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Help Me: Opportunistic Smart Rescue Application and System Osnat (Ossi) Mokryn, Dror Karmi, Akiva Elkayam, Tomer Teller
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Chile 2010 Japan 2011 Haiti 2010 Indian Ocean 2004 China 2010 Turkey 2011,2012 Disaster Areas
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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?
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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
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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
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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
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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
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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
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Classification Accuracy Obtained With Tailored Partial Dictionaries Based on globally available general database with categories
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Rescue Categories Root Non-specific MedicalRescue Law & Order xxxxxxxfirexxxx water? rescu e emergency Hierarchy of categories Each category is divided to several sub- categories
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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
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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
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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
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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
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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 fire rescu e 4 0 4 4
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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
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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
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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
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iPhone Implementation
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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)
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Smartphone App Lifecycle
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Initial Screens
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Experiments: The effects of Availability on Load 4 devices corresponding to 2 skilled personnel and 2 victims 4 different experiments with different availability settings
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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
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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
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Thank you. Questions?
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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
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