Data Dissemination in Wireless Networks - 7DS/MobEyes

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Data Dissemination in Wireless Networks-
7DS/MobEyes

• Mario Gerla and Uichin Lee
• uclee_at_cs.ucla.edu

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7DS

• Introduction
• Motivation
• Overview of 7DS
• Performance analysis on 7DS
• Conclusions
• Future work

Slides from Maria Papadopouli Henning
Schulzrinne hgs_at_cs.columbia.edu http//www.cs.colu
mbia.edu/IRT
Maria Papadopouli Henning Schulzrinne, Effects
of power conservation, wireless coverage and
cooperation on data dissemination among mobile
devices, Mobihoc01
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Characteristics of Wireless Data Access Settings
Heterogeneity of devices access methods
Changes in data availability due to host mobility
Heterogeneous application requirements on delay,
bandwidth, accuracy
Spatial locality of information
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Limitations of 802.11

• Good for hotspots, difficult for complete
  coverage
• Manhattan 60 km2 ? 6,000 base stations (not
  counting vertical)
• With 600,000 Manhattan households, 1 of
  households would have to install access points
• Almost no coverage outside of large coastal cities

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Mobile Data Access

• Hoarding grab data before moving
• 802.11, 3G, Bluetooth wireless last-hop access
  technology
• Ad-hoc networks
• Wireless nodes forward to each other
• Routing protocol determines current path
• Requires connected network, some stability
• Mobility harmful (disrupts network)
• 7DS networks
• No contiguous connectivity
• Temporary clusters of nodes
• Mobility helpful (propagates information)

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Limitations of Infostations Wireless WAN

• Require communication infrastructure
• not available field operation missions,
  tunnels, subway
• Emergency
• Overloaded
• Expensive
• Wireless WAN access with low bit rates high
  delays

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Challenge

• Accelerate data availability enhance
  dissemination discovery of information under
  bandwidth changes intermittent connectivity to
  the Internet due to host mobility
• considering energy, bandwidth memory
  constraints of hosts

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Our Approach 7DS

• 7DS Seven Degrees of Separation
• Increase data availability by enabling devices to
  share resources
• Information sharing
• Message relaying
• Bandwidth sharing
• Self-organizing
• No infrastructure
• Exploit host mobility

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7DS

• Application
• Zero infrastructure
• Relay, search, share disseminate information
• Generalization of infostation
• Sporadically Internet connected
• Coexists with other data access methods
• Communicates with peers via a wireless LAN
• Energy constrained mobile nodes

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Family of Access Points
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Examples of Services using 7DS
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Information Sharing with 7DS
cache miss
Host C
WLAN
cache hit
data
Host B
Host A
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7DS options
Cooperation Server to client Peer to peer
Querying active (periodic) passive
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Scalability Issues for Information Dissemination
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Boundary Policies for Information Dissemination
Restrict the dissemination of a query
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Simulation Environment
pause time 50 s mobile user speed 0 .. 1.5
m/s host density 5 .. 25 hosts/km2 wireless
coverage 230 m (H), 115 m (M), 57.5 m
(L) ns-2 with CMU mobility, wireless
extension
querier
wireless coverage
1m/s
pause
mobile host
data holder
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Data Holders () after 25 min
high transmission power
P2P
Mobile Info Server
Fixed Info Server
2
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Scaling Properties of Data Dissemination

R
If cooperative host density transmission power
are fixed, data dissemination remains the same
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Scaling Properties of Data Dissemination
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Average delay (s) vs. dataholders ()Fixed Info
Server
one server in 2x2 high transmission power
4 servers in 2x2 medium transmission power
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Average Delay (s) vs Dataholders ()Peer-to-Peer
schemes
high transmission power
medium transmission power
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MobEyes Smart Mobs for Proactive Urban
Monitoring with VSN

• Introduction
• Scenario
• Problem Description
• Mobility-assist Meta-data Diffusion/Harvesting
• Diffusion/Harvesting Analysis
• Simulation

Uichin Lee, Eugenio Magistretti, Biao Zhou,
Mario Gerla, Paolo Bellavista, Antonio Corradi
"MobEyes Smart Mobs for Urban Monitoring with a
Vehicular Sensor Network," IEEE Wireless
Communications
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Vehicular Sensor Network (VSN)

• Onboard sensors (e.g., video, chemical, pollution
  monitoring sensors)
• Large storage and processing capabilities (no
  power limit)
• Wireless communications via DSRC (802.11p)
  Car-Car/Car-Curb Comm.

