Research Profile of My Group

Guoliang XingDepartment of Computer Science

City University of Hong Kong

Facts of My Group

• Members– Three PhD students

• CityU, CityU-USTC, CityU-WuhanU

– One Master student– Two research assistants (joint supervision)

• Part of CityU wireless group– 6 faculty members– more than 20 research staff/students– ~3 million HK$ government funding in 2007-08

Research Directions

• Controlled mobility

• Data fusion based target detection

• Power management

• Sensing coverage

2007-08 Conference Publications • Controlled mobility

– Rendezvous Design Algorithms for Wireless Sensor Networks with a Mobile Base Station, G. Xing, T. Wang, W. Jia, M. Li, MobiHoc 2008, 44/300=14.6%.

– Rendezvous Planning in Mobility-assisted Wireless Sensor Networks, G. Xing, T. Wang, Z. Xie and W. Jia; RTSS 2007, 44/171=25.7%.

• Data fusion based target detection– Mobility-assisted Spatiotemporal Detection in Wireless Sensor

Networks, G. Xing; J. Wang; K. Shen; Q. Huang; H. So; X. Jia, ICDCS 2008, 102/638=16%.

– Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility, R. Tan, G. Xing, J. Wang and H. So, IWQoS 2008

• Power management – Link Layer Support for Unified Radio Power Management in Wireless

Sensor Networks. K. Klues, G. Xing and C. Lu, IPSN 2007 38/170=22.3%.

– Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks. H. Luo, G. Xing, M. Li, and X. Jia, MSWiM 2007, 41/161=24.8%.

Earlier Work on Sensor Networks

ACM/IEEE Transactions Papers

1. 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), 2007, extended MobiHoc 2005 paper

2. 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, extended MobiHoc 2004 paper

3. 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), 2005, extended SenSys 2003 paper, one of the most widely cited work on the coverage problem of sensor networks, total number of citations is 358 in Google Scholar.

Focus of this Talk

• Controlled mobility – Rendezvous Planning in Mobility-assisted Wireless

Sensor Networks, G. Xing, T. Wang, Z. Xie and W. Jia; RTSS 2007, 44/171=25.7%.

• Power management – Link Layer Support for Unified Radio Power

Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu, IPSN 2007 38/170=22.3%.

• Sensing Coverage– 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), 2005, extended SenSys 2003 paper

Motivations

• Sensor nets face the fundamental performance bottleneck– Many applications are data-intensive– Multi-hop wireless relays are power-consuming– A tension exists between the sheer amount of data

generated and limited power supply

• Controlled mobility is a promising solution– Number of related papers increases significantly in

last 3 years: MobiSys, MobiHoc, MobiCom, IPSN

Mobile Sensor Platforms

• Low movement speed (0.1~2 m/s)– Increased latency of data collection– Reduced network capacity

Networked Infomechanical Systems (NIMS) @ CENS, UCLA

Robomote @ USC [Dantu05robomote]

XYZ @ Yale http://www.eng.yale.edu/enalab/XYZ/

A Data Collection Tour

Base Station

50K bytes

100K bytes 200K bytes100K bytes

150K bytes

1 m

inut

e

2 minute

1 minute 1 minute

• Analogy – What's the most reliable way of sending 1000 G bytes of

data from Hong Kong to Suzhou?

Static vs. Mobile

All-static networks

Mobility-assisted Networks

Delay Low High

Energy Consumption

High nonreplenishable

High

replenishable

Bandwidth Medium Medium to high

Basic idea

• Some nodes serve as “rendezvous points” (RPs)– Other nodes send their data to the closest RP– Mobiles visit RPs and transport data to base station

• Advantages – In-network caching + controlled mobility– Mobiles can collect a large volume of data at a time– Minimize disruptions due to mobility

• Mobiles contact static nodes at RPs at scheduled time

mobile node

rendezvous point

An Example

source node

The field is 500 × 500 m2 The mobile moves at 0.5 m/s

It takes ~20 minutes to visit six randomly distributed RPs

It takes > 4 hours to visit 200 randomly distributed nodes.

The Rendezvous Planning Problem

• Choose RPs s.t. mobile nodes can visit all RPs within data collection deadline

• Total network energy of transmitting data from sources to RPs is minimized

• Joint optimization of positions of RPs, motion paths of mobile, and routing paths of data

Assumptions

• Only one mobile is available

• Mobile moves at a constant speed v

• Mobile picks up data at locations of nodes

• Data collection deadline is D– User requirement: “report every 10 minutes and

the data is sampled every 10 seconds”– Recharging period: e.g., Robomotes powered

by 2 AA batteries recharge every ~30 minutes

Data Aggregation

• Data from different sources can be aggregated– Reduces the amount of network traffic– "what's the lowest temperature of this region"?

• Without aggregation– Optimal routing tree is the shortest path tree

• With aggregation– Optimal routing tree is the minimum

spanning/Steiner tree

Geometric Network Model• Transmission energy is proportional to distance• Base station, source nodes and branch nodes

are connected with straight lines

a multi-hop route is approximated by a straight line

Source nodes

Source nodes

approximated data route

real data route

Non-source nodes

Branch nodes

Rendezvous points

a branch node lies on two or more source-to-root routes

Problem Formulation

• Given a tree T(V,E) rooted at B and sources {si}, find RPs, {Ri}, and a tour no longer than L=vD that visits {B}U{Ri}, and

• The problem is NP-hard (reduction from the Traveling Salesman Problem)

minimized is ),( isS

iiT Rsd

dT(si,Ri) – the on-tree distance between si and Ri

Rendezvous Planning under Limited Mobility

• The mobile only moves along routing tree– Simplifies motion control and improves reliability

