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The Dark Crypto World! IEEE Internet Comput. (IF 3.2) Pub Date : 2024-03-07 Muhammad Abulaish, Harshita Dalal
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Hierarchical Network Data Analytics Framework for 6G Network Automation: Design and Implementation IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-27 Youbin Jeon, Sangheon Pack
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Remote Learning and Work IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 René Kizilcec, John Mitchell
Drawing on platforms and methods that had been developed but not widely adopted, the COVID-19 pandemic suddenly forced remote learning and remote work worldwide. While the first few months were too rushed to provide time for iteration or reflection, continuing years gave educators, students, employers, and workers an unprecedented opportunity to explore and find value in remote operation. Recognizing
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Data Management Challenges in Blockchain-Based Applications IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 Stanly Wilson, Kwabena Adu-Duodu, Yinhao Li, Ellis Solaiman, Omer Rana, Schahram Dustdar, Rajiv Ranjan
Effective data management is crucial to ensure the security, integrity, and efficiency of blockchain systems. This study proposes a detailed data management taxonomy specifically designed for blockchain technology. The taxonomy provides a structured framework to categorize and address various aspects of data management in blockchain networks. It covers essential aspects, such as data flow, data storage
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Call for Papers: IEEE Internet Computing Special Issue on Civilizing and Humanizing AI IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16
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A Grateful Farewell and a Warm Welcome IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 George Pallis
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The EMPWR Platform: Data and Knowledge-Driven Processes for the Knowledge Graph Lifecycle IEEE Internet Comput. (IF 3.2) Pub Date : 2024-02-16 Hong Yung Yip, Amit Sheth
The unparalleled volume of data generated has heightened the need for approaches that can consume these data in a scalable and automated fashion. Although modern data-driven, deep-learning-based systems are cost-efficient and can learn complex patterns, they are black boxes in nature, and the underlying input data highly dictate their world model. Knowledge graphs (KGs), as one such technology, have
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Enabling 6G and Beyond Network Functions from Space: Challenges and Opportunities IEEE Internet Comput. (IF 3.2) Pub Date : 2024-01-30 Lixin Liu, Wei Liu, Yuanjie Li, Hewu Li
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Privacy-Preserving Recommendation based on Shuffled Federated Graph Neural Network IEEE Internet Comput. (IF 3.2) Pub Date : 2024-01-05 Qinbo Liu, Lichen Yang, Yang Liu, Jiaqi Deng, Guorui Wu
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3-D Point Cloud Map Compression for Connected Intelligent Vehicles IEEE Internet Comput. (IF 3.2) Pub Date : 2023-12-15 Youngjoon Choi, Hannah Baek, Jinseop Jeong, Kanghee Kim
In autonomous vehicles, 3-D point cloud (PCD) maps are widely employed. By matching a point cloud acquired from a 3-D ranging sensor in real time with the PCD map, the ego vehicle can be localized with high accuracy. However, the PCD maps must be compressed and customized to the vehicles because they typically have low computing power, a small memory space, and low-resolution sensors. In this study
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Adapting to Online and Remote Learning: Examining the Educational Assessment Experiences of U.S. College Students Amidst COVID-19 IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-28 Teresa M. Ober, Ying Cheng
We administered a survey to examine the impact of the COVID-19 pandemic on academic assessment in a sample of 992 U.S. college students (mean age = 22.36 years) between February and June 2021. The survey included multiple-choice and open-ended questions asking about students’ experiences before (fall 2019 to early spring 2020) and during the pandemic-affected periods (late spring 2020 to spring 2021)
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Are Remote Educational Escape Rooms Designed During the Pandemic Useful in a Postpandemic Face-to-Face Setting? IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-23 Daniel López-Fernández, Aldo Gordillo, Sonsoles López-Pernas, Edmundo Tovar
