multi-channel real-time communications in wireless sensor networks by xiaodong wang nov 18th, 2008

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 NetworksBy Xiaodong Wang, Xiaorui Wang,

Xing Fu, Guoliang Xing,

Nitish Jha

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

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

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

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

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

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 s i to t

Each path delay is bounded by a value W


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


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

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

FD E ts CB

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

FD E ts CB

V(I)


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

FD E ts CB

V(I)


Current iteration ends with a search path reaches to t

FD E ts C

X

B

V(I)


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

FD E

V(I)

ts C

X

B

V(I-1)


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

FD E

V(I)

ts CB

V(I-1)

X


If there is no match of V(I), we backtrack to the search path of V(I-1) to find a match

FD E

V(I)

ts CB

V(I-1)

X


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

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.

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

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


Performance with different deadlines MCRT outperforms others on different deadlines

Performance with different packet rate MCRT shows low miss ratio and energy consumption


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

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

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.

Second Paper

RACNet: Fine-Grained and

Large-Scale Data Center SensingBy Chieh-Jan Mike Liang, Jie Liu,

Liqian Luo, Andreas Terzis

Johns Hopkins University, Microsoft Research

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

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

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

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

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

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)

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

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 gatewayServe as a node live signal

If time out, abandon parent or childrenLocal TDMA is used

A time slot T is given for all one node childrenith child use time slot Remaining slot is used by tree construction


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


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

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

Evaluation

Tree settling time evaluation 100 nodes (10x10) is simulated in a

100x100ft networks

Data collection evaluation Real Test Bed HEARTBEAT interval (s)


Application level evaluation: latency and data loss Network Density (simulation)

Data latency (test bed)

Real Data Center Deployment

100 masters real deployments

Real Data Center Deployment (cont’)

174 masters real deployment Channel balancing

Multichannel impact

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

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

Comparison

MCRT RACNet

Traffic Pattern Flow traffic Tree based

Multi channel protocol

Yes Yes

Data collection approach

Push approach Pull approach

Metric for realtime Transmission count

Transmission count

Channel Assignment

Centralized Distributed

Real test bed No Yes

multi-channel real-time communications in wireless sensor networks by xiaodong wang nov 18th, 2008

Documents

multichannel wsns

realtime application

low multi channel

realtime requirement

communication link

communication relationships

interference link

communication main components