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Efficient Bulk Transport and Mobility Control in Wireless Sensor Networks

Guoliang Xing

Department of Computer Science City University of Hong Kong

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Outline

• Motivation

• Model-driven medium access control

• Mobility-assisted spatiotemporal detection

• Other projects– Spatiotemporal query service for mobile users– Relationship bw coverage and connectivity

• Future work

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Sensing Applications

• Redwood Ecophysiology, Occidental, CA [Culler05]

– "40-50 nodes per tree, 5 min sample interval, temp, light, humidity…"

• Structural health monitoring [Szewczyk04]

– "sample vibration at 100Hz, 30 min/day”, 100 G data gathered per year

• Volcano monitoring, [Tungurahua 2007]

– "16 nodes, sample at 100Hz, 19 days“, most data processed locally, 107 M data gathered

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Promise of Sensor Networks

"Dense monitoring and analysis of complex phenomena over large regions of space for long periods"

----- David Culler

"Dense monitoring and analysis of complex phenomena over large regions of space for long periods"

----- David Culler

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Application Characteristics

• Data-intensive

• Long-lived– 1 month ~ years on limited power supplies

• Delay-tolerant– "sample accelerometers at 100Hz and report

data hourly"

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Outline

• Motivation• Model-driven medium access control

– Establish empirical power control and interference models

– Enable concurrent transmissions of multiple nodes

• Mobility-assisted spatiotemporal detection• Other projects

– Spatiotemporal query service for mobile users– Relationship bw coverage and connectivity

• Future work

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Understanding Interference

• Traditional MACs try to completely avoid interference– Back-off and channel reservation– Only one link within the interference range can transmit

s1

r1

s2

r2

Noise + Interference

Received Signal Strength (RSS)

Signal-to-interference-noise ratio (SINR) model

+

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Improve Throughput by Concurrency

• Enable concurrency by controlling senders' power

s1

r1

s2

r2 +

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Existing Models

• How does radio power decay?– Classical exponential decay model:

• How does packet reception relate to SINR?– Classical deterministic SINR model:

RSS = P / distα

log(RSS) = log(P)- α log(dist)

Noise + Interference

Received Signal Strength (RSS)

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Model Estimation Experiments

• 18 Tmotes with Chipcon 2420 radio– 32 tunable power leves, -25 – 0 dBm– One sender, multiple receivers at different

positions

• Four environments– Office, corridor, parking lot, grass field

• Over two weeks of experiments

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Received Signal Strength

Transmission Power LevelTransmission Power Level

Re

ce

ive

d S

ign

al S

tren

gth

(dB

m)

Re

ce

ive

d S

ign

al S

tren

gth

(dB

m)

• Near-linear RSSdBm vs. transmission power level• Non-linear RSSdBm vs. log(dist), different from the classical model!• Observable difference in hourly measurements

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Pair-wise Power Control Model

• Received signal strength (RSS) at r when s transmits with power Ps is given by

RSSr(s) = a x Ps + b

• a and b are interpolated using multiple measurements – a is estimated once– b is updated periodically

s

r

Ps

RSSr(s)

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Packet Reception Ratio vs. SINR

• Classical model doesn't capture the gray region

office, no interfererparking lot, no interferer office, one interferer

Noise + Interference

Received Signal Strength (RSS)

0~3 dB is

"gray region"

Packet R

eception Ratio (%

)

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Probabilistic SINR Model

• PRR(SINRi ) (1≤ i ≤ m)

PRR(0.5) PRR(1) PRR(1.5) PRR(2) …..