Numerous initiatives were conducted online during the COVID-19 pandemic, and today it is necessary to analyze whether it is better to continue conducting these initiatives online, or should they be done face-to-face and even readapted to this format. This article compares an educational escape room for learning software engineering conducted online during the confinement caused by the pandemic and
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Trustworthy AI and Data Lineage IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-17 Elisa Bertino, Suparna Bhattacharya, Elena Ferrari, Dejan Milojicic
AI trustworthiness properties are at the top of concerns for industry, governments, and academia. However, the AI and its models are only as good as the data used to train it. Data lineage could be tracked in many ways, including using metadata, from its generation usage, deployment, and verification. New standards, blueprints, best practices, and repositories for data are required to address requirements
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Understanding Responsible Computing via Project Management for Sustainability IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-17 Hoa Khanh Dam, Aditya Ghose, Nigel Gilbert, Munindar P. Singh
Everyone acknowledges the importance of responsible computing, but practical advice is hard to come by. Important Internet applications are ways to accomplish business processes. We investigate how they can be geared to support responsibility as illustrated via sustainability. Sustainability is not only urgent and essential but also challenging due to engagement with human and societal concerns, diverse
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Addressing the Faults Landscape in the Internet of Things: Toward Datacentric and System Resilience IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-17 Sultan Altarrazi, Tomasz Szydlo, Schahram Dustdar, Satish Narayana Srirama, Rajiv Ranjan
In the Internet of Things (IoT) context, the landscape of weaknesses in the IoT spectrum sheds light on addressing faults by researchers due to the number of IoT components that unveil immense vulnerabilities to failures. Hence, there is a need to comprehend the faults dynamics to facilitate identifying potential hazards in a developer’s design, deliver methodologies to mitigate the risks, and ensure
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Digital Transformation in Remote Learning and Work—An Externality of the COVID-19 Pandemic IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-21 Kevin K.W. Ho, Shaoyu Ye, Dickson K.W. Chiu, Takuya Sekiguchi
The COVID-19 pandemic changed the world in multiple ways. The global lockdown forced people to adopt remote learning and work, creating many examples of different organizations (government agencies, businesses, and educational institutions) adapting to this new paradigm through digital transformation. In this article, we review how educational institutions have digitally transformed teaching and learning
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Empowering Database Learning Through Remote Educational Escape Rooms IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-21 Enrique Barra, Sonsoles López-Pernas, Aldo Gordillo, Alejandro Pozo, Andres Muñoz-Arcentales, Javier Conde
Learning about databases is indispensable for individuals studying software engineering or computer science or those involved in the IT industry. We analyzed a remote educational escape room for teaching about databases in four different higher education courses in two consecutive academic years. We employed three instruments for evaluation: a pre- and posttest to assess the escape room’s effectiveness
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Remote Work and Gender Inequality: Unmasking the Challenges and Seeking Solutions IEEE Internet Comput. (IF 3.2) Pub Date : 2023-11-21 Hem Chandra Joshi, Sandeep Kumar
The COVID-19 pandemic has transformed the way we work, especially in the domain of remote work (RW). It compelled employees and organizations to embrace RW arrangements, which were previously voluntary, part time, or occasional, as a means to curtail the spread of the virus and minimize business disruptions. RW has introduced a flexible arrangement and other benefits but has also brought forth numerous
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A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning IEEE Internet Comput. (IF 3.2) Pub Date : 2023-10-06 Guanqin Zhang, Jiankun Sun, Feng Xu, Yulei Sui, H.M.N. Dilum Bandara, Shiping Chen, Tim Menzies
