Research Profile of My Group
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Transcript of Research Profile of My Group
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!