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A Study on IOT Applications and Technologies in Logistics
Abstract
One of the most trending state-of-the-art technologies due to broadening usages of logistics is Internet of Things (IOT) applications which has been enabled by new expandable platforms which are occupied in many applications like transportation, smart city, smart home and etc. To broaden this field of study there is need to classify and group all documents to find useful patterns and speed up and accurate future studies in order to achieve to greatest results. In this paper, some analysis to fulfil the pattern of studies and determine latest IOT applications in logistics is done. Results demonstrate that new inclines in IOT applications with logistics are embedded in airports and railways and some technologies like WSN, RFID and GIS are top of useful devices in this direction.
... It is able to transfer data over a network automatically without the need for anyone or manual systematical intervention [1]. IoT systems, which are rapidly expanding their application area, range from health and agriculture to logistic and industry [2][3][4][5][6]]. An IoT system consists of embedded systems such as processors, sensors and communication hardware, and these devices are responsible for collecting data from their environment and sending it to the cloud. ...
... The leader or alpha wolf is the first wolf that the beta and delta wolves follow; in the hunting mechanism, the omega wolves follow the first three wolves and update their own position to prey. the mathematical model of the hunting mechanism is given in the following Eqs. (1)(2)(3)(4)(5)(6)(7)(8): ...
In this study, a new hybrid method is proposed by using the advantages of Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The proposed hybrid metaheuristic algorithm tries to find the near-optimal solution with high efficiency by using the advantage of both algorithms. At the same time, the shortcomings of each will be eliminated. The proposed algorithm is used to solve the edge computing node localization problem, which is one of the important problems on the Internet of Things (IoT) systems, with the least error rate. This algorithm has shown a successful performance in solving this problem with a smooth and efficient position update mechanism. It was also applied to 30 famous benchmark functions (CEC2015 and CEC2019) to prove the accuracy and general use of the proposed method. It has been proven from the results that it is the best algorithm with a success rate of 54% and 57%, respectively.KeywordsInternet of ThingsEdge computing nodesMetaheuristicLocalizationOptimization problems
... Leveraging cutting edge technologies like Internet of Things (IoT) and machine-to-machine communication enables objects to be controlled or sensed in a remote way across the existing network infrastructure and creates a good opportunity to directly integrate the physical word into the cyberspace [2]. We can embed logistical IoT applications in railways, industries and airports, using GIS, RFID and a Wireless Sensor Network Platform (WSN) [3]. Integrating the IoT and CPPSs into logistics permits tracking assets in real-time, handling transport in a better way and managing risks accurately. ...
... Actually, we discuss the industrial case implementation. The interaction mechanisms in IoT-SA are deployed in the wireless sensor network platform using the 3 Waspmote technology. Thus latter permits identifying risk according to the RSSI measure and ensuring distance estimation in IoT-SA. ...
... Leveraging cutting edge technologies like Internet of Things (IoT) and machine-to-machine communication enables objects to be controlled or sensed in a remote way across the existing network infrastructure and creates a good opportunity to directly integrate the physical word into the cyberspace [2]. We can embed logistical IoT applications in railways, industries and airports, using GIS, RFID and a Wireless Sensor Network Platform (WSN) [3]. Integrating the IoT and CPPSs into logistics permits tracking assets in real-time, handling transport in a better way and managing risks accurately. ...
... Actually, we discuss the industrial case implementation. The interaction mechanisms in IoT-SA are deployed in the wireless sensor network platform using the 3 Waspmote technology. Thus latter permits identifying risk according to the RSSI measure and ensuring distance estimation in IoT-SA. ...
