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

– Optimal algorithm under limited mobility– Heuristic under unlimited mobility

• Protocol design• Performance evaluation

Challenges for Data-intensive Sensing Applications

• Many applications are data-intensive – Structural health monitoring

• Accelerometer@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

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

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/

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

mobile node

rendezvous point

Rendezvous-based Data Collection

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.

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

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

The Rendezvous Planning Problem

• Choose RPs s.t. the data collection tour of mobile node is no longer than L=vD

• 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

Illustration of Problem Formulation

Objective: minimize length of routes from sources to RPs

Constraint: mobile tour is no longer than L=vD

The problem is NP-hard

Source nodes

Rendezvous points data route

branch nodes

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

• Set the intersections between the tour and the routing tree as RPs

2

3

Illustration

• 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

# of sources in the descendents

1

1

1 1

3

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

c(x) = the increased length of the mobile tour

the decreased length of data routes

obtained by running a Traveling Salesman

Problem solver

Illustration

two RP candidates

A

B

CE

G

D F

Agenda

• Motivation• Problem formulation• Rendezvous planning algorithms

– Optimal algorithm under limited mobility– Heuristic under unlimited mobility

• Protocol design• Performance evaluation

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

Source nodes

Source nodes

Rendezvous points

approximated data route

real data route

Non-source nodes

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

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

Network Energy Consumption

Impact of Variance of Mobile Speed• Mean mobile speed is 1m/s, with a

variance + α m/s

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