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 ofBachelor of Engineering

InComputer Science and Engineering

OfVISVESVARAYA TECHNOLOGICAL UNIVERSITY, BELGAUM

ByM.KARTHIK

(1GV10CS022)Under the guidance

OfMrs. 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

CERTIFICATEThis 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)

ACKNOWLDGEMENTThe 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.

CONTENTSSl.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 no1

23

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

Fog Based Emergency System 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.



1.1 Ambient Assisted LivingThere 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



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



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 ComputingCloud 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.



1.3 Fog ComputingFog 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 ComputingFog 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 ComputingTech 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.


http://whatis.techtarget.com/definition/smart-grid.html


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 ComputingFog 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



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



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.



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 ApproachA 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 ApproachThe 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 OrchestrationThe 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 serviceA 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 serviceA 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 logicIn 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



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 translationThe 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 networkingThe 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.



4.6 Extreme EdgeEach 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 Dept. of CSE, Dr TTIT, KGF 19 2016-17

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, Dept. of CSE, Dr TTIT, KGF 20 2016-17


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.



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)



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.



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