This chapter shed lights on a decentralized approach which aims at monitoring resources securely and dealing with products in IoT-based warehouses, and this is part of hybrid logistic decision-making aided processes. These latter are composed of two phases. The first phase consists in logistic centralized decision-making in relation to terminal product allocations, utilizing advanced product classifications, floating locations and the method of static potential fields. The second phase consists in decentralized decision making, hence assuring at the shop floor level the reactive product-trajectory control through the use of the method of dynamic potential fields, while taking into account product-resource interactions. The proposal can achieve that safer warehouse management which is based on the reactivity of smart shelves and products against any critical surrounding risk. Some scenarios for negotiated interaction mechanisms that concern the safe storage and picking operations are proposed. Using intelligence in Warehouse Management System (WMS) results in proposing an "IoT-controlled Safe Area" (IoT-SA) as a new concept. Our contribution is to make decisional, communicational and informational abilities near the warehouse because of those communicating objects. Thus, we can achieve safety assurance while decreasing certain delays in decision-making and increasing the effectiveness of solving dynamic and local disruptions. This leads to avoid the inherent WMS-centralization short comings. Therefore, we present an implementation case for IoT-SA concept, which utilizes Labview software and a platform of a ZigBee wireless sensor network.
This research work focuses on the hybrid concentrated Photovoltaic-Thermo electric (PV-TEG) module. The electrical characteristics of this framework give rise to complex control problems which can be classified as nonlinear, multiple local maxima monotonic control problems. Due to the existence of several gradient peak points, the conventional controllers cannot distinguish among the local and global maximum power points. Yet another problem with large-scale concentrated PV-TEG systems connected to the grid is the undesired large fluctuations in electrical power generation. Such surges and drops in voltage imbalances the grid oscillation balance and may cause failure and safety issues. This new approach is the first step toward stable power generation. For PV-TEG system power optimization, a hybrid Chaos Game Optimization Algorithm-General Neural Regression Network (CGOA-GRNN) is devised. A comprehensive comparison is made for performance evaluation with Henry gas optimization (HGO), Grey wolf optimization (GWO), and Particle Swarm Optimization (PSO). The statistical, qualitative, and quantitative results show superior performance by the proposed CGO-GRNN for concentrated solar thermal control application. The experimental simulation results indicate that the efficiency of the CGO-GRNN reaches up to 35.33%, TEG achieves 0.6% added efficiency, while proposed MPPT control achieves 100% GMPP detection score, with 99.9% power tracking efficiency. The tracking time under fast varying solar concentration is on average 80 ms, while under partial shading (PS) conditions it is up to 90–110 ms. Additionally, the statistical analysis shows the robustness of the proposed methodology achieving a very low measure of variability.
Artificial Intelligence (AI) is the technology which is gaining popularity in the recent years. The application of AI can be seen in all industries. This article is focusing particularly on the application of AI in the supply chain. Looking at the numerous benefits of AI, many industries, researchers and practitioners across the world had started exploring this area. The AI consists of many tools such as machine learning, deep learning, expert systems, big data, artificial neural networks, speech processing, natural language processing, robotics etc. This study elaborates the applications of AI tools among different stakeholders of the supply chain. These stakeholders are the suppliers, manufacturers, logistics, retailers and the customers. Several benefits of AI have also been explored in this study through literature review. This study adds knowledge base in the field of technology adoption such as AI in the supply chain for sustainable development in emerging countries.
Today, plastics occupy a predominant place in the world’s industrial production of which polystyrene (PS) is one of the most abundant plastics. In the context of sustainable industrial technology, scientific research favors the elaboration of plastics reinforced by organic waste, considering their low cost and their very interesting thermal and mechanical properties that will benefit the matrix. In the present study, we focused on acorn as bio-reinforcement, the PS was reinforced by the acorn pericarp (AP), and the acorn cupule (AC), separately. The results obtained show an integration of the reinforcement in the matrix, the FTIR spectra show the appearance of the OH band, the diffractograms show a decrease in intensity and thus a slight decrease in crystallinity, which remains very acceptable. Morphologically, the SEM images showed laminates for AP while the incorporation of AC caused pores in the matrix.
KeywordsPolystyreneCompositeAcorn wasteIn situ polymerization
Artificial intelligence (AI)—defined as a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.
Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002-2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.
