Phd Research Opportunities on Internet of Things Logistics
Introduction
As the Internet’s coverage expands, so does the number of physical items that may connect to it. By 2011, there were roughly 12.5 billion Internet-connected things, and with the arrival of 5G-Internet [3, it is expected to surpass 25 billion by the end of 2020 and 50 billion by the end of 2050 [1]. As a result, the Internet of Things (IoT) has gotten a lot of attention, especially in recent years, because it has led to significant changes in lifestyle as well as emerging technologies like Machine-to-Machine (M2M) communication [2], context-aware computing, and Radio Frequency Identification (RFID). On an open, gigantic, self-configured, and dynamic internet-based network, these technologies make it possible to identify, connect, adapt, and localise, as well as track and monitor such objects as wearable devices, smart home appliances, intelligent vehicles and drones, and smart applications for industrial automation and logistics.
Despite various analyses of IoT applications, there has yet to be a detailed examination of its implementation in logistics. However, the aforementioned collection of common IoT features may fully support all logistics functions, including the proper commodities, quantity, location, quality, time, and pricing.
Logistics
In a Supply Chain (SC), logistics management includes short-term operations such as material and inventory handling, as well as communications. There are four perspectives on logistics vs SCs: 1) re-labeling, 2) traditionalist, 3) inter-sectionist, and 4) unionist. The first merely changes the name of logistics to SC. The second places SC within logistics, the third asserts they intersect, and the fourth considers logistics to be a part of SC. As a result, opinions differ on how SC management interacts with logistics. So, in keeping with the previous point of view, this blog focuses on the term “logistics,” on which current research has taken two contrasting approaches: economic versus behavioral. The latter, on the other hand, focuses on psychological and sociological factors, which are primarily gathered through questionnaires, interviews, and case studies. Despite their fundamental differences, both methods regard Information Technology (IT) as the most important factor in facilitating logistics in both internal and external activities. Despite their fundamental differences, both methods regard IT as the most important factor in facilitating logistics in both internal and external activities. Because the IoT is such a significant advance in the world of IT, it is especially important for today’s modern logistics.
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IOT-BASED GENERAL LOGISTICS
IoT technology, which incorporates RFID, Near Field Communications (NFC), and GPS, can not only provide real-time visibility for operations but also create value for both consumers and suppliers on the market. It can use Big Data Analysis, smart tags and objects, and ad-hoc predictive maintenance applications to optimize business and industrial process flows while providing effective logistics service solutions. This cutting-edge technology aids logistics service providers in making timely decisions about how to track, route, and deliver items to their clients, thereby increasing their competitiveness. This is because providing autonomous and self-controlled movement of products from sender to consignee reduces the traditional logistics network’s long reaction time to several days for L-IoT.
IOT-BASED PRODUCTION LOGISTICS
Aside from smart product design and smart machine systems, which are both part of the emerging industry 4.0 concept, and Industrial Internet-of-Things (I-IoT), IoT technology combines IT and SC to produce a higher-quality product in a more agile process by providing real-time visibility. It also aids producers in cutting down on data analysis and decision-making time. Because it generates dynamic control over production logistics systems by providing fast, accurate, and timely information flow. As a result, the Internet of Things, especially on a worldwide scale, can be a source of many useful changes in industrial logistics. Because manufacturing logistics accounts for approximately 95% of the whole production process, both intra- and inter-firm.
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IOT-BASED INTELLIGENT CARGO AND OBJECTS LOGISTICS
Not just manufacturers, but also the logistics business, are concerned about how to handle goods in today’s world. RFID tags can give and update freight information, such as storage, inventory, and delivery conditions, for rapid control, monitoring, and tracking via a network connection, in answer to this problem. They can detect cargo to prevent loss and displacement, and weight sensors can also be used to prevent vehicle overburden. Furthermore, GPS and the Global System for Mobile Communications (GSM) can be used to track the location of cars in real time. The expensive cost of these sensors to overcome performance restrictions such as operating in hard and unclean environments with extreme temperatures, dealing with unmanageable data, the requirement for extended reading ranges, the diversity of the standards, and so on, makes tagging every retail item uneconomical.
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IOT-BASED SMART INVENTORY CONTROL
More technically, using the IoT technology in manufacturing logistics is based on the EPC as a unique global identifier of each product, which is used to track and trace products. Objects with RFID tags and unique EPCs provide information services to subscribers not only locally, but also remotely. By this means, RFID technology with the involvement of the EPC in warehouses can eliminate industrial risks and theft problems, improve productivity, and make products easier to track. However, security, safety, and reliability are all concerns when using IoT in large industrial applications. As a result, while RFID tags can store all of the information about an object, they rarely do. And the information is distributed through the use of an Object Naming Service (ONS), which is analogous to the Domain Name System (DNS).
