An Efficient Centralized Scheduling Algorithm in IEEE 802

An Efficient Centralized Scheduling Algorithm in IEEE 802. 15. 4 e TSCH Networks SPEAKER: YEI-REI CHEN ADVIDOR: DR. HO-TING WU DATE: 2017/04/20

Outline Introduction System model and Problem solution Throughput Simulation Reference Maximization scheduler and Result

Introduction TSCH scheduling Centralized : TASA Distributed : De. TAS

Introduction Bipartite graph

Introduction Complete Bipartite Graph

Introduction Maximum weighted bipartite matching(MWBM) G’ =( U’, V’, E’) be a weighted bipartite graph where U’ = {u’ 1, u’ 2, . . . , u’N}, V’= {v’ 1, v’ 2, . . . v’F} and E’ = {(u’, v’)|u’ ∈ U’, v’∈ V‘} A maximum weighted matching denoted by I* is to find a matching with the maximum total weight.

Introduction Hungarian alogorithm

System model and Problem solution Network Model Directed graph G = (V, E, d) V = {n 0, n 1, n 2, . . . , n. N} E is the set of links d consist of a set of physical distances , di, j Node communicateion randge Ri Directed link li, j

System model and Problem solution Network Model [Uk, Qk] Uk =[Uk, f, t] Qk is the number of packets in the buffer of nk Ck, f, t as the channel capacity of link lk, f at time t.

System model and Problem solution Network Model Uk, f, t is defined as the maximum number of packets that can be transmitted over lk, f at time t. Uk, f, t T is the slot frame duration

System model and Problem solution Network Mk, f, t Model is defined as effective rate of link lk, f, t

System model and Problem solution Network Model

System model and Problem solution Interference GI Model =( VI, EI). Interference occurs when a node have more than one communication in a single time slot transmitting and receiving at the same time receiving from multiple nodes

System model and Problem solution Interference Model Consider node ni, the transmission of node nj will interfere with the transmission of ni if

System model and Problem solution Throughput Maximization Problem Combinatorial optimization problem Solved by graph-based theoretical algotithm in polynomial time Graph-base theoretical based on matching theory such that problem transformed to another MWBM problem

System model and Problem solution Throughput Maximization Problem

System model and Problem solution Throughput Maximization Problem Π be the maximization problem formulation and z ≥ 1. A feasible solution s of an instance I of Π is a z objective optimal OΠ(s) function value OΠ(s) objective function value OΠ’(I) ≥ (OΠ’(I)/z).

System model and Problem solution Throughput Maximization Problem

System model and Problem solution Throughput Maximization Problem Π’ be the optimization problem obtained from Π by substitutin X of Π can be converted to a solution of Y of Π’ with OΠ’(Y )=OΠ(X) in polynomial time

System model and Problem solution Throughput Maximization Problem

Throughput Maximization scheduler

Simulation and Result Deployment : randomly deployed in a square area of 200 m*200 m Slot : 15 ms Slotframe : 50 slots equal to 0. 75 s Simulation total time : 3000 slotframes equal to 37. 5 min Number of nodes : 10 to 100

Simulation and Result

Simulation and Result

Reference Mike Ojo, Stefano Giordano, ” An Efficient Centralized Scheduling Algorithm in IEEE 802. 15. 4 e TSCH Networks”, Conference on Standards for Coummunications and Networking(CSCN)