data dissemination in wireless networks - 7ds/mobeyes mario gerla and uichin lee [email protected]
TRANSCRIPT
7DS*
Introduction Motivation Overview of 7DS
Performance analysis on 7DS Conclusions Future work
Slides from:Maria Papadopouli
Henning [email protected]
http://www.cs.columbia.edu/IRT
* Maria Papadopouli Henning Schulzrinne, Effects of power conservation, wireless coverage and cooperation on data dissemination among mobile devices, Mobihoc’01
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
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
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
Our Approach: 7DS
7DS = Seven Degrees of SeparationIncrease data availability by enabling devices to share
resources– Information sharing– Message relaying– Bandwidth sharing
Self-organizing No infrastructure Exploit host mobility
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
Family of Access Points
Disconnected Infostation
2G/3G
access sharing7DS
Connected Infostation
WLAN
Examples of Services using 7DS
schedule infoautonomous
cache
traffic, weather, maps, routes, gas station
WANWANnews
where is the closest Internet café ?
service location queries
events in campus,pictures
pictures, measurements
Host B
Host C
data cache hit
cache miss
data
Host A
query
WANWAN Host A Host D
query
WLAN
WLAN
Information Sharing with 7DS
7DS options
Forwarding
Host A Host B
query
FWquery
Host C
time
Querying
active (periodic)
passive
Energy conservation
on
off time
communication enabled
CooperationServer to client
Peer to peer
server to client only server shares datano cooperation among clients• fixed info server (infostation model)• mobile info server peer to peer data sharing among peers
Scalability Issues for Information Dissemination
Boundary Policies for Information Dissemination
Restrict the dissemination of a query
Simulation Environment
pause time 50 smobile user speed 0 .. 1.5 m/shost density 5 .. 25 hosts/km2
wireless coverage 230 m (H), 115 m (M), 57.5 m (L)
ns-2 with CMU mobility, wireless extension
pause1m/s
mobile host data holder
querierwireless coverage
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25
Density of hosts (#hosts/km )
Dataholders (%)
P2P data sharing(power cons.)
P2P data sharing
P2P data sharing & FW(power cons.)
Fixed Info Server
Mobile Info Server
Data Holders (%) after 25 min
high transmission power
2
Fixed Info Server
Mobile Info Server
P2P
Scaling Properties of Data Dissemination1
km
1 km
If cooperative host density & transmission power are fixed, data dissemination remains the same
R
2 km
2 k
m
R
wireless coverag
e
Scaling Properties of Data Dissemination
R
R/2
For fixed wireless coverage, the larger the density of cooperative hosts, the more efficient the data dissemination
wireless coverage
Average delay (s) vs. dataholders (%)Fixed Info Server
0
200
400
600
800
1000
1200
0 5 10 15 20 25 30 35Dataholders (%)
Average Delay (s)
Fixed Info Server(medium transmission power) 4 initial dataholders (servers) in 2x2
Fixed Info Server (high transmission power ) one initial dataholder (server) in 2x2
one server in 2x2high transmission power
4 servers in 2x2medium transmission power
Average Delay (s) vs Dataholders (%)Peer-to-Peer schemes
0200400600800
1000120014001600
0 10 20 30 40 50 60 70 80 90 100Dataholders (%)
Average Delay (s)
P2P (high transmission power) one initial dataholder & 20 cooperative hosts in 2x2
P2P(medium transmission power) one initial dataholder & 20 coperative hosts in 1x1
medium transmission power
high transmission power
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
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.
VSN-enabled vehicle
Inter -vehiclecommunications
Vehicle -to-roadsidecommunications
Roadside base station
Vid e o Ch e m.
Sensors
S to ra g e
Systems
P ro c.
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
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)
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
CRASH
- Sensing - P rocessing
Crash Summary Reporting
Summary Harvesting
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]
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”
MobEyes Architecture
MSI (Sensor I nterface )
DSRC Compliant Driver
MDP (Data Processing)
J 2SE
J MF AP IJ ava Comm.
AP IJ ava Loc .
AP I
GP SRadio T ransceiver
Bio /ChemSensors
A/VSensors
MDHP (Diffusion/Harvesting)
SummaryDatabase
Raw DataStorage
MSI : Unified sensor interface MDP : Sensed data processing s/w (filters) MDHP : opportunistic meta-data diffusion/harvesting
Mobility-assist Meta-data Diffusion/Harvesting Let’s 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)
Mobility-assist Meta-data Diffusion/Harvesting
+ Broadcasting meta-data to neighbors+ Listen/store received meta-data
Periodical meta-data broadcasting
Agent harvests a set of missing meta-data from neighbors
HREQ
HREP
Diffusion/Harvesting Analysis Metrics
Average summary delivery delay? Average delay of harvesting all summaries?
Analysis assumption Discrete time analysis (time step Δt) N disseminating nodes Each node ni advertises a single summary si
Diffusion Analysis
Expected number (α) of nodes within the radio range ρ : 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
2Rs=vΔt
Harvesting Analysis
Agent harvesting summaries from its neighbors (total α nodes)
A regular node has “passively” collected so far Et summaries Having a random summary with probability Et/N
A random summary found from α neighbor nodes with probability 1-(1-Et/N)
E*t : Expected number of summaries harvested by the agent
Numerical Results
Numerical analysis
Area: 2400x2400m2
Radio range: 250m # nodes: 200Speed: 10m/sk=1 (one hop relaying)k=2 (two hop relaying)
Simulation
Simulation Setup Implemented using NS-2 802.11a: 11Mbps, 250m tran
smission range Network: 2400m*2400m Mobility Models
Random waypoint (RWP) Real-track model:
Group mobility model Merge and split at intersections Westwood map
Westwood Area
Simulation
Summary harvesting results with random waypoint mobility
Simulation
Summary harvesting results with real-track mobility