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IOT-BASED ELECTRONIC LOGISTICS
Logistics has become increasingly important as e-commerce has grown in popularity. Unlike other economic sectors, e-commerce refers to the use of information and communication technology (ICT) for business transactions. In such businesses, vendors should be able to supply a wide range of services and items in acceptable volumes and quality using the pull method. As a result, Interplant Planning and Logistics Integration (IPLI) is required to connect all Material Requirements Planning (MRP) systems via Electronic Data Interchange (EDI) in order to reduce information flow delays and costs while increasing flexibility and efficiency. Because such information flow systems can efficiently monitor online consumer orders in real time while ensuring a quick flow of items covered by a Time-Based Management (TBM) programmer, they are becoming increasingly popular.
IOT-BASED CHEMICAL LOGISTICS
In 2018–2022, the worldwide chemical logistics market is expected to grow by about 14% annually, up from 5.48 percent in 2013–2018. Chemical logistics, on the other hand, is non-eco-efficient, combustible, explosive, poisonous, and caustic, among other things. As a result, its market isn’t totally standardized, and despite its high price, there’s little information about it. Despite this, the Internet of Things (IoT) presents new prospects for improving chemical logistics in globalized situations.
FUTURE RESEARCH DIRECTION FOR PHD SCHOLAR
| S.No | Research Direction | Future Scope | References |
| 1 | Focusing on mathematical approaches for optimally modeling and designing IoT-based systems | The cloud-based optimization methodologies are used to reduce energy consumption. However, research into the integration of mathematical modeling and optimization methodologies in the L-IoT, as well as the development of user-friendly decision support systems that can employ these models, is still in its infancy. As a result, how to develop IoT-based decision support systems as well as mathematical models to best support the L-IoT, as well as how to solve models, have become major future research potentials. | [3] |
| 2 | Developing tools to deal with sources of uncertainty | The Internet of Things can reduce risk and uncertainty by monitoring and regulating uncertainty-related misjudgment. As a result, in both the investment and operation stages, the creation of methodologies and tools to handle this conflict could be a promising research opportunity. Due to the significance of uncertainty, it seems fairer to devote more attention to resilient optimization techniques than to other mathematical optimization approaches in this regard. | [4] |
| 3 | Using a case-based research strategy | Research Proposal is a general design for an L-IoT network, there is no common method for case adaptation. This could be because attempting to extract modification rules in such a fast-changing pull (customer-driven) network can be a very complicated task. As a result, future research could focus on building a general mechanism that can be utilised practically unchanged for almost all logistical applications. The construction of a diverse, integrated, and effective hardware infrastructure that enables L-IoT adoption can be beneficial in this regard.
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[5] |
| 4 | Focusing on the cost-efficiency of the L-IoT | The expense of connecting to an IoT terminal may not be significant for major corporations, but it is for small and medium businesses (SMEs). Its growth is primarily influenced by its impact on SMEs, which are considered the economic backbone of many countries. This could exacerbate the cost issue for L-IoT. Because it needs to put large-scale infrastructures in place, such as sensors and computers, in every container, vehicle, and warehouse, as well as hire qualified managers and train employees. | [6] |
| 5 | Focusing on Waste Electrical and Electronic Equipment (WEEE)/ Electronic-waste (E-waste) management. | Internet of Things is increasing E-waste, it is also a useful tool for managing it [549]. As a result of the increased interest in IoT technology, E-waste management will become increasingly crucial in the future. As a result, research on reverse logistics and closed-loop SC of E-waste, as reviewed in, could be a promising area for future study. In this context, EPCs can facilitate information sharing among diverse agents in a reverse logistics network. In the meantime, advancing toward smart and expert infrastructures for E-waste identification, tracing, correct separation, on-time collection, and recovery could be a promising Research Topic. | [7] |
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References
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- Prasad, R., & Rohokale, V. (2020). Internet of Things (IoT) and Machine to Machine (M2M) Communication. In Cyber Security: The Lifeline of Information and Communication Technology (pp. 125-141). Springer, Cham.
- Tu, M., Lim, M. K., & Yang, M. F. (2018). IoT-based production logistics and supply chain system–Part 2: IoT-based cyber-physical system: a framework and evaluation. Industrial Management & Data Systems.
- Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440.
- Sears, J. R. Coverage of conference documents in scientific databases: viewpoint of Cambridge Scientific Abstracts. Science & Technology Libraries, 9(2), 35-45.
- Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362-372.
- Islam, M. T., & Huda, N. (2018). Reverse logistics and closed-loop supply chain of Waste Electrical and Electronic Equipment (WEEE)/E-waste: A comprehensive literature review. Resources, Conservation and Recycling, 137, 48-75.
- Boubellouta, B., & Kusch-Brandt, S. (2021). Cross-country evidence on environmental Kuznets curve in waste electrical and electronic equipment for 174 countries. Sustainable Production and Consumption, 25, 136-151.
- Cofta, P., Karatzas, K., & Orłowski, C. (2021). A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks. Sensors, 21(5), 1827.
- Ismail, S., Shah, K., Reza, H., Marsh, R., & Grant, E. (2021). Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study. Computers, 10(3), 27.
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