Rendezvous Planning in Mobilityassisted Wireless Sensor Networks

1
Rendezvous Planning in Mobility-assisted Wireless
Sensor Networks

• Guoliang Xing Tian Wang Zhihui Xie Weijia Jia
• Department of Computer Science City University
  of Hong Kong

2
Agenda

• Motivation
• Problem formulation
• Rendezvous planning algorithms
• Without data aggregation
• With data aggregation
• Performance evaluation
• Other projects

3
Wireless Sensor Networks
physical environments homes/offices
Structural monitoring (Golden gate bridge, SF)
Healthcare (Boston Medical Center)
Industrial automation (Intel fabrication plant)
Pervasive computing

• Market
• US 100M in 2005, 500M by 2010, Kris Pister,
  CEO of Dust Networks, Professor of UC Berkeley
• Challenges
• Limited power supplies batteries, small solar
  panels
• Long lifetime requirements months to tens of
  years
• Must minimize network power consumption

4
Challenges for Data-intensive Sensing Applications

• Many applications are data-intensive
• Structural health monitoring
• Accelerometers sample at 100Hz
• Micro-climate and habitat monitoring
• Mics/cameras generate a few K bytes every minute
• Multi-hop wireless relays are power-consuming
• A Mica2 mote can only transmit a couple days
• A tension exists between the sheer amount of data
  generated and limited power supply

5
Mobility-assisted Data Collection

• Mobile nodes move close to sensors and collect
  data via short-range communications
• Mobile nodes can transport data mechanically
• Mobile nodes are less power-constrained
• Can move to wired power sources
• Number of wireless relays is reduced

6
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

7
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 fastest way of sending 100 G bytes of
  data from Hong Kong to Phoenix?

8
Static vs. Mobile
9
Rendezvous-based Data Collection

• 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

10
Rendezvous-based Data Collection
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
11
The Rendezvous Planning Problem

• Total network energy of transmitting data from
  sources to RPs is minimized
• Choose RPs s.t. mobile nodes can visit all RPs
  within data collection deadline
• Joint optimization of positions of RPs, motion
  paths of mobile, and routing paths of data

12
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

13
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

14
Agenda

• Motivation
• Problem formulation
• Rendezvous planning algorithms
• Without data aggregation
• With data aggregation
• Performance evaluation
• Other projects

15
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
16
Problem Formulation

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

dT(si,Ri) the on-tree distance between si and Ri
17
Illustration of Problem Formulation

• Objective
• Minimize length of routes from sources to RPS
• Constraint
• Tour length L

Source nodes
branch nodes
Rendezvous points
data route
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
Illustration
of sources in the descendents

• All edges have a length of 50m
• Deadline 500 s, v 0.5 m/s
• L 0.5 m/s x 500 s 250 m
• Correctness
• Edges chosen are connected
• Optimality
• A tour can cover at most L/2 edges
• L/2 mostly 'used' edges are chosen

3
3
2
1
1
1
1
21
A Heuristic for Unlimited Mobility

• 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
22
Illustration
G
A
B
two RP candidates
C
E
F
D
23
Agenda

• Motivation
• Problem formulation
• Rendezvous planning algorithms
• Without data aggregation
• With data aggregation
• Protocol design
• Performance evaluation
• Other projects

24
Rendezvous Planning w Aggregation

• Given a base station B, and sources si,
  find trees Ti(Vi, Ei), BUsi UVi, such that
• 1) the shortest tour visiting the roots of Ti
  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
25
An Approx. Algorithm

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

26
The Improved Algorithm

• 1. Find T - an approx. Steiner min tree of
  BUsi
• 2. YL/2
• 3. Depth-first traverse T from B until cover Y
  length, denote I as the set of intersections
• 5. if X L - TSP(I) gt d
• YYX/2 goto 3
• else exit
• TSP(R) the length of tour visiting points in R,
  computed by a Traveling Salesman Problem solver

27
Illustration
1. Find T - an approx. Steiner min tree of
BUsi 2. YL/2 3. Depth-first traverse T from
B until cover Y length, denote I as the set of
intersections 5. if X L - TSP(I) gt d YYX/2
goto 3 else exit
28
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)

29
Proof Sketch I
B

• A is opt solution
• RB U Ri
• SB U Si
• T is the tree used in input
• SMT(X) - SMT connecting points in set X
• TSP(X) - length of the shortest tour visiting
  points in R

R1
R3
R2
30
Proof Sketch II
B
A U SMT(R) is a Steiner tree connecting
S c(A) c(SMT(R)) c(SMT(S)) SMT is a
lower bound of TSP problem c(SMT(R)) lt
c(TSP(R)) L ? c(A) gt c(SMT(S)) L gt c(T)/
a - L
R1
R3
R2
Our solution c(T)-L/2
31
Simulation Settings

• 100 sources are randomly distributed in a 300m X
  300m field, base station is on the left corner
• Each source generates 2 bytes/second, delivery
  deadline is 20 minutes
• Implemented USC model Zuniga et al. 04 to
  simulate lossy links on Mica2 motes
• Baseline algorithms
• NET collect data via the routing tree without
  using mobile nodes
• Sector mobile moves on a sector of 45o
• RP-CP the optimal algorithm with limited
  mobility
• RP-UG the utility-based heuristic
• RP-SRC choose a subset of sources as RPs
• RP-Agg the basic algorithm with aggregation
• RP-Agg the improved algorithm with aggregation