0 1 2 3 4 SINR (dB)

Packet R

eception Ratio (%

)

20

40

60

80

100

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Online Model Estimation

• Each node bcasts N beacons at K power levels• Each node estimates neighbors' power control

models and its own interference model• Nodes exchange their model parameters

s1

r1

s2

r2

ID a b PRR(0.5) PRR(1) PRR(1.5) PRR(2) …..

s1

s2

model parameters

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System Components

ConcurrentTransmission

Engine

Power Control Model

InterferenceModel

handshaking

On

line M

od

el Estim

ation

Currency Check

Throughput Prediction

Throughput Prediction

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Throughput Prediction• s tries to transmit to r• s overhears m packets (belonging to K links) • s finds power P that maximizes

• If negative, abort, otherwise transit a block of B packets

}{rKv Σ PRRv( SNRv ) – |K|

assuming 100% PRR for all active links

RSSr(P)

Interferencer+Noiser

SNRr =

obtained from handshaking

RSS model

PRR model

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Performance Evaluation• Implemented in TinyOS 1.x• 16 Tmotes deployed in a 25x24 ft office• 8 senders and 8 receivers

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Sample Experimental Results

system th

rou

gh

pu

t (Kb

ps)

system th

rou

gh

pu

t (Kb

ps)

Time (second) Number of Senders

Improve throughput linearly w num of senders

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Summary of Contributions

• Established power control and interference models using experimental data

• Developed a model-driven MAC protocol for concurrent bulk-data transmissions– Evaluated system performance on an 18-node

test-bed• Improved network capacity by ~3 times

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Outline

• Motivation• Model-driven medium access control• Mobility-assisted spatiotemporal detection• Other projects

– Spatiotemporal query service for mobile users– Relationship bw coverage and connectivity

• Future work

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Mission-critical Target Detection

• Stringent Spatiotemporal QoS requirements– High detection probability, e.g., 90%– Low false alarm rate, e.g., 5%– Bounded detection delay, e.g., 20s

• Network and environmental dynamics– Death of nodes (battery depletions, attacks…)– Changing noise levels and target profiles

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State of the Art

• Over-provisioning of sensing capability– Careful advance network planning– Dense node deployment– Incremental redeployment

• Issues– High deployment costs and long redeployment

periods– Fails to adapt to network and environmental changes

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Mobility-assisted Target Detection

• Mobile sensors collaborate with static sensors in target detection– Achieve higher signal-to-noise ratios by

moving closer to possible targets– Reconfigure sensor coverage dynamically

target

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Mobile Sensor Platforms

• Limitations– Low movement speed (0.1~1 m/s)– High power consumption (~60 W for PackBot)

PackBot @ iRobot.com Robomote @ USCKoala @ NASA

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Overview of Our Approach

• Data-fusion-based detection model for collaboration between mobile and static sensors

• Optimal sensor movement scheduling algorithm – Minimizes the moving distance of sensors– Meets spatiotemporal QoS requirements

• High detection probability, low false alarm rate, and bounded detection delay

• Simulations based on real data traces of target detection

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Signal and Noise Models

• Target energy decays quadratically with distance • Noise energy follows the Normal distribution• Sensor reading = decayed target energy + noise energy

Plotted based on real acoustic sensor data traces in military vehicle detection

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Fusion-based Detection Model

• Sensors send readings to the cluster head

• Cluster head sums up all readings and compare against a threshold

sensor reading distribution

detection threshold

noise energy distribution

energy

false alarm rate

detection probability

sensor reading distribution

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Problem Formulation

• Mobile sensors are assumed to move synchronously at equal-distant steps

• Find two detection thresholds and a movement schedule– Minimizes the moving distance of mobile sensors – Detection probability ≥ α, false alarm rate ≤ β, detection delay ≤ T

target

Example movement schedule:

M1: t0 - one step, t3 - two steps …M2: t1 - one step, t2 - one step…M3: t1 - two steps, t2 - one step…

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A Two-phase Detection Scheme

• First phase – static detection – All sensors send readings to cluster head– Cluster head makes a detection decision, if

positive, starts the 2nd phase

• Second phase – movement scheduling– Mobile sensors move toward the possible target

according to a movement schedule– Cluster head makes the final detection decision

