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Mobile Sensor Systems: Performance Results for Data Gathering Thomas F. La Porta ( tlp@cse.psu.edu ) & Guohong Cao ( gcao@cse.psu.edu ) The Pennsylvania State University Brian Farabaugh ( bfarabaugh@3eti.com ) & Ryon Coleman ( RColeman@3eti.com ) 3ETI

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mobile sensor systems performance results for data gathering

Mobile Sensor Systems: Performance Results for Data Gathering

Thomas F. La Porta (tlp@cse.psu.edu) & Guohong Cao (gcao@cse.psu.edu)

The Pennsylvania State University

Brian Farabaugh (bfarabaugh@3eti.com) & Ryon Coleman (RColeman@3eti.com)

3ETI

Students: Hosam Rowaihy, Mike Lin, Jie Teng, Tim Bolbrock

Executive Summary

Schedule

Performance Results for Data Gathering (2Q milestone)

executive summary data gathering
Executive Summary: Data Gathering
  • Network example
    • Remotely deployed robots
    • RFID systems (active tag hierarchies)
  • Problem 1: How to efficiently retrieve data with mobile sinks
    • naïve solutions result in high overhead, high latency, and data loss
    • target mobile RFID readers
  • Benefits to Vendors
    • more robust networks with simple management (distributed)
    • efficient data gathering meeting both performance and network lifetime goals
schedule
Schedule
  • Milestones:
  • Q1: Mobile Robot Design/Parts, Protocol designs
  • Q2: Mobile Robot, Relocation implementation, Gathering simulation
  • Q3:Relocation performance, Gathering implementation
  • Q4:Integration with robot
  • 3ETI Cost Share:
  • Consulting on RFID platform
  • Review and consulting protocol designs
  • Specification of performance objectives
  • Detailed commercialization plan
  • Performance evaluation
  • Platform
  • Custom (small) robot
  • Linux Laptops
  • RFID?
data gathering application mobile rfid readers
Data Gathering Application: Mobile RFID readers
  • Mobile reader attempts to gather data from large warehouse
  • Limited remote communication available due to distance and obstructions
    • tags in crates may be difficult to reach
    • large numbers of collisions due to large numbers of tags
    • both reader and crates may be moving
  • Solution: distributed algorithms and protocols to pass and store data
    • local aggregation of data to simple, but more capable, smart-tags
    • data reader communicates with smart-tags
    • smart tags form a mesh network that accommodates low-rate mobility
    • will drastically reduce number of messages, hence reducing collisions

Passive tags

reader

Smart tag

crate

data gathering solution approach
Data Gathering: Solution Approach
  • Assumptions
    • reader has large power supply
    • smart tags have limited power, memory, bandwidth
  • Goals
    • respond to queries within time constraint
      • may be “real-time”
      • may be batch, e.g., overnight inventory
    • meet network lifetime goal, T
  • Query types
    • simple, e.g., contents of crate #1172
    • compound (complex), e.g., item existence, location, serial number, quantity, etc.
  • Algorithms and protocols
    • Backbone formation: network structured into cells
      • Picking cell leaders
      • Forming backbone
    • Query processing
      • Locate data
      • Determine optimal method of retrieval
data gathering query resolution hybrid scheme
Data Gathering: Query Resolution – Hybrid Scheme
  • Find data, decide location from which to retrieve data
  • Goal: get data from as close to the course as possible to reduce energy consumption
    • consider latency requirement, Tconstraint
  • Step 1: robot sends query to closest index node
    • learns where data is and path to data
  • Step 2: robot determines where to pick up data
    • Mi is time to transfer data i-hops, , S = size of data, B = data rate, d = # of hops
    • Mi* is the time to transfer data x-hops, where x is the distance to the source once the robot moves
    • l is location where data will be picked up
    • Ciis the time to move the robot to hop i
    • Tresponse is time to get initial response to query
    • Tdecision is time for robot to inform of where data should be sent
  • Step 3: robot moves and data is transferred
data gathering status
Data Gathering Status
  • Protocol specification complete
    • Driven by PSU in consultation with 3ETI and Cisco
  • Evaluation
    • JAVA simulation complete (results in following charts)
  • Next steps
    • Implement protocols: 50% complete
    • Simulate with realistic environment
      • Full day meeting with Cisco (Art Howarth)
    • Examine RFID platforms (3ETI)
systems compared
Systems Compared
  • Hybrid (our proposed scheme)
  • the reader chooses the best mid-point from which it collects the data
  • No movement:
  • queries are resolved over the peer-to-peer network consisting of all active RFID tags
  • the reader does not move
  • Data Pickup:
  • the reader moves all the way to the data source
  • no peer-to-peer data transfer is used in this scheme
simulation environment
Simulation Environment
  • Communication range of nodes and reader = 25m
  • Robot Speed (Reader starts at center of deployment)
    • slow: 0.5m/s, fast: 1m/s
  • Nodes are deployed in a grid
    • 100m x 100m (441 nodes)
    • 250m x 250m (2601 nodes)
  • Queries
    • time constraints uniformly distributed
      • 50 - 100s (small network) and 100 - 200s (large network)
    • answer size uniformly distributed between 1 - 100 packets
    • data source of each query is randomly selected from all nodes
    • bandwidth was varied between 5 packets/s (pps) and 15 pps in 0.5 increments
  • Network lifetime is defined as the time from the start of deployment until the first node dies
  • Energy consumption model:
    • for idle nodes: no energy consumption
    • for transmitting nodes: 0.2 units/packet
results
Results
  • Four experiments:
    • a small-sized network (100m x 100m) with slow robot (robot speed = 0.5m/s)
    • a small-sized network (100m x 100m) with fast robot (robot speed = 1m/s)
    • a large-sized network (250m x 250m) with slow robot (robot speed = 0.5m/s)
    • a large-sized network (250m x 250m) with fast robot (robot speed = 1m/s)
  • Metrics (average over 10 runs)
    • Network Lifetime: number of attempted queries before the network dies
    • Query Success Rate: fraction of queries that were successfully resolved within the time constraint
    • Distance travelled by reader: number of meters the mobile reader travelled to resolve the quires
network lifetime
Network Lifetime

Order of results: no movement, hybrid, data pick-up

  • Data pick-up has longest life-time
    • very little energy spent transmitting data
    • cannot resolve many queries within time constraint
  • Hybrid extends lifetime beyond no movement case and has good query success rate
  • No movement has high query success, but short lifetime
    • data transmitted a large number of hops
network lifetime12
Network Lifetime

Order of results: no movement, hybrid, data pick-up

network lifetime13
Network Lifetime

Order of results: no movement, hybrid, data pick-up

query success rate small network
Query Success Rate: Small Network

Slow reader

Fast reader

  • Data pick-up: cannot meet most time constraints
  • Hybrid: approximates no movement
    • In small number of cases, reader is far from data source due to previous movement
query success rate large network
Query Success Rate: Large Network

Slow reader

Fast reader

slide16

Distance Traveled by Reader

Data pick-up requires one order of magnitude more movement

conclusion
Conclusion
  • Hybrid approach achieves balance between lifetime and latency
    • Virtually matches no movement in terms of query success rate
    • Extends network lifetime by requiring fewer data transmissions
  • Next steps:
    • Simulation of implementation
    • Map to real-world scenario
    • Map to RFID platform