Mobile Sensor Systems: Performance Results for Data Gathering

1. 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)

2. 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

3. 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?

4. 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

5. 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

6. 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

7. 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)

8. 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

9. 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

10. 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

11. 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

12. Network Lifetime Order of results: no movement, hybrid, data pick-up

13. Network Lifetime Order of results: no movement, hybrid, data pick-up

14. 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

15. Query Success Rate: Large Network Slow reader Fast reader

16. Distance Traveled by Reader Data pick-up requires one order of magnitude more movement

17. 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