Milcom 2008 - Elisa Rondini

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  • Presentation Title June 6, 2009 Speaker Name
  • Milcom 2008 - Elisa Rondini

    1. 1. Distributed Wireless Ad Hoc Grids With Bandwidth Control For Collaborative Node Localisation Elisa Rondini University College London, London, UK (e.rondini@cs.ucl.ac.uk) Stephen Hailes University College London, London, UK (s.hailes@cs.ucl.ac.uk) Li Li Communications Research Centre, Canada (li.li@crc.ca)
    2. 2. Agenda <ul><li>Tactical WSNs Military Scenario </li></ul><ul><li>Motivations </li></ul><ul><li>Contributions </li></ul><ul><li>DWAG Paradigm </li></ul><ul><li>DWAG for Collaborative Localisation </li></ul><ul><li>The Bandwidth Problem </li></ul><ul><li>BATS Strategy </li></ul><ul><li>Experimental Evaluation </li></ul><ul><li>Conclusions </li></ul>
    3. 3. Tactical WSNs Military Scenario <ul><li>Emergency event (CBRNE). </li></ul><ul><li>Indoor environment (e.g. building, train station, underground). </li></ul><ul><li>Need for timely in-field environmental gathering and information provisioning to the external Command & Control ( C 2 ). </li></ul><ul><li>Need for fast collaborative localisation algorithms (e.g. CCA-MAP *) to compute node positions without external support. </li></ul>* L. Li and T. Kunz, “Cooperative Node Localization for Tactical Wireless Sensor Networks”, in Proceedings of the IEEE/Boeing MILCOM, October 2007. * L. Li and T. Kunz, “Cooperative Node Localization Using Non-Linear Data Projection”, in ACM Transactions on Sensor Networks, 2008.
    4. 4. Motivations <ul><li>Computationally intensive applications. Resource-constrained devices. </li></ul><ul><li>Limited available bandwidth. Radio communication interference. </li></ul>
    5. 5. Contributions <ul><li>Distributed Wireless Ad hoc Grids (DWAG) to implement distributed algorithms in WSNs formed by resource-constrained devices. </li></ul><ul><li>Bandwidth-Aware Task Scheduling (BATS) to load share location computing tasks among sensors by assessing both node computational capabilities and local network conditions . </li></ul>
    6. 6. Distributed Wireless Ad hoc Grid DWAG Paradigm
    7. 7. DWAG Applicability Steps <ul><li>Applications need to be parallelisable: </li></ul><ul><li>Tasks code need to be loaded onto sensors: </li></ul><ul><li>Tasks need to be profiled: </li></ul><ul><li>by hand </li></ul><ul><li>automatically </li></ul><ul><li>on-the-fly </li></ul><ul><li>pre-deployment </li></ul><ul><li>statically </li></ul><ul><li>dynamically </li></ul>
    8. 8. CCA-MAP Localisation Algorithm Task 2 : GA computes its “ local map ” with CCA . GA chooses among its neighbours the one that shares the greatest number of neighbours with itself, request and obtains its local map. Deliverable : GA has two maps to be patched together . Task 4 : TE performs the SVD transformation. Deliverable : TE outputs three matrices back to GA. Task 3 : GA patches a “ global map ” by merging neighbouring map with its own local one. Deliverable : GA outputs a “global map” . Grid Activator: Task Executor: Task 1 : GA broadcasts a message to discover its neighbours and collect their adjacency matrices. Deliverable : GA establishes its own neighbourhood.
    9. 9. Existing Work Assumptions <ul><li>Radio communication issues are disregarded. </li></ul><ul><li>Usage of simulators with oversimplified radio models: </li></ul><ul><ul><li>Error – free. </li></ul></ul><ul><ul><li>Circular transmission radius. </li></ul></ul><ul><ul><li>Bidirectional/symmetrical links. </li></ul></ul><ul><ul><li>Isotropic (received signal is the same in all directions). </li></ul></ul><ul><ul><li>Monotonic distance decay (shorter distance means better link quality). </li></ul></ul>Previous CCA-MAP evaluations used centralised simulations (i.e. Matlab V7.2 – 1.60GHz Pentium M – 1GB RAM) and perfect radio communication .
