Epidemic Dissemination, Network Coding and MIMO Multicasting Dawn Project Review, UCSC Sept 5, 06 Mario Gerla Computer Science Dept, UCLA gerla@cs.ucla.edu; www.cs.ucla.edu/NRL

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

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

MIMO-CAST Overview No RTS, CTS, and ACK IEEE 802.11 MAC broadcast/multicast mode Contention and collision Throughput loss Hidden terminal problem Solutions MPR: reducing duplicate packet transmissions MIMO: blocking interference signals

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

Motivation Using Network Coding: Using Rate Diversity and Network Coding

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.

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

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

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

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

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

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)

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

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?

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.

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.

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.

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 7/31/2007 45

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) 7/31/2007 46

Evaluation Framework Simulation setup Candidate algorithms NS-2 simulation IEEE 802.11 (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 7/31/2007 47

Performance Results Aggregate number of harvested data 7/31/2007 48

Performance Results Impact of vehicle speed 7/31/2007 49

Performance Results Insensitivity of DTF performance to parameters 7/31/2007 50

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