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
• Optimal algorithm under limited mobility
• Heuristic under unlimited mobility
• Protocol design
• Performance evaluation

3
Challenges for Data-intensive Sensing Applications

• Many applications are data-intensive
• Structural health monitoring
• Accelerometer_at_100Hz, 30 min/day, 80Gb/year
• Micro-climate and habitat monitoring
• Acoustic video, 10 min/day, 1Gb/year
• Most sensor nodes are powered by batteries
• A tension exists between the sheer amount of data
  generated and the limited power supply

4
Mobility-assisted Data Collection

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

5
Mobile Sensor Platforms
Robomote _at_ USC Dantu05robomote
XYZ _at_ Yale http//www.eng.yale.edu/enalab/XYZ/
Networked Infomechanical Systems (NIMS) _at_ CENS,
UCLA

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

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Rendezvous-based Data Collection

• Some nodes serve as rendezvous points (RPs)
• Other nodes send their data to the closest RP
• Mobiles pick up data from RPs and transport to BS
• In-network caching controlled mobility
• Mobiles can collect a large volume of data at a
  time
• Mobiles contact static nodes at RPs at scheduled
  times and disruptions to network topology are
  reduced

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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
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Assumptions

• Only one mobile is available
• Average speed of mobile is v m/s
• Mobile picks up data at locations of nodes
• Data collection deadline is D seconds
• 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

9
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
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The Rendezvous Planning Problem

• Choose RPs s.t. the data collection tour of
  mobile node is no longer than LvD
• Total network energy of transmitting data from
  sources to RPs is minimized
• Joint optimization of positions of RPs, motion
  path of mobile, and routing paths of data

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Illustration of Problem Formulation

• Objective minimize length of routes from sources
  to RPs
• Constraint mobile tour is no longer than LvD
• The problem is NP-hard

Source nodes
branch nodes
Rendezvous points
data route
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Rendezvous Planning under Limited Mobility

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

XYZ _at_ Yale
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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
• Set the intersections between the tour and the
  routing tree as RPs

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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
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A Heuristic under Unlimited Mobility

• Add virtual nodes s.t. each edge is no longer
  than L0
• In each iteration
• Choose the RP candidate x with the max utility
  defined by c(x)
• Remove RPs with zero utility
• Terminate if all sources become RPs or no more
  RPs can be chosen without violating the
  constraint of L

the decreased length of data routes
c(x)
the increased length of the mobile tour
obtained by running a Traveling Salesman Problem
solver
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Illustration
G
A
B
two RP candidates
C
E
F
D
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Agenda

• Motivation
• Problem formulation
• Rendezvous planning algorithms
• Optimal algorithm under limited mobility
• Heuristic under unlimited mobility
• Protocol design
• Performance evaluation

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Initialization

• Mobile computes locations of RPs
• Find real nodes around the computed RPs
• Find the nodes along the routing tree
• Mobile travels to RPs and discover real nodes

Non-source nodes
Source nodes
Rendezvous points
approximated data route
real data route
Source nodes
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Handling Unexpected Delays

• Movement of mobile node is subject to various
  delays
• Obstacles, mechanical failures
• RPs should cache data as long as possible without
  violating the deadline
• Mobile node may adjust motion path online e.g.,
  skips some of the RPs

20
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

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Network Energy Consumption
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Impact of Variance of Mobile Speed

• Mean mobile speed is 1m/s, with a variance a m/s

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Conclusions

• Proposed a rendezvous-based data collection
  approach
• In-network caching controlled mobility
• Developed two rendezvous planning algorithms
• An optimal algorithm under limited mobility
• A efficient heuristic under unlimited mobility
• Designed the rendezvous-based data collection
  protocol