Scheduling Granularity in Underwater Acoustic Networks Kurtis Kredo - PowerPoint PPT Presentation

Scheduling Granularity in Underwater Acoustic Networks 1/21 Scheduling Granularity in Underwater Acoustic Networks Kurtis Kredo II 1 Prasant Mohapatra 2 1 Electrical and Computer Engineering Department California State University, Chico 2 Computer Science Department University of California, Davis ACM WUWNet December 1, 2011

Scheduling Granularity in Underwater Acoustic Networks 2/21 Introduction Underwater Scheduling Spectrum Goals Explore the many scheduling options in underwater networks Find balance between complexity and performance Tight Scheduling Disadvantages Tight Scheduling Advantages Higher state overhead Decreased frame size Inflexible communication Higher throughput opportunities Lower latency Less resiliency to Increased sleep opportunities synchronization error

Scheduling Granularity in Underwater Acoustic Networks 3/21 Channel Scheduling Spectrum Frame Size CDF Underwater Network Terrestrial Network 1 1 TDMA TDMA Node Node 0.8 0.8 Group Group Cumulative Distribution Link Cumulative Distribution Link Slot Slot 0.6 0.6 0.4 0.4 0.2 0.2 0 0 100 200 300 400 500 600 700 800 150 200 250 300 350 400 450 500 Frame Size (slots) Frame Size (slots)

Scheduling Granularity in Underwater Acoustic Networks 4/21 Channel Scheduling Spectrum Five Scheduling Methods Scheduling Spectrum Examples Network E C A → C: 4 Slots B → D: 2 Slots A B D Example Frames TDMA A B C D Node A TX B Intf C RX D

Scheduling Granularity in Underwater Acoustic Networks 5/21 Channel Scheduling Spectrum Five Scheduling Methods Scheduling Spectrum Examples Network E C A → C: 4 Slots B → D: 2 Slots A B D Example Frames Node A B C D Group TX A B Intf RX C D

Scheduling Granularity in Underwater Acoustic Networks 6/21 Channel Scheduling Spectrum Five Scheduling Methods Scheduling Spectrum Examples Network E C A → C: 4 Slots B → D: 2 Slots A B D Example Frames Group A B C D Link A TX Intf B C RX D

Scheduling Granularity in Underwater Acoustic Networks 7/21 Channel Scheduling Spectrum Five Scheduling Methods Scheduling Spectrum Examples Network E C A → C: 4 Slots B → D: 2 Slots A B D Example Frames Link A B C D Slot TX A B Intf RX C D

Scheduling Granularity in Underwater Acoustic Networks 8/21 Channel Scheduling Spectrum Schedule State Requirements Scheduling State Requirements Scheduling Options State Class Characteristics S D P TDMA Fixed-length blocks × Node Variable-length blocks × × × Group Group neighbors into rings × × × Link Single link transmission × × × Slot Multiple link transmissions × × × State Variables State Description S Slot for each element D Duration for each element P Propagation delay or ring number

Scheduling Granularity in Underwater Acoustic Networks 9/21 Scheduling Problem Communication Characteristics Device Capabilities Single half-duplex radio Optional DSSS with single packet reception Stationary nodes Network Conflicts j k k i j i j i i RX−RX TX−RX−TX TX−TX TX−RX Each network conflict results in a schedule constraint

Scheduling Granularity in Underwater Acoustic Networks 10/21 Scheduling Problem TX-RX Constraint Formulation Example Frame p j s j s +m j s i ∆ i Network Conflicts j k k i j i j i i RX−RX TX−RX−TX TX−TX TX−RX

Scheduling Granularity in Underwater Acoustic Networks 10/21 Scheduling Problem TX-RX Constraint Formulation Example Frame p j s j s +m j s i ∆ i Schedule Constraints s i ≥ s j + ∆ j + p j T s j + p j T + m ≥ s i + ∆ i

Scheduling Granularity in Underwater Acoustic Networks 11/21 Scheduling Problem TX-RX Schedule Constraint Node j Transmits First Node j Transmits Second s i ≥ s j + ∆ j + p j s j + p j T ≥ s i + ∆ i ⊕ T s j + p j s i + m ≥ s j + ∆ j + p j T + m ≥ s i + ∆ i ⇓ T Combined Constraints s i + mo ij ≥ s j + ∆ j + p j T s j + p j T + m (1 − o ij ) ≥ s i + ∆ i ⇓ ∆ i − p j T − m ≤ s j − s i − mo ij ≤ − ∆ j + p j T

Scheduling Granularity in Underwater Acoustic Networks 12/21 Scheduling Problem Scheduling Problem Given: Propagation delays p Required transmission durations ∆ Find: Transmission slots s Satisfy all schedule constraints B ij − m ≤ s j − s i − mo ij ≤ B ij

Scheduling Granularity in Underwater Acoustic Networks 13/21 Numerical Evaluation Methodology Summary Find schedules using CPLEX Topology 13 nodes distributed in a grid 10 time slots from each node 1 time slot to each node Central sink as destination 100 random networks Evaluate: Impact of synchronization error Use of DSSS Best scheduling options to evaluate in simulation

