reliable and energy-efficient data collection in wireless ...€¦ · wireless mesh network,...

Reliable and Energy-Efficient Data Collection in Wireless Sensor Networks

Feng Wang Department of Computer and Information Science

University of Mississippi

January 2014

January 2014 Reliable and Energy-Efficient Data Collection in Wireless Sensor Networks

About Myself

2/18

Feng Wang

Assistant Professor (Aug 2012- )

Department of Computer and Information Science

University of Mississippi

Email: [email protected]

Webpage: www.cs.olemiss.edu/~fwang

Research interest: computer networking Wireless mesh network, Wireless sensor network

Peer-to-peer network, Socialized content sharing

Cloud computing, Big data

mailto:[email protected]://www.cs.olemiss.edu/~fwang


Research Work

3/18

Peer-to-peer overlay networks

Live video streaming: exploit stable peers to improve QoS Partly adopted by PPTV (one major video streaming company in China)

File sharing (e.g. BitTorrent) Tracker: balance resource availability and traffic locality

Peer: apply fountain codes to speed up sharing performance

Socialized content sharing

UGC video (e.g., YouTube/Twitter Vine video) sharing Adaptively prioritize data requests with collaborations

Popularity prediction of videos shared in online social networks

Instant video clip sharing over mobile social network platforms


Research Work (cont.)

4/18

Cloud computing

Service migration/optimization Cloud assisted live video streaming for diversified/globalized demands

Optimization of cloud-based distributed interactive applications

Architecture design/performance analysis Utilization of customer-provided resources for cloud computing

Virtualization in cloud storage/synchronization services (e.g., Dropbox)

Network performance in virtual machine based cloud

Big data

Network-aware load balance and data locality in MapReduce

Crowdsourced live media streaming


Research Work (cont.)

5/18

Wireless mesh networks

Path capacity estimation and optimization Analyze available additional capacity without violating QoS

Path diversified multi-QoS optimization in multi-channel networks

Wireless sensor networks

Local calibration assisted time synchronization Calibrate by local crystal oscillator’s properties to reduce messages

Data collection/diffusion Location-oblivious: hybrid push-pull and adaptive ultra-node selection

Error-bounded: filter goes along paths to suppress unnecessary report

Reliable and energy-efficient: to be discussed in this talk


Outline

6/18

Reliable and energy-efficient data collection

Background and overview Wireless sensor network

Data collection service

Issues, challenges and solutions Wireless sensor deployment

Control message dissemination

Sensing data gathering

Case study: Guangzhou New TV Tower


Wireless Sensor Network (WSN)

Network of collaborative wireless sensor nodes Computing, sensing, communication and storage

Often powered by battery and expected to work for long time Relatively small and deployed in great number

Need careful design to be energy-efficient

Nowadays widely used in many applications, such as

Habitat monitoring

Battlefield surveillance

Building control

Volcano monitoring

Water monitoring

Structure health monitoring

7/18


Data Collection

8/18

Data collection service

Sensor nodes deployed at specific locations

Sensing ambient environment

Data forwarded to base station for further processing

Traditional (wired) approach More impact to ambient environment

Need extra efforts and costs Line protection

Find and repair a broken line


Challenges and Issues in Data Collection WSN

9/18

From WSNs

Limited hardware capacity and energy budgets

Wireless communication Major source of energy consumption

Loss due to interference and collision

From data collection Various application and networking requirements

Reduce/balance energy costs to extend network lifetime Heterogeneous data rate (temperature, acceleration, ...)

Hard to apply aggregation: “many-to-one” traffic pattern


How Data Collection WSNs Work ?

10/18

In practice, three stages

Deploy sensors to fulfill various application and

networking requirements

Disseminate messages (setup/management/

commands) to all sensors

Deliver sensing data from each sensor to base station


Deployment Stage

11/18

How can we use min. # nodes to connect the network or max. network lifetime for given node #?

Previous work mainly focuses on network connectivity

For data collection Heterogeneous data rate

“Many-to-one” traffic pattern

Traffic-aware deployment Generalized Euclidian Steiner minimum

tree problem Hybrid algorithm to bypass local optima and

yield high quality results

Optimal discrete node assignment on topology

Topology adjustment to fit discrete node assignment

x x

x x


Message Dissemination Stage

12/18

How to efficiently disseminate message to all nodes?

