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《The Journal of High Technology Management Research》2023,34(2):100477
Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge. 相似文献
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《International Journal of Information Management》2016,36(5):748-758
The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. 相似文献
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《Information processing & management》2022,59(1):102762
Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness. 相似文献
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当前,理论界与实践界都认为企业需要构建恰当的物联网商业模式,从而有效地释放和获取物联网技术的商业价值。为此,运用模块化理论深入解析物联网商业模式的复杂结构、可能形态与构建路径。研究发现:用户企业物联网商业模式是由能够独立设计而又功能集成的技术模块和业务模块构成;按照用户企业处理看得见的设计规则可将物联网商业模式划分为封闭型、开放型与自适应型3种类型;用户企业通过模块整合构建物联网商业模式,包括技术模块整合、技术模块与业务模块整合、跨企业的模块整合3个层次。 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100468
As time went on, technological progress inevitably altered our daily routines. Many new technologies, such as the Internet of Things (IoT) and cryptocurrency, offer revolutionary possibilities. To put it simply, the blockchain is a distributed, public, and auditable database that can be used to record financial transactions. The IoT, or “Internet of Things,” is a system of interconnected electronic devices that can communicate with one another and be remotely monitored and handled. This paper reviews the most recent findings in the field of blockchain and Internet of Things with the goal of examining blockchain as a possible answer to secure IoT data management within supply networks. There is a dearth of literature in the early stages of both blockchain and IoT study because they are such novel topics. The study's findings suggest that in order to improve their leadership quality to intentionally impact employee performance, industry managers should pay attention to human resource management indicators like collaboration, involvement, actualization, perception, and teamwork. This is primarily because of the inherent limitations of IoT devices and the distributed ledger architecture of the blockchain technology. There is potential for IoT to provide many advantages if blockchain capabilities can be optimized for it. 相似文献
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Didier Grimaldi Vicenc Fernandez 《The Journal of High Technology Management Research》2019,30(1):27-39
The Internet of Things (IoT) technology provides a lot of possibilities to develop IT projects that offer to the citizens platform of services for better conditions of living and transporting. Our study contributes to the topical subject of smart cities analysing the performance of an IoT-platform based solution. It evaluates if an IoT platform project can achieve business, environmental and social objectives all together. We select and test a project developed in Nice (France) where 5.000+ sensors are deployed on parking slots to improve the urban transport. The benefits of the IT project are determined comparing data extracting from Nice and compared with two other similar cities (Marseille and Toulon) which play the role of control group. The analysis of the value creation and value capture suggest a governance model for a private and public collaboration. Our findings help public managers to understand better private-public partnership and then prepare the future cities development. 相似文献
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Technology advancements in cloud computing, big data systems, No-SQL database, cognitive systems, deep learning, and other artificial intelligence techniques make the integration of traditional ERP transaction data and big data streaming from various social media platforms and Internet of Things (IOTs) into a unified analytics system not only feasible but also inevitable. Two steps are prominent for this integration. The first, coined as forming the big-data ERP, is the integration of traditional ERP transaction data and the big data and the second is to integrate the big-data ERP with business analytics (BA). As ERP implementers and BA users are facing various challenges, managers responsible for this big-data ERP-BA integration are also seriously challenged. To help them deal with these challenges, we develop the SIST model (including Strategic alignment, Intellectual and Social capital integration, and Technology integration) and propose that this integration is an evolving portfolio with various maturity levels for different business functions, likely leading to sustainable competitive advantages. 相似文献
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物联网是什么？物联网不是互联网、传感网、产品电子代码,也不单纯是一种技术应用。物联网将“互联网”和“物”连接在一起,就意味着把破坏性创新引进到当今的信息和通信技术世界。与互联网不同,物联网是物、网络、语义等视角的综合而形成的集网络、应用服务于一体的技术融合系统。在物联网语境中,物联网技术像人一样形成了人为的自主特征。物联网意味着一种潜在的技术异化的环境：个人隐私以多种方式受到威胁。而现有对隐私的制度规约存在诸多的不完备性。 相似文献
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The Internet of Things (IoT) might yield many benefits for organizations, but like other technology adoptions may also introduce unforeseen risks and requiring substantial organizational transformations. This paper analyzes IoT adoption by organizations, and identifies IoT benefits and risks. A Big, Open, Linked Data (BOLD) categorization of the expected benefits and risks of IoT is made by conducting a comprehensive literature study. In-depth case studies in the field of asset management were then executed to examine the actual experienced, real world benefits and risks. The duality of technology is used as our theoretical lens to understand the interactions between organization and technology. The results confirm the duality that gaining the benefits of IoT in asset management produces unexpected social changes that lead to structural transformation of the organization. IoT can provide organizations with many benefits, after having dealt with unexpected risks and making the necessary organizational changes. There is a need to introduce changes to the organization, processes and systems, to develop capabilities and ensure that IoT fits the organization’s purposes. 相似文献
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基于我国物联网产业发展具有代表性的9个省(市)的相关统计数据,构建物联网产业技术研发评价指标体系,运用曼奎斯特(Malmquist)指数方法对2007—2013年间我国物联网产业的技术研发效率进行动态评价。通过研究发现:我国物联网产业技术研发效率较为低下,且各地区研发效率差异较大;技术衰退是导致物联网产业研发效率下降的主要原因。 相似文献