Most of the advancements are now carried out by interconnecting physical devices with computers; this is what known as Internet of Things (IoT). The major problems facing by blind people fall in the category of navigating through indoor and outdoor environments consisting of various obstacles and recognition of person in front of them. Identification of objects or person only with perceptive and audio information is difficult. An intelligent, portable, less expensive, self-contained navigation and face recognition system is highly demanded for blind people. This helps blind people to navigate with the help of a Smartphone, global positioning system (GPS) and a system equipped with ultrasonic sensors. Face recognition can be done using neural learning techniques with feature extraction and training modules. The images of friends, relatives are stored in the database of user Smartphone. Whenever a person comes in front of the blind user, the application with the help of neural network gives the voice aid to the user. Thus this system can replace the regular imprecise use of guide dogs as well as white sticks to help the navigation and face recognition process for people with impaired vision.In this paper, we have proposed a novel image recognition and navigation system which provides precise and quick messages in the form of audio to visually challenged people so that they can navigate easily. The performance of the proposed method is comparatively analyzed with the help of ROC analysis.
This paper explores the role of Internet of Things (IoT) and its impact on supply chain management (SCM) through an extensive literature review. Important aspects of IoT in SCM are covered including IoT definition, main IoT technology enablers and various SCM processes and applications. We offer several categorisation of the extant literature, such as based on methodology, industry sector and focus on a classification based on major supply chain processes. In addition, a bibliometric analysis of the literature is also presented. We find that most studies have focused on conceptualising the impact of IoT with limited analytical models and empirical studies. In addition, most studies have focused on the delivery supply chain process and the food and manufacturing supply chains. Areas of future SCM research that can support IoT implementation are also identified.
Circular economy (CE) is a term that exists since the 1970s and has acquired greater importance in the past few years, partly due to the scarcity of natural resources available in the environment and changes in consumer behavior. Cutting-edge technologies such as big data and internet of things (IoT) have the potential to leverage the adoption of CE concepts by organizations and society, becoming more present in our daily lives. Therefore, it is fundamentally important for researchers interested in this subject to understand the status quo of studies being undertaken worldwide and to have the overall picture of it. We conducted a bibliometric literature review from the Scopus Database over the period of 2006–2015 focusing on the application of big data/IoT on the context of CE. This produced the combination of 30,557 CE documents with 32,550 unique big data/IoT studies resulting in 70 matching publications that went through content and social network analysis with the use of ‘R’ statistical tool. We then compared it to some current industry initiatives. Bibliometrics findings indicate China and USA are the most interested countries in the area and reveal a context with significant opportunities for research. In addition, large producers of greenhouse gas emissions, such as Brazil and Russia, still lack studies in the area. Also, a disconnection between important industry initiatives and scientific research seems to exist. The results can be useful for institutions and researchers worldwide to understand potential research gaps and to focus future investments/studies in the field.
This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.
With the arrival of Industry 4.0, the overall transformation using digital integration and intelligent engineering has taken a giant leap towards futuristic technology. All devices today are equipped with machine learning, automation has become a priority and thus another industrial revolution is in the making. In this state-of-the-art paper, we have performed bibliometric analysis and an extensive survey on recent developments in the field of “Industry 4.0”. In bibliometric analysis, different performance metrics are extracted, such as: total papers, total citations, and citation per paper. Further, top 10 of the most productive and highly cited authors, major subject areas, sources or journals, countries, and institutions are evaluated. A list of highly influential papers is also assessed. Later on, a detailed discussion of the most cited papers is analysed and a sectional classification is provided. This paper summarizes the growth structure of Industry 4.0 during the last 5 years and provides the concise background overview of Industry 4.0 related works and various application areas.
In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.
Big data sensing system (BDSS) plays an important role in the Internet of Things, in which how to reduce power consumption is one crucial problem. Currently, low energy adaptive clustering hierarchy (LEACH) protocol is one well-known algorithm used in BDSS with low energy cost. In this paper, a new variant of bat algorithm combined with centroid strategy is introduced. Three different centroid strategies with six different designs are introduced. In addition, the velocity inertia-free update equation is also provided. The optimization performance is verified by CEC2013 benchmarks in those designs against standard BA. Simulation results prove that the bat algorithm with weighted harmonic centroid (WHCBA) strategy is superior to other algorithms. By integrating WHCBA into LEACH protocol, we develop a two-stage cluster-head node selection strategy and can save more energy compared to the standard LEACH protocol.
