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First Paper

Multi-Channel Real-Time Communications in Wireless Sensor Networks by Xiaodong Wang Nov 18th , 2008. First Paper. Flow-Based Real-time Communication In Multi-Channel Wireless Sensor Networks By Xiaodong Wang, Xiaorui Wang, Xing Fu, Guoliang Xing, Nitish Jha. Introduction.

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First Paper

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  1. Multi-Channel Real-Time Communications in Wireless Sensor Networksby Xiaodong Wang Nov 18th, 2008

  2. First Paper Flow-Based Real-time Communication In Multi-Channel Wireless Sensor Networks By Xiaodong Wang, Xiaorui Wang, Xing Fu, Guoliang Xing, Nitish Jha

  3. Introduction • Real-time Requirement • A lot of application of WSN require real time service quality: • Wood fire monitor • Battle field application • Real time service quality hamper • Existence of interference • Packet cannot be received because of collision • Long packet routing path • Too many service request • Border Intruder Monitor • Alarm System

  4. Related Works • Single channel real-time communication • Implicit EDF • Collision-free real-real time scheduling • SPEED • Enforcing uniform communication speed • None of them take advantage of multi-channel • Multi-channel protocols and channel assignment • Node-based protocol • Require switching channel • Interference free assignment • Some require synchronization • Transmission power control • RPAR • High power transmission incur interference to others • Most do not deal with real-time requirement

  5. Single Channel Multiple Channel Empirical Study on Multi-Channel Communication • Power adaptation in RPAR • With single channel, increasing power has significant impact to other’s transmission • Almost 40% drop ratio when the communication power is low • Multi channel highly mitigate this problem. Experimental Setup

  6. Multi-Channel Real-Time Protocol • Multi-Channel Real-Time Protocol (MCRT) • Especially designed for the real-time application in multi-channel WSNs • It is designed for meeting the end-to-end delay • Application traffic type: many to one communication • Main components: • Flow-based channel allocation • Power-efficient real-time routing • Contributions: • Formulate the constrained optimization problem • Heuristics is proposed • Incorporating power efficient component • Massive Simulations

  7. Flow Based Channel Allocation • Link Weight • Packet Reception Ratio (PRR) • 3 Communication Relationships: • Communication link: PRR > 90% • Interference link: 90%>PRR>10% • Cannot communicate: PRR<10% • Number of retries = 1/PRR • Worst case one hop delay • End to end delay: • Summation of the one hop delay along a path

  8. Flow Based Channel Allocation (cont’) • Channel assignment requirement: • Each data flow is assigned a different channel • Each data flow should be 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 • Each path delay is bounded by a value W

  9. Flow Based Channel Allocation (cont’) • DBPD is NP-complete • Proof of the NP-completeness of DBPD by reducing to MLBDP • MLBDP problem: Maximum Length Bounded Disjoint Path Problem • Find the greatest common denominator c for all the link weights • Every link weight is rational number • Transform a single link to a chain • Inserting I-1 node • Add a fake source • The bound is W*c +1

  10. Flow Based Channel Allocation (cont’) • Disjoint Path Search Algorithm • Centralized algorithm • Phase I: Initial solution set searching • To search an initial solution set with some disjoint paths • The shortest path algorithm is used in this paper • Phase II: Augmentation algorithm • Get as many disjoint paths as possible • Phase I can only give a fast searching solution set, but not complete enough • Depth first searching • Matching to the existence solution • Phase II is running iteratively. Every round of phase II will add one more new disjoint path to the solution set

  11. t s C B E D F V(I) Phase II: Augmentation algorithm • Using DFS to search a path on the free vertex, which should be bounded. DFS keeps proceeding when there is a free neighbor and the path to the free neighbor is bounded

  12. t s C B E D F V(I) Phase II: Augmentation algorithm (cont’) • If a search path does not have free neighbor to meet requirement any more, a match between non-free neighbor is performed

  13. t s C B E D F V(I) Phase II: Augmentation algorithm (cont’) • If a match is found, it changes the existing solution set and a new search path established, and continue the DFS( in this example, from D) • Every node keep a match forbidden list

