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1.
Artificial intelligence (AI) will transform business practices and industries and has the potential to address major societal problems, including sustainability. Degradation of the natural environment and the climate crisis are exceedingly complex phenomena requiring the most advanced and innovative solutions. Aiming to spur groundbreaking research and practical solutions of AI for environmental sustainability, we argue that AI can support the derivation of culturally appropriate organizational processes and individual practices to reduce the natural resource and energy intensity of human activities. The true value of AI will not be in how it enables society to reduce its energy, water, and land use intensities, but rather, at a higher level, how it facilitates and fosters environmental governance. A comprehensive review of the literature indicates that research regarding AI for sustainability is challenged by (1) overreliance on historical data in machine learning models, (2) uncertain human behavioral responses to AI-based interventions, (3) increased cybersecurity risks, (4) adverse impacts of AI applications, and (5) difficulties in measuring effects of intervention strategies. The review indicates that future studies of AI for sustainability should incorporate (1) multilevel views, (2) systems dynamics approaches, (3) design thinking, (4) psychological and sociological considerations, and (5) economic value considerations to show how AI can deliver immediate solutions without introducing long-term threats to environmental sustainability.  相似文献   

2.
Human Intelligence is considered superior compared to Artificial Intelligence (AI) because of its ability to adapt faster to changes. Due to increasing data deluge, it is cumbersome for humans to analyse the vast amount of data and hence AI systems are in demand in today's world. However, these AI systems lack self-awareness, social skills, multitasking and faster adaptability. Cognitive Computing (CC), a subset of AI, acts as an effective solution in solving these challenges by serving as an important driver for knowledge-rich automation work. Knowing the latest research and state of the art in CC is one of the initial steps needed for researchers to make progress in this front. Thus, this paper presents a comprehensive survey of prior research in the CC domain along with the challenges, solutions and future research directions. Specifically, CC-based techniques solving real-world problems in four widely-researched application areas, namely, healthcare, cybersecurity, big data and IoT, have been reviewed in detail and the open research issues are discussed.  相似文献   

3.
Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed.  相似文献   

4.
In the era of the Internet of Things (IoT), emerging artificial intelligence (AI) technologies provide various artificial autonomy features that allow intelligent personal assistants (IPAs) to assist users in managing the dynamically expanding applications, devices, and services in their daily lives. However, limited academic research has been done to validate empirically artificial autonomy and its downstream consequences on human behavior. This study investigates the role of artificial autonomy by dividing it into three types of autonomy in terms of task primitives, namely, sensing, thought, and action autonomy. Drawing on mind perception theory, the authors hypothesize that the two fundamental dimensions of humanlike perceptions—competence and warmth—of non-human entities could explain the mechanism between artificial autonomy and IPA usage. Our results reveal that the comparative effects of competence and warmth perception exist when artificial autonomy contributes to users' continuance usage intention. Theoretically, this study increases our understanding of AI-enabled artificial autonomy in information systems research. These findings also provide insightful suggestions for practitioners regarding AI artifacts design.  相似文献   

5.
As mobile networks and devices being rapidly innovated, many new Internet services and applications have been deployed. However, the current implementation faces security, management, and performance issues, which are critical to the use in business environments. Migrating sensitive information, management facilities, and intensive computation to security hardened virtualized environment in the cloud provides effective solutions. This paper proposes an innovative Internet service and business model to provide a secure and consolidated environment for enterprise mobile information management based on the infrastructure of cloud-based virtual phones (CVP). Our proposed solution enables the users to execute Android and web applications in the cloud and connect to other users of CVP with enhanced performance and protected privacy. The organization of CVP can be mixed with centralized control and distributed protocols, which emulates the behavior of human societies. This minimizes the need to handle sensitive data in mobile devices, eases the management of data, and reduces the overhead of mobile application deployment.  相似文献   

6.
User-created automation applets to connect IoT devices and applications have become popular and widely available. Exploring those applets enables us to grasp the patterns of how users are utilizing and maximizing the power of connection by themselves, which can deliver practical implications for IoT service design. This study builds an IoT application network with the data of the IFTTT(if this then that) platform which is the most popular platform for self-automation of IoT services. The trigger-action relationships of the IFTTT applets currently activated are collected and used to construct an IoT application network whose nodes are IoT service channels, and links represent their connections. The constructed IoT network is then embedded by the node2vec technique, an algorithmic framework for representational learning of nodes in networks. Clustering the embedded nodes produces the four clusters of IoT usage patterns: Smart Home, Activity Tracking, Information Digest, and Lifelogging & Sharing. We also predict the IoT application network using node2vec-based link prediction with several machine learning classifiers to identify promising connections between IoT applications. Feasible service scenarios are then generated from predicted links between IoT applications. The findings and the proposed approach can offer IoT service providers practical implications for enhancing user experiences and developing new services.  相似文献   

