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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

Agenda

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

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

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

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

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

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?

Static vs. Mobile

All-static networks Mobility-assisted Networks

Delay Low High

Energy Consumption High nonreplenishable High replenishable

Bandwidth Medium Medium to high

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

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

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

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

Agenda

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

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

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

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

Rendezvous Planning under Limited Mobility

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

reliability

XYZ _at_ 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

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

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

Illustration

G

A

B

two RP candidates

C

E

F

D

Agenda

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

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

An Approx. Algorithm

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

length

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

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

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)

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

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

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

Without Aggregation

With Aggregation

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

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!

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

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

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

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

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!

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!

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

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

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

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

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

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

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

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

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

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.