Most previous work: assume all nodes active all the time

Real world: nodes may work in low duty-cycles to save energy - Ratio between active and full active/dormant period

Topology of active nodes may change frequently and dramatically

Wireless losses further aggravate above issues

Duty-cycle-aware message dissemination

Transform the problem to a graph problem Min. cost/delay: find the shortest path

Centralized optimal solution: dynamic programming

Distributed implementation Scalability, reliability, efficiency


Data Delivery Stage

13/18

New scenario: structural health monitoring for

high-rise structures

Much longer distance to base station

Much higher data concentration near base station

EleSense - base station on moving elevator

Optimal solution to min. delay and cost

Resolve practical issues Hardware constraint

Local search algorithm guided by evaluation function

Elevator operates in cycle pattern

Use known “short future” to further improve performance


Guangzhou New TV Tower: A Case Study

14/18

World’s tallest TV tower (Nov 2010, 600m)

Hyperbolic shape

Uneven horizontal + extensive vertical dimension More variances in node capacity/wireless interference

System design (hierarchical architecture)

Close nodes as a cluster, sub-station as head Intra-cluster collection: traffic-aware deployment

Inter-cluster collection: EleSense framework

Stand-by mode: low duty-cycle Switch mode by command message dissemination

Collection mode: fully operate Back to stand-by mode if no new command


Sensor Deployment for Civil Requirements

15/18

Sensor Type Monitoring Items Qty.

Weather station Temperature, humidity, rain, air pressure

1

Anemometer Wind speed and direction 2

Wind pressure sensor Wind pressure 4

Tiltmeter Inclination of tower 2

GPS Displacement 2

Vibrating wire gauge Strain, shrinkage and creep 60

Thermometer Temperature of structure 60

Digital video camera Displacement 3

Seismograph Earthquake motion 1

Corrosion sensor Corrosion of reinforcement 3

Accelerometer Acceleration 22

Fiber optical sensor Strain and temperature 120 Sensor Deployment on GNTVT


System Deployment and Verification

16/18

Sensor node hardware

StanfordMote + Tokyo Sokushin AS-2000 (accelerometer)

Experiment results

Base station successfully receives all data packets while moving with elevator at both directions

Wireless transmissions could easily reach 55Kbps

Accelerometer Deployment Sub-station Base Station on Elevator


Preliminary Evaluation for Full Tower

17/18

Due to limited time/area access

Emulation with real data/settings from

GNTVT to examine full tower performance

Results

Throughput gain: 212.7%

Communication cost reduction: 58.7%

Throughput Communication Cost

Example of Collected Acceleration Data


Summary

18/18

Wireless sensor data collection

Greatly reduce deployment/maintenance costs

Pose new challenges such as reliability and energy-efficiency

Propose a full range of solutions across different stages

Traffic-aware deployment

Message dissemination with low duty-cycle

Elevator-assisted data delivery

Partially integrated in GNTVT’s new monitoring system


Email: [email protected]

Thank you!

19/40

Question and Comments?

mailto:[email protected]


Deployment Stage

20/-

• Bai et al. “Complete Optimal Deployment Patterns for Full-Coverage and k-Connectivity (k


System Model and Problem Statement

21/-

System model

M source nodes (S-nodes) with location S={s1,…,sM} and data rate

Location of base station: s0

Traffic-aware deployment problem

Given N relay nodes (R-nodes), find their locations {f1,…,fN} with communication ranges R={r1,…,rN} and data paths for S-nodes P={p1,…,pM} to

Communication range:

Forwarding path connectivity:

S-nodes and sink connectivity:

},,{ 1 M

..)]([maxmin1,

1tsr

Mjpi

isendrecvjNi

j

EE

m a xRrRr ,

lirffPxxxpiii xxxl

1,,1110

lll xxxlrsfsfPxxxpSs 010 ,,,


Our Solution

22/-

Optimal solution for single source case

Single traffic flow: deploy nodes from source with distance of

L/N (Theorem 1)

Multi traffic flow: merge all

flows into one and apply Theorem 1 (Theorem 2)

Transform general case into directed graph G=(V,E)

V={v0,v1,...,vM,vM+1,...} vi=si, for ; vj: merge vertices, for j>M

E={e1,e2,…}, for edge ei with length : total data rate of flows through edge

: number of assigned R-nodes

: maximum R-node energy cost

ie

ieL

ien

ieE

Mi


Our Solution (cont.)