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Vehicular Sensor Applications

• Traffic engineering
• Road surface diagnosis
• Traffic pattern/congestion analysis
• Environment monitoring
• Urban environment pollution monitoring
• Civic and Homeland security
• Forensic accident or crime site investigations
• Terrorist alerts

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MobEyes Smart Mobs for Proactive Urban
Monitoring with VSN

• Smart mobs people with shared interests/goals
  persuasively and seamlessly cooperate using
  wireless mobile devices (Futurist Howard
  Rheingold)
• Smart-mob-approach for proactive urban monitoring
  
• Vehicles are equipped with wireless devices and
  sensors (e.g., video cameras etc.)
• Process sensed data (e.g., recognizing license
  plates) and route messages to other vehicles
  (e.g., diffusing relevant notification to drivers
  or police agents)

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Accident Scenario Storage and Retrieval

• Private Cars
• Continuously collect images on the street (store
  data locally)
• Process the data and detect an event (if
  possible)
• Create meta-data of sensed Data -- Summary
  (Type, Option, Location, Vehicle ID, )
• Post it on the distributed index
• The police build an index and access data from
  distributed storage

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Problem Description

• VSN challenges
• Mobile storage with a sheer amount of data
• Large scale up to hundreds of thousands of nodes
• Goal developing efficient meta-data
  harvesting/data retrieval protocols for mobile
  sensor platforms
• Posting (meta-data dissemination) Private Cars
• Harvesting (building an index) Police
• Accessing (retrieve actual data) Police

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Searching on Mobile Storage- Building a
Distributed Index

• Major tasks Posting / Harvesting
• Naïve approach Flooding
• Not scalable to thousands of nodes (network
  collapse)
• Network can be partitioned (data loss)
• Design considerations
• Non-intrusive must not disrupt other critical
  services such as inter-vehicle alerts
• Scalable must be scalable to thousands of nodes
• Disruption or delay tolerant even with network
  partition, must be able to post harvest
  meta-data

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MobEyes Architecture

• MSI Unified sensor interface
• MDP Sensed data processing s/w (filters)
• MDHP opportunistic meta-data diffusion/harvestin
  g

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Mobility-assist Meta-data Diffusion/Harvesting

• Lets exploit mobility to disseminate
  meta-data!
• Mobile nodes are periodically broadcasting
  meta-data of sensed data to their neighbors
• Data owner advertises only his own meta-data
  to his neighbors
• Neighbors listen to advertisements and store them
  into their local storage
• A mobile agent (the police) harvests a set of
  missing meta-data from mobile nodes by actively
  querying mobile nodes (via. Bloom filter)

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Mobility-assist Meta-data Diffusion/Harvesting
Agent harvests a set of missing meta-data from
neighbors
Periodical meta-data broadcasting
Broadcasting meta-data to neighbors
Listen/store received meta-data
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Diffusion/Harvesting Analysis

• Metrics
• Average summary delivery delay?
• Average delay of harvesting all summaries?
• Analysis assumption
• Discrete time analysis (time step ?t)
• N disseminating nodes
• Each node ni advertises a single summary si

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Diffusion Analysis

• Expected number (a) of nodes within the radio
  range
• ? network density of disseminating nodes
• v average speed
• R communication range
• Expected number of summaries passively
  harvested by a regular node (Et)
• Prob. of meeting a not yet infected node is
  1-Et-1/N

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Harvesting Analysis

• Agent harvesting summaries from its neighbors
  (total a nodes)
• A regular node has passively collected so far
  Et summaries
• Having a random summary with probability Et/N
• A random summary found from a neighbor nodes with
  probability 1-(1-Et/N)?
• Et Expected number of summaries harvested by
  the agent

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Numerical Results

• Numerical analysis

Area 2400x2400m2Radio range 250m nodes
200Speed 10m/sk1 (one hop relaying)k2 (two
hop relaying)
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Simulation

• Simulation Setup
• Implemented using NS-2
• 802.11a 11Mbps, 250m transmission range
• Network 2400m2400m
• Mobility Models
• Random waypoint (RWP)
• Real-track model
• Group mobility model
• Merge and split at intersections
• Westwood map

Westwood Area
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Simulation

• Summary harvesting results with random waypoint
  mobility

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Simulation

• Summary harvesting results with real-track
  mobility