7.
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   

8.
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   

9.
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   

10.
As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges for computer scientists, fashion studies have received increasing attention in computer vision, machine learning and multimedia communities in the past few years. In this paper, introduce the progress in fashion research and provide a taxonomy of these fashion studies that include low-level fashion recognition, middle-level fashion understanding and high-level fashion applications. Finally, we discuss the challenges that when the fashion industry faces AI technologies.  相似文献   

11.
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   

12.
Conventional grant-based random access scheme is inappropriate to massive Internet of Things (IoT) connectivity since massive devices results in large number of collisions. This is unacceptable for the low latency requirement in 5 G and future networks. It is also not possible to assign orthogonal pilot sequences to all users to perform user activity detection (UAD) due to the massive number of devices and limited channel coherence time. In this paper, a novel grant-free (GF) UAD scheme is proposed with extremely low complexity and latency in an IoT network with a massive number of users. We exploit multiple antennas at the base station (BS) to produce spatial filtering by a fixed beamforming network (FBN), there then the inter-beam interference can be mitigated. Moreover, intra-beam interference is removed in temporal domain by orthogonal multiple access (OMA) technology. Joint UAD and multiuser detection (MUD) is realized by a bank of spatial-temporal matched filters at BS. The proposed method is efficient and the complexity is much less than the existing compressed sensing (CS)-based GF non-orthogonal multiple access (GFNOMA) algorithms. Performances of the proposed method is extensively analyzed in terms of the successful activity detection rate (SADR) as well as the Receiver operating characteristic (ROC) based on Neyman-Pearson (NP) decision rule. Numerical results demonstrate that it is comparable to the recently proposed iterative Maximum Likelihood (ML) algorithm, yet the computation load of the proposed scheme is extensively reduced.  相似文献   

13.
随着云计算、物联网、大数据、移动互联、人工智能、增强现实等新技术的不断涌现,数据量呈指数级增长,催生了新的业务——数据分析。从数据的分类、标准化、质量管理等方面入手,提出基于结合生命周期(lifecycle)、需求层次(need hierarchy)和大数据(big data)技术3种方法的数据(资源)管理三维度的LNB管理方法,探讨数据分析的维度、步骤、工具,及其在现代科技管理中所起关键作用。  相似文献   

14.
Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google. Some of the most prominent and commonly used FL-powered applications are Android’s Gboard for predictive text and Google Assistant. FL can be defined as a setting that makes on-device, collaborative Machine Learning possible. A wide range of literature has studied FL technical considerations, frameworks, and limitations with several works presenting a survey of the prominent literature on FL. However, prior surveys have focused on technical considerations and challenges of FL, and there has been a limitation in more recent work that presents a comprehensive overview of the status and future trends of FL in applications and markets. In this survey, we introduce the basic fundamentals of FL, describing its underlying technologies, architectures, system challenges, and privacy-preserving methods. More importantly, the contribution of this work is in scoping a wide variety of FL current applications and future trends in technology and markets today. We present a classification and clustering of literature progress in FL in application to technologies including Artificial Intelligence, Internet of Things, blockchain, Natural Language Processing, autonomous vehicles, and resource allocation, as well as in application to market use cases in domains of Data Science, healthcare, education, and industry. We discuss future open directions and challenges in FL within recommendation engines, autonomous vehicles, IoT, battery management, privacy, fairness, personalization, and the role of FL for governments and public sectors. By presenting a comprehensive review of the status and prospects of FL, this work serves as a reference point for researchers and practitioners to explore FL applications under a wide range of domains.  相似文献   

15.
In recent years, the United Arab Emirates (UAE) has been a leader in the global adoption of AI and online schooling. Despite using the traditional educational structure, military colleges have embraced this new technology. This research analyzed the present adoption rate, difficulties, and solutions for implementing an AI-based online education system. The results demonstrate that digital technology has a tangible impact on all facets of higher education if it is supported by the institution. The results also indicate that the organization plays a crucial role in the integration of digital technology into teaching and learning, and that an examination of the materiality already present in the Collaborative Technical Education (CTE) organization is necessary for comprehending the potential effects of new digital technology. Finally, this paper addressed how AI can make e-learning more interesting, efficient, and tailored to each individual learner, all of which contribute to better learning outcomes and wider access to technical education. The details of how artificial intelligence can be used to promote diversity and equity in the classroom are laid out. To motivate educators to create mixed-reality artifacts and conduct further research to support collaborative educational environments, this article discusses current works and visualizes the current state of the field.  相似文献   