23/-

Solution for general case

Theoretical solution in continuous space To , we need and have ([Olariu06])

Find topology (merge vertices) to minimize total weighted edge length

Generalized Euclidian Steiner Minimum Tree problem (NP-hard, [Xue99])

Hybrid algorithm: bypass local optima and yield high quality results

Practical solution on discrete R-node deployment Fractional node number: up to 40% degradation by simply rounding

Discrete R-node assignment algorithm: optimal assignment on topology

Merge vertex adjustment algorithm: fit discrete node assignment

)(i

i

ii

e

e

een

LE

ii

eEe

E

maxmin 21 ee

EE

Ee

e

Ee

ee

e

ee

e

ee

i

i

i

ii

n

L

n

L

n

L)(

2

22

1

11

• Olariu et al. “Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting”, IEEE INFOCOM 2006

• Xue et al. “Computing the minimum cost pipe network interconnecting one sink and many sources”, SIAM J. Optimiz., 1999

N


Performance Evaluation

24/-

Our solution: significantly extend network lifetime

Up to 7 times of Connectivity-Only and 15 times of Direct-Connection

Close to theoretical upper bound (difference 13.5%) Upper bound of optimal solution with small M ( 15 in our evaluation)

Network Lifetime by Numerical Analysis Network Lifetime by ns-2 simulations


Message Dissemination Stage

25/-

Efficiently deliver messages to all nodes at low costs

Conventional assumption in previous works

All nodes active all the time ([Stann2006, Kyasanur2006])

Real world

Nodes may work in low duty-cycles to save energy - Ratio between active and full active/dormant period

Topology of active nodes may change frequently and dramatically

Active/dormant pattern is often application-specific

May not be determined before node deployment

Should not be disturbed by message dissemination

Wireless losses further aggravate above issues • Kyasanur et al. “Smart Gossip: An Adaptive Gossip-based Broadcasting Service for Sensor Networks,” IEEE MASS, 2006 • Stann et al. “RBP: Robust Broadcast Propagation in Wireless Networks,” ACM SenSys, 2006


Problem Formulation

26/-

Duty-cycle-aware message dissemination problem:

Notations Active/dormant state of node i at t: (1: active; 0: dormant)

Neighbor set of node i:

Forwarding Sequence (FS):

Node set covered by i-th forwarding:

For message dissemination starting from s at t0, find

s.t. Duty-cycle constraint:

Forwarding order constraint:

Coverage constraint:

To minimize , a function of message and time costs A common linear combination form:

)},(,),,(),,{( 2211 mm tututuS )( 21 mttt

)(tX i

iC

iN

)},(,),,(),,{( 2211 mm tututuS

}1)(,|{},{;1)( 0 ijuiiu tXNjjCsCtX ii

miCuttjsu jiij ,,3,2,,,;1

nCm

i

i

0

),( 0ttSf m

)(),( 00 ttSttSf mm

)( 10 mttt


Centralized Optimal Solution

27/-

Transform to time-space graph

Vertex : nodes in R have message at t

Edge : Time edge: no forwarding at t

Forwarding edge: forward to nodes in at t

Weight W Time edge:

Forwarding edge: (p: forwardings at t)

Minimize : find shortest path from to last row

Dynamic programming solution

Recurrence relation: : total weights of shortest path from to

tRv ,),( ',', tRtR vv

RR '

),( ',', tRtR vv

)'( ttp

0},{ tsv),( 0ttSf m

)),()(( ',',,),(,

min'',,,

tRtRtREvvv

vvWvFtRtRtR

)( ',' tRvF ',' tRv0},{ tsv

)( ',' tRvF


Distributed Implementation

28/-

Scalable forwarding sequence generating

CovSet: nodes being covered within 2-hop

Compute 2-hop optimal FS from current CovSet Re-compute if CovSet update does not match computed FS

Accommodate wireless loss

RcvSet: nodes having received message within 2-hop RcvSet may NOT be equal to CovSet due to wireless loss

Set CovSet to RcvSet periodically

Expedite information updating

Piggy-back RcvSet with messages

Overhearing during active mode



29/-

RBP ([Stann06]): unacceptable when duty-cycle


Data Delivery Stage

30/-

Diverse application-specific QoS requirements

Reliability [Xu04], delay [Song06], throughput

[Ahn06], energy consumption [Burri07], …

New scenario: Structural health monitoring

(SHM) for high-rise structures

High-rise structure: normal horizontal dimension

but extensive vertical dimension

New challenges Much longer distance to base station

Much higher data concentration near base station

• Xu et al. “A Wireless Sensor Network For Structural Monitoring,” ACM SenSys, 2004 • Song et al. “Time-Optimum Packet Scheduling for Many-to-One Routing in Wireless Sensor Networks,” IEEE MASS, 2006 • Ahn et al. “Funneling-MAC: A Localized, Sink-Oriented MAC For Boosting Fidelity in Sensor Networks,” ACM SenSys, 2006 • Burri et al. “Dozer: Ultra-Low Power Data Gathering in Sensor Networks,” ACM/IEEE IPSN, 2007


Elevator-Assisted Data Delivery

31/-

EleSense - elevator-assisted data collection

Base station on elevator Collect data when serving passenger

Reduce distances between nodes and base station

Effectively balance traffic relaying among sensor nodes

Still a series of issues to address

Elevator not controlled by base station Various node capacity to transmit data to base station

Relay by neighbour or wait for base station arrival?

