1. Epidemic Dissemination, Network Coding and MIMO Multicasting
Dawn Project Review,
UCSC Sept 5, 06
Mario Gerla
Computer Science Dept, UCLA

2. Outline MIMO cross-layer designs Network Coding Epidemic Dissemination
MIMO-Cast
MIMO and TCP
MIMO and multipath routing
Network Coding
NC and multicast over variable rate channels
NC and TCP (Piggycode)
Epidemic Dissemination
Dissemination/Harvesting Models
Bio-inspired Multi-agent Harvesting
Mobility Models
Secure incentives for data dissemination

3. MIMO-CAST: A Cross-Layer Ad Hoc Multicast Protocol Using MIMO Radios
Soon Y. OH and Mario Gerla
Computer Science Dept.
University of California, Los Angeles

4. MIMO-CAST Overview
No RTS, CTS, and ACK IEEE MAC broadcast/multicast mode
Contention and collision
Throughput loss
Hidden terminal problem
Solutions
MPR:
reducing duplicate packet transmissions
MIMO:
blocking interference signals

5. MULTI POINT RELAY
Lear topology up to 2 hops using Hello packet exchange
Selecting MPR neighbors (MPRs)
Only MPRs retransmit packets
Other nodes receive and process packets, but do not retransmit them
11 retransmission to diffuse a message up to 3 hops
Retransmission node
24 retransmissions to diffuse a message up to 3 hops
Retransmission node

6. MIMO MAC PROTOCOL MIMO benefit MIMO-CAST uses beamforming technique
Multiple signals
Increasing point-to-point capacity
Beamforming
Achieving space reuse A B C D
Interference range of A
Interference range of B
Reduced
interference
range of A A B C D E F
MIMO-CAST uses beamforming technique
Before transmitting a data packet, sending a CL (Channel Learning) packet including weight vector
Upon receiving CL packet, a node adjusts own weight vector
Blocking interference
If a node receive another CL packet, it recalculates own weight vector nulling other signals

7. SIMULATION RESULTS Node 1,2,3, and 4 receive duplicated packets
Compare MIMO-CAST with MPR with single antenna and original ODMRP 2 3 4 1
Source
Forwarder
Receiver

8. SIMULATION RESULTS
Data sending rate increases from 10packets/s to 200packets/s
1 source, 100 members among 200 nodes
Throughput and Normalized Overhead

9. Enhanced Multi-Path Data Transmission Using MIMO
Brian Choi and Mario Gerla

10. Assumptions Fading is flat (i.e. freq. independent).
Channel is symmetric and quasi-static.
Rich scattering - H is of full rank
We ignore the additive channel noise.
Perfect synchronization between nodes

11. Problem A B source destination receiving mode sending mode
A and B exchange two independent streams on the two paths
Assume 4x2 MIMO (4 transmitter x 2 receiver antennas)
The problem is valid from 1 to N parallel paths
Transmissions alternate left to right.

12. Sending Mode 4 DOFs needed at the transmitter
2 for transmitting 2 streams
2 for removing interference
Use Dirty Paper Coding to separate the streams and remove inter-symbol interference B A C
What Dirty Paper Coding (effectivelv) does is to enable A to estimate the interference that its signals (which are targeting specific antennas at B and C) will have on other antennas, and precode the signals by subtracting these interference before hand, essentially creating two non-interfering signals.
signals from A to B and C
interference-canceling signals
signals from other nodes

13. Six-fold Throughput Gain
C/3
C = single-hop capacity
traditional network
C/2
MIMO solution C
For N-path case, NC is achievable given that there are enough antennas to use.
bi-directional … NC 2C
bi-directional, 2 paths
bi-directional, N paths

14. Future Work in MIMO crosslayer
Realistic accounting of Channel Acquisition overhead
MIMO-Cast and Network Coding

15. Outline MIMO cross-layer designs Network Coding Epidemic Dissemination
MIMO-Cast
MIMO and TCP
MIMO and multipath routing
Network Coding
NC and multicast over variable rate channels
NC and TCP (Piggycode)
Epidemic Dissemination
Dissemination/Harvesting Models
Bio-inspired Multi-agent Harvesting
Mobility Models
Secure incentives for data dissemination

16. Luiz Filipe M. Vieira Archan Misra Mario Gerla
Network-Coding in Multi-Rate Wireless Environments for Multicast Applications
Luiz Filipe M. Vieira
Archan Misra
Mario Gerla

17. Problem Description
Investigate how Network Coding and Rate Diversity can improve multicast communications.
Interaction between network coding and link-layer transmission rate diversity in multihop wireless networks

18. Multi-Rate 802.11a/b/g Exploit rate diversity rate-range tradeoff
can reduce broadcast latency
rate-range tradeoff
if the same transmission power is used for all link transmission rates, then, in general, the faster the transmission rate, the smaller is the transmission range

19. Motivation
Node 1 wants to broadcast packet A and node 2 wants to broadcast packet B.
Broadcast scheduling using min rate:

20. Motivation Using Network Coding:
Using Rate Diversity and Network Coding

21. Rate Diversity and Network Coding
As expected Increasing the maximum transmission rate, we increase the throughput.
Increasing NC level increases the throughput.
Key observation: Rate diversity has a bigger determinant on throughput than Network Coding.

