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PPT – An Energy and Delay Efficient Scheduling Algorithm for Data-Centric Wireless Sensor Networks PowerPoint presentation | free to download - id: 180137-ZDc1Z

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An Energy and Delay Efficient Scheduling

Algorithm for Data-Centric Wireless Sensor

Networks

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Outline

- Introduction
- Problem Description
- Getting Primal Feasible Solution
- Experiment Result
- Conclusion

Background

- Wireless Sensor Networks (WSNs)
- WSNs consist of a number of small nodes with

sensing, computation, and wireless communication

abilities. - Each sensor in the WSNs is capable of probing and

collecting environmental information such as

temperature, ocean current, and atmospheric

pressure. - The deployment of these sensors would be

typically in a random fashion. - Recharging the batteries of a moribund sensor

would not be feasible. - Several principal issues of WSNs
- Data Aggregation
- Routing
- Minimize End-to-end Delay

Background(contd)

- Data-centric Routing Algorithms
- Shortest Path Tree (SPT)
- Center Nearest Source (CNS)

B. K., D. E. S. W., Modeling Data-Centric

Routing in Wireless Sensor Networks

Background(contd)

- Data-centric Routing Algorithms
- S-MAC
- T-MAC
- D-MAC

S-Mac W. Y., J. H., D. E., An

energy-efficient MAC protocol for wireless sensor

networks. T-Mac T. V. D., K. G. L An

Adaptive Energy-Efficient MAC Protocol for

Wireless Sensor Networks G. L., B. K., C.

R., An Adaptive Energy-Efficient and Low-Latency

MAC for Data Gathering in Sensor Network

Motivation

- Drawback of the existing protocols
- SPT, CNS idle listening
- S-MAC, T-MAC sleep latency
- D-MAC data aggregation
- The tradeoff between energy consumption and

latency

Problem Description

Routing Problem

Sensor Node

Source Node

Sink Node

Problem Description(contd)

Scheduling Problem

Problem Description (contd)

- Assumption
- There is a centralized node to determine the

activities of all sensors - A network with strict synchronization
- Propagation delay can be ignored
- Link delay can be considered as a constant
- Given
- The set of all sensor nodes
- The set of all data sources
- The sink node
- The set of all candidate paths for each data

source to reach sink node - Longest hops along shortest path from sink node

to reach the farthest data source - An arbitrary large number M
- End-to-end delay requirement T
- Objective
- To minimize the energy consumption of the entire

wireless sensor network

Problem Description(contd)

- Subject to
- Routing constraint
- Tree constraint
- Number of Neighbors constraint
- Collision Free constraint
- To determine
- Routing path for each data source
- Transmission radius for each sensor node
- Whether a link should be on the data aggregation

tree - The data aggregation tree
- The wake up time of each sensor node on data

aggregation tree - The aggregation complete time of each sensor node

on data aggregation tree - The transmission finish time of each sensor node

on data aggregation tree

Notation Decision Variables

Decision Variables Decision Variables

Notation Description

1 if the data source node uses the path p to reach the sink node

1 if the link (u, v) is on the tree

The transmission radius of node u

When the node v must wake up

When the node v will complete its aggregation

When the node v could finish its transmission and turn off hits raido

1 if the node v is covered within transmission radius of the node u

1 if the node v will be interfered by the node u

Notation Decision Variables(contd)

Decision Variables Decision Variables

Notation Description

1 if the maximum end-to-end delay from leaf nodes to node u is large than the minimum begin time of all flows to node v.

1 if the maximum end-to-end delay from leaf nodes to node v is large than the minimum begin time of all flows to node u.

The difference between and

Problem Formulation

- Objective Function
- Zip min
- Subject to

Problem Formulation

Problem Formulation

Go

Lagrangean Relaxation

- In (IP), by introducing Lagrangean multiplier

vector u1, , u16 we dualize Constraints (2),

(5), (10), (11), (12), (16), (17), (18), (22) ,

(23), (24), (25), (26), (27), (28), and (29) to

obtain the following Lagrangean relaxation

problem(LR).

- We can decompose (LR) into 11 independent

subproblems

Getting Primal Feasible Solution

S6

S2

Sink

S1

S4

S5

S3

Getting Primal Feasible Solution

N5

N6

Rerouting Heuristic

Experiment Environment

Experiment Environment and Parameters Experiment Environment and Parameters

Parameter Value

Number of Iterations 2000

Improvement Counter 30

Initial Upper Bound Solution of 1st Getting Primal Feasible

Initial Upper Multiplier 0

Initial Scalar of step size 2

Test Platform CPU Intel(R) Pentium-IV 2.8GHz

Test Platform RAM 1024 MB

Test Platform OS Windows 2000 SP 4

Experiment Scenarios

- Random Network with Different Number of Sensor

nodes - Depolyment Manner Ransom Source, Congregated

Source. - Grid Network with Different Number of Sensor

nodes - Random Network with Different Number of Source

nodes

Random Network with Different Number of Sensor

Nodes (Random Source)

Random Network with Different Number of Sensor

Nodes (congregated source)

End-to-end Delay of DifferentSensor Nodes

Grid Network with Different Number of Sensor

Nodes

Energy Cost of Different Source Number

No. of Sensor Nodes 100

End-to-end Delay of Different Source Number

Experiment Discussion

- Number of Source Nodes
- Sensor Deployment manner
- The Sequence of Paths Selection

Conclusion

- Contribution
- We propose a mathematical formulation to model

this problem as an Integer Programming Problem. - By Lagrangean Relaxation and our two-phases

heuristic, we can near-optimally solve this

problem. - By the reroute heuristic, we can verifies whether

the algorithm we proposed achieves energy

efficiency, data aggregation and ensures the

latency within a reasonable range.

