Yu Gu and Tian He Minnesota Embedded Sensor System (MESS) Department of Computer Science & Engineering This work is supported by National Science Foundation

Sleep Latency in Low Duty-Cycle Sensor Networks Sleep now. Wake up in 35 seconds Sleep now. Wake up in 4 seconds Sleep now. Wake up in 57seconds Sleep now. Wake up in 13 seconds 35s latency 57s latency 4s latency13s latency A B C D E Yu

Unreliable Radio Links 90% 95% 50% 70% A B C D E Yu

State-of-the-art Solutions: ETX (MobiCom’03) 50%, 100s 40%, 10s ETX = 1/ /0.5 = 4 ETX = 1/ /0.4 = 5 Expected E2E delay is 400s Expected E2E delay is 50s A B C D Sole link quality based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! ETX only considers link quality Yu

State-of-the-art Solutions: DESS (INFOCOM’05) 10%, 10s 100%, 20s DESS = = 20s DESS = = 40s Expected E2E delay is 200s Expected E2E delay is 40s A B C D Sole sleep latency based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! DESS only considers sleep latency Yu

State-of-the-art Solutions (2) Only Consider impact of link qualities Only Consider impact of Duty Cycling 80 fold performance degradation! 20 fold performance degradation! Intelligent MAC protocols (B-MAC, S-MAC, SCP-MAC …) provide significant performance improvement at the MAC layer. We focus on further performance improvement at the network layer. Yu

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Sensor States Representation Scheduling Bits ( )* Switching Rate 0.5 HZ  16 s round time On Off Yu

Data Delivery Process Sleep latency is 1Sleep latency is 2Sleep latency is 3 E2E Delay is 6 ( )*( ( ( Yu

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1 st attempt: Sleep latency is 1 Main Idea ( )*( ( ( Sleep latency is 1 2 nd attempt: Sleep latency is =11 i th attempt: Sleep latency is * (i-1) ( )* nd attempt: Sleep latency is =2 We should try a sequence of forwarding nodes instead of a fixed forwarding node! Dynamic Switching-based Forwarding (DSF) is important in extremely low duty-cycle sensor networks. Yu

Optimization Objectives EDR: Expected Delivery Ratio EED: Expected End-to-End Delay EEC: Expected Energy Consumption Assisted Living Target Tracking Border Control Disaster Response Habit Monitoring Environmental Monitoring Space Monitor Traffic Control Precision Agriculture Yu

Optimization Objectives(1) : EDR (100)* EDR = 90% (001)* EDR = 80% (010)* EDR = 70% % 50% 40% EDR: Expected Delivery Ratio. 0.6*0.7+ (1-0.6)*0.5*0.8 + (1-0.6)*(1-0.5)*0.4*0.9 EDR for node 1 is (EDR 1 ): Forwarding Sequence Yu

Optimization Objectives(1) : EDR (100)* EDR = 90% (001)* EDR = 80% (010)* EDR = 70% % 50% 40% EDR: Expected Delivery Ratio. 0.6*0.7+ (1-0.6)*0.5*0.8 + (1-0.6)*(1-0.5)*0.4*0.9 EDR for node 1 is (EDR 1 ): Forwarding Sequence Yu

Optimization Objectives(2) EDR: Expected Delivery Ratio EED: Expected End-to-End Delay EEC: Expected Energy Consumption Yu

Optimizing EDR (100)* (001)* EDR = 80% 2 2 (010)* EDR = 70% 100% If only node 3 is selected as forwarding node: EDR 1 = 1 * 0.8 = 0.8 We should only choose a subset of neighboring nodes as forwarding nodes! Shall we try all available neighbors? If both node 2 and node 3 are selected as forwarding nodes: EDR 1 = 1 * 0.7 = 0.7 Yu

Optimizing EDR with dynamic programming (100)* EDR = 90% (001)* EDR = 80% (010)* EDR = 70% 60% 50% 40% Select only a subset of neighbors as forwarders Node 4 has to be selected Then we attempt to add more nodes into the forwarding sequence backwardly. Try or skip Try or drop Yu

Distributed Implementation sink EDR = 98%, EED = 2, EEC = 1EDR = 99%, EED = 15, EEC = 2 EDR = 100%, EED = 0, EEC = 0 EDR = 97%, EED = 20, EEC = 5 EDR = 90%, EED = 90, EEC = 12 Yu

Interesting Findings Temporary routing loops may be helpful on reducing E2E Delay (111111)* (010000)* (111111)* (000010)* (100%,1) (90%,1) (100%,1) Yu

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Evaluations Both testbed implementation and large-scale simulations Baseline solutions: ETX by Douglas S.J. De Couto et al. in Mobicom’03 PRR*D by Karim Seada et al. in SenSys’04 DESS by Gang Lu et al. in INFOCOM’05 Yu

Testbed Results 20 MicaZ nodes, 27,398 bytes code memory and 1,137 bytes data memory Yu

Simulation Results (1) DSF Yu

Simulation Results (2) DSF DSF converges to DESS at perfect link Yu

Simulation Results (3) DSF and ETX Yu

Conclusion A Dynamic Switch-based Forwarding (DSF) scheme for extremely low duty-cycle sensor networks Addressed both sleep latency and lossy radio links Dynamic switching is essential Distributed model for data delivery ratio (EDR), E2E delay (EED) and energy consumption (EEC). Optimal forwarding on these three metrics A generic metrics that converge to ETX (in always-awake networks) and DESS (in perfect-link networks) Yu