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1
Khorramshahr: A Scalable Peer to Peer Architecture for Port Warehouse Management System
2
01
03
02
Introduction
Problem Statement
Preliminaries of The Used Filters
04
06
05
The Architecture of Khorramshahr
Performance Evaluation
Conclustion
Outline
3
Internet of Things in Smart Industry
Management of products in huge
warehouse
Stock checking is a time
consuming task and requires considerable
effort
Product moves from one
warehouse to anotherPort the key
gateway to industrial products
Introduction
4
InnovationsKhorramshahr uses a peer to peer (P2P) architectural stylewhich makes it scalable in the number of transactions,number of warehouses and the geographical distribution.
The double chord approach on both distributed hash tables(product types and product information) is used, to facilitateusers from inside or outside of the port to access therequired information from different warehouses.
A distributed discovery service is designed to support accessto the product catalogs, stock and property checking.
Memory efficient data structures such as Bloom filter andQuotient filter are utilized to reduce the response time andmemory usage. Chord based DHT is implemented withboth of the filters and the performances are analyzed
To increase the efficiency of the system in looking up product types in object name server (ONS) which is usually in variant, a client server architectural style with replicateddata repository is used. Therefore, Khorramshahr is a hybridarchitecture, which uses both P2P and client serverstyles in different sections.
5
Radio Frequency Identification (RFID) RFID Reader
Physical Components
Soft ware Components
Middleware EPC Information Service (EPCIS) Object Name Service (ONS) Discovery Service (DS)
EPC Global Architecture
6
Problem Statement
W = {w1;w2; …; wm} w= warehouse (1)
T= {t1; t2; …; tn} T= good types (2)
G = {g1; g2; …; gx} G= goods (3)
Wti = {tij |t j ∈ T} (4)C={c1, c2,…ck,…., cp} C= companies (5)
∩k=1 p Cwk = ∅ (7)
f (gj) = wiG W (8)
Cwk = {wi |wi ∈ W} (6)
7
Dynamic Bloom Filter
Quotient Filter
Preliminaries of the used filters 14 13 12 11 10 9 8 7 6 5 4 3 2 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bloom filter A: a vector of bits initially all set to 0s
0 1 0 1 1 0 1 0 0 1 0 0 1 0Programming phase: insert each element xi in S into the A, A[Hj(xi)]=1
x1 x2 x3
0 1 0 1 1 0 1 0 0 1 0 0 1 0
Querying phase: if all A[Hj(y)]=1 return Yeswith false positive probability, otherwise return No
y1 y2y1 is definitely not amember of S
y2 is a member of S(false positive)
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The Architecture of Khorramshahr
9
Physical Layer
Interface Layer
Application Layer
The Architecture of Khorramshahr
Product Serial No .
(16bit)
Product Type
(16bit)
Warehouse Prefix
( 16bit )
The assigned ID format for each product in the port EPC ID
(up to 111 bit)
Assigned ID
(48 bit)
The format of product ID in Khorramshahr architecture
10The deployment view of the Khorramshahr
architecture
11
Handling Incoming
ShipmentsTracking a
Stocked Product
Handling Outgoing
ShipmentsStock List
The Architecture of KhorramshahrBehavioral Model
12
Performance Evaluation
Simulation Tools
OMNET++ Version 4.1
OverSim ( release 20101103)
Value Parameter18000(s) Simulation Time (Global)
No churn Num Replica churn Type (Global)
10 Mbps Ethernet Channel Data Rate (Global)
10(ms) Ethernet Channel Delay (Global)
[10000-50000] Num Get Request
60s Test Interval
300 Test TTL
10(s) Failure Latency
[25-250] Number of Terminal
13
10000 20000 30000 40000 50000
0.8790.875 0.877 0.877
0.8820.889
0.885 0.8860.89 0.892
0.8640.86 0.862
0.867 0.869
0.8560.861
0.855 0.8550.851
Terminal 50ODSA Base DHT Bloom Quotient
Number of Request
Get Latency
10000 20000 30000 40000 500000.83
0.84
0.85
0.86
0.87
0.88
0.89
0.9
Performance Comparison & Scalability
14
10000 20000 30000 40000 500000.85
0.9
0.95
1
1.05
1.1
1.15
1.2
Terminal 250
BaseBloomQuotientODSA
Number of requests
Get Latency
10000 20000 30000 40000 500000.85
0.9
0.95
1
1.05
1.1
1.15
1.2
1.09
1.131.15 1.15
1.17
0.991 0.995 0.997 0.997 0.9920.978 0.983 0.985 0.987 0.984
1.03
1.061.07 1.07
1.09
Performance comparison & scalability
15
50 100 150 200 2500
50
100
150
200
250
300
Memory efficiency
BaseBloomQuotientODSA
Number of Terminals
Required memorySize (KB)
50 100 150 200 2500
50
100
150
200
250
300
50.2
100.5
145
194.4
249.8
35.4
71.8
125
168.3
218.2
42.9
83.4
134.6
178.2
229.2
50.3
100.6
145
194
249
Memory Efficiency
16
10000 20000 30000 40000 500000
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
False Positive Rate
Bloom
Quotient
Number of requests
False positiveRate(%)
10000 20000 30000 40000 500000
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.015
0.012
0.011
0.012
0.011
0.0011 0.00110.0015 0.0017
0.0011
False Positive Rate
17
Conclusion• New architecture warehouse management
• Architecture uses a hybrid of P2P and client server paradigms
• The architecture is scalable in terms of the number of requests and the number of warehouses
• To boost the lookup procedure two different filters( Bloom and Quotient filters)