22. Network Coding-Future Work
TCP and Network Coding over multiple paths
Efficient use of TCP + multiple paths in challenged scenarios
Fairness and congestion control in multicast (with and w/o Network Coding)
Investigate ’throughput-latency’ tradeoffs of Network Coding in multi-rate environments

23. Outline MIMO cross-layer designs Network Coding Epidemic Dissemination
MIMO-Cast
MIMO and TCP
MIMO and multipath routing
Network Coding
NC and multicast over variable rate channels
NC and TCP (Piggycode)
Epidemic Dissemination
Dissemination/Harvesting Models
Bio-inspired Multi-agent Harvesting
Mobility Models
Secure incentives for data dissemination

24. 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.
Applications: traffic engineering, environment monitoring, civic and homeland security

25. Accident Scenario: Forensic Search
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

26. 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]
The goal of this thesis is to develop efficient and secure data harvesting protocols for mobile sensor platforms
But we have the following challenges.
1. First of all, sensor are mobile
2. In the case of vehicular network, we have to deal with large-scale deployment such as VSH in Los Angeles
3. In the case of underwater sensors, we are using acoustic channel. And also energy, and positioning is challenging.
From this, here are planned contributions.
First, we define mobile sensor platforms functions, requirements, characteristics
Propose, new data harvesting protocols for VSN and USN.
Evaluate protocols using analytical models, simulation and experiments

27. Mobility-assist Meta-data Diffusion/Harvesting
Exploit “mobility” to disseminate meta-data!
Mobile nodes are periodically broadcasting meta-data to 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)

28. Mobility-assist Meta-data Diffusion/Harvesting
HREP
HREQ
Agent harvests a set of missing meta-data from neighbors
So the cars moving. (Brown and Red cars)
Periodical meta-data broadcasting
+ Broadcasting meta-data to neighbors
+ Listen/store received meta-data

29. 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
We want to analyze the protocol in terms of average delay.
Here are the assumptions we have.
There are N disseminating nodes; that is regular mobile nodes.
And each node advertises event and moves on average speed “v”
Rho is the network density of disseminating nodes.
We use discrete time analysis at each time step delta t.
We want to estimate average event delivery delay
and average delay of harvesting all events?

30. Numerical Results Numerical analysis
Area: 2400x2400m2 Radio range: 250m # nodes: 200 Speed: 10m/s k=1 (one hop relaying) k=2 (two hop relaying)
Next we model the latency for an agent to harvest all events.
Let E*_t be expected number of events harvested by the agent.
This is very similar but we have blue terms. This is because, the agent
Queries its neighbors. Each node has collected E_t so far, it’ll return this
To the agent. But the problem here is that due to replication, we have more redundant
harvesting as time passes. To deal with this, let’s simply use ‘r_t’ as a damping function that is inversely proportional to time.

31. Simulation Simulation Setup Random waypoint (RWP) Real-track model:
Implemented using NS-2
802.11a: 11Mbps, 250m transmission range
Network: 2400m*2400m
Mobility Models
Random waypoint (RWP)
Real-track model:
Group mobility model
Merge and split at intersections
Westwood map
Westwood Area
We implemented this protocol using NS-2. Communication range used is 250m and the average speed was 10 m/s. The width of network is 2400m and for the mobility model we use RWP and RT.
RT uses group mobility where groups can merge and split at an intersection.
We use Westwood map for realistic simulations.

32. Simulation Summary harvesting results with random waypoint mobility
This graph shows data harvesting delay with RWP.
As you can see simulation results fit well with analytical results.

33. Multi-agent Harvesting Problem
Challenges
Dynamic nature of the environment: continuous creation and movement of data
Scale of operation: harvesting region may range over multiple city blocks
Location and nature of the critical information not known a priori
Approach
Social animals solve a similar problem – foraging to find reliable food sources
Animals solve the foraging problem quite efficiently using simple communications
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34. Algorithm Design Data-taxis Collision avoidance
Similar to the chemotactic behavior of E. coli
Modes of locomotion: tumble, swim, search
Algorithmic view: greedy approach with random search
Three modes of agent operation
Collision avoidance
Avoids collecting the same data by different agents
Implicit detection vs. pheromone trail
Move in a direction to minimize collision (Levy jump)
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35. Evaluation Framework Simulation setup Candidate algorithms
NS-2 simulation
IEEE (11Mbps, 250m)
Manhattan mobility model
Map of 7x7 grid (streets 2 and 6 with valuable information)
Up to 4 agents
Candidate algorithms
RWF (Random Walk Foraging)
BRWF (Biased RWF)
PPF (Preset Pattern Foraging)
DTF (Data-taxis Foraging)
7x7 Manhattan grid
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36. Performance Results Aggregate number of harvested data
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37. Performance Results Impact of vehicle speed
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38. Performance Results Insensitivity of DTF performance to parameters
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39. Epidemic Dissemination: Future Work
Scaling Laws for throughput and delay with limited buffer resources
Optimal replication rate to achieve faster dissemination (utility function approach)
Accounting for mobility pattern
Congestion prevention
Bio inspired dissemination/harvesting (cont)
Mobility models characterization