Next-Gen Emergency-Aware Platform Based on Microservices and …€¦ · Next-gen emergency-aware service is a customizable, context-aware machine-to-machine (M2M) solution, leveraging

Next-Gen Emergency-Aware Platform Based on Microservices and Smart Computing

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9-1-1 has long been the standard emergency awareness/response system in the US

Started in 1967, 9-1-1 emergency number was made available to 50% of the population in next 25 years. By 1999, the availability was 93% and by 2014, 99%.

An estimated 240 million calls are made to 9-1-1 in the US each year. In many areas, 70% or more are from wireless devices.

The client supports three million 9-1-1 emergency calls a year and delivers more than 40 million next-generation (wireless) 9-1-1 calls.

Wire-Line Calls

Exchange

PSTN (Public

Switched Telephone Network)

SELECTIVE ROUTER

ANI (Automatic

Number Information)

ANI (Automatic

Location Information)

Location Database

All Databases MSAG

(Master Sheet Address Guide)

PSAP (Public Safety

Answering Point)

Computer-Assisted Dispatch System

Caller Location Information

Cellular Network

Emergency Response

Wireless Calls

GPS/A-GPS Data

Radiolocation Data

Network-Based Data (Phase II) Cell-Tower Location Data

Emergency Services

ANI

PSAP

Voice + ANI

PSAP

PSAP

Voice + ANI

911 Tandem

Local Exchange Carrier Service Voice + ANI

911


The major issues with traditional emergency response are lack of relevant and useful information and technical challenges with legacy systems

Lack of interconnectivity Traditional emergency response had very minimal automation because of less connectivity. Also it was very difficult to customize as and when the need arises because of monolithic architecture.

E.g., During 9/11 attack, many fire-fighters lost their lives in a building collapse as their systems were not integrated with the warning system issued by the NYPD.

Lack of relevant Information The amount of information received from traditional emergency response is limited as it is dependent on the amount of information conveyed through a call with some additional supplementary location information.

E.g., The average 911 response time to high-priority incidents is 11 minutes. Majority of this time goes in explaining who the victim is, giving the exact location, the nature of the emergency, etc. Other supplementary information, like temperature, fire threat, etc., is also not easily available

Lack of real-time intelligence The traditional emergency response could not predict a situation because of the lack of real-time user data. This resulted in slower communication during emergency and non-proactive action.

E.g., During Hurricane Katrina, thousands of volunteers and amateur radio operators came to the area. However, their efforts were scattered as there was no clear information on the worst affected areas.

Extreme interdependence of modules The emergency response consisted of 3 modules – Device Module, User Module and Service Module. Scaling up of individual modules is not possible.

With newer sensors and social media information being constantly integrated, the non-modular nature of the architecture causes technical challenges.

Upgrading or altering is difficult

Entire web application needs to be shut down

even for a minor change in one of the modules.

E.g., To update the location and capacity of newly installed water hydrants in just a couple of blocks, the entire application needs to be shut down which is difficult for a mission critical application.

Challenges With traditional Emergency Response


The next-generation emergency response will address challenges in traditional emergency response with Microservices and smart computing

Next-gen emergency-aware service is a customizable, context-aware machine-to-machine (M2M) solution, leveraging smart objects and sensors to provide improved safety and security, better situational awareness, and improved risk management.

Next-gen emergency response allow for additional information sources to be plugged in easily because of its Microservices architecture while enhancing reliability and scalability.

Built-in smart computing surface relevant information for 911 call handlers and dispatchers. Machine learning algorithms provide predictive information enhancing quality of service for call handlers.

Weather Sensor

Fire/Smoke Sensor

SE

NS

OR

DA

TA

Video

Chemical Sensor

Air Quality Sensor

THE CLIENT’S EMERGENCY-AWARE CLOUD

MOBILE DEVICE

RU

LE

-BA

SE

D

DE

CIS

ION

S

RE

AL

-TIM

E S

EN

SO

RS

WEB INTERFACE


Why Microservices and smart computing

Extreme interdependency of modules

Upgrading or altering is difficult

Lack of interconnectivity

Technical Challenges

Data-Related Challenges

Lack of relevant Information

Lack of real-time intelligence

MICROSERVICES

SMART COMPUTING

Each Microservice is independent of the other one – this allows for individual altering, upgrading, and scaling up of module without disturbing others.