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《Information processing & management》2023,60(2):103212
The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges. 相似文献
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《Information processing & management》2023,60(3):103275
The food industry is composed of a complex network of processing food and supply to the market. Supplying the food product to the market requires logistics with its operations. Nowadays the management of the food supply chain plays a vital role. In the food industry, supplying food with high quality and minimizing cost is required throughout the implementation of supply chain management. The quality of food is an essential thing for tracing the issues in food safety. Due to the increase in demand for quality food products by consumers IoT-based Food Supply Chain Management (FCM) is required for satisfying the consumer's needs. The IoT-based FCM helps the suppliers in the aspects of managing food safety procedures and tackling the issues in food safety, monitoring the lifespan of food on the manufacturing side, analyzing the process of reproduction or farming of raw material, warehousing, transporting the food product, sales in wholesaler etc. The main aim of using IoT is to receive sensor signals for monitoring the proper growth of crops in good quality soil, weather status, and so on. Also, in the transporting of food products, the sensors, and Radio-Frequency Identification (RFID) tags transmit the information to the server for monitoring the food safety parameters like temperature level, frozen level, etc. The issues are unsafe, ambiguous, the cost is high, low quality, and inaccurate. To overcome these issues, this paper proposed Internet of Things (IoT) based Food Supply Chain management is implemented. This paper proposes Tree-Augmented Naïve Bayes (TANB) with particle swarm optimization algorithm (TANB-PSO). This proposed work uses IoT which track, trace and manages the operations of food supply chain management. TANB-PSO handles optimized chronological data with IoT. The accuracy rate of three models in the FSCM of our proposed work TANB-PSO produces a higher accuracy rate of 95.02%. TANB got 92.44%, and PSO got 93.68%. The error rate of root mean square error (RMSE) and mean error (MAE) for the proposed work TANB-PSO is 0.017 and 0.031. 相似文献
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数字情境下制造服务化的治理机制:契约治理与关系治理研究 《科学学研究》2022,40(2):269-277
服务化被看做制造型企业保持竞争优势的有效途径。数字技术在服务化过程中的应用为制造商改变了设备运行方式与服务方式。数字化情境下的服务化不仅提供了新的用企互动类型,同时企业与用户的关系也发生了变化。因此,在该过程中采用不同的治理机制变得尤为重要。此研究基于308份中国制造型企业的问卷调查数据,实证检验了服务化对创新绩效的影响。研究结果发现：（1）服务化对创新绩效有正向影响;（2）高数字化水平下的服务化对创新绩效影响受关系治理的显著调节,低数字化水平下的服务化对创新绩效的影响受契约治理的显著调节。该研究的管理启示是,管理者依据企业所处的数字化情境不同,应采取不同的治理机制。与低数字化情境相比,在数字化水平高的情况下,企业应多关注关系治理这类非经济因素的作用。 相似文献
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《Journal of The Franklin Institute》2023,360(8):5354-5374
In this paper, we provide a methodology to evaluate the capacity of a Massive multiple-input multiple-output (MIMO) supported Internet of Things (IoT) system in which a large number of low cost low power IoT devices transmit and receive sporadic data. Numerous IoT devices are supported by a single cell Massive MIMO base station (BS) with maximum-ratio (MR) processing. Orthogonal reference signals (RSs) or pilots are assigned randomly to all the IoT devices for channel estimation purpose. The number of simultaneously active IoT devices follows Poisson distribution. Due to the tremendous number of IoT devices, orthogonal RSs are heavily reused, which severely degrades the receiver signal quality. One of the most important performance criteria for this kind of system is the blocking probability which shows the percentage of the outage IoT devices, and how we maintain the low blocking probability while supporting all the IoT devices simultaneously is particularly important. Due to RS reuse, we can divide IoT devices into two groups based on their interference levels. We provide detailed theoretical analyses, and show that the blocking primarily happens to the group with higher interference level. Increasing the number of service antennas and/or reducing the number of IoT devices can help to improve the performance of the blocking probability, however there is a regime in which the parameter adjustment helps little to improve the performance. Based on these factors, we provide a useful algorithm to improve the performance of blocking probability. A number of simulation results are also provided to validate the theoretical analysis. 相似文献
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With the development of information technology and economic growth, the Internet of Things (IoT) industry has also entered the fast lane of development. The IoT industry system has also gradually improved, forming a complete industrial foundation, including chips, electronic components, equipment, software, integrated systems, IoT services, and telecom operators. In the event of selective forwarding attacks, virus damage, malicious virus intrusion, etc., the losses caused by such security problems are more serious than those of traditional networks, which are not only network information materials, but also physical objects. The limitations of sensor node resources in the Internet of Things, the complexity of networking, and the open wireless broadcast communication characteristics make it vulnerable to attacks. Intrusion Detection System (IDS) helps identify anomalies in the network and takes the necessary countermeasures to ensure the safe and reliable operation of IoT applications. This paper proposes an IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology. According to the existing literature and algorithms, this paper introduces the modeling scheme of migration learning model and data feature extraction. In the experimental part, KDD CUP 99 was selected as the experimental data set, and 10% of the data was used as training data. At the same time, the proposed algorithm is compared with the existing algorithms. The experimental results show that the proposed algorithm has shorter detection time and higher detection efficiency. 相似文献