ADVISOR : Professor Yeong-Sung Lin STUDENT : Hung-Shi Wang An Energy and Delay Efficient Data - Centric Scheduling Algorithm for Wireless Sensor Networks ADVISOR : Professor Yeong-Sung Lin STUDENT : Hung-Shi Wang Presented by Hung-Shi Wang 2005/12/20 2019/2/15

Outline Introduction Problem Description Related Work Motivation Notation Problem Formulation Lagrangean Relaxation Subproblem and Solution Approach 2019/2/15

Related work SMAC TMAC DMAC Max-Min Fair Collision-Free Scheduling Fixed duty cycle, sleep latency TMAC Sleep latency DMAC Data aggregation Max-Min Fair Collision-Free Scheduling ELECTION Not Optimal 2019/2/15

Motivation To propose an algorithm that achieves energy efficiency, data aggregation and ensures low latency. 2019/2/15

Problem description 2019/2/15

Problem Description Given Objective: Subject to: 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 Average packet arrival rate for each sensor node on data aggregation tree Maximum propagation delay for transmission data packet Transmission time for RTS, CTS, ACK frame waiting time for SIFS, DIFS Objective: To minimize the total energy consumption Subject to: Routing constraint Tree constraint Maximum end-to-end delay constraint Minimum begin time constraint 2019/2/15

Problem Description To determine: Routing path for each data source. Whether a link should be on the data aggregation tree The data aggregation tree Maximum end-to-end delay for each sensor node on data aggregation tree Minimum begin time of each sensor node on data aggregation tree 2019/2/15

Notation – Given Parameter 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 A The possible maximum link delay Q The sink node  Packet arrival rate 2019/2/15

Notation – Given Parameter(cont.) Description  Maximum propagation delay for transmitting data packet Average random backoff time Average network allocation vector(NAV) SIFS Short inter-frame space time DIFS Distributed inter-frame space time RTS Transmission time for RTS frame CTS Transmission time for CTS frame Es Energy consumption when sensor nodes are sending Er Energy consumption when sensor nodes are receiving 2019/2/15

Notation – Given Parameter(cont.) Description The indicator function which is 1 if the link (u, v) is on the path p and 0 otherwise The indicator function 1 if the node v is covered within transmission radius of the node u and 0 otherwise Estimate error 2019/2/15

Notation – Decision Variables 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 Data transmission delay from the node u to the node v Data transmission time from the node u to the node v Maximum end-to-end delay from leaf nodes to the node v on data aggregation tree Minimum begin time of all flows to node v 1 if the node v is covered within interference range of the node u 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. 2019/2/15

The relationship between nu mu nv and mv 2019/2/15

The relationship between nu mu nv and mv if the communication between node u and node v exist interference and if 2019/2/15

Problem Formulation Min subject to: Routing Constraint Tree Constraint 2019/2/15

Problem Formulation(cont.) Minimum and Maximum End to End delay Constraint Number of neighbors Constraint 2019/2/15

Problem Formulation(cont.) 2019/2/15

Approximated Function For convenience of applying our solution approach to this model, we make some transformations on constrain (17) in order to make (IP) solvable. Constraint (17) can be approximated by : 2019/2/15

Approximated Function We take natural logarithm on both sides in order to make this function solvable 2019/2/15

Relaxation In (IP), by introducing Lagrangean multiplier vector u1,u2, u3, u4, u5, u6, u7, u8, u9, u10 , u11 , u12 , u13 we dualize Constraints (2), (4), (8), (9), (10), (11), (12), (13), (14) , (15) , (16) , (17) and (18) to obtain the following Lagrangean relaxation problem(LR). 2019/2/15

Relaxation(cont.) Min 2019/2/15

Relaxation(cont.) 2019/2/15

Relaxation(cont.) subject to: 2019/2/15

Relaxation(cont.) 2019/2/15

Subproblem relate objective function min s.t. (SUB 1) can be further decomposed into independent shortest path problems 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Transformation After transforming, we can decompose (SUB 3) into | V | independent subproblems. For each node u, s.t. calculate 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Transformation After transforming, we can decompose (SUB 4) into | V | independent subproblems. For each node u, s.t. calculate 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Transformation After transforming, we can decompose (SUB 6) into | V X V | independent subproblems. For each link (u, v), s.t. calculate 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Transformation (SUB 7) can be decomposed into | V X V | independent subproblems. For each link (u, v), s.t. calculate 2019/2/15

Subproblem relate objective function min s.t. 2019/2/15

Transformation (SUB 8) can be decomposed into | V X V | independent subproblems. For each link (u, v), s.t. calculate 2019/2/15

End Thanks for your listening To conclude: we designed a new sleep scheduling scheme for wireless active sensor networks. The scheme exploits spatio-temporal correlation to adaptively adjust sleep cycles of nodes. The analytical and simulation results show that our scheme outperforms leach and teen with respect to energy efficiency, latency and responsive. 2019/2/15