Mobile sensor systems performance results for data gathering
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Mobile Sensor Systems: Performance Results for Data Gathering Thomas F. La Porta ( [email protected] ) & Guohong Cao ( [email protected] ) The Pennsylvania State University Brian Farabaugh ( [email protected] ) & Ryon Coleman ( [email protected] ) 3ETI

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Mobile sensor systems performance results for data gathering l.jpg

Mobile Sensor Systems: Performance Results for Data Gathering

Thomas F. La Porta ([email protected]) & Guohong Cao ([email protected])

The Pennsylvania State University

Brian Farabaugh ([email protected]) & Ryon Coleman ([email protected])


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

Executive Summary


Performance Results for Data Gathering (2Q milestone)

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Executive Summary: Data Gathering 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

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Schedule Gathering

  • 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?

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Data Gathering Application: Mobile RFID readers Gathering

  • 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


Smart tag


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Data Gathering: Solution Approach Gathering

  • 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

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Data Gathering: Query Resolution – Hybrid Scheme Gathering

  • 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

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Data Gathering Status Gathering

  • 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)

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Systems Compared Gathering

  • 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

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Simulation Environment Gathering

  • 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

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Results Gathering

  • 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

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Network Lifetime Gathering

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

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Network Lifetime Gathering

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

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Network Lifetime Gathering

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

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Query Success Rate: Small Network Gathering

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

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Query Success Rate: Large Network Gathering

Slow reader

Fast reader

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Distance Traveled by Reader Gathering

Data pick-up requires one order of magnitude more movement

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Conclusion Gathering

  • 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