Help Me: Opportunistic Smart Rescue Application and System

1. Help Me: Opportunistic SmartRescue Application and System • Osnat (Ossi) Mokryn, Dror Karmi, Akiva Elkayam, Tomer Teller

2. Disaster Areas China 2010 Haiti 2010 Japan 2011 Turkey 2011,2012 Indian Ocean 2004 Chile 2010

3. 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?

4. 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

5. 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

6. 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

7. 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

8. 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

9. Classification Accuracy Obtained With Tailored Partial Dictionaries Based on globally available general database with categories

10. Root Law & Order emergency Medical Rescue Non-specific rescue xxxx fire xxxx xxx water? Rescue Categories • Hierarchy of categories • Each category is divided to several sub-categories

11. 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

12. 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

13. 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

14. 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

15. 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 4 Rescue 4 rescue fire 4 0

16. 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

17. 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

18. 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

19. iPhone Implementation

20. 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)

21. Smartphone App Lifecycle

22. Initial Screens

23. Experiments: The effects of Availability on Load • 4 devices corresponding to 2 skilled personnel and 2 victims • 4 different experiments with different availability settings

24. 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

25. 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

26. Thank you. Questions?

27. 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