Abstract
Purpose – The emergent field of Internet of Things (IoT) has been evolving rapidly with a geometric
growth in the number of academic publications in this field. The purpose of this paper is to review the
literature of IoT in past 16 years using rigorous bibliometric and network analysis tools, offering at
the same time future directions for the IoT research community and implications for managers and
decision makers.
Design/methodology/approach – The authors adopted the techniques of bibliometric and
network analysis. The paper reviewed the articles published on IoT from 2000 to 2015.
Findings – This study identifies top contributing authors; key research topics related to the field; the
most influential works based on citations and PageRank; and established and emerging research
clusters. Scholars are encouraged to further explore this topic.
Research limitations/implications – This study focuses only on vision and applications of IoT.
Scholars may explore various other aspects of this area of research.
Originality/value – To the best of authors’ knowledge, this is the first study to review the literature
on IoT by using bibliometric and network analysis techniques. The study is unique as it spans a long
time period of 16 years (2000-2015). The study proposes a five-cluster classification of research themes
that may inform current and future research in IoT.
The Internet of Things (IoT), also called the Internet of Everything or the Industrial Internet, is a new technology paradigm envisioned as a global network of machines and devices capable of interacting with each other. The IoT is recognized as one of the most important areas of future technology and is gaining vast attention from a wide range of industries. This article presents five IoT technologies that are essential in the deployment of successful IoT-based products and services and discusses three IoT categories for enterprise applications used to enhance customer value. In addition, it examines the net present value method and the real option approach widely used in the justification of technology projects and illustrates how the real option approach can be applied for IoT investment. Finally, this article discusses five technical and managerial challenges.
The term “Internet-of-Things” is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internet-of-Things.
Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts
across many areas of modern day living. This offers the ability to measure,
infer and understand environmental indicators, from delicate ecologies and
natural resources to urban environments. The proliferation of these devices in
a communicating-actuating network creates the Internet of Things (IoT),
wherein, sensors and actuators blend seamlessly with the environment around us,
and the information is shared across platforms in order to develop a common
operating picture (COP). Fuelled by the recent adaptation of a variety of
enabling device technologies such as RFID tags and readers, near field
communication (NFC) devices and embedded sensor and actuator nodes, the IoT has
stepped out of its infancy and is the the next revolutionary technology in
transforming the Internet into a fully integrated Future Internet. As we move
from www (static pages web) to web2 (social networking web) to web3 (ubiquitous
computing web), the need for data-on-demand using sophisticated intuitive
queries increases significantly. This paper presents a cloud centric vision for
worldwide implementation of Internet of Things. The key enabling technologies
and application domains that are likely to drive IoT research in the near
future are discussed. A cloud implementation using Aneka, which is based on
interaction of private and public clouds is presented. We conclude our IoT
vision by expanding on the need for convergence of WSN, the Internet and
distributed computing directed at technological research community.
Presently the necessity of building anticollision RFID systems with dynamic location change of tags appear more often. Such solutions are used in identification of moving cars, trains (automatic identification of vehicles – AVI processes) as well as moving parts and elements in industry, commerce, science and medicine (internet of things). In the paper there were presented operation stages in the RFID anticollision system necessary to communicate with groups of tags entering and leaving read/write device interrogation zone and communication phases in conditions of dynamic location change of tags. The mentioned aspects influence RFID system reliability, which is characterized by the efficiency coefficient and the identification probability of objects in specific interrogation zone. The communication conditions of correct operation of multiple RFID system are crucial for efficient exchange of data with all tags during their dynamic location changes. Presented problem will be the base to specify new application tag parameters (such as maximum speed of tag motion) and synthesis of interrogation zone required for concrete anticollision RFID applications with dynamic location change of tags.