Conclusion

- Future Work
- More experiments for the different sequence of

paths selection - Take load-balance or the other factors into

consideration

The End

- Q A
- - Thanks for your listening

The End

- Appendix

Notation Given Parameters

Given Parameters Given Parameters

Notation Description

V The set of sensor nodes

Ps The set of all possible paths from the data source s node to the sink node

S The set of all data source nodes

H Longest distance of shortest path to reach farthest data source node

M An arbitrary large number

q The sink node

T End-to-end delay requirement

R The set of all possible transmission radii that sensor node v can adopt.

Notation Given Parameters(contd)

Given Parameter Given Parameter

Notation Description

e(ru) Energy consumption function of node u, which is a function of sensors transmission radius

V Data volume of a message, which is a constant

K Processing cost of each incoming message, which is a constant

Er Energy consumption rate when sensor nodes are receiving

Eidle Energy consumption rate when sensor nodes are idle

Esleep Energy consumption rate when sensor nodes are sleeping

The indicator function which is 1 if the link (u, v) is on the path p and 0 otherwise

Relation between wu, nv, and mv

Scheduling Problem

Back

Lagrangean Relaxation

- In (IP), by introducing Lagrangean multiplier

vector u1, , u16 we dualize Constraints (2),

(5), (10), (11), (12), (16), (17), (18), (22) ,

(23), (24), (25), (26), (27), (28), and (29) to

obtain the following Lagrangean relaxation

problem(LR). - ZLR (u1, , u16 )
- min

Relaxation(contd)

We can decompose (LR) into 11 independent

subproblems.

Relaxation(contd)

- Subject to

Relaxation(contd)

Relaxation(contd)

We can decompose (LR) into 11 independent

subproblems.

Subproblem 1 (related to decision variable )

- min
- Subject to

Subproblem 2 (related to decision variable )

- min
- Subject to

Subproblem 3 (related to decision variable )

- min
- Subject to

Transformation

- After transforming, we can decompose (SUB 3) into

V independent subproblems. For each node u, - s.t.

min

Subproblem 4 (related to decision variable )

- min
- Subject to

After transforming, we can decompose (SUB 4) into

V independent subproblems. For each node u,

s.t.

min

Subproblem 5 (related to decision variable )

- min
- subject to

After transforming, we can decompose (SUB

4) into V independent subproblems. For each

node u, s.t.

min

Subproblem 6 (related to decision variable )

- min
- subject to

We can be further decomposedthis

subproblem into V independent subproblems. For

each node u s.t.

min

Subproblem 7 (related to decision variable )

- min
- subject to

We can decompose this subproblem into

V independent subproblems. For each link ( u,v

), s.t.

min

Subproblem 8 (related to decision variable )

- min
- subject to

We can decompose this subproblem into

V x V independent subproblems. For each

link ( u,v ), s.t.

min

Subproblem 9 (related to decision variable )

- min
- subject to

We can decompose this subproblem into

V x V independent subproblems. For each link (

u,v ), s.t.

min

Subproblem 10 (related to decision variable

)

- min
- subject to

We can decompose this subproblem into

V x V independent subproblems. For each link (

u,v ), s.t.

min

Subproblem 11 (related to decision variable

)

- min
- subject to

We can decompose this subproblem into

V x V independent subproblems. For each link (

u,v ), s.t.

min

Topology Sort Algorithm

V1

V1

V1

V0

V2

V2

V2

V4

V4

V4

V3

V3

V5

V5

V5

V3

V0

initial

V1

V1

V4

V4

V4

V5

V2

V5

V1

V4

Back

Getting Primal Feasible Solution

- Phase 1 Routing Policy
- Step 1 Set the arc weight for link (u,v) to be

,

and then run - Dijkstras algorithm to get the

shortest path from each source node. - .
- Step 2 Choose a path with the smallest cost, set

each value of to be one, if this - is on the corresponding path ,

and adjust the arc weight of these links to be - zero.
- Step 3 Repeat Step 1-2 until all source have a

path to sink node. - Step 4 Set each value of to be the

nearest value from , if the corresponding - is one.
- Step 5 Using to construct a data

aggregation tree.

Getting Primal Feasible Solution

- Phase 2 Scheduling Policy
- Step 6 By running topology-sort algorithm, we

can derive the outliers of the data

aggregation tree. Put these outliers into

stack_number1. - Step 7 Sort stack_number1 by out-degree, and pop

a node with the smallest out-degree. - If this node doesnt interfere the

transmissions of the previous slot, do Step 9, - else put this node into stack_number2.

- Step 8 Set each value of to be

current_slot, each value of to be the

minimum - of its children, and each value

of to be the maximum of its

children. - Repeat Step 5-7 until stack_number1 is

empty. - Step 9 Swap the values of stack_number1 and

stack_number2 and set current_slot to - be current_slot plus one.
- Step 10 Repeat Step 6-9 until both stack_number1

and stack_number2 are empty.

Rerouting Heuristic

- Step 1Identify the path (denoted as P) that

incurs the highest end-to-end - delay.
- Step 2 Investigate nodes located on P one by

one. For each checked node - (denoted as n) , examine each node

(denoted as k) If the end-to-end - delay of node n plus one unit of delay

is smaller than that of k, then - reroute the path from n to k.
- Step 3 Update the corresponding decision

variables and reconstruct data - aggregation tree.
- Step 4 Repeat Step 1 3, until msink within the

Maximum end-to-end delay - requirement.

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