In the growing trend of globalization, the logistics and transportation play the key role in many industries. Products are shipped from one country to another. The heart of this transportation is the port, where large quantities of goods are imported and exported on daily bases. Applying Internet of Things (IoT) technologies to port warehouses is the main area of this work. Centralized warehouse management systems are prone to a single point of failure problem and are not fault tolerant. They have also limited scalability. Another issue is that the port warehouses may run by different companies and the privacy of the detailed product information must be preserved. In this paper, we design an IoT based architecture for warehouse management, according to the facts gathered from the Khorramshahr port. The main issues that this paper tries to address are: scalability, fault tolerance, and privacy. The devised architecture is named Khorramshahr (named after the name of the port). The tailored version of Chord architecture is exploited for a Distributed Hash Table (DHT), which inherits the required scalability and fault tolerance. According to the devised architecture, each company can solely manage its dedicated nodes and preserve the privacy. To boost the lookup process the design is enhanced by Bloom filter and Quotient filter. Moreover, to gain performance the architecture uses a hybrid approach, which combines both the client server and the peer to peer paradigms. To evaluate the performance, the DHT of the architecture is simulated by OMNet++ and OverSim. The simulation results show that by the scaling the number of terminals from 25 to 250 the access time for an item increases only 38%. Besides, the increase in the number of requests from 10,000 to 50,000 depicts 5% and 10% improvement respectively for the lookup message latency compared to the ODSA. The simulation results also exhibit lower false positive rate for the Quotient filter approach, which makes it the first implementation candidate. Only in cases with strict constraints on memory consumption the Bloom filter approach is favored. https://www.sciencedirect.com/science/article/pii/S1084804516302223

1. 1
Khorramshahr: A Scalable Peer to
Peer Architecture for Port Warehouse
Management System

2. 2
01
03
02
Introduction
Problem Statement
Preliminaries of The Used Filters
04
06
05
The Architecture of Khorramshahr
Performance Evaluation
Conclustion
Outline

3. 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
another
Port the key
gateway to
industrial products
Introduction

4. 4
Innovations
Khorramshahr uses a peer to peer (P2P) architectural style
which 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 facilitate
users from inside or outside of the port to access the
required information from different warehouses.
A distributed discovery service is designed to support access
to the product catalogs, stock and property checking.
Memory efficient data structures such as Bloom filter and
Quotient filter are utilized to reduce the response time and
memory usage. Chord based DHT is implemented with
both 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 replicated
data repository is used. Therefore, Khorramshahr is a hybrid
architecture, which uses both P2P and client server
styles in different sections.

5. 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. 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) = wi
G W (8)
Cwk = {wi |wi ∈ W} (6)

7. 7
Dynamic Bloom Filter
Quotient Filter
Preliminaries of the used filters
1413121110987654321
00000000000000
Bloom filter A: a vector of bits initially all set to 0s
01011010010010
Programming phase: insert each element xi in S into the A, A[Hj(xi)]=1
x1 x2 x3
01011010010010
Querying phase: if all A[Hj(y)]=1 return Yes
with false positive probability, otherwise return No
y1 y2
y1 is definitely not a
member of S
y2 is a member of S
(false positive)

8. 8
The Architecture of Khorramshahr

9. 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

10. 10
The deployment view of the Khorramshahr
architecture

11. 11
Handling Incoming
Shipments
Tracking a Stocked
Product
Handling Outgoing
Shipments
Stock List
The Architecture of Khorramshahr
Behavioral Model

12. 12
Performance Evaluation
Simulation Tools OMNET++
Version 4.1
OverSim
( release 20101103)
ValueParameter
18000(s)Simulation Time (Global)
No churnNum Replica churn Type (Global)
10 MbpsEthernet Channel Data Rate (Global)
10(ms)Ethernet Channel Delay (Global)
[10000-50000]Num Get Request
60sTest Interval
300Test TTL
10(s)Failure Latency
[25-250]Number of Terminal

13. 13
0.879
0.875 0.877 0.877
0.882
0.889
0.885 0.886
0.89 0.892
0.864
0.86 0.862
0.867 0.869
0.856
0.861
0.855 0.855
0.851
10000 20000 30000 40000 50000
Get Latency
Number of Request
Terminal 50
ODSA Base DHT Bloom Quotient
0.83
0.84
0.85
0.86
0.87
0.88
0.89
0.9
10000 20000 30000 40000 50000
Performance Comparison & Scalability

14. 14
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
10000 20000 30000 40000 50000
Get Latency
Number of requests
Terminal 250
Base
Bloom
Quotient
ODSA
1.09
1.13
1.15 1.15
1.17
0.991 0.995 0.997 0.997
0.992
0.978
0.983 0.985 0.987 0.984
1.03
1.06
1.07 1.07
1.09
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
10000 20000 30000 40000 50000
Performance comparison & scalability

15. 15
0
50
100
150
200
250
300
50 100 150 200 250
Required memory
Size (KB)
Number of Terminals
Memory efficiency
Base
Bloom
Quotient
ODSA
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
0
50
100
150
200
250
300
50 100 150 200 250
Memory Efficiency

16. 16
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
10000 20000 30000 40000 50000
False positive
Rate(%)
Number of requests
False Positive Rate
Bloom
Quotient
0.015
0.012
0.011
0.012
0.011
0.0011 0.0011
0.0015 0.0017
0.0011
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
10000 20000 30000 40000 50000
False Positive Rate

17. 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)