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Help Me: Opportunistic Smart
Rescue Application and System
Osnat (Ossi) Mokryn, Dror Karmi, Akiva Elkayam, Tomer Teller
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
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)
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
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