Multi-channel Real-time Communications in Wireless Sensor Networks

1. Multi-channel Real-time Communications in Wireless Sensor Networks Xiaodong Wang

2. Introduction to Wireless Sensor Networks • A wireless sensor network (WSN) consists of spatially distributed inexpensive miniature devices to cooperativelymonitor physical or environmental conditions [Wikipedia] • Cooperatively: communication with each other over wireless channels • Monitor: capable of computation and sensing • Inexpensive: Ideally less than 1 cent • Have a wide range of potential applications • industry, science, transportation, civil infrastructure, security, etc.

3. Comparison with Traditional Wireless Networks • Integration of sensors, processors and radio • Sensors are networked • Very limited resource and very low cost • Deeply embedded and networked • Stationary locations • May allow limited mobility • Large Scale Deployment • Data-centric communication • Care about data instead of identity: mostly many to one communication • Different from Internet or wireless ad hoc networks

4. Some Issues with WSN • Energy Consumption • Energy resource is critical in WSN: provided by batteries. • Normally don’t expect recharging or battery replacement • Research goal: extend network life • End-to-end Communication Delay • Real-time is important for a lot of application • Coverage • The range of monitoring and the communication connectivity • Synchronization • Distributed system requires good timing knowledge. Clock drift on the local crystal causes trouble • Security • Wireless channel is insecure

5. Real-time Property • Packet Reception Ratio (PRR) • The probability of a packet to be successfully received by the receiver • Real-time metric: , the total transmissions we need to successfully receive a packet. • What is impacting real time service quality? • Interference • Packet cannot meet the required deadline because of collision and contention • Long packet routing path • High workload • Application type decides traffic pattern: • Event driven: End-to-end communication • Data collection monitoring: May require data aggregation

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

7. 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.

8. 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

9. Outline • 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

10. 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

11. 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

12. 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

13. Power-Efficient Real-Time Routing • Real-time forwarding • Velocityrequired(s, d) =dist(s, d)/Tslack • Tslack: the time remaining until the packet deadline expires • Velocityprovided(n, p, c) =(dist(s, d) − dist(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 d Vrequired n Vprovided s

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

15. 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

16. 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

17. 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

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

19. 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

20. 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

21. 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

22. 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

23. 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

24. Critiques • No explicit end-to-end delay boundary is provided in the paper • No explicit analysis for the real-time property after the channel is assigned. • Worst case is a theoretical approach, but not realistic enough in real networks.

25. Exploiting Overlapping Channels for Minimum PowerConfiguration in Real-Time Sensor Networks Department of EECSUniversity of Tennessee, Knoxville

26. Introduction • Multi-channel application • Multiple channels supported by hardware • Limited orthogonal channels, adjacent channels are overlapping • Should we use overlapping channels? • More channels resources to use is a benefit. • The interference between overlapping channels is not negligible

27. Empirical Study for Overlapping Channel • Overlapping channel interference • Transmission pair uses Channel 16 and power level 15 • Jammer pair uses Channel 15 • Both pairs achieve good PRR when jammer use power level 16 -18

28. Problem to Solve • Problem: • A WSN with multiple data flows from different sources to the base station • Assign channels (including overlapping channels) to the data flows • Determine a transmission power level for every node • Goal: • To minimize overall (transmission) power consumption of the network • To guarantee average end-to-end delay of each data flow to stay within a deadline

29. Contributions • Overlapping channel interference reality • Overlapping channels can be used for improving real-time performance • Empirical models • Received signal strength (RSS) vs. transmission power • Packet reception ratio (PRR) model • Power and channel configuration problem formulated • Constrained optimization problem formulated • Heuristic algorithm proposed • Testbed established • Experiment conducted on a 25-motes testbed.