Limited queuing buffer: rate control and fairness

Interference/collision at both nodes and moving base station


Solution Design

32/-

Elevator-assisted data delivery problem

Assumption: elevator movement known a priori

Modeled as a cross-layer optimization problem Link scheduling, packet routing, end-to-end delivery

Theoretical solution

Transform into a graph problem

Solve optimally by dynamic programming

Good for analysis but hard to apply to WSNs Intensive computation/memory requirement

Base on a priori information


Resolve Practical Issues

33/-

Accommodate hardware constraint

Local search algorithm Explore vertices by order based on evaluation function

Limit search range to finish in one time unit

Work without a priori information

Naive approach Use current elevator location

An observation Elevator operates in cycle pattern

Use known “short future” movements

to further improve performance



34/-

EleSense achieves good reliability and fairness

Throughput gain: 30.7% to 159.6% over StaticSense and 40.9% to 423.2% over Ele802.11

Communication cost: 58.9% to73.1% of the runner-up

Communication Cost Throughput


Guangzhou New TV Tower: A Case Study

35/-

Guangzhou New TV Tower (GNTVT)

World’s tallest TV tower (600m) Locate in Guangzhou, China

Fully operate in Nov 2010 and broadcast

16th Asia Games

Hyperbolic shape: more challenges Uneven horizontal dimension

60mX80m at ground, minimum of 20.65mX

27.5m at 280m, 40.5mX54m at top (454m)

Extensive vertical dimension Main tower: 454m, antennary mask: 146m

Cause more variances in node capacity

and wireless interference/collision


Sensor Deployment for Civil Requirements

36/-

Sensor Type Monitoring Items Qty.

Weather station Temperature, humidity, rain, air pressure

1

Anemometer Wind speed and direction 2

Wind pressure sensor Wind pressure 4

Tiltmeter Inclination of tower 2

GPS Displacement 2

Vibrating wire gauge Strain, shrinkage and creep 60

Thermometer Temperature of structure 60

Digital video camera Displacement 3

Seismograph Earthquake motion 1

Corrosion sensor Corrosion of reinforcement 3

Accelerometer Acceleration 22

Fiber optical sensor Strain and temperature 120 Sensor Deployment on GNTVT


Prototype System Design

37/-

Hierarchical architecture

Nodes close to each other form a cluster

Sub-station as cluster head Intra-cluster collection: traffic-aware deployment

Inter-cluster collection: EleSense framework

Two-mode working pattern

Stand-by mode All nodes in low duty-cycle to conserve energy

Switch mode by command message dissemination

Collection mode Nodes fully operate for data collection

Back to stand-by mode unless new command disseminated


System Deployment and Verification

38/-

Sensor node hardware

Node: StanfordMote

Accelerometer: Tokyo Sokushin AS-2000

16-bit ADC with sample rate as 50Hz

Data traffic from each sensor

Total 50x60x2=6000 bytes

acceleration data per minute,

further divided into 20 packets

Accelerometer Deployment


System Deployment and Verification (cont.)

39/-

GNTVT still in construction during our experiments

Only section below 240m allowed for temporary access 4 subsections, each for 60m

Experiments at both up/down directions for all subsections to fully understand wireless capacity

Sub-station Base Station on Elevator


System Deployment and Verification (cont.)

40/-

Experiment results

Base station successfully receives all data packets while moving with elevator at both directions

Wireless transmissions could easily reach 55Kbps

Elevator Movement Start Height End Height Packet from Each Node Delivery Ratio

From bottom to top

0m 60m 20 100%

60m 120m 20 100%

120m 180m 20 100%

180m 240m 20 100%

From top to bottom

240m 180m 20 100%

180m 120m 20 100%

120m 60m 20 100%

60m 0m 20 100%


Preliminary Evaluation for Full Tower

41/-

Limited time/area access due to construction phase

Emulation with real data/settings from

GNTVT to examine full tower performance

Results

Throughput gain: 212.7%

Communication cost reduction: 58.7%

Throughput Communication Cost

Example of Collected Acceleration Data


Summary and Discussion

42/-

Wireless sensor data collection

Greatly reduce deployment/maintenance costs

Pose new challenges such as reliability and energy-efficiency

Propose a full range of solutions across different stages

Traffic-aware deployment

Message dissemination with low duty-cycle

Elevator-assisted data delivery

Partially integrated in GNTVT’s new monitoring system

Other issues worth further exploring

Learn/model elevator movement for further optimization

Multiple base stations for collaborative data collection

reliable and energy-efficient data collection in wireless ...€¦ · wireless mesh network,...

Documents