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On Enhancing Network-Lifetime Using Opportunistic Routing in Wireless Sensor Networks

19 th Intl. Conf. on Computer Communication Networks August, 2010, Zurich, Switzerland. Chien -Chun Hung et al. On Enhancing Network-Lifetime Using Opportunistic Routing in Wireless Sensor Networks.

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On Enhancing Network-Lifetime Using Opportunistic Routing in Wireless Sensor Networks

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  1. 19th Intl. Conf. on Computer Communication Networks August, 2010, Zurich, Switzerland Chien-Chun Hung et al. On Enhancing Network-Lifetime UsingOpportunistic Routing in Wireless SensorNetworks Author: Chien-Chun Hung†§, Kate Ching-Ju Lin§, Chih-Cheng Hsu†, Cheng-Fu Chou† and Chang-Jen Tu* Presenter: Chien-Chun Hung (August 3, 2010) §Network and Mobile System Group(NMSGroup) Research Center for Innovation Technology Information (CITI) Academia Sinica, Taipei, Taiwan †Communication and Multimedia Laboratory (CMLab) Dept. of Computer Science & Information Engineering (CSIE) National Taiwan University (NTU), Taipei, Taiwan * Institute for Information Industry, Taipei, Taiwan

  2. Wireless sensor networks (WSNs) • Measuring data collection: • Sensors propagate measuring data toward sinks • Numerous data transmissions dominate energy expenditure • Challenging characteristic: • Sensors with limited energy resource • The duration of network availability is restricted • WSNs demand energy-efficient routing design • Network-lifetime: the amount of data gathered by the sinks before the first sensor depletes its energy

  3. Routing in WSNs • Specific routing • Pre-determined route for each origin-destination pair before actual transmission • Easy and simple, but lack of path diversity • Fixed-path routing • Dynamic-path routing • Opportunistic routing • A group of possible forwarders are chosen • Adaptively select the best route at each intermediate hop • Sophisticated and demand an effective metric

  4. Specific routing • Fixed-path routing • Construct the constant route for a transmission pair • E.g., geographic routing scheme • Minimize the hop stretch of the routing path • Hop-stretch: The ratio of the hop counts of a given route to the hoop counts of the shortest path • Utilize the same route at anytime • Sensors traversed by the selected route are likely to deplete their energy quickly • Ex: GPSR1、GFG2 • 1 B. Karp, “GPSR: greedy perimeter stateless routing for wireless networks”, MobiCOM’2000 • 2 F. Kuhn, “Geometric ad-hoc routing: of theory and practice”, PODC’ 2003

  5. Specific routing (cont.) • Dynamic-path routing • Assess different energy capability for each sensor • Distribute traffic load over the sensors with higher residual energy to prolong network lifetime • Avoid the energy depletion on critical sensors • Detour a long way in order to utilize the sensors with more energy • The additional overhead is harmful for future transmission • Route is static after data transmission starts • Cannot adapt to per-hop dynamics, • e.g., packet loss, residual energy • Ex: OML3、BLM4 • 3 J. Park, “An Online Heuristic for Maximum Lifetime Routing in Wireless Sensor Networks”, IEEE Transactions on Computer 2006 • 4 C. Wu, “A Novel Load Balanced and Lifetime Maximization Routing Protocol in Wireless Sensor Networks”, VTC’2008

  6. Opportunistic routing (OR) • Utilize the characteristics of wireless channel • Broadcast nature、spatial diversity • Multiple forwarders are involved • Improve transmission reliability • Reduce retransmissions, as well as energy cost F1 0.8 The probability of at least one forwarder receives the packet: 1 – 0.3 × 0.2 × 0.4 = 0.976, which is larger than any link. 0.7 F2 D S F3 0.6 • S. Biswas, “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks”, SIGCOM’2005