• First phase – static detection – All sensors send readings to cluster head– Cluster head makes a detection decision, if

positive, starts the 2nd phase

• Second phase – movement scheduling– Mobile sensors move toward the possible target

according to a movement schedule– Cluster head makes the final detection decision

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Optimal Movement Schedule

• Theorem I: max sum of readings in the 2nd phase leads to min total moving distance

• Optimal schedule maximizes the sum of energy readings

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Examples of Optimal Schedules• Assume that all sensors can move one step every

second, and detection delay is T seconds• Case 1: only one step allowed

– Opt schedule: move B/C one step at time zero• Case 2: two steps allowed

– Schedule I: move B and C one step at time zero– Schedule II: move A two steps at time zero

A

C

BAll move combinations must be considered to find the optimal schedule!

sample T-2 secondsample T-1 second

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Finding the Optimal Schedule

• Theorem II: if a sensor moves in the 2nd phase, it moves continuously before a stop– Num of move combinations is limited – Dynamic programming algorithm

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Simulations

• Public dataset of detecting military vehicles [Duarte04]

• Sensors are randomly deployed in a 50×50m2 field

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Performance Results

Total 6 sensors are deployed MD-random1: randomly choose one sensor and moves to the targetMD-random2: randomly move the next step from an arbitrary sensor

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Outline

• Motivation

• Model-driven medium access control

• Rendezvous-based bulk-data transport

• Mobility-assisted spatiotemporal detection

• Other projects– Spatiotemporal query service for mobile users– Relationship bw coverage and connectivity

• Future work

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Spatiotemporal Query

“report the temperature within 100m in every 2s; data can be at most 1s old.”

• Spatial constraint– All and only the sensors within 100m should respond

• Temporal constraints– Data are no older than 1s, and must be delivered within 2s

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Implementation• Implemented MobiQuery on Mica2 motes• Acroname PPRK robot carrying Stargate was used to emulate the user• Demonstrated at Sensys 04

International Conference on Distributed Computing Systems (ICDCS), 2005 International Symposium on Information Processing in Sensor Networks (IPSN) 2005

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Coverage + Connectivity

• Backbone nodes must achieve:– K-coverage: every point is monitored by at least K active

sensors– N-connectivity: network is still connected if N-1 active

nodes fail

Sleeping node

Communicating nodes

Active nodes

Sensing range

A network with 1-coverage and 1-connectivity

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Connectivity vs. Coverage: Analytical Results

• Network connectivity does not guarantee coverage– Connectivity only concerns with node locations– Coverage concerns with all locations in a region

• If Rc/Rs 2– K-coverage K-connectivity– Implication: given requirements of K-coverage and N-

connectivity, only needs to satisfy max(K, N)-coverage– Solution: Coverage Configuration Protocol (CCP)

• If Rc/Rs < 2– CCP + connectivity mountainous protocols

ACM Transactions on Sensor Networks, Vol. 1 (1), 2005. First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003

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Acknowledgements

• Collaborators– Jianping Wang, Xiaohua Jia, Weijia Jia, Minming Li,

Hing Cheung So (CityU of HK)– Chenyang Lu, Robert Pless (Washing Univ.), Gang

Zhou (College of William and Mary), Benyuan Liu (Univ. of Mass, Lowell)

• Students and associates– PhDs: Hongbo Luo (Ph.D, joint supervision with Dr.

