Research Profile of My Group

1
Research Profile of My Group

• Guoliang Xing
• Department of Computer ScienceCity University of
  Hong Kong

2
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

3
Research Directions

• Controlled mobility
• Data fusion based target detection
• Power management
• Sensing coverage

4
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, 44/30014.6.
• Rendezvous Planning in Mobility-assisted Wireless
  Sensor Networks, G. Xing, T. Wang, Z. Xie and W.
  Jia RTSS 2007, 44/17125.7.
• 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,
  102/63816.
• 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 38/17022.3.
• Dynamic Multi-resolution Data Dissemination in
  Storage-centric Wireless Sensor Networks. H. Luo,
  G. Xing, M. Li, and X. Jia, MSWiM 2007,
  41/16124.8.

5
Earlier Work on Sensor Networks

• ACM/IEEE Transactions Papers
• 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
• 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
• 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.

6
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, 44/17125.7.
• Power management
• Link Layer Support for Unified Radio Power
  Management in Wireless Sensor Networks. K. Klues,
  G. Xing and C. Lu, IPSN 2007 38/17022.3.
• 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

7
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

8
Mobile Sensor Platforms
XYZ _at_ Yale http//www.eng.yale.edu/enalab/XYZ/
Robomote _at_ USC Dantu05robomote
Networked Infomechanical Systems (NIMS) _at_ CENS,
UCLA

• Low movement speed (0.12 m/s)
• Increased latency of data collection
• Reduced network capacity

9
A Data Collection Tour
Base Station
1 minute
150K bytes
50K bytes
2 minute
1 minute
1 minute
100K bytes
100K bytes
200K bytes

• Analogy
• What's the most reliable way of sending 1000 G
  bytes of data from Hong Kong to Suzhou?

10
Static vs. Mobile
11
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

12
An Example
mobile 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 gt 4 hours to
visit 200 randomly distributed nodes.
rendezvous point
source node
13
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

14
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

15
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

16
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
Rendezvous points
Non-source nodes
a branch node lies on two or more source-to-root
routes
Source nodes
Branch nodes
approximated data route
real data route
Source nodes
17
Problem Formulation

• Given a tree T(V,E) rooted at B and sources si,
  find RPs, Ri, and a tour no longer than LvD
  that visits BURi, and
• The problem is NP-hard (reduction from the
  Traveling Salesman Problem)

dT(si,Ri) the on-tree distance between si and Ri
18
Rendezvous Planning under Limited Mobility

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

XYZ _at_ Yale
19
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

20
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

the decreased length of data routes
the increased length of the mobile node tour
TSP(W) computes the distance to visit nodes in W
using a Traveling Salesman Problem solver
21
Rendezvous Planning w Aggregation

• Given a base station B, and sources si,
  find trees Ti(Vi, Ei), BUsi 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

B
s6
R4
R1
s5
R3
s1
R2
s4
s2
A special case when L0, the opt solution is
Steiner minimum tree that connects B U si
s3
22
An Approx. Algorithm

• Find an approx. Steiner min tree of BUsi
• Depth-first traverse the tree until covers L/2
  length

23
Approx. Ratio

• The approximation ratio of the algorithm is
  aß(2a-1)/2(1-ß)
• a is the best approximation ratio of the Steiner
  Minimum Tree problem
• ß L/SMT(B U si)
• Assume L ltlt SMT(B U si)

24
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, 44/17125.7.
• Power management
• Link Layer Support for Unified Radio Power
  Management in Wireless Sensor Networks. K. Klues,
  G. Xing and C. Lu, IPSN 2007 38/17022.3.
• 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

25
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

26
Traditional Core Radio Functionality
Incoming and Outgoing data buffers
State machine
Integrated Radio Power Management
CCA Functionality
Real Implementations do not separate these
functional components so nicely
27
Solution UPMA

• Unified Radio Power Management Architecture

• Monolithic --gt Composable radio stack architecture

• Pluggable power management policies

• Separation of power management features

• Cross layer in nature

28
Unified Power Management Architecture
interfaces of sleep schedulers
Protocol 2
Protocol 1
Protocol 3
Protocol 0

SyncSleep
AsyncSleep
Other Interface

parameters specified by upper-level protocols
OnTime
Mode
Param 0
OffTime
Preamble
Param 1
DutyCycling Table
LPL Table
Other Table
Power Management Abstraction

• Consistency check
• Aggregation

Power Manager
sleep scheduling protocols

Async Listening
Others
Sync Scheduler
MAC
PreambleLength
ChannelMonitor
On/Off
interfaces with MAC
PHY
29
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

30
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, 44/17125.7.
• Power management
• Link Layer Support for Unified Radio Power
  Management in Wireless Sensor Networks. K. Klues,
  G. Xing and C. Lu, IPSN 2007 38/17022.3.
• 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

31
Power Management under Performance Constraints
base station

• 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

32
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 lt 2
• CCP SPAN chen et al. 01

33
Greedy Forwarding with Coverage

• Always forward to the neighbor closest to
  destination
• Simple, local decision based on neighbor
  locations
• Fail when a node cant find a neighbor better
  than itself
• Always succeed with coverage when Rc/Rs gt 2
• Hop count from u and v is

shortest Euclidean distance to destination
Rc
A
destination
B
34
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

x and y are candidates
Rc
x
y
u
z
v
not a candidate
35
Analytical Results
Dilation
GF bound is high when Rc/Rs ? 2
result of four-hop analysis
Both performs well for high Rc/Rs
BVGF bound
result of one-hop analysis
result of two-hop analysis
Dilation
36
Thanks!