Building and managing Microservices-based emergency response is easier in a cloud-based architecture.

Allows for integration of third-party API (application programmable interface).This gives access to third party data sources.

Allows real-time change in data in emergency response without changing the entire code, unlike the previous version.


Microservices will allow for easy integration with multiple systems while maintaining a decoupled architecture

Microservices architecture is a service-oriented architecture composed of loosely coupled elements that have bounded contexts. These services are small, highly decoupled and focus on doing a small task thereby facilitating a modular approach to system-building.

Emergency Response is built using a Microservices architecture which is a software architecture style in which complex applications are composed of small, independent processes communicating with each other using language-agnostic APIs.

Without Microservice components

Deploy and run the entire application as one big unit

With Microservice components

Deploy and run individual parts of a solution separately and independently

Microservices will also improve reliability since individual components can continue to operate even if parts of the system fail

Deployment Package Deployment Package

Page 1 Page 2 Page 3


Customer Product Cart

Rating Order Account

User Experience

Application Controller

Business Logic

Data Access

Application Controller A

Application Controller B

Data Access A

Data Access B

Data Access C



Customer Product Cart

Rating Order Account


Smart computing will provide actionable information to 911 operators and dispatchers

A new generation of integrated hardware, software, and network technologies that provide IT systems with real-time awareness of the real world and advanced analytics.

It helps in making more intelligent decisions about alternatives and actions that will optimize business processes and business balance sheet results.

Real-time or in-memory analytics is a key technology for making sense of the transactional data coming from awareness devices.

Accessing multiple sources of data and being able to surface the most relevant data is the focus of smart computing in emergency response.

Awareness Sensors, radio frequency ID, video monitoring, machine-to-machine, global positioning system data, social intelligence, data repositories

Au

dit

ab

ilit

y Analysis Real-time analytics, big data tools

Alternatives Business process management (BPM), rules engines, workflow

Mobility, smartphones, tablets Access

Collaboration environment, dynamic case management, project portfolio management, supplier risk and performance management, BPM

Assembly

Process applications, BPM, dynamic case management Actions

Sensors

RFID

M2M Video Monitoring

Social Media Monitoring

GPS Location Signals

Sta

tus

&

Co

nd

itio

n

Ide

nti

ty a

nd

L

oca

tio

n

Physical Assets Human Capital Assets


Emergency response architecture – based on Microservices and smart computing

Cloud Connectivity

Gateway Connectivity

Direct Connectivity

Smart Computing – Context-aware engine receives data from external sources and discovers the category of data like law, fire, medical, etc. It then uses different algorithms to enable quick and efficient incident response, e.g., location and distance of nearest water hydrant and its capacity.

Orchestration Node J.S

Emergency Response Mobile

Authentication & Authorization Secured With

OAuth 2.0

Emergency Response Application

Emergency Response Core

Services

Device Management

Services

Me

ssa

ge

Message

Event Services

Rule Engine Services

Data Analytics

Event Publisher

Notification Service

Prediction Service

Data Visualization

External API

DATABASE SERVICE

Da

ta

Message

Context-Aware Engine

Context-Aware Service – Receives data from external source, predicts the context and sends notifications to the relevant user, e.g., based on the feeds coming from building CCTV cameras, sensor information, social media, etc. it can notify fire department of a possible fire even before somebody calls and asks for help.

External Data Source Service - Integrates third party APIs enabling access to external sources of information like air quality, weather information, flood levels, hydrant information


Emergency response using Microservices and smart computing - benefits

Proactive monitoring of critical infrastructure, assets, and resources

Improved asset utilization and performance because of layered architecture of Microservices

Improved enterprise risk management and situational awareness

Intelligently process multiple data elements enabling in-depth logging, trending and analytical capabilities

Increased safety and security of employees and assets

Emergency Response UX Touch Point Apps/Devices

Touch-Point-Specific Microservices APIs

Map Display Social Media Feeds Notifications/SMS

Reporting/Analytics Local Storage

Alarm Event Rule Engine Context Aware

Event Publishing External Data Source

Emergency Response Core Device Management

Traditional Applications

UX

La

ye

r B

usi

ne

ss L

ay

er

Emergency Response Microservices API Within Layered Structure

Business Specific Microservices APIs


Next-generation emergency-aware services

Fully integrated communication system

Wired and wireless telephony, radio, text-based communication for incoming and outgoing communications, including new channels based on visual components and social media.

