An Efficient Centralized
Scheduling Algorithm in
IEEE 802.15.4e 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
: DeTAS
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
= {n0,n1,n2,...,nN}
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 200m*200m
 Slot
: 15ms
 Slotframe
: 50 slots equal to 0.75s
 Simulation
total time : 3000 slotframes equal
to 37.5min
 Number
of nodes : 10 to 100
Simulation and Result
Simulation and Result
Reference

Mike Ojo, Stefano Giordano,” An Efficient Centralized Scheduling Algorithm in
IEEE 802.15.4e TSCH Networks”,Conference on Standards for
Coummunications and Networking(CSCN)