Research on Smart Logistics Model Based on Internet of Things Technology

Tang, Xiuli

doi:10.1109/access.2020.3016330

Cited by 43 publications

(21 citation statements)

References 27 publications

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“…However, as Tang (2020) emphasizes Smart logistics refers to a logistics distribution network system that integrates informatization, intelligence and systemization by using the Internet of Things technology and information technology. It mainly uses high-tech and modern management methods to achieve high efficiency and low efficiency of the logistics distribution system.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…SL striving for a systemic recognition in its environment reduces the number of people's control activities by "supervising" the material and non-material flow in the supply chain (Tiejun, 2012), integrates logistic technologies (Hribernik et al, 2010), using appropriate software (Uckelmann, 2008), allows to reduce logistics costs and increase the efficiency of transport processes (Blecker et al, 2012), through e.g., fleet location and management (Miragliotta et al, 2012), ensures the security of data flow. In addition, SL considers the territorial (spatial) and object-oriented aspect of functioning e.g., in an enterprise, local market, region (Kirch et al, 2017), allows for mapping and assessment of gaps in processes (Singh et al, 2017), monitoring and integration of resources using IoT, planning and implementation of contracts and the use of advanced enterprise management tools (Arumugam et al, 2018;Tang, 2020).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Smart Logistics Infrastructure in Peripheral Region

Korczak¹,

Sasin²,

Janiszewska³

2021

ERSJ

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Purpose:The main aim of the article is to find out the extent to which enterprises in Central Pomerania, which is an example of a peripheral region, are equipped with smart logistics infrastructure and infrastructure, indicating the conditions for the development of enterprises towards the use of smart logistics solutions. Approach/Methodology/Design: The empirical part is based on a survey conducted on a sample of enterprises (n = 353) located in the region of Central Pomerania. The research model covers the key areas of smart logistics infrastructure used in enterprises. In the analytical part, basic statistical methods were used, based on the CHI-square test of independence, enabling the assessment of the dependence of enterprise infrastructure on the type of business activity. Findings: The obtained results indicate that there is a relationship between the type of business activity and the enterprise's infrastructure and smart solutions. This applies to both Internet of Things (IoT) devices as well as devices for communication and digitization. The industry that has the least developed in its activities Smart Logistics is tourism, gastronomy and entertainment, trade and construction. . Industrial processing and transport stand out positively in this respect. Practical Implications: Practical implications boil down to identifying which enterprises develop the SL function less and encounter development barriers, which in turn may be helpful in creating a support policy. Originality/Value: Study is one of the few that provides knowledge on the use of SL in a wide group of enterprises. It provides knowledge about the relationship between the type of enterprise and the development of its logistics functions in the context of the use of smart solutions and technologies.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Smart Logistics Infrastructure in Peripheral Region

Korczak¹,

Sasin²,

Janiszewska³

2021

ERSJ

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“…T HE proliferation of Internet of Things (IoT) and deployment of massive amounts of IoT devices in cyberphysical infrastructure systems such as Smart Factories [1], [2], Smart Grids [3], Smart Logistics [4] and others, brought forward an increasing number of cyber-security [5] and property management challenges [6]. For example, Smart Factory or Smart Logistics operations include asset management, intelligent manufacturing, performance optimization and monitoring, planning, and human-machine interaction, but neither of them takes into account full cyber-security protection or data management of Industrial IoT scale [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

Savić

Lukić

Danilovic³

et al. 2021

IEEE Access

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The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increased interest. In this paper, motivated by the upcoming surge of 5G IoT connectivity in industrial environments, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture. The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy. We design, integrate, demonstrate and evaluate a testbed that implements the above service in a real-world deployment integrated within the 3GPP Narrow-Band IoT (NB-IoT) mobile operator network.

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“…Tang designed and implemented a warehouse management system using B/S architecture, which adopted Java WEB three-tier architecture in J2EE system to reduce coupling degree of the system and improve scalability and easy maintenance. SQL Server was used in database to improve concurrent data and computing capability of the system [12]. Cui et al use AOP technology in the design of its warehouse management system, which makes the code of the system highly modularized and structured, only paying attention to the local content while ignoring the information transmission between modules, reconstructing the business system and simplifying the system structure [13].…”

Section: Introductionmentioning

confidence: 99%

Logistics and Warehousing Intelligent Management and Optimization Based on Radio Frequency Identification Technology

Du¹

2021

Journal of Sensors

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In today’s competitive global business environment, companies and organizations are emphasizing return on assets. By analyzing the status quo of Internet of things and warehouse management, this paper puts forward the application of radio frequency identification technology in logistics warehouse management. Based on the intelligent management of logistics and warehousing, the overall structure of intelligent management of logistics and warehousing based on radio frequency identification technology is established, and the functions of each functional module of the system are introduced in detail. Warehouse management system uses B/S architecture mode design; warehouse management system determines the location of goods in the warehouse management program, through radio frequency identification technology reader collection of goods in and out of the warehouse data and data processing results reported to the warehouse management system. Through the optimization of warehouse management process, the intelligent management of logistics and warehousing is realized efficiently. By testing the response time of the system and comparing the test efficiency before and after optimization, the reliability and efficiency of the warehousing intelligent management system are verified, which provides a basis for the study of logistics warehousing intelligent management.

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Research on Smart Logistics Model Based on Internet of Things Technology

Cited by 43 publications

References 27 publications

Smart Logistics Infrastructure in Peripheral Region

Smart Logistics Infrastructure in Peripheral Region

Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

Logistics and Warehousing Intelligent Management and Optimization Based on Radio Frequency Identification Technology

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