Abstract
Realtime object security and vehicle tracking have not been successfully implemented in commercial vehicles due to limitations in processing large amount of data and hardware capabilities. This paper aimed to improve the object security, processing power and data storage in a distributed environment. The proposed system (HLAiUE) consists of a three-layer architecture, first layer is used to collect data, second layer distributes the data processing and the last layer stores the processed data. The designed architecture improves the performance and security by applying data compression algorithms in a real time and removes the hardware dependency by processing the data in a distributed environment using Hadoop and Spark framework. The results demonstrate that the proposed architecture improves processing time by 80% compared to other algorithms and provides more reliability, security and flexibility due to ubiquity and the absence of hardware dependency in comparison with other existing architectures which are hardware dependent. Also, using an NOSQL database server in a distributed environment optimizes data storage by up to 80% and is flexible because all the infrastructure is in a distributed environment. The proposed architecture improves the object tracking by implementing compression technique in a distributed architecture. Thus, this system improves the accuracy up to 80% without depending on hardware resources.
Highlights
Nowadays it is essential for almost every organization to use some forms of technology (Valentine and Stewart, 2013; Cao et al, 2014) to increase efficiency, productivity and provide the best value to the customer to survive and stay competitive in the market (Leten et al, 2016)
With increased popularity and availability of the Internet, many traditional logistics companies have improved their business by enhancing their technological capabilities through replacing traditional with electronic commerce (Maity and Dass, 2014) or mobile commerce (m-commerce) (Yoshii and Sumita, 2016) and are moving towards ubiquitous computing (u-computing) (Chung, 2014)
Logistics plays a major role in this because every organization is involved either in Business to Business (B2B) (Vlachos et al, 2016), Business to Consumer (B2C), Consumer to Consumer (C2C) (Paris et al, 2016) or Government to Government (G2G) (Christou and Michalakos, 2010) requires logistics services for the movement of goods, people or services from one place to another and the value of these resources can be measured in millions of dollars (Min and Joo, 2009)
Summary
Nowadays it is essential for almost every organization to use some forms of technology (Valentine and Stewart, 2013; Cao et al, 2014) to increase efficiency, productivity and provide the best value to the customer to survive and stay competitive in the market (Leten et al, 2016). There are several devices and Internet Of Things (IOT) based applications that are required to make transportation secure and reliable (Bandyopadhyay and Sen, 2011). The collected data first need to be processed by integrating business scenarios so that they can be converted into information Due to large size and complex nature of data, Imani and Braga-Neto (2018) proposed approximate MMSE filtering and smoothing algorithms to manage computational and memory requirement to enhance performance, where as, several other authors (Gong et al, 20018; Jin et al, 2018; Ghoreishi and Allaire, 2017) proposed optimization algorithms which can be used to reduce cost increase efficiency and reliability (Su et al, 2014). We will discuss proposed architectures to solve these issues and their limitations
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