  14. t s C X B E D F V(I) Phase II: Augmentation algorithm (cont’) • Current iteration ends with a search path reaches to t

  15. t s C X B E V(I-1) D V(I) F Phase II: Augmentation algorithm (cont’) • If X does not meet the requirement, there is no neighbor for D to choose to perform DFS or match, then search path go back to C to perform DFS or match

  16. t s C B X E V(I-1) D V(I) F Phase II: Augmentation algorithm (cont’) • If the search path go back to node s, which means the previous matching is an unsuccessful one, we should return to the search path before the previous matching

  17. t s C B X E V(I-1) D V(I) F Phase II: Augmentation algorithm (cont’) • If there is no match of V(I), we backtrack to the search path of V(I-1) to find a match

  18. Flow Based Channel Allocation (cont’) • Algorithm 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 • More fault tolerant • More energy efficient neighbor to choose for forwarding

  19. Power-Efficient Real-Time Routing (RPAR) • Real-time forwarding • velocityrequired(s, d) =dis(s, d)/slack • velocityprovided(n, p, c) =(dis(s, d) − dis(n, d))/delay(n, p, c) • Neighborhood Management • Power adaptation • Neighborhood discovery, using Routing Request (RR) packet • Trade off between decrease the overhead and interference.

  20. Evaluation • Baseline design • SIMPLE • A flow based distributed heuristic to find disjoint path • Require initialization phase to establish path • Using explorer packet • Multi-hop ack is used • Channel switching • Guarantee disjoint path • Node-based scheme • Every node has default listening channel • Node need to switch channel for listening and transmitting • RR packet is broadcasted on two channels • Real-time Power Aware Routing (RPAR) • Single channel protocol

  21. Evaluation (cont’) • 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 grid • Main evaluation metric • Deadline miss ratio • Energy consumption per data packet • To see in order to successfully finish a work load within a deadline, how many energy does it require

  22. Evaluation (cont’) • Performance with different deadlines • MCRT outperforms others on different deadlines • Performance with different packet rate • MCRT shows low miss ratio and energy consumption

  23. Evaluation (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

  24. Conclusion • The proposed MCRT protocol can • Effectively utilizing the multichannel based on flow traffic pattern • Greatly reduce the deadline miss ratio • Outperform a state-of-art real-time protocol

  25. Critique • Should find a way to transform the DBPD bound to a real-time delay bound, which makes more sense. • The baseline SIMPLE performs similar to the MCRT protocol • With small network, the MCRT could only support few network flows.

  26. Second Paper RACNet: Fine-Grained and Large-Scale Data Center Sensing By Chieh-Jan Mike Liang, Jie Liu, Liqian Luo, Andreas Terzis Johns Hopkins University, Microsoft Research

  27. Data Center Power Consumption • 61 billion kWh energy consumption is consumed by data center in US alone in 2006 • Enough to power up 5.8 million average households • Estimated to be double in 2001 • Power consumption components: • IT equipment • Computer Room Air Conditioning (CRAC) • Water chillers • (de-)humidifiers • Power Usage Effectiveness (PUE) • Ratio of the total facility power consumption over IT equipment • 2 is good, but some could be as high as 3.5 • The reason of high PUE • Lack of visibility of the data center’s operating conditions • Limited means to diagnose and handle the situation

  28. Cooling Management in Data Center • Cold-aisle-hot-aisle cooling design • Usual means to manage the data center cooling system • Computational Fluid Dynamics (CFD) simulations • Cool the whole room

  29. Solution • Dense and real-time environmental monitoring system • Troubleshoot thermo-alarms • Help decisions on rack lay out and server deployment • Innovate on facility management • Wireless sensor network • Advantage • Low-cost • Non-intrusive • Wide coverage • No need to change infrastructure • Challenge and requirement • Density is high: high packet collision possibility • Real-time data collection

  30. RACNet • Large-scale sensor network for fine-grained data center monitoring • Part of the project called Data Center Genome (DC Genome). • Understand the energy consumption • Optimize the control datacenter resources • Planning to deploy 2000 sensors • Reliable Data Collection Protocol (rDCP) • Customized sensor hardware: Genomotes • Uses multiple wireless channels: 802.15.4 have 16 channels • In-network bi-directional collection tree