Deep neural networks (DNNs) have widespread applications in industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, previous work has shown that the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations)
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Rethinking Certification for Trustworthy Machine-Learning-Based Applications IEEE Internet Comput. (IF 3.2) Pub Date : 2023-10-06 Marco Anisetti, Claudio A. Ardagna, Nicola Bena, Ernesto Damiani
Machine learning (ML) is increasingly used to implement advanced applications with nondeterministic behavior, which operate on the cloud–edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions to assess applications’ nonfunctional properties (e.g., fairness, robustness, and privacy) with the aim of improving their trustworthiness. Certification has been clearly identified
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Balancing Trustworthiness and Efficiency in Artificial Intelligence Systems: An Analysis of Tradeoffs and Strategies IEEE Internet Comput. (IF 3.2) Pub Date : 2023-08-24 Yifei Wang
As artificial intelligence (AI) systems become more prevalent in various domains, ensuring their trustworthiness and efficiency becomes increasingly important. This article presents an analysis of the tradeoffs between different dimensions of trustworthy AI, including transparency, robustness, fairness, accountability, efficiency, privacy, and human interaction. The challenges and opportunities that
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Connected Living—Part II IEEE Internet Comput. (IF 3.2) Pub Date : 2023-07-17 Ye Sun, Weisong Shi
With the recent remarkable advancements in the Internet of Things, wireless communication, edge/cloud computing, and AI, the connection between human beings and the surrounding environments is dramatically increased, entering a new era of connected living. New challenges and opportunities are also brought up in the complex interaction between human beings and technologies. The focus of this special
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Edge Intelligence—Research Opportunities for Distributed Computing Continuum Systems IEEE Internet Comput. (IF 3.2) Pub Date : 2023-07-17 Victor Casamayor Pujol, Praveen Kumar Donta, Andrea Morichetta, Ilir Murturi, Schahram Dustdar
Edge intelligence and, by extension, any distributed computing continuum system will bring to our future society a plethora of new and useful applications, which will certainly revolutionize our way of living. Nevertheless, managing these systems challenges all previously developed technologies for Internet-distributed systems. In this regard, this article presents a set of techniques and concepts
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Can We Explain Privacy? IEEE Internet Comput. (IF 3.2) Pub Date : 2023-07-17 Gönül Aycı, Arzucan Özgür, Murat Şensoy, Pınar Yolum
Web users want to protect their privacy while sharing content online. This can be done through automated privacy assistants that are capable of taking actions by detecting privacy violations and recommending privacy settings for content that the user intends to share. While these approaches are promising in terms of the accuracy of their privacy decisions, they lack the ability to explain to the end
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Measuring the Energy of Smartphone Communications in the Edge-Cloud Continuum: Approaches, Challenges, and a Case Study IEEE Internet Comput. (IF 3.2) Pub Date : 2023-05-24 Chiara Caiazza, Valerio Luconi, Alessio Vecchio
As computational resources are placed at different points in the edge-cloud continuum, not only is the responsiveness on the client side affected, so too is the amount of energy spent during communications. We summarize the main approaches used to estimate the energy consumption of smartphones and the main difficulties typically encountered. A case study then shows how such approaches can be put into
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Connected Living—Part I IEEE Internet Comput. (IF 3.2) Pub Date : 2023-05-10 Ye Sun, Weisong Shi
With the recent, remarkable advancements in the Internet of Things, wireless communication, edge/cloud computing, and artificial intelligence, the connection between human beings and their surrounding environments has dramatically increased, entering a new era of connected living. New challenges and opportunities are also brought up in the complex interaction between human beings and technologies.