30. Outline • Introduction & contributions • Related work review • Empirical modeling of overlapping channel • Overlapping Channel RSS Model • Packet Reception Model • Minimum transmission power configuration • Empirical Results • Conclusion

31. Interference Strength • The interference strength is decided by the received signal strength (RSS) from the interference transmission. • Higher interference signal strength -> more severe interference. • Signal strength is essentially decided by the transmission power at the sender • Overlapping Channel RSS Model • Sender uses Channel 16 • Linear RSS vs. Power model

32. Packet Reception Model • Packet Reception Ratio (PRR) • Decided by Signal to Interference and Noise Ratio (SINR) • Relationship between SINR and RSS • Packet reception model • PRR-SINR-Channel relationship (SINRv, cv, PRRv)

33. Problem Solving Flow Transmission Power RSS vs. Power ? Real-time constrained power minimization problem RSS SINR vs. PRR PRR

34. Outline • Introduction & contributions • Related work review • Empirical modeling of overlapping channel • Minimum transmission power configuration • Problem Formulation • Average PRR Estimation • Collision Probability Calculation • Algorithm Design • Empirical Results • Conclusion

35. Problem Formulation • System assumption • Many-to-one traffic pattern • Each source generates a data flow independently • All flows are disjoint • Base station is assumed to be a super node, with multiple radio interfaces • Power optimization problem formulation Subject to the constraints: • Average packet reception ratio • Soft real-time guarantees for Wireless Sensor Networks • Sources generate independent random flows

36. Average PRR Estimation • Average packet reception ratio estimation • Low probability for more than two nodes to transmit at the same time • Average packet reception ratio : • P(u,w) : probability that node w's transmission collide with node v's reception of its own sender u • PRR(u,v,w) : packet reception ratio of v's reception from its sender under w's interference • Example:

37. Collision Probability Calculation • Probability of collision • Verification of the average PRR estimation

38. Problem Solving Flow Transmission Power RSS vs. Power RSS SINR vs. PRR ? Real-time constrained power minimization problem Goal: most energy efficient power configuration PRR

39. Algorithm Design • The configuration search space is huge: jnkm • m nodes forming n flows in the network. The total available number of channels on the equipment is j. Each mote can use k different power levels to transmit. • Simulated Annealing (SA): probabilistic method for global optimization problems • Reason for using SA: • commonly used to find suboptimal solutions when the search space is huge and discrete

40. Algorithm Design (cont’) • Algorithm based on Simulated Annealing Choose Tini, calculate the Cini and Pini Find neighbor state ΔP > 0 Calculate ΔP ΔP < 0 Accepted by Probability? No Yes Calculate Delayi Meet Constraint? No Add Penalty Yes Reduce Temperature T Program Terminate? No Yes end

41. Problem Solving Flow Transmission Power RSS vs. Power RSS Simulated Annealing SINR vs. PRR Real-time constrained power minimization problem Goal: Best power configuration PRR +

42. Outline • Introduction & contributions • Related work review • Empirical modeling of overlapping channel • Minimum transmission power configuration • Empirical Results • Testbed and baselines • Four different set of experiments • Conclusion

43. Empirical Result • Testbed setup and baselines • 25 Tmote Invent motes are used • Two Baselines: • Orthogonal: orthogonal channels with simulated annealing power consumption algorithm • Random: Random channel assignment with simulated annealing power consumption algorithm

44. Different Delay Constraints • Topology I is used in this experiment • Three channels, 16, 17 and 18, are used. • Totally four flows are formed.

45. Different Delay Constraints (cont’) • Overlapping scheme achieves a smaller average end-to-end transmission count and power consumptions • Loose constraint gives larger search space leading to better performance

46. Different Packet Rates • Higher packet rate leads to increased retransmission count and power consumption for each packet. • higher degree of packet collision

47. Different Flow Numbers • Topology II is used in this experiment • Five channels, from 16 to 20, are used. • Flow number is increased by one for each time

48. Different Flow Numbers (cont’) • More data flows bring more interference into the network • More flows are sharing the same channels for data transmissions

49. Different Network Size • Topology III is used in this experiment • Five channels, from 16 to 20, are used. • Four flows are formed • Node number is increased by one in each flow for each time

50. Different Network Size (cont’) • More nodes result in larger end-to-end transmission count and power consumption • More inter-channel and intra-channel interference is incurred