32
Without Aggregation
33
With Aggregation
34
Recent Projects

• Rendezvous Planning in Mobility-assisted Wireless
  Sensor Networks,
• 28th IEEE Real-Time Systems Symposium (RTSS),
  2007, acceptance ratio 44/17125.7
• Dynamic multi-resolution data dissemination
• 10th ACM/IEEE International Symposium on
  Modeling, Analysis and Simulation of Wireless and
  Mobile Systems (MSWiM), 2007, acceptance ratio
  41/16124.8
• Unified Radio Power Management Architecture
• International Symposium on Information Processing
  in Sensor Networks (IPSN), 2007, acceptance ratio
  38/17022.3

35
Challenges of Data Dissemination

• Queries have different temporal resolutions
• "report temperature readings every 1 minute"
• "report light readings every 2 minutes"
• Queries are dynamic
• New queries can arrive anytime
• Data rates of existing queries can change
• Optimal data dissemination tree is not fixed!

36
Impact of Data Rate (Resolution)

• Data rate determines total power cost
• Radio power cost varies in different states
• Tx 106.8, Rx/Idle 32, Sleeping 0.001 mW
  (cc1000)
• Energy cost is sum of power cost in each state
  weighted by the time in the state
• Exploring diversity of data rates reduces power
  due to broadcast wireless channel

37
Modeling Power Consumption

• Node s transmits to t at a rate of R bps

s
z
B is the bandwidth e(s,t) is the expected number
of transmissions due to the lossy link
?(a,b)
t2
t
z
t1

• Node s broadcasts to t1,t2

38
Min-power Multi-resolution Data Dissemination
(MMDD)

• Given traffic demands I(ti , ri ) arriving
  online, find a tree T(V, E) minimizing

node cost, independent of data rate
d(u) set of decedents of u c(u) set of children
of u

• Sleep scheduling power-aware multicast
• MMDD is NP-Hard

39
Lightweight Tree Adaptation

• When data rates of existing requests change
• Power efficiency of a tree degrades
• Constructing a new tree is expensive
• Path-quality based tree adaptation
• Monitor the quality of each path
• Find a new path if quality drops below a
  threshold
• Reference-rate based tree adaptation
• Monitor the reference of all data rates
• Find a new tree if reference exceeds a threshold

40
Path Quality Estimation with Decreased Data Rate

• Yl and Yh are best paths from s to t under rl and
  rh
• Shortest paths under metric z r ?(u,v)
• Theorem I If rh drops to rl, then power cost of
  Yh is no more than the min power under rl by
• Significance path quality degradation can be
  estimated solely by known information

all symbols are known!
41
Path Quality Estimation with increased Data Rate

• Theorem II If rl increases to rh, then power
  cost of Yl is no more than min power under rh by

all symbols are known!
42
Path-quality based Tree Adaptation

• Suppose sink t changes rate from r to r
• Computes ?P, the difference between current power
  and the min power under r
• If ?PT gt ß, find a new path using r, otherwise,
  continue to use the existing path
• T is the duration of new rate r
• ßis the energy cost of finding a shortest path

43
Reference-rate based Tree Adaptation

• Find paths using same rate r for all sinks
• Significantly reduces the overhead
• Theorem for D data requests with rates in rmin,
  rmax, the performance ratio is D(rmax/rmin), if
  rmin r rmax holds

44
Recent Projects

• Rendezvous Planning in Mobility-assisted Wireless
  Sensor Networks,
• 28th IEEE Real-Time Systems Symposium (RTSS),
  2007, acceptance ratio 44/17125.7
• Dynamic multi-resolution data dissemination
• 10th ACM/IEEE International Symposium on
  Modeling, Analysis and Simulation of Wireless and
  Mobile Systems (MSWiM), 2007, acceptance ratio
  41/16124.8
• Unified Radio Power Management Architecture
• International Symposium on Information Processing
  in Sensor Networks (IPSN), 2007, acceptance ratio
  38/17022.3

45
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

46
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
47
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

48
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
49
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

50
Conclusions

• Rendezvous based data collection
• Combined In-network caching controlled mobility
• Developed rendezvous planning algorithms
  with/without data aggregation
• Dynamic multi-resolution data dissemination
• Modeled impact of data rate on power consumption
• Proposed two dynamic tree adaptation algorithms
• Unified radio power management architecture
• Designed and implemented a link-layer power
  management architecture

51
References

• 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), acceptance ratio
  44/17125.7
• Dynamic Multi-resolution Data Dissemination in
  Storage-centric Wireless Sensor Networks, Hongbo
  Luo, Guoliang Xing, Minming Li, Xiaohua Jia, 10th
  ACM/IEEE International Symposium on Modeling,
  Analysis and Simulation of Wireless and Mobile
  Systems (MSWiM), acceptance ratio 41/16124.8.
• Link Layer Support for Unified Radio Power
  Management in Wireless Sensor Networks, Kevin
  Klues, Guoliang Xing, Chenyang Lu, International
  Symposium on Information Processing in Sensor
  Networks (IPSN), acceptance ratio 38/17022.3.