Help Me: Opportunistic Smart

Rescue Application and System

Osnat (Ossi) Mokryn, Dror Karmi, Akiva Elkayam, Tomer Teller

Chile 2010

Japan 2011 Haiti 2010

Indian Ocean 2004

China 2010

Turkey 2011,2012

Disaster Areas

Disaster Areas

• When disaster strikes

- Communication infrastructure is damaged

- Rescue forces take time to arrive, organize

• First hours are crucial

- Skilled people, no communication

- Everybody (almost) has a smartphone with 802.11

- How do we enable smart communication between people over the spontaneously formed ad-hoc 802.11 network of smartphones?

HelpMe In a Nutshell

• A self-learning ad-hoc network of smartphones formed opportunistically

• Smart communication:A request is delivered to the best matching person that is close enough

• Messages are forwarded based on matching of user generated content to users’ skills

• Ad-hoc routing based on our matching algorithm within the opportunistic network

• Messages are routed to the best receiver

- The network is unlimited in size, locality considerations

Problem Formalizing

• Unlimited number of people, with different skills

- Nodes number is not bounded (N)

- Each node has a set of skills |k|={0,1,...K}

- No global knowledge

• People can ask or request anything

- Unlimited number of possible classifications

- Spontaneous requests, no local \ global knowledge

• Power limitations at some or all of the nodes

Scenario Limitations

• Let us consider a cloud-based Q&A scenario

- Questions are classified using Google

- “Apple” is 50% hi-tech, 50% fruit

- Matching can be based on

- Users’ ratings, location, etc.

- Overall knowledge

• Crisis situation

- Classification based on local dictionary

Prerequisites

• When a user registers and downloads HelpMe:

- Cloud service. Please be prepared.

- Specifies skills

- Can be automated with corresponding agencies

- Service creates

- A list of categories of skills (or none)

- Tailored dictionary for classification

- Downloaded app is tailored to each user

Local Tailored Dictionary

• Classification requires a dictionary

• Smartphones are limited in resources

- Memory, power consumption

• Per user tailored dictionary created at registration

- Either skills-based or general

• Classification using local dictionary

Classification Accuracy Obtained With Tailored Partial

Dictionaries

Based on globally available general database with categories

Rescue Categories

Root

Non-specific Medical Rescue

Law & Order

xxxxxxxfirexxxxwater?

rescue

emergency

• Hierarchy of categories

• Each category is divided to several sub-categories

When a disaster strikes..

• Activate app

• Smartphone is used in a peer-to-peer mode over the spontaneous opportunistic ad-hoc network formed by the app

• Requests are generated spontaneously upon need

• Neighboring devices exchange skill sets and location coordinates during a short hello

Initial hello - exchange skill sets

WiFi: received power (in dBm) decays ~ as a function of the log of the

distance.Each 802.11b hop: indoor 50m, outdoor 80-120m

Questions Classification

• Each word is classified and returns its set of values per category (if at all)

- Using a Naive base classification

• The union of all values per category is calculated:

• Resulting classification

- Only the highest category is chosen and published

- The n-th top categories are chosen and published

How to Match?

• Matching algorithm tries to route to best matching person to help

- Compares classified query categories to neighbors skills

• A nearby may seem able to help, but doesn’t..

- Create ranks per skill per person

- Prefer a highly ranked neighbor

Ranks

• Each node’s set of skills are assigned ranks

• A rank corresponds to the user’s

- Responsiveness

- Quality of help

• To enable ranking a feedback mechanism

must be employed (i.e., )

Root

Rescue

firerescu

e4 0

4

4

Matching Algorithm

• Given a peer k with m subscribed interests:

• Given a request R is classified to categories as follows:

• The request R is matched to peer k if:

where T is a predefined threshold

Matching Based Routing

• A request is classified at the sending side

• Categories are matched to neighbors ranked skills

• Forwarded (directly) to best matching neighbor

• Re-classification at receiving node

• Forwarding (directly) if a better matching exists AND {number of hops} < Threshold

• ==> End receiver is the best possible match

User Controlled Load

• Users can control their received load automatically

- A highly skilled professional who helps can be overloaded

• An availability setting determines load:

- Accept all: users become forwarding hubs.

- Accept by skills: normal matching

- Accept by expertise only: filter out non-specific requests within expertise

- Accept only emergency

iPhone Implementation

Haggle: A publish-subscribe middleware for exchanging interests [Diot

et al., 2006]. • MobiClique: Middleware for Mobile Social Networking Users that share interests are notified of each other• The MobiSoC Middleware for Mobile Social Computing: Challenges, Design, and Early Experiences Applications• Using Haggle to Create an Electronic Triage Tag• Socially-Aware Routing for Publish-Subscribe in Delay-Tolerant Mobile Ad Hoc Networks (predict routing according to social knowledge)

Smartphone App Lifecycle

Initial Screens

Experiments: The effects of

Availability on Load • 4 devices

corresponding to 2 skilled personnel and 2 victims

• 4 different experiments with different availability settings

Server Post-Processing

• All communication is stored locally

• When the server is available, everything is upload to it

• Location of all neighbors through out crisis

- Missing people services

• Stats

Conclusions• We presented a tailored application

- Applicable also to rural areas, hiking, etc.

• The solution is general for any spontaneous ad-hoc opportunistic network

- Who wants to go play tennis/ swim?

- Who wants to share a taxi to Larnaka?

- Where can I find a good sea-food restaurant around?

• Ranking makes it reliable

Thank you. Questions?

Our HelpMe System

• Efficient the emergency service

• Creates on-the-fly routes between people

• Finds the most suitable person to help within a neighborhood

• Post event, when communication is restored

- Analyze the events

- Help in locating lost people