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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