    10. 10. The Bandwidth Problem <ul><li>Message collisions and radio interference generate a high probability of packet loss and corruption. </li></ul><ul><li>Unpredictable hardware failures require control mechanisms. </li></ul><ul><li>Load distribution in DWAG needs to consider traffic congestion effects. </li></ul>
    11. 11. Load Distribution Algorithm <ul><li>An Auction* reactive/sender-initiated algorithm is selected and adapted to load distribute localisation tasks. </li></ul>* E. Rondini, S. Hailes and L. Li, “Load Sharing and Bandwidth Control in Mobile 2P2 Wireless Sensor Networks”, in Proceedings of the 5 th IEEE MP2P08 in conjunction with PerCom, March 2008. GA broadcasts a Task request message (with CPU and Bandwidth details). TE i sends a Bid (with CPU and Bandwidth availability details). The Winner is... TE2!!! GA selects the Best Available Node (through BATS scheme). GA uploads the Task Result back. GA offloads the Task to the Auction Winner.
    12. 12. Bandwidth-Aware Task Scheduling BATS Strategy S(i) = w C * C(i) + w B * B(i) S max = max{ S(i) } i=1,...,N <ul><li>i = GA’s TE neighbours (i=1, ..., N) </li></ul><ul><li>N = max number of neighbouring TEs </li></ul><ul><li>C(i) = CPU availability of TE “i” </li></ul><ul><li>B(i) = Bandwidth availability of TE “i” </li></ul><ul><li>S(i) = score of TE “i” </li></ul><ul><li>w C = weight CPU </li></ul><ul><li>w B = weight Bandwidth </li></ul>Best Candidate TE <ul><li>BATS allows localisation tasks distribution according to both TEs’ computational capabilities and local network conditions. </li></ul><ul><li>Parameters: </li></ul>C(i ) = difference between the maximum number of processes executable on each TE and the ones actively running on it at the request time. B(i) = percentage of TE local bandwidth availability calculated by maintaining the historical information of the last 100 temporal time slots in which the radio channel was clear or busy (reading CCA value from CC2420). The percentage was calculated by dividing the “m” number of times in which the channel was clear by 100.
    13. 13. Experimental Set-Up <ul><li>Evaluation on the Heterogeneous Experimental Network ( HEN - http://www.cs.ucl.ac.uk/research/hen/ ) Sensor Test-bed deployed at the Dept. of Computer Science at UCL. </li></ul><ul><ul><li>40 Tmote Sky sensors. </li></ul></ul><ul><ul><li>Random Deployment. </li></ul></ul><ul><ul><li>Fully Distributed Network. </li></ul></ul><ul><ul><li>Remotely Accessible. </li></ul></ul><ul><ul><li>Remotely Programmable. </li></ul></ul><ul><ul><li>Fast Kernel Flashing. </li></ul></ul><ul><ul><li>Contiki OS. </li></ul></ul>
    14. 14. Experimental Set-Up (II) <ul><li>Different topological sets of 15 sensors from HEN. </li></ul><ul><li>Up to 10 GAs and 4 TEs. </li></ul><ul><li>1 Streaming node injecting background traffic. </li></ul><ul><li>GAs radio power levels set to 0x04 (~400-500cm packet radio range) and 0x1F (all nodes are in rage with each other). </li></ul><ul><li>Streaming node with radio power lever 0x03 (~ 250cm). </li></ul><ul><li>Performance metric: average job execution time . </li></ul>
    15. 15. Performance Results – 1 st Set
    16. 16. Performance Results – 2 st Set
    17. 17. Conclusions <ul><li>We presented the novel DWAG paradigm which is the convergence of mobile ad hoc WSNs and Computational Grids. </li></ul><ul><li>We implemented the localisation algorithm CCA-MAP using the DWAG paradigm on a real Tmote Sky WSN test-bed. </li></ul><ul><li>We proposed a BATS strategy to distribute location computing tasks among sensor nodes assessing both their load and traffic conditions. </li></ul><ul><li>Experimental results proved that: </li></ul><ul><ul><li>Physical network conditions have a major impact on the performance of job collaborations between nodes. </li></ul></ul><ul><ul><li>Significant performance improvements in terms of latency can by achieved by applying simple BATS mechanisms taking into account both computational capabilities and network traffic conditions . </li></ul></ul>
    18. 18. Thank you! ... questions?

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