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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 – PowerPoint PPT presentation

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Title: Data Dissemination in Wireless Networks - 7DS/MobEyes


1
Data Dissemination in Wireless Networks-
7DS/MobEyes
  • Mario Gerla and Uichin Lee
  • uclee_at_cs.ucla.edu

2
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
3
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
4
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

5
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)

6
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

7
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

8
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

9
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

10
Family of Access Points
11
Examples of Services using 7DS
12
Information Sharing with 7DS
cache miss
Host C
WLAN
cache hit
data
Host B
Host A
13
7DS options
Cooperation Server to client Peer to peer
Querying active (periodic) passive
14
Scalability Issues for Information Dissemination
15
Boundary Policies for Information Dissemination
Restrict the dissemination of a query
16
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
17
Data Holders () after 25 min
high transmission power
P2P
Mobile Info Server
Fixed Info Server
2
18
Scaling Properties of Data Dissemination

R
If cooperative host density transmission power
are fixed, data dissemination remains the same
19
Scaling Properties of Data Dissemination
20
Average delay (s) vs. dataholders ()Fixed Info
Server
one server in 2x2 high transmission power
4 servers in 2x2 medium transmission power
21
Average Delay (s) vs Dataholders ()Peer-to-Peer
schemes
high transmission power
medium transmission power
22
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
23
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.

24
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

25
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)

26
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

27
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

28
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

29
MobEyes Architecture
  • MSI Unified sensor interface
  • MDP Sensed data processing s/w (filters)
  • MDHP opportunistic meta-data diffusion/harvestin
    g

30
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)

31
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
32
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

33
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

34
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

35
Numerical Results
  • Numerical analysis

Area 2400x2400m2Radio range 250m nodes
200Speed 10m/sk1 (one hop relaying)k2 (two
hop relaying)
36
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
37
Simulation
  • Summary harvesting results with random waypoint
    mobility

38
Simulation
  • Summary harvesting results with real-track
    mobility
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