  7. OR (cont.) • Different ORs require different metrics • Forwarder selection • Forwarder prioritization • Can we directly apply OR to WSNs? • Most ORs target on reducing retransmissions • Energy cost reduction ≠ lifetime-enhancement • OR demands more sophisticated design for WSNs

  8. Summary for previous works • Fixed-path routing • Constantly utilize the same route for a transmission • Dynamic-path routing • Dynamically adjusts path each time • Not adapt to per-hop dynamics • Overly detour a long path • Opportunistic routing • Reduce energy cost • Merely reducing total energy consumption is not enough for lifetime enhancement

  9. EFFORTEnergy-Efficient Opportunistic Routing • A lifetime-enhancing OR • Reduce energy consumption • Minimize number of retransmissions and reduce energy cost • Prolong network-lifetime • Assess different energy capabilities of sensors • Determine the best route at each hop • Design issues • An index for the impact of energy cost on lifetime • A metric served as the criteria of OR operation • OR framework operates on the proposed metric

  10. EFFORT components • SE-Cost index • Scarcity Energy Cost • Indicate the sustainability of each sensor • OEC metric • Opportunistic Energy Cost • Represent the end-to-end SE-Cost from each sensor to sink • EFFORT framework • Network initialization • Routing decision • Data forwarding • Routing update

  11. Scarcity energy costthe impact of energy consumption -100% -20% -25% -33% -50% • Definition: the ratio of energy cost to residual energy • The impact of the energy consumption to its residual energy • The less SE-Cost, the less damage to the network-lifetime. • Reducing total SE-Cost is to mitigate the damage to network-lifetime • SE-Cost is an effective index for lifetime enhancement S5 S1 S2 S3 S4

  12. Opportunistic energy costthe operation metric of OR f1 • OEC: end-to-end SE-Cost from sensor to sinks • Comprehensively model the utilization of multiple forwarders • Recursively integrate the end-to-end information s f2 d … fj Multi-forwarder utilization End-to-end integration

  13. OEC formulation ECRx:fwds f1 ECTx:sfwd: Tx / Res ECRx:fwds: Σ [Rx / Ref] OECfwdd: Σ [OECj × Pj] Ecfwdd s f2 d … • Intuitively, OECs,d is composed of: • The transmit energy cost consumed by the sender s • The receiving energy cost consumed by all the forwarders • The OEC consumed from the forwarders to d • OECs,d = ECTx:sfwd + ECRx:fwds + OECfwdd ECTx:sfwd fj ECTx:sfwd: Tx / Res ECRx:fwds: Σ [Rx / Ref] OECfwdd: Σ [OECj × Pj]

  14. Network initializationcompletion rule 1.1 1.1 ∞ 4.2 4.5 4.2 ∞ 0 Sink 2.0 2.0 2.4 ∞ 3.1 3.1 ∞ 1.5 ∞ 1.5 1.7

  15. Routing decisionforwarder selection 2.5 3.2 8.3 7.9 ∞ 7.8 7.4 7.2 7.6 5.0 4.6 4.8 4.6 ∞ 4.0 3.3 7.4 2.4 1.9 All neighbors Extraction stage Candidate forwarders Forwarding set Inclusion stage

  16. Data forwarding & routing updateprioritization rule F5 {2.3} F1 {3.2} F1 {3.2} F4 {1.6} S {8.0} F2 {4.1} F2 {4.1} F3 {4.5} F3 {4.5} F6 {3.2}

  17. NS2 simulation setting • Testing field: 250000 square meters (500m × 500m) • 150 - 350 sensor nodes randomly scattered over the field • Each node sequentially starts a transmission every 1000 seconds • Parameters setting based onMICAz • 4 sinks randomly distributed over the field • Assume that sink nodes are re-chargeable and have unlimited energy S 500 m 500 m S S S