Geographic information systems (GIS)

Location and positioning solutions such as call and asset location, vehicle positioning and tracking,

possibility of adding multiple layers of information from different sources, including 3D views, CCTV,

traffic, and weather information.

Solutions for all operational phases

Call acceptance, resource dispatching, and event management to case

completion and evaluation

Reporting and analysis

Support for reports, statistics and operations analysis, including management dashboards

Planning tools

For staff, vehicles, bases, and overall equipment needed by emergency response organizations.

Highest guaranteed security standards

Backup, business continuity program (BCP), and disaster recovery (DR)


A next-generation emergency response in action

Take the example of a hurricane hitting the Atlantic coast. The next-gen emergency response can integrate air quality data, sensor data, current weather, live-stream from nearby cameras, and social media information.

Emergency response can directly take input from various CCTV sources, social media feeds, sensors etc., and identify secure spots for relocation.

It can also integrate all the wireless and wired communications coming its way to dispatch the relevant help at the required places.

Based on the analytical reports from emergency response, danger zones can be identified and monitored. On the basis of forecast, alerts can be issued and services dispatched even before the incident.

EMERGENCY ALERT SYSTEM

Issued a Tornado Warning


Future of emergency-aware services

Emergency response is evolving into a plug-and-play architecture model where

other services can easily leverage

features of the next-generation emergency response platform.

The future of emergency response may be

deployment of artificial intelligence robots and a system which could provide a more streamlined way for robots and drones

to communicate with humans in difficult situations, including during emergency-

response operations. Real-time or in-memory analytics is a key for making sense of the

transactional data coming from the devices.

The emergency-aware services can be further used across industry verticals like

manufacturing, mining, etc., for

safety and faster communication with less/no

customizations in various services.

Ability to communicate to 911 through any connected device and share any form of data. This is done

using emergency services IP network (ESInet) which in turn needs 911

services to be IP-based.

911

Chennai

Johannesburg

New York

Dallas Tualatin

Amsterdam

London

THANK YOU!

Prodapt Solutions Pvt. Ltd. Chennai:

INDIA

1. Prince Infocity II, OMR Ph: +91 44 4903 3000 Fax: +91 44 4903 3010

2. “Chennai One” SEZ, Thoraipakkam Ph: +91 44 4230 2300 Fax: +91 44 4903 3010

SOUTH AFRICA USA

Prodapt North America Tualatin: 7565 SW Mohawk St., Ph: +1 503 636 3737 Fax: +1 503 885 0850

Dallas: 222 W. Las Colinas Blvd., Irving Ph: +1 972 201 9009 Fax: +1 972 501 9019

New York: 1 Bridge Street, Irvington Ph: +1 646 403 8158

Prodapt SA (Pty) Ltd. Johannesburg: No. 3, 3rd Avenue, Rivonia Ph: +27 (0) 11 259 4000 Fax: +27 (0) 11 259 4111

THE NETHERLANDS

Prodapt Solutions Europe Amsterdam: Zekeringstraat 17A, 1014 BM Ph: +31 (0) 20 4895711

Prodapt Consulting BV Rijswijk: De Bruyn Kopsstraat 14 Ph: +31 (0) 70 4140722 Fax: +31 70 3030047

UK

Prodapt (UK) Limited London: Devonshire House, 60 Goswell Road, EC1M 7AD Ph: +44 (0) 11 8900 1068 Fax: +44 (0) 11 8900 1069

Bengaluru Thank You

Documents

Next-Gen Emergency-Aware Platform Based on Microservices and …€¦ · Next-gen emergency-aware service is a customizable, context-aware machine-to-machine (M2M) solution, leveraging