16.
The development of Management Information Systems (MIS) is impossible without the use of machine learning (ML). It's a type of Artificial Intelligence (AI) that makes predictions using statistical models. When it comes to financial analysis, there are numerous risk-related concerns to contend with today (FI). In the financial sector, machine learning algorithms are used to detect fraud, automate trading, and provide financial advice to investors. To better serve its customers, the financial sector can now save borrower data according to specific criteria thanks to MIS. In fact, there is a large amount of data about debtors, making load management a difficult task. ML can examine millions of data sets in a short period of time without being explicitly programmed to improve the results. This type of algorithm can aid financial institutions in making grant selections for their clients. For the objective of classifying FI in terms of fraud or not, the Intelligent Information System for Financial Institutions (IISFI) relying on Supervised ML (SML) Algorithms has been created in this work. Bayesian Belief Network, Neural Network, Decision trees, Naïve Bayes, and Nearest Neighbor has been compared for the purpose of classifying FI risks using the performance measures asfalse positive rate, true positive rate, true negative rate, false negative rate, accuracy, F-Measure, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Med AE, Receiver Operating Characteristic (ROC) area,Precision Recall Characteristic (PRC) area, and measures of PC.  相似文献   

17.
The Internet of Things (IoT) has sparked a revolution in the manufacturing sector, providing numerous advantages to companies that adopt it. Using IoT, factories can boost productivity, cut expenses, and develop a more sustainable business model. The rise of digital networking and real-time communication are compelling manufacturers to adopt cutting-edge technologies in order to compete in today's fast-paced, international marketplace. The Internet of Things (IoT) to facilitate the virtualization of manufacturing processes and the gathering of real-time data to guarantee seamless supply chain operations. There has been abductive qualitative research done. Case studies of the heavy-duty vehicle sector provided empirical data, while a review of the relevant literature provided the theoretical underpinnings. Information system issues and people and structure issues were cited as barriers to analytics adoption. In this study works on challenges and security of manufacturing. Finally, suitable themes for analysis have been derived using a thematic analysis. The results show that manufacturing firms can benefit from analytics solutions for production activities even if they are not highly automated or complicated. The Internet of Things (IoT) offers numerous opportunities for growth in the business models of manufacturing companies. Businesses can boost efficiency, cut expenses, and develop a more robust business model by implementing IoT. Successfully integrating IoT, however, calls for meticulous preparation and execution.  相似文献   

18.
国外农业物联网技术发展及对我国的启示   总被引：5，自引：0，他引：5       下载免费PDF全文
随着现代信息技术的发展,物联网已广泛应用于农业生产的各个领域。国际上,一些国家在农业物联网感知技术、数据传输技术、智能处理技术等方面取得了重要的进展,出现了物联网在农业领域的典型应用。这些技术进展和相关应用对我国农业物联网的快速发展具有重要的借鉴意义。文章通过对国外农业物联网技术最新进展的调研和分析,描述了农业物联网在感知技术、数据传输技术、智能处理技术等方面的国际先进经验,例举了国外农业物联网在农业资源监测和利用、农业生态环境监测、农业生产精细管理、农产品安全溯源、农业物联网云服务等领域的典型应用,并针对我国农业物联网技术的发展,在发展微型化传感器、寻求系统节能策略、努力降低传感器成本、传感器网络安全性和抗干扰问题、节点自动配置问题等方面提出了建设性意见。  相似文献   

19.
《Journal of The Franklin Institute》2021,358(18):10232-10249
In this paper, we investigate the physical-layer security for an Internet of Things (IoT) relaying network employing non-orthogonal multiple access (NOMA) in the context of non-linear energy harvesting. In particular, a power-constrained source transmits its confidential information to a destination via an IoT device, which first decodes the received signal and then forwards the decoded data together with its own information using the NOMA technique to their respective destinations, while an eavesdropper is overhearing this transmission. Considering the scenario that both the power-constrained source and the IoT device are assumed to work under non-linear energy harvesting modes, and are capable of harvesting energy from a power beacon with a time switching protocol, both analytical and asymptotic expressions for secrecy outage probability as well as the analytical expression for the probability of strictly positive secrecy capacity are derived. In addition, those expressions are also verified with Monte-Carlo simulations.  相似文献   

20.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.  相似文献   

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