Khorramshahr: A scalable peer to peer architecture for port warehouse management system

Abstract

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.