Multi-Channel Real-Time Communications in Wireless Sensor Networks by Xiaodong Wang Nov 18th, 2008

Page 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

Page 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

Page 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

Page 5 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 Single Channel Multiple Channel

Page 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

Page 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

Page 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 s 1,…, s k and a destination vertex t  Goal:  Find k disjoint paths, one from each source s i to t  Each path delay is bounded by a value W

Page 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

Page 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

Page 11 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 F DE t sCB V(I)

Page 12 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 F DE t sCB V(I)

Page 13 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 F DE t sCB V(I)

Page 14 Phase II: Augmentation algorithm (cont’) Current iteration ends with a search path reaches to t F DE t sC X B V(I)

Page 15 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 F DE V(I) t sC X B V(I-1)

Page 16 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 F DE V(I) t sCB V(I-1) X

Page 17 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 F DE V(I) t sCB V(I-1) X

Page 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

Page 19 Power-Efficient Real-Time Routing (RPAR) Real-time forwarding  velocity required (s, d) =dis(s, d)/slack  velocity provided (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.

Page 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

Page 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 150x150m 2 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

Page 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

Page 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

Page 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

Page 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.

Page 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

Page 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

Page 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

Page 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

Page 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: have 16 channels  In-network bi-directional collection tree

Page 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

Page 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)

Page 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

Page 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)  ETTC i 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

Page 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

Page 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

Page 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

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

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

Page 40 Real Data Center Deployment 100 masters real deployments

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

Page 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

Page 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

Page 44 Comparison MCRTRACNet Traffic PatternFlow trafficTree based Multi channel protocol Yes Data collection approach Push approachPull approach Metric for realtimeTransmission count Channel Assignment CentralizedDistributed Real test bedNoYes

Page 45 Q&A