  31. RACNet Architecture • DC Genome system: • The fewer gateways, the better • Multiple base-station mounts on the gateway, periodically pull data from master mote (see below) • Genomtes: customized mote • Masters and slaves • Master at the top of a rack • Master has 1MB flash Memory, rechargeable battery, radio, humidity sensor • Slaves has 2 serial ports forming a chain • Master use polling protocol to get data from slave, and store in the RAM • Master/slave approach decreases the collision, facilitates the deployment of server rack

  32. Reliable Data Collection Protocol (rDCP) • Placing routing and data retrieval operations: • Distributed ways • Centralized ways • rDCP employs a hybrid way • Genomotes cooperatively determine touting topology • Topology Control Layer (TCL) • Gateways initiate data downloads • Data Download Layer (DDL)

  33. Topology control for rDCP • BiTree construction • Tree topology network with bi-directional link, supporting point-to-point communication • Gateway broadcasting HEARTBEAT message • Genomote receiving the HEARTBEAT, compete to join the tree by JOIN_REQ, parent will grant joint by JOIN_GRANT • Children list is added in the HEARTBEAT • Two way hand shake process • After join tree, generate their own HEARTBEAT message

  34. Topology control for rDCP (cont’) • Parent selection requirement • Potential parent does not have maximum children number • Path quality to the gate way: • Expected total transmission count (ETTC) • ETTCi is included in the HEARTBEAT message for recursively calculation • HEARTBEAT is periodically sent out from gateway • Serve as a node live signal • If time out, abandon parent or children • Local TDMA is used • A time slot T is given for all one node children • ith child use time slot • Remaining slot is used by tree construction

  35. Topology control for rDCP (cont’) • Channel assignment (tree establishing) • High density reduce the throughput • Multiple gateways • Utilize channel diversity to build bitrees on different frequency • Every base station node has a fixed channel • Non-basestation node start scanning channels sequentially • Wait two intervals of HEARTBEAT time on each channel • After scanning, switch to the optimal parent’s channel • Delete this child in other channel

  36. Topology control for rDCP (cont’) • BiTree balance • Unbalanced tree lead to long overall time of collecting all the data • Tree balance choosing requirement : • Longest total data collecting time • Must exceed the average collecting time to a threshold • The only tree to do balance meets • Switching probability to channel i • If channel i tree’s collecting time is longer than average, the probability to switch to channel i is 0 • Otherwise, the less time channel i use, the highest probability to switch to it • If cannot find parent in channel i, switch back • The switch decision is transmitted through START_BAL message

  37. Data Download • Centralized, pull-based approach from gateway • Upload approach will course collision • Gateway sequentially poll each node for data • Downstream Route Construction • Every node only knows the parent and children • Gateway merge children list • Data reliability and integrity • CRC (Cyclic Redundancy Check) for integrity • End to end ack used for reliability • Data time stamping • Time stamping on HEARTBEAT • Gateway provide global timestamp • Each node provide local timestamp

  38. Evaluation • Tree settling time evaluation • 100 nodes (10x10) is simulated in a 100x100ft networks • Data collection evaluation • Real Test Bed HEARTBEAT interval (s)

  39. Evaluation (cont’) • Application level evaluation: latency and data loss • Network Density (simulation) • Data latency (test bed)

  40. Real Data Center Deployment • 100 masters real deployments

  41. Real Data Center Deployment (cont’) • 174 masters real deployment • Channel balancing • Multichannel impact

  42. Conclusion • RACNet is the first attempt to provide fine-grained and real-time visibility into data center cooling system • Compared with CTP, rDCP is more reliable and flexible • RACNet can provide a holistic understanding of key operation and performance parameters of the data center energy saving based on the effective data

  43. Critique • The pulling approach of data collection may decrease the collision, but will trade the time, especially when network is dense • The time stamping is important for latency calculation. But the scheme proposed in the paper require both global timing and local timing, which is complicate • From the experiment, the tree balancing takes too long time. It need a fast balancing approach to improve

  44. Comparison

  45. Q&A

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