EECS Department University of Tennessee, Knoxville

1. Flow-Based Real-time CommunicationIn Multi-Channel Wireless Sensor NetworksXiaodong Wang, Xiaorui Wang, Xing Fu, *Guoliang Xing, Nitish Jha EECS DepartmentUniversity of Tennessee, Knoxville *CSE Department Michigan State University

2. Outline • Introduction & Background • Multi-channel real-time protocol • Disjoint path search algorithm • Experiment result • Conclusion

3. Introduction • Real-time requirement • A lot of WSN applications require real time service quality: • Wood fire monitoring • Battle field application • Factors that impact real time service quality • Interference • Packet cannot meet the required deadline because of collision and contention • Long packet routing path • High workload • Border Intruder Monitoring • Alarm System

4. Single Channel Multiple Channel Experimental Setup Empirical Study on Multi-Channel Communication • With single channel, increasing sending power has significant impact to others’ transmission • Almost 40% drop ratio when the communication power is low • Using multiple channels significantly mitigates the interference to other transmissions.

5. Related Works • Single channel real-time communication • Implicit EDF • Collision-free real-real time scheduling • SPEED • Enforcing uniform communication speed • None of them takes advantage of multiple channels • Multi-channel protocols and channel assignment • Node-based protocols • Requires channel switching • Interference free channel assignment • Requires synchronization • Transmission power control • Most do not deal with real-time requirement

6. Outline • Introduction & Background • Multi-Channel Real-Time protocol (MCRT) • Network metric design • Flow based channel allocation • Power-efficient real-time routing (RPAR) • Disjoint path search algorithm • Experiment result • Conclusion

7. Multi-Channel Real-Time Protocol • Multi-Channel Real-Time protocol (MCRT) • Especially designed for the real-time application in multi-channel WSNs • Designed to meet the end-to-end delay • Application traffic type: many to one communication • Major components: • Flow-based channel allocation • Power-efficient real-time routing • Contributions: • Formulate a constrained optimization problem • Propose a heuristic to solve the problem • Incorporate power efficient component

8. Network Metric Design • Link weight calculation • Link weight = Number of transmissions = 1/PRR • PRR : Packet Reception Ratio • 3 Communication Relationships defined by PRR: • Communication link: PRR > 90% • Interference link: 90%>PRR>10% • Cannot communicate: PRR<10% • Worst-case one-hop delay • End to end delay: • Summation of the worst-case one-hop delay along a path

9. Flow Based Channel Allocation • Channel assignment requirements: • Different channels for different data flows • Data flows are mutually disjoint • Disjoint Path with Bounded Delay problem (DPBD) • Directed graph G=(V,E) with weighted edges • K source vertices s1,…, sk and a destination vertex t • Goal: • Find k disjoint paths, one from each source si to t • All the path delay are bounded by a value W • DBPD is NP-complete • NP-completeness of DBPD can be proved by reducing it to MLBDP • MLBDP problem: Maximum Length Bounded Disjoint Path Problem

10. Power-Efficient Real-Time Routing • Real-time forwarding • Velocityrequired(s, d) =dis(s, d)/slack • Slack time: the time remaining until the packet deadline expires • Velocityprovided(n, p, c) =(dis(s, d) − dis(n, d))/delay(n, p, c) • s = current node, d = destination, n = neighbor, p = power, c = n’s channel • Feasible next hop: Velocityprovided > velocityrequired • Power and neighborhood management • Power adaptation • If a set of neighbors are feasible, decrease power to transmit to the least power consumer • Increasing the power to transmit to the max velocity provider, if no neighbors are feasible • Neighborhood discovery • If the transmission to all neighbors are requiring max power, but still cannot meet deadline, start neighborhood discovery by using Routing Request (RR) packet

11. Outline • Introduction • Multi-channel real-time protocol • Disjoint Path Search Algorithm • Experiment result • Conclusion

12. Disjoint Path Search Algorithm • DBPD Problem: Directed graph with weighted edge, k sources and 1 destination • Find k disjoint paths, one from each source si to t • All the path delay are bounded by a value W • Disjoint path search algorithm includes two phases • Phase I: Initial solution set searching • To search an initial solution set with some disjoint paths • Dijkstra's shortest path algorithm is implemented • Phase II: Augmentation algorithm • Get as many disjoint paths as possible • Phase I can only provide an incompletesolution set by fast searching scheme • Depth first searching • Matching scheme to the existing solution • Phase II is iterative. • Every round of phase II will add one more new disjoint path to the solution set • Centralized algorithm

13. X X X X X X X Y Y Y Y Y Y Y S S S S S S S B B B B B B B C C C C C C C T T T T T T T U U U U U U U V V V V V V V W W W W W W W Phase II: Augmentation algorithm Match DFS New Path DFS/match One more path

14. Disjoint Path Search Algorithm (cont’) • Analysis of the augmentation algorithm: • Time complexity: O(W’2|V||E|) • DFS: O(W’|E|) • Matching algorithm O(W’2|V||E|) • W’ is the edge number boundary • V – number of nodes • E – number of edges • Extended DBPD problem • Better fault tolerance • More energy efficient neighbors to choose for real time forwarding

15. Outline • Introduction • Multi-channel real-time protocol • Disjoint path search algorithm • Experiment result • Baseline design • Simulation result • Conclusion

16. Baseline Design • SIMPLE • A flow-based distributed heuristic to find disjoint path • Requires an initialization phase to establish path • Using explorer packet • Node-based scheme • Every node has a default listening channel • Node need to switch channel between receiving and transmitting • Real-time Power Aware Routing (RPAR) • Single channel real time protocol

17. Simulation Setup • Simulation setup • NS-2 simulation, based on the characteristic of Mica2 sensor mote • Probabilistic radio model from USC is implemented • 130 nodes in a 150x150m2 square scenario, divided into 130 grids • Major evaluation metrics • Deadline miss ratio • The percentage of packet that is missing the required service deadline • Energy consumption per data packet • Energy required for each scheme to successfully finish a work load under a certain deadline requirement

18. Simulation Result (cont’) • Performance with different deadlines • MCRT outperforms other baselines on all the different deadlines • Performance with different packet rate • MCRT shows low miss ratio and energy consumption

19. Simulation Result (cont’) • Performance with different number of flows • MCRT is not impacted significantly by number of flows • Performance for different network density • MCRT is not sensitive to density

20. Conclusion • The proposed MCRT protocol can effectively utilize the multiple channels for the many to one flow traffic pattern application • MCRT greatly reduces the deadline miss ratio compared with a single channel real-time protocol and two baselines • MCRT is the most energy efficient scheme among the four schemes • MCRT has good scalability compared with others

21. Q&A Thank You!