Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks

IEEE Communications Society 2004

Chi Ma, Ming Ma and Yuanyuan Yang

Outline Introduction Data-Centric Energy Efficient Scheduling

Communication-Centric Initialization Phase Characteristics of the traffic in sensor networks Data-centric scheduling phase

Power Shutdown Scheme Performance Comparisons with Existing

Protocol Conclusion

Introduction Previous work:

Predictive power management strategy Only highly correlated requests can benefit from

it Markov Chain method based on historic data

analysis [7] point out that this is not suitable for today’s

low energy and low computation sensor Power mode scheduling

Does not distinguish between the routing data and the sensing data

Introduction

All previous work assume that both sensing and routing packets come as homogeneous traffic

They propose DCe2S (Data-centric energy efficient scheduling) to minimizing the power dissipated under heterogeneous packet traffic

Data-Centric Energy Efficient Scheduling

DCe2S protocol consists of two phases: 1. Communication-centric initialization

phase 2. Data-centric scheduling phase

Communication-Centric Initialization Phase

Determine node’s lengths of sleep according to sensor density (not uniform)

IAR Energy dissipation Probability to lose packet Higher density (after CCI)

Communication-Centric Initialization Phase

:the probability the packet is not lose of node p

:numbers of neighbor of node k Given , if any routing node i of sending

node p has that ,then the can be guaranteed at sending node p

P

IARp

IARp

IARp IARp

IARp

Characteristics of the traffic in sensor networks

There are two types of data: Sensing packets Routing packets

Previously proposed protocols assume that both types of traffic follow are homogeneous Poisson distribution

Apparently, it cannot model real traffic (ex. traffic monitoring)

Even the sensing traffic is homogeneous, the routing traffic cannot not be homogeneous

Characteristics of the traffic in sensor networks

Path length

Sensing traffic

Characteristics of the traffic in sensor networks There are k path Traffic out of Di : t : latency for each node Consider traffic from A

Consider traffic from both A and C

Characteristics of the traffic in sensor networks

When sensing traffic is heterogeneous Poisson traffic

Suppose A has sensing rate of

When ,the case is equivalent to A broadcasts packets at homogeneous rate , and A` broadcasts after t1

And is the same

Data-Centric Scheduling Algorithm

Use exponentially weighted average time to combine and to obtain

is a threshold means a sudden change A sliding window with size W is used to

cache the recent packet arrival intervals

//exponentially weighted

//average of the window

Power Shutdown Scheme

DCS algorithm uses the shut-down scheme in [8]

The shut-down latency for turning on/off : Sensing unit 30ms Transmitter 5ms Receiver 5ms

Power Shutdown Scheme

Power Shutdown Scheme

Derive a set of sleep time threshold{Tth,k}

if ti<Tth,k will result net energy loss

next event

Performance Comparisons with Existing Protocol

The Time Out Protocol Node switches to sleeping blindly for a

time period of Tout

The Greedy Protocol Without any power control protocol

The Power Mode Scheduling Protocol (PMS)

Power dissipation (homogeneous)

100ms500ms

Packet Loss Rate (homogeneous)

61.4% better than Greedy

31% better than PMS

Unstable because of predict

Heterogeneous traffic 1000 packets First phase: 200 packets Second phase: 400 packets Third phase: 400 packets Packet Lost Rate

Power dissipation (heterogeneous)

Packet Loss Rate (heterogeneous)

Events are not uniformly distributed

Conclusions

They first prove the routing traffic is heterogeneous with Poisson sensing traffic

Then proposed a well defined power model to extend the lifetime without compromising their performance

Presented DCe2S in this paper and try to achieve maximum lifetime