing edge network growth.
Dept. of CSE, Dr TTIT, KGF 11 2016-
17

3. 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 defined boundaries. A user who’s outside the
established geographical boundaries is classified 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 realtime location.
Overall, we can dissect the system into three basic virtual layers, as Figure 3.1
illustrates.
Figure 3.1 System architecture.

Fog Based Emergency System Fog Based System
Architecture
The cloud orchestrates the virtual fog layer’s resources and the services.
(LoST: location-to-service translation, SDN: software-defined networking)
3.2 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 sufficient resources, ensuring uninterrupted operation
of the system.
3.3 Fog-Based Approach
The classic fog computing paradigm is a dispersed version of the cloud, where
distributed devices at the network’s edge host certain services 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.
3.4 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 management, the orchestrator is responsible for
deploying virtual services that facilitate the infrastructure’s intelligence.

Fog Based Emergency System Fog Based System
Architecture
Figure 3.4 Orchestration

4. System Overview
4.1 Profiling service
A profiling mechanism implemented in the fog separates users into two
categories: volunteers and persons of interest. The service maintains a non-SQL
database of user profiles stored in the fog, and containing personal, health, and
positioning information. It also contains users’ current status as safe or unsafe. User
profiles are dynamically updated by other services or authorities.
A user profile 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 first distress
message must be granted access to the full personal and medical information
contained inside the user’s profile. Volunteer responders, who will receive
complementary alert messages, require access only to basic user information along
with first-response instructions. To perform that task, the service creates two different
dynamic HTML5 pages containing the appropriate information for each actor type.
4.2 Positioning service
A positioning service periodically obtains the user’s received signal strength
indicator (RSSI) between the embedded device and the inhouse 5G small-cell Wi-Fi
interface. As long as the service receives RSSI measurements from the embedded
device, the user remains classified 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 profiling service, which
classifies 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

Fog Based Emergency System System
Overview
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 profile by probing the profiling service. Finally, the
service logic module acquires the user’s profile from the profiling service and notifies
the geographically nearest authority and possible nearby first responders by sending
them an alert banner containing information from the user’s profile and geographical
location, customized for each actor.
Figure 4.2 5g Small Cell
4.3 Service logic
In an emergency, first-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
service first 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 profile, along with the list of the nearest volunteers, from the
profiling service. After having collected all this information, it sends the nearest
PSAP an alert banner containing the user’s full profile and location. To reduce first
response time, the service also sends all nearby volunteers an alert banner containing
the user’s limited profile and location, along with a set of basic instructions on how to

Overview
respond and attend to the user in need. Lastly, it sends the limited user profile, along
with an interface-enabling signal, back to the embedded device.
4.4 Location-to-service translation
The LoST service uses the LoST protocol to find the geographically nearest
emergency response authority. As input, the service receives the user’s location and it
returns the URI of the nearest PSAP.
4.5 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–specific links, and connectivity management,
such as creating virtual networks required by the system.

Overview
4.6 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 service, 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.
Figure 4.6 Extreme Edge

5. Use Case Scenario
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 lowenergy, or BLE) interface to use as a
beacon. The interface, using the Google Eddystone open protocol
(https://github.com/google/eddystone/blob/master/ protocol-specification.md), broadcasts
a distress signal containing the user’s limited profile, 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 5.1 shows the architecture of the
embedded device.
Figure 5.1 Block view of embedded device architecture layers. (BLE: Bluetooth
low energy)
We divide our use case scenario into two phases. In the first phase, the user is
within the household boundaries and classified as safe, as depicted in Figure 5.2 . 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

Fog Based Emergency System Usecase
Scenario
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 predefined time period, the positioning service
notifies the service logic, which classifies the user as unsafe.
Figure 5.2 Overview of the system within the radius of the small cell. The user is
indoors and classified as safe.
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,

Scenario
which, in turn, locates and informs the authorities responsible for the user by invoking a
LoST service, providing them with the user’s full profile and geographical location
(Figure 5.3). In addition, the service logic module acquires a list of the nearest volunteer
responders from the profiling service, and provides them with a brief user profile, a set of
first-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 first-response instructions to any person passing by. Once found,
the user is classified as safe by the authority in charge of the situation or the system
administrator.
Figure 5.3 full profile banner for the public safety answering point
Figure 5.4 illustrates the second phase, and Figure 5.5 shows the sequence in
which the services are deployed and interacted with each other, along with the messages
they exchange.

Scenario
Figure 5.4 Overview of the system outside the radius of the small cell. The user is
outside of the household boundaries and thus classified as unsafe. (LoST: location-to-
service translation)

Scenario
Figure 5.5 Sequence diagram describing the interaction between the system
entities . (BLE: Bluetooth low energy, PSAP: public safety answering point, RSSI:
received signal strength indicator)

6. Performance Evaluation
To demonstrate the system’s functionality and efficiency, we defined several
experiments to validate the basic use case scenario where a user drifts away from the
predefined safety radius. The service logic classifies the user as unsafe and acquires the
URI of the geographically closest PSAP by invoking the LoST server, and consequently
collects the contact information of the geographically closest volunteers by probing the
profiling 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 first value is the time needed for the service to acquire the PSAP URI from
the moment the user is classified 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 predefined radius and notify the nearest liable authority, along with any
possible nearby volunteers, in approximately five seconds. The response time can
fluctuate 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.

CONCLUSION
Our future work will focus on adding telemedicine functionalities to the
proposed system, providing health measurements such as pulse, blood oxygen level,
airflow (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 services, but as contributors to the
infrastructure’s federated IT resource pool.
The proposed system can play a significant 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.

Bibliography
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[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, “Services and Applications in an
Ambient 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 Virtualization and Software
Defined Network (NFV-SDN 15), 2016, pp. 13–14.
Dept. of CSE, Dr TTIT, KGF 26
2016-17

Report-Fog Based Emergency System For Smart Enhanced Living Environment

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