A Framework to Determine Secure Distances for Either Drones or Robots Based Inventory Management Systems

Rahmadya, Budi; Sun, Ran; Takeda, Shigeki; Kagoshima, Kenichi; Umehira, Masahiro

doi:10.1109/access.2020.3024963

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…This encompasses not only the quantitative aspects but also delves into the strategic analysis of ensuring the precise quantity of stock required to meet dynamic customer demands [4]. In its operational continuum, IMS encapsulates the comprehensive journey of inventory, commencing from the point of receipt and extending through various stages until the final sale or utilization [5]. This intricate process involves the dynamic management of products, real-time tracking of inventory stages, and judicious reordering strategies to maintain optimal stock levels.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cloud-Based Inventory Management: A Hybrid Blockchain Approach With Generative Adversarial Network and Elliptic Curve Diffie Helman Techniques

Irshad,

Hussain,

Hussain

et al. 2024

IEEE Access

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In contemporary business organizations, the pivotal role of automation in control processes is evident through Inventory Management Systems (IMS), which leverage advanced techniques and data analytics algorithms to optimize inventory levels, enhance accuracy, and minimize costs. However, existing security techniques for Inventory Management Systems (IMS), including access controls, firewalls, and audits, face challenges in effectively addressing the evolving threat landscape. These limitations, including struggles with dynamic user roles, susceptibility to data manipulation, and challenges in thwarting various cyber threats, necessitate innovative solutions for robust real-time management and security. Consequently, this work proposes a novel hybrid approach that integrates blockchain with RFID data, Generative Adversarial Networks, and Elliptic Curve Diffie-Hellman cryptographic techniques. In the developed hybrid approach, RFID readers are leveraged to collect inventory data, while the Generative Adversarial Network is specifically designed for processing the raw dataset, encompasses data filtering, normalization, and error correction tasks. The utilization of the Elliptic Curve Diffie-Hellman technique is integral for generating both private and shared keys, facilitating secure transmission between the IMS client and cloud-based servers. The blockchain module is engineered to enhance data security and protect shared secret keys, which is achieved through a two-layer mechanism involving encryption via the Advanced Encryption Standard algorithm and SHA-256 hashing function. Additionally, it incorporates the Artificial Algae Algorithm and an Elman Neural Network to ensure robust data access and integrity. To assess the effectiveness of the proposed hybrid approach, it is implemented on a publicly available dataset. The performance assessment involves a comprehensive comparison with state-of-the-art security methods, considering key metrics such as encryption time, decryption time, key generation time, throughput, latency, and data confidentiality rate. Simulation results conclusively demonstrate that the proposed hybrid approach significantly reduces encryption time, decryption time, key generation time, and latency. Furthermore, it notably improves throughput and data confidentiality rates while aligning with stringent IMS security requirements.

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Section: Introductionmentioning

confidence: 99%

Enhancing Cloud-Based Inventory Management: A Hybrid Blockchain Approach With Generative Adversarial Network and Elliptic Curve Diffie Helman Techniques

Irshad,

Hussain,

Hussain

et al. 2024

IEEE Access

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“…The development of the so-called industry 4.0 and 5.0 has allowed the integration of these systems in an architecture that responds to a smart city environment. Collecting the data of the products in stock through inventory management within an industry allows to provide information to the citizens in a quick and simple way [10,11]. The following paragraphs present a review of the most relevant articles on the use of UAVs in the development of inventories.…”

Section: Introductionmentioning

confidence: 99%

UAVs for Business Adoptions in Smart City Environments: Inventory Management System

et al. 2023

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Unmanned aerial vehicle (UAV)-assisted inventory management, used for inventory control within an enterprise with large warehousing and manufacturing systems, is currently common practice. There are many applications for UAVs within an enterprise, but they are generally used in tasks that have high risk connotations to perform activities quickly, such as in the cases of large enterprises that generally require inventory of many products quickly and efficiently connected in a smart city environment. Having an up-to-date inventory as quickly as possible in a smart city environment allows users to have the necessary information to verify the availability of products, reducing waiting times. The use of UAVs systems compared to conventional systems, such as unmanned ground vehicles, has several advantages, including the fact that UAVs have their own cameras and can move independently within the warehouse. This paper proposes the development of a Supervisory Control and Data Acquisition system (SCADA) for the inventory management of the company DHL, using drones. This study evaluates the OPC communication protocols for the virtual environment and the 232 serial connection protocol for the real environment, using LabView and Factory I/O programs. For the development of the system, the use of a drone was evaluated for the recognition of the availability of merchandise with the use of infrared technology incorporated in the drone by means of an Arduino. The appropriate use of communication protocols allowed the necessary information to be obtained for the communication of the LabView and Factory I/O environment in which a virtual environment based on the DHL inventory system is available. The system allowed the development of a SCADA system for the management of inventory monitoring in real time of the availability of goods in the virtual environment, allowing to obtain the necessary information for its integration in a smart city environment.

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“…Currently, solving problems related to determining the distance to an object is practically significant in various robotic systems, including computer vision systems [1]. Modern computer vision systems obtain real information about the distance to objects using various sets of sensors, such as radar [2], ultrasonic sensors [3], lidars [4], stereo mate [5], etc.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering

Kalita¹,

Lyakhov²

2022

Preprint

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The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.

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A Framework to Determine Secure Distances for Either Drones or Robots Based Inventory Management Systems

Cited by 15 publications

References 33 publications

Enhancing Cloud-Based Inventory Management: A Hybrid Blockchain Approach With Generative Adversarial Network and Elliptic Curve Diffie Helman Techniques

Enhancing Cloud-Based Inventory Management: A Hybrid Blockchain Approach With Generative Adversarial Network and Elliptic Curve Diffie Helman Techniques

UAVs for Business Adoptions in Smart City Environments: Inventory Management System

Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering

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