Xiaohua Jia), Rui Tan, Zhaohui Yuan, Shucheng Liu,

– Masters: Mo Sha – Research Staff: Tian Wang, Shaoliang Peng (joint

supervision with Prof. Weijia Jia)

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Research Summary• Systems

– C-MAC: concurrent model-driven MAC [TR08]– UPMA: unified power management architecture [IPSN 07]– MobiQuery: spatiotemporal query service for mobile users [ICDCS 05, IPSN 05]– nORB: light-weight real-time middleware for networked embedded systems

[RTAS 04]

• Algorithms, protocols, and analyses– Mobility-assisted spatiotemporal detection [ICDCS 08,IWQoS 08]– Rendezvous-based data transport [MobiHoc 08, RTSS 07]– Minimum power configuration [MSWiM 07, MobiHoc 05, TOSN 3(2)]– Integrated coverage and connectivity configuration [TOSN 1(1), SenSys 03]– Impact of sensing coverage on geographic routing [TPDS 17(4), MobiHoc 04]– Real-time power-aware routing in sensor networks [IWQoS 06]– Data fusion for target detection [IPSN 04]

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Relevant PublicationsACM/IEEE Transaction Papers:1. Rendezvous Planning in Wireless Sensor Networks with Mobile Elements, G. Xing, T. Wang, Z. Xie and W.

Jia, IEEE Transactions on Mobile Computing (TMC), accepted for publication, to appear.2. Minimum Power Configuration for Wireless Communication in Sensor Networks, G. Xing C. Lu, Y. Zhang,

Q. Huang, R. Pless, ACM Transactions on Sensor Networks, Vol 3(2), 20073. Integrated Coverage and Connectivity Configuration for Energy Conservation in Sensor Networks , G.

Xing; X. Wang; Y. Zhang; C. Lu; R. Pless; C. D. Gill, ACM Transactions on Sensor Networks, Vol. 1 (1), 20054. Impact of Sensing Coverage on Greedy Geographic Routing Algorithms, G. Xing; C. Lu; R. Pless; Q.

Huang. IEEE Transactions on Parallel and Distributed Systems (TPDS),17(4), 2006

Conference Papers:

1. Rendezvous Design Algorithms for Wireless Sensor Networks with a Mobile Base Station, G. Xing, T. Wang, W. Jia, M. Li, the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), May 26-30, 2008, Hong Kong, acceptance ratio 44/300=14.6%.

2. Mobility-assisted Spatiotemporal Detection in Wireless Sensor Networks, G. Xing, J. Wang, K. Shen, Q. Huang, X. Jia and H. So, the 28th International Conference on Distributed Computing Systems (ICDCS), Beijing, China, Jun 17-20, 2008, acceptance ratio 102/638=16%.

3. Rendezvous Planning in Mobility-assisted Wireless Sensor Networks, Guoliang Xing, Tian Wang, Zhihui Xie and Weijia Jia, The 28th IEEE Real-Time Systems Symposium (RTSS), December 3-6, 2007, Tucson, Arizona, USA.

4. Minimum Power Configuration in Wireless Sensor Networks, G. Xing; C. Lu; Y. Zhang; Q. Huang; R. Pless, The Sixth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2005,acceptance ratio: 40/281=14%

5. On Greedy Geographic Routing Algorithms in Sensing-Covered Networks, G. Xing; C. Lu; R. Pless; Q. Huang. The Fifth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), May, 2004, Tokyo, Japan, acceptance ratio: 24/275=9%

6. Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks , X. Wang; G. Xing; Y. Zhang; C. Lu; R. Pless; C. D. Gill, First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003, acceptance ratio: 24/135=17.8%

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Concurrency Check

1. Overhear m packets (say, belonging to K links)2. For each of link uv, predict the PRR if s

transmits with min power PRRv(SNRv)

3. If the PRR of any link would drop below α (i.e., 20%), fails

RSSv(Pu)

RSSv(Psmin) + Ir+Nr

SNRv =

stored in data packet

RSSv(Psmin) = av Psmin + bv

compute from v's RSS model

compute PRRv(SNRv) from v's interference

model

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Mobile Platforms• Rreplenishable power supplies and large storage

capacity• Low movement speed (0.1~2 m/s)

– It takes a mobile of 0.5 m/s > 4 hours to visit 200 nodes in a 500×500 m2 field

Networked Infomechanical Systems (NIMS) @ UCLA

Robomote @ USC XYZ @ Yale