Scheduling Granularity in Underwater Acoustic Networks 14/21 Numerical Evaluation Throughput and Latency Network Performance Uplink Latency Aggregate Throughput 1600 0.8 Optimal Aloha Average Maximum Uplink Latency (Slots) TDMA 1400 TDMA Node 0.7 Node Group 1200 Group Link Normalized Throughput Link Slot 0.6 Distributed Scheduling 1000 Optimal Latnecy 0.5 800 0.4 600 0.3 400 0.2 200 0 0.1 0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 Synchronization Error, σ (s) Synchronization Error, σ (s) Summary Performance along spectrum as expected Scheduling options scale well with synchronization error Slot provides no throughput improvement Higher throughput than optimal Aloha, without collisions

Scheduling Granularity in Underwater Acoustic Networks 15/21 Numerical Evaluation Performance with DSSS DSSS Aggregate Throughput Summary 1.2 Performance along TDMA Node 1 spectrum as expected Group Link Normalized Throughput Slot Slot provides no 0.8 Distributed Scheduling Optimal Frame Size throughput improvement ST-MAC 0.6 DSSS provides no benefit above SF = 1 0.4 Comparable throughput 0.2 performance with 0 ST-MAC (centralized) No 1 2 4 8 DSSS DSSS Spreading Factor

Scheduling Granularity in Underwater Acoustic Networks 16/21 Simulation Results Simulation Results Summary Using OMNeT++ Discrete Event Simulator Topologies of 13 and 29 nodes Central sink as destination Variable data rate and transmit power 30 random networks Evaluate: Metrics unavailable in numerical evaluation Performance compared to related protocols Protocol convergence time

Scheduling Granularity in Underwater Acoustic Networks 17/21 Simulation Results Efficiency Efficiency Summary 1.1 1 Link scheduling 0.9 Protocol Efficiency (bits/mJ) delivers data for lower 0.8 0.7 energy 0.6 Node Group Link scheduling 0.5 Link Aloha 0.4 provides 300% ST-Lohi UT-Lohi 0.3 efficiency 0.2 0.1 improvement 0 20 40 60 80 100 120 Average Node Data Rate (bits/s)

Scheduling Granularity in Underwater Acoustic Networks 18/21 Simulation Results Throughput and Latency Throughput Uplink Latency 1400 3000 Node 1200 2500 Group Network Throughput (bits/s) Link Aloha 1000 Packet Latency (s) 2000 ST-Lohi cUT-Lohi aUT-Lohi 800 1500 Node Group 600 Link 1000 Aloha ST-Lohi 400 cUT-Lohi aUT-Lohi 500 200 0 0 0 50 100 150 200 20 40 60 80 100 120 Average Node Data Rate (bits/s) Average Node Data Rate (bits/s) Summary Link scheduling has 200%–300% higher throughput Link scheduling provides low latency

Scheduling Granularity in Underwater Acoustic Networks 19/21 Simulation Results DSSS Link Scheduling Average Frame Size Spreading Factor, SF Average Frame Size (slots) Summary No DSSS 128.2 1 86.1 Frame size 2 163.5 scales with 4 314.1 SF 8 610.8 DSSS reduces Schedule State Size (bits) distributed No DSSS DSSS schedule Scheduling Method Mean Max Mean Max state Node 364.9 848 165.0 464 Group 378.0 1040 178.7 656 Link 347.8 1176 146.5 400

Scheduling Granularity in Underwater Acoustic Networks 20/21 Simulation Results Scheduling Convergence Epoch Size in Frames No DSSS DSSS Scheduling Method Mean Max Mean Max Node 17.4 22 8.2 12 Group 15.2 19 8.4 13 Link 12.2 17 7.6 10 Summary More specific scheduling converges faster Using DSSS causes faster convergence Results provide indication of required epoch size

Scheduling Granularity in Underwater Acoustic Networks 21/21 Conclusion Conclusions Several options for schedule granularity Numerical results evaluated five scheduling methods TDMA scheduling provides poor performance Slot scheduling requires significant resources Simulation results evaluated three best candidates Link scheduling provides best balance Better performance than other methods and protocols DSSS Provides no benefit to traffic metrics Reduces control state requirements Decreases schedule convergence time

Scheduling Granularity in Underwater Acoustic Networks 22/21 Conclusion Thank You! Questions?

Scheduling Granularity in Underwater Acoustic Networks 18/21 Appendix TDMA Constraints Time Slot Set Size � p a � �� Λ = max ∆ a + T a Constraint Λ − m ≤ s b − s a − mo ab ≤ − Λ

Scheduling Granularity in Underwater Acoustic Networks 19/21 Appendix Node Constraints Time Slot Set Size Λ a = ∆ a + p a T Constraint Λ a − m ≤ s b − s a − mo ab ≤ − Λ b