next-gen emergency-aware platform based on microservices and …€¦ · next-gen emergency-aware...
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Next-Gen Emergency-Aware Platform Based on Microservices and Smart Computing
Copyright © Prodapt Solutions
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
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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
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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
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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.
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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
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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
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Customer Product Cart
Rating Order Account
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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
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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
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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
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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)
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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
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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
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