  18. Compared protocols • OML † • Minimize the weighted cost based on residual energy • Dynamically adjust each end-to-end routing path before transmission • GCF ‡ • Maximize packet advancementat each intermediate hop • Select forwarders based on fixed geographic metric • EFFORT • Minimize the OEC metric based on Scarcity Energy Cost • Instantly adapt routing at each intermediate hop during transmission † J. Park, “An Online Heuristic for Maximum Lifetime Routing in Wireless Sensor Networks”, IEEE Tran. on Computer 2006 ‡ K. Zeng, “On Geographic Collaborative Forwarding in Wireless Ad Hoc and Sensor Networks“, WASA 2007

  19. Lifetime EnhancementThe amount of data gathered by the sinks before the first sensor drains out its energy

  20. Residual EnergyCumulative Distribution Function

  21. Conclusion • An energy-efficient opportunistic routing scheme: • End-to-end integration • Per-hop adaptation • Distributed computation • An effective framework • Improve transmission reliability • Reduce energy consumption • Enhance network-lifetime

  22. Thank you for your attendance! Chien-Chun Hung shinglee@citi.sinica.edu.tw §Network and Mobile System Group(NMSGroup) Research Center for Innovation Technology Information (CITI) Academia Sinica, Taipei, Taiwan †Communication and Multimedia Laboratory (CMLab) Dept. of Computer Science & Information Engineering (CSIE) National Taiwan University (NTU), Taipei, Taiwan

  23. Appendix

  24. Data forwardingprioritization rule F5 {2.3} F1 {3.2} F1 {3.2} F4 {1.6} S {8.0} F2 {4.1} F2 {4.1} F3 {4.5} F3 {4.5} F6 {3.2}

  25. Prioritization rule • Importance • Ensure all packets are sent by any forwarder • Ensure each packet is sent by only one forwarder • Implementation • A record is maintained at each hop • Record the status of each packet • Apply ACK and notification mechanism • Notify each forwarder the status of the packets

  26. Update eliminationcandidate exclusion 1.3 4.0 F1 F4 S 3.5 4.5 2.1 F2 3.0 F3

  27. Geographic Collaborative Forwarding (GCF) • Multiple forwarders are involved based on EPA • Packet advancement • Link reliability • Number of forwarders • Residual energy is not taken into account • The static routing decision drains out the energy on bottleneck • One-hop information is limited • May not reflect the end-to-end condition • Reduce the number of retransmission • Reduce the total energy cost • K. Zeng, “On Geographic Collaborative Forwarding in Wireless Ad Hoc and Sensor Networks“, WASA’ 2007

  28. MICAz • Tiny wireless measurement system • Designed specifically for deeply embedded sensor networks • High data rate • Low power • Specification • IEEE 802.15.4 compliant • Frequency band: 2.4 GHz • Transmit data rate: 250 Kbps • RF power: -24 dBm ~ 0.0 dBm • Transmission range: up to 90 m • http://www.xbow.com/Products/productdetails.aspx?sid=164 • http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/MICAz_Datasheet.pdf

  29. Energy Utility

  30. End-to-end Delay

  31. Mathematical property of OEC • Prioritization rule • Determine the relay sequence • Avoid repetition • Monotonic property • OEC values of forwarders are smaller than sender • Candidate extraction • Candidate exclusion

  32. The characteristics of OEC • 1. Prioritization Rule • Assign priority based on OEC value in ascending order. • 2. Monotonic Property (Candidate Extraction) • The OEC value of the sender is definitely larger than all the ones of the forwarders. • Otherwise, extracting the forwarder with higher or equal OEC value than the sender would obtain a better OEC value. • 3. Candidate Exclusion • Exclude the node with higher OEC from the forwarding set.

  33. 1. Prioritization rule 1.3 F1 2.1 S F2 3.0 F3

  34. 2. Monotonic Property 1.3 F1 2.1 S 3.5 F2 3.0 F3

  35. 2.1 Candidate Extraction 1.3 F1 2.1 S 2.8 2.5 F2 3.0 F3

  36. 3. Candidate Exclusion 1.3 4.0 F1 F4 S 3.5 4.5 2.1 F2 3.0 F3

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