XYZ @ Yale

An Optimal Algorithm

• Sort edges in the descending order of the number of sources in descendents

• Choose a subset of (partial) edges from the sorted list whose length is L/2

• The mobile tour is the pre-order traversal of the chosen edges

A Heuristic for Unlimited Mobility

• Add virtual nodes s.t. each edge is no longer than L0

• In each iteration, choose the RP candidate with the max utility defined by c(x)

• Terminate if no more RPs can be chosen or all sources become RPs

)(}){(

}){,(),(

)(QTSPxQTSP

xQsdQsd

xc SsiT

SsiT

ii

the decreased length of data routes

the increased length of the mobile node tourTSP(W) computes the distance to

visit nodes in W using a Traveling Salesman Problem solver

Rendezvous Planning w Aggregation

Given a base station B, and sources {si}, find trees Ti(Vi, Ei), {B}U{si} UVi, and a tour visiting the roots of Ti such that

1) the tour is no longer than L;

2) the total length of edges of Ti is minimized

R1

s1

s5

s4

B

s2s3

R2

R3

R4

s6

A special case when L=0, the opt solution is Steiner minimum tree that connects {B} U {si}

An Approx. Algorithm

• Find an approx. Steiner min tree of {B}U{si}

• Depth-first traverse the tree until covers L/2 length

Approx. Ratio

• The approximation ratio of the algorithm is α+β(2α-1)/2(1-β)– α is the best approximation ratio of the Steiner

Minimum Tree problem

– β = L/SMT({B} U {si})

– Assume L << SMT({B} U {si})

Focus of this Talk

• Controlled mobility – Rendezvous Planning in Mobility-assisted Wireless

Sensor Networks, G. Xing, T. Wang, Z. Xie and W. Jia; RTSS 2007, 44/171=25.7%.

• Power management – Link Layer Support for Unified Radio Power

Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu, IPSN 2007 38/170=22.3%.

• Sensing Coverage– 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), 2005, extended SenSys 2003 paper

Problem

• Communication power cost is high Explosion in the development of various radio power

management protocols

• Protocols make different assumptions No single protocol is suited to the needs of every

application• Existing radio stack architectures are monolithic

Hard to develop new protocols or tune existing ones to

specificapplication requirements

MAC

Send/ReceiveBuffers

Radio Power Management

Clear ChannelAssessment

Backoff Controller

Radio State Machine

Send/Receive Interfaces Power Management Interfaces Backoff Control Interfaces

Radio Component

Traditional Core Radio Functionality

CCA Functionality

Incoming and Outgoing data

buffers State machine Integrated Radio Power Management

Real Implementations do not separate these functional components so nicely

Solution: UPMA

• Unified Radio Power Management Architecture

• Monolithic --> Composable radio stack architecture

• Pluggable power management policies

• Separation of power management features

• Cross layer in nature

Unified Power Management Architecture

SyncSleep Other Interface

Protocol 3Protocol 2

MAC

PHY

Async Listening OthersSync Scheduler …

DutyCycling Table

OnTime

OffTime

LPL Table

Mode

Preamble

Other Table

Param 0

Param 1

PreambleLengthChannelMonitor On/Off

…

…

Protocol 0 Protocol 1

Power Management Abstraction

Power Manager

parameters specified by upper-level protocols

AsyncSleep

interfaces of sleep schedulers

sleep scheduling protocols

1. Consistency check2. Aggregation

interfaces with MAC

Implementation

• Implemented UPMA in TinyOS 2.0 for both Mica2 and Telosb motes

• Developed interfaces with different types of MAC– CSMA based: S-MAC [Ye et al. 04], B-MAC [Polastre et al. 04]

– TDMA based: TRAMA [Rajendran et al. 05]

– Hybrid: 802.15.4, Z-MAC [Rhee et al. 05]

• Separated sleep scheduling modules from B-MAC• Implemented two new sleep schedulers on top of

B-MAC

Focus of this Talk

• Controlled mobility – Rendezvous Planning in Mobility-assisted Wireless

Sensor Networks, G. Xing, T. Wang, Z. Xie and W. Jia; RTSS 2007, 44/171=25.7%.

• Power management – Link Layer Support for Unified Radio Power

Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu, IPSN 2007 38/170=22.3%.

• Sensing Coverage– 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), 2005, extended SenSys 2003 paper

Power Management under Performance Constraints

• Performance constraints– “Any target within the region must be detected” K-coverage: every point is monitored by at least K active sensors– “Report the target to the base station within 30 sec” N-connectivity: network is still connected if N-1 active nodes fail Routing performance: route length can be predicted

• Focus on fundamental relations between the constraints

base station

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 + SPAN [chen et al. 01]

Greedy Forwarding with Coverage

A destination

shortest Euclidean distance to destination

B

• Always forward to the neighbor closest to destination– Simple, local decision based on neighbor locations

• Fail when a node can’t find a neighbor better than itself

• Always succeed with coverage when Rc/Rs > 2

– Hop count from u and v is sc RR

uv

2

||

Rc

Bounded Voronoi Greedy Forwarding (BVGF)

• A neighbor is a candidate only if the line joining source and destination intersects its Voronoi region

• Greedy: choose the candidate closest to destination

u

v

x and y are candidates

not a candidate

x y

z

Rc

BVGF bound

Analytical Results

62.43

38max

GF bound is high when Rc/Rs 2

Both performs well for high Rc/Rs

result of one-hop analysis

result of two-hop analysis

result of four-hop analysis

Dila

tion

Dilation = c / Rdistance

count hop

Thanks!