Data Dissemination in Wireless Networks - 7DS/MobEyes Mario Gerla and Uichin Lee

7DS* Introduction  Motivation  Overview of 7DS Performance analysis on 7DS Conclusions Future work Slides from: Maria Papadopouli Henning Schulzrinne * 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 Good for hotspots, difficult for complete coverage Manhattan = 60 km 2  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

Mobile Data Access Hoarding: grab data before moving , 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)

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 Separation Increase 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 sharing 7DS Connected Infostation WLAN

Examples of Services using 7DS schedule info autonomous cache traffic, weather, maps, routes, gas station WAN news 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 WAN Host A Host D query WLAN Information Sharing with 7DS

7DS options Forwarding Host A Host B query FW query Host C time Querying active (periodic) passive Energy conservation onon off time communication enabled Cooperation Server to client Peer to peer  server to client only server shares data no 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 s mobile user speed m/s host density hosts/km 2 wireless coverage 230 m (H), 115 m (M), 57.5 m (L) ns-2 with CMU mobility, wireless extension pause 1m/s mobile host data holder querier wireless coverage

Data Holders (%) after 25 min high transmission power 2 Fixed Info Server Mobile Info Server P2P

Scaling Properties of Data Dissemination 1 km If cooperative host density & transmission power are fixed, data dissemination remains the same R 2 km R wireless coverage

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 one server in 2x2 high transmission power 4 servers in 2x2 medium transmission power

Average Delay (s) vs Dataholders (%) Peer-to-Peer schemes 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.

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

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 : 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 Δ t)  N disseminating nodes  Each node n i advertises a single summary s i

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 ( E t )  Prob. of meeting a not yet infected node is 1-E t-1 /N 2R s=v Δ t

Harvesting Analysis Agent harvesting summaries from its neighbors (total α nodes) A regular node has “passively” collected so far E t summaries  Having a random summary with probability E t /N A random summary found from α neighbor nodes with probability 1-(1-E t /N)  E* t : Expected number of summaries harvested by the agent

Numerical Results Numerical analysis Area: 2400x2400m 2 Radio range: 250m # nodes: 200 Speed: 10m/s k=1 (one hop relaying) k=2 (two hop relaying)

Simulation Simulation Setup  Implemented using NS-2  a: 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

Simulation Summary harvesting results with random waypoint mobility

Simulation Summary harvesting results with real-track mobility