Adaptive Data Collection Strategies for Lifetime-Constrained Wireless Sensor Networks

Xueyan Tang Jianliang Xu Sch. of Comput. Eng., Nanyang

Technol. Univ., Singapore;

Parallel and Distributed Systems, IEEE Transactions onJune 2008

Outline Introduction Problem Formulation

Single-hop networks Optimal Data Update Solution (Off-line) Adaptive Data Update Strategy (On-line) Adaptive Aggregate Data Update

Multi-hop networks Performance Evaluation Conclusion

Data Report Problem (1/3)-Single-hop Networks

Consider 10 solar radiation readings 369, 330, 264, 266, 274, 279, 260, 233, 225

Assume the total energy budget of a sensor is three updates (i.e., send only three updates)

Periodically update strategy Sends the 1-th, 4-th, and 7-th readings 369, skip, skip, 266, skip, skip, 260, skip, skip

Approximate readings 369, 369, 369, 266, 266, 266, 260, 260, 260

Reconstructed data

Data Report Problem (2/3)- Single-hop Networks

Data Error (Deviation) Exact readings:

369, 330, 264, 266, 274, 279, 260, 233, 225

Approximate readings: 369, 369, 369, 266, 266, 266, 260, 260, 260

Error = 0+39+105+0+8+13+0+27+35 = 227.

error

Data Report Problem (3/3)- Single-hop Networks

Better Update Strategy sends the 1-th, 4-th, and 8-th

readings369, skip, skip, 266, skip, skip, skip,

233, skip approximate readings

369, 369, 369, 266, 266, 266, 266, 233, 233

Error = 0+39+0+2+10+15+4?+0+ 8 = 78error

Problem Formulation (1/3)-Single-hop Networks

Problem: Exact readings:

369, 330, 264, 266, 274, 279, 260, 233, 225…………

Find M updates such that root-mean-square of collected data error is minimized.

Problem Formulation (2/3)- Single-hop Networks

Assume Exact readings (T: given network lifetime):

d1, d2, …, dT Energy budget (at most): M updates Data updates at times: v1=1, v2, v3,…, vM

Ex: v1=1 1-th reading (first update) v2=3 3-th reading (second update)

Approximate readings: MMM vvvvvvvvv ddddddddd ,....,,...,,,,...,,,

222111

Problem Formulation (3/3)-Single-hop Networks

Find v1=1, v2, v3,…, vM such that is minimized. where

Optimal Data Update Solution (Off-line Version)

Assume that all sensor readings are known a priori Exact readings d1, d2, …, dT are known

Solve by a dynamic programming algorithm.

Dynamic Programming (1/4) Let be an optimal

solution to the (t, m)-optimization problem.

Claim: must be an optimal

solution to the (t -1, m -1)-optimization problem.

Dynamic Programming (2/4)

Proof Assume there exists a better solution

Dynamic Programming (3/4)

Dynamic Programming (4/4) Let A(t, m) be the minimal achievable total square

error to the (t, m)-optimization problem. Let B(t, m) be the time of the last data update in th

e optimal solution.

Adaptive Data Update Strategy (On-line Version)

Idea Let the sensor node update a new

reading with the base station only when the new reading substantially differs from the last update.

i.e., update only ifWdd elast updatnew ||

Example: W = 40

369, 330, 264, 266, 274, 310, 260, 233, 225

Adaptive Data Update Strategy (On-line Version)

Issues The number of updates are decided by W How to dynamically adjust W

Assume that the energy budgets: 3 updates

Expected data update period : Once every 3 time units

369, 330, 264, 266, 274, 279, 260, 233, 225

Adaptive Data Update Strategy (On-line Version)

Measure the data update period every time a new reading is updated. Estimate of data update period

Compare with the expected data update period IE :

oldupdatelastc ITTI )1() (

)1( if )1(

)1( if )1(

E

E

IIWW

IIWW

Adaptive Data Update Strategy (Algorithm)

Initialization

Adaptive Data Update Strategy (Algorithm)

Adaptive Aggregate Data Update-Multi-hop networks

Problem in multi-hop networks

bottleneck

Node A : receive 6 updates

sends 3 updates

Adaptive Aggregate Data Update-Multi-hop networks

Node A : receive 6 updates

sends 8 updates

Node A : receive 6 updates

sends 3 updates

Allocating Number of Updates

The number of updates that node can send is

bottleneck

)( vCs

e

i

i

send

receive

Total energy

Allocating Number of Updates-Idea

2022

24

Round t

Round t+1

22tAVG

Assume thresholds WA = 3, WB=2, WC=2

2119

22

|22-19| > WB

22 20

22

19

|21-20| < WC

3.20tAVG

|22-20.3| < WA

6

3

3 3

6

3 3 3 3 3

6 6

Goal The objective is to let the sensor nodes se

nd as many updates as possible subject to the energy constraints

Update Allocation Algorithm-An Example ui : unused energy budget

xi: min(xi , xpi)

ci: allocated number of updates Assume that s = 1 units (send) and v = 1 units (receive)

ui/xi/ci

A:

ui = 12 (initial)

xi = 12/(2+1) = 4

ci = min(4, ∞)=4

Round 1

Update Allocation Algorithm-An Example ui : unused energy budget

xi: min(xi , xpi)

ci: allocated number of updates Assume that s = 1 units (send) and v = 1 units (receive)

ui/xi/ci

B:

ui = 12 (initial)

xi = 12/(3+1) = 3

ci = min(4, 3) = 3

Round 1

Update Allocation Algorithm-An Example ui : unused energy budget

xi: min(xi , xpi)

ci: allocated number of updates

Round 2

A:

ui = 12-4-6 = 2

xi = 2/(0+1) = 2

ci = min(2, ∞)+4=6

Update Allocation Algorithm

Performance Evaluation

Experimental Setup

Performance Evaluation-Single-hop (without aggregation)

Performance for Parameter Settings

Performance Evaluation-Multi-hop (MAX Aggregation)

Performance Evaluation-Multi-hop (Average Aggregation)

Conclusion This paper developed adaptive strategies

for both individual and aggregate data collections to make full use of the energy budgets of sensor nodes.

Experimental results show that, compared to the periodic strategy, adaptive strategies significantly improve the accuracy of collected data.