1

Khorramshahr: A Scalable Peer to Peer Architecture for Port Warehouse Management System

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01

03

02

Introduction

Problem Statement

Preliminaries of The Used Filters

04

06

05

The Architecture of Khorramshahr

Performance Evaluation

Conclustion

Outline

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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

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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.

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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

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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)

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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

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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

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Handling Incoming

ShipmentsTracking a

Stocked Product

Handling Outgoing

ShipmentsStock List

The Architecture of KhorramshahrBehavioral Model

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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

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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

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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

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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

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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

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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)