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EECS Department University of Tennessee, Knoxville

Exploiting Overlapping Channels for Minimum Power Configuration in Real-Time Sensor Networks Xiaodong Wang , Xiaorui Wang, * Guoliang Xing, Yanjun Yao. EECS Department University of Tennessee, Knoxville. * CSE Department Michigan State University. Introduction.

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EECS Department University of Tennessee, Knoxville

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  1. Exploiting Overlapping Channels for Minimum Power Configuration in Real-Time Sensor NetworksXiaodong Wang, Xiaorui Wang, *Guoliang Xing, Yanjun Yao EECS DepartmentUniversity of Tennessee, Knoxville *CSE Department Michigan State University

  2. Introduction • Multi-channel reality in Wireless Sensor Networks (WSNs) • Multiple channels are supported by hardware. • e.g. CC2420 provides 16 channels, 5MHz spacing between adjacent channels. • Number of orthogonal channels is limited • Adjacent channels may interfere with each other.[Wu et al. 2008 Infocom] • Frequencies of adjacent channels are overlapped. • Should we use overlapping channels? • More channels to use can be beneficial. • Inter-channel interference is not negligible.

  3. Transmission pair Jammer pair Empirical Study for Overlapping Channels • Overlapping channel interference • Transmission pair uses Channel 16 and power level 15 • Jammer pair uses Channel 15 • Use Tmote Invent mote • Both pairs achieve good PRR when jammer uses power levels 16 -18

  4. Problem to Solve • Problems: • Assign channels (including overlapping channels) to nodes • Determine a transmission power level for every node • Goals: • To minimize overall (transmission) power consumption of the network • To guarantee the average end-to-end delay of each data flow to stay within a deadline • To establish a relationship between transmission power and transmission delay

  5. Outline • Introduction • Empirical Modeling of Overlapping Channel • Overlapping channel RSS model • Packet reception ratio vs. signal strength • Minimum Transmission Power Configuration • Empirical Results • Conclusion & Contributions

  6. Received Signal Strength • Inter channel Received Signal Strength (RSS) • Overlapping channel RSS model • Linear RSS vs. Power model sender u receiver v channel 16 channel 14-18

  7. Packet Reception Ratio vs. SINR • Packet Reception Ratio (PRR) • Decided by Signal to Interference and Noise Ratio (SINR) • Relationship between SINR and RSS • PRR vs. SINR • PRR-SINR in one channel • One lookup table in each channel transmission interference A C B

  8. Correlate Delay to Transmission Power Transmission Power RSS vs. Power RSS SINR SINR vs. PRR Real-time constrained power minimization problem Delay: 1/PRR

  9. Outline • Introduction • Empirical Modeling of Overlapping Channel • Minimum Transmission Power Configuration • Problem formulation • Algorithm design • Empirical Results • Conclusion & Contributions

  10. Problem Formulation • Power minimization problem formulation • Subject to the constraints: • Channel constraint: • Delay constraint: • Proper power and channel configuration transmission power transmission count

  11. Algorithm Design • The configuration search space is huge: • m nodes forming n flows in the network. • j available channels • Each mote can use k different power levels to transmit. • Simulated Annealing (SA): probabilistic method for global optimization problems • Sub-optimal solutions • Take solutions probabilistically Search Space ~ jnkm

  12. Problem Solving Diagram Transmission Power RSS vs. Power RSS SINR Simulated Annealing SINR vs. PRR Goal: sub-optimal power and channel configuration Real-time constrained power minimization problem Delay: 1/PRR +

  13. Outline • Introduction • Empirical Modeling for Overlapping Channel • Minimum Transmission Power Configuration • Empirical Results • Testbed and baselines • Experiment results • Conclusion & Contributions

  14. Empirical Results • Testbed setup • 29 Tmote Invent motes are used to organize a grid topology. • Two baselines: • Orthogonal: only use orthogonal (non-adjacent) channels • Random: randomly assign channels • Power is calculated by Simulated Annealing. • Two topologies

  15. Different Delay Constraints • Overlapping scheme achieves a smaller average end-to-end transmission count and power consumptions • Loose constraint gives larger search space leading to better performance Three channels, 16, 17 and 18, are used.

  16. Different Flow Numbers • More data flows bring more interference into the network • More flows are sharing the same channels for data transmissions Five channels, from 16 to 20.

  17. Conclusion & Contributions • Overlapping channels can improve real-time and power efficiency • Contributions • Empirical studies to establish a model between power and delay • Power and channel configuration -> a constrained optimization problem • Proposed a heuristic solution based on SA

  18. Q&A Thank You!

  19. Appendix A: • Distributed Simulated Annealing • Krishna et al. “Distributed simulated annealing algorithms for job shop scheduling”, IEEE Transactions on System, Man and Cybernetics. 1995 Jul • Complexity of Simulated Annealing: Convergence rate highly related to cost function • Galen H. Sasaki and Bruce Hajek, “The time complexity of maximum matching by simulated annealing”, Journal of the ACM, Volume 35, Issue 2 . pp387 – 403, 1988 • Debasis Mitra, Fabio Romeo and Alberto Sangiovanni-Vincentelli, “Convergence and Finite-Time Behavior of Simulated Annealing” Advances in Applied Probability, Vol. 18, No. 3 (Sep., 1986), pp. 747-771 Published by: Applied Probability Trust

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