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FlyNet: Drones on the Horizon IEEE Internet Comput. (IF 3.2) Pub Date : 2023-05-10 Alicia Esquivel Morel, Chengyi Qu, Prasad Calyam, Cong Wang, Komal Thareja, Anirban Mandal, Eric Lyons, Michael Zink, George Papadimitriou, Ewa Deelman
Over the past few years, due to the boom of advances in image processing, edge computing, and wireless networking, unpiloted aerial vehicles, often referred to as drones, have become an important enabler to support a wide variety of scientific applications, ranging from environmental monitoring, disaster response, and wildfire monitoring to the survey of archaeological sites. In this article, we present
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SAV-D: Defending DDoS with Incremental Deployment of SAV IEEE Internet Comput. (IF 3.2) Pub Date : 2023-05-10 Linbo Hui, Lei Zhang, Yannan Hu, Jianping Wu, Yong Cui
Large-scale Internet Protocol (IP) spoofing distributed denial-of-service (DDoS) attacks is one of the major cyber threats. Current commercial defenses focus on eliminating attacks at the destination end, which raises concerns about the cost of appliances and the impact on quality of service. As complementaries, source-end schemes using source address validation (SAV) can block IP spoofing traffic
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Serverless Edge Computing—Where We Are and What Lies Ahead IEEE Internet Comput. (IF 3.2) Pub Date : 2023-05-10 Philipp Raith, Stefan Nastic, Schahram Dustdar
The edge–cloud continuum combines heterogeneous resources, which are complex to manage. Serverless edge computing is a suitable candidate to manage the continuum by abstracting away the underlying infrastructure, improving developers’ experiences, and optimizing overall resource utilization. However, understanding and overcoming programming support, reliability, and performance engineering challenges
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Serverless Vehicular Edge Computing for the Internet of Vehicles IEEE Internet Comput. (IF 3.2) Pub Date : 2023-05-01 Faisal Alam, Adel N. Toosi, Muhammad Aamir Cheema, Claudio Cicconetti, Pablo Serrano, Alexandru Iosup, Zahir Tari, Majid Sarvi
Rapid growth in the popularity of smart vehicles and increasing demand for vehicle autonomy brings new opportunities for vehicular edge computing (VEC). VEC aims at offloading the time-sensitive computational load of connected vehicles to edge devices, e.g., roadside units. However, VEC offloading raises complex resource management challenges and, thus, remains largely inaccessible to automotive companies
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Optimizing Drone Delivery in Smart Cities IEEE Internet Comput. (IF 3.2) Pub Date : 2023-04-19 Babar Shahzaad, Balsam Alkouz, Jermaine Janszen, Athman Bouguettaya
We propose a novel context-aware drone delivery framework for optimizing package delivery through skyway networks in smart cities. We reformulate the problem of finding an optimal drone service delivery pathway as a more congruent and elegant drone delivery service composition problem. In this respect, we propose a novel line-of-sight heuristic-based context-aware composition algorithm that selects
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Enhancing Winter Road Maintenance via Cloud Computing IEEE Internet Comput. (IF 3.2) Pub Date : 2023-04-10 Mohammad Hossein Tavakoli Dastjerdi, Zhen Liu, Behnam Azmoon, Xiaoyong Yuan
In this article, we develop an AI-enhanced cloud computing framework to enable the autonomous decision-making quality and precision of winter road operations for connected living, namely, the Smart Maintenance Decision Support System (SmartMDSS). With support from the Federal Highway Administration and Michigan Department of Transportation, we are among the first to develop AI-enabled tools for practical
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Federated Learning for Network Intrusion Detection in Ambient Assisted Living Environments IEEE Internet Comput. (IF 3.2) Pub Date : 2023-04-05 Ana Cholakoska, Hristijan Gjoreski, Valentin Rakovic, Daniel Denkovski, Marija Kalendar, Bjarne Pfitzner, Bert Arnrich
Given the Internet of Things’ rapid expansion and widespread adoption, it is of great concern to establish secure interaction between devices without worsening the quality of their performance. The use of machine learning techniques has been shown to improve detection of anomalous behavior in these types of networks, but their implementation leads to poor performance and compromised privacy. To better
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User-Centric Federated Matrix Factorization Based on Differential Privacy IEEE Internet Comput. (IF 3.2) Pub Date : 2023-04-03 Yang Liu, Wanyin Xu, Jiaxin Lai, Jiabo Wang
Matrix factorization is a popular recommendation method used in many fields, but it often lacks sufficient privacy protection. To address privacy concerns in connected living, we propose a user-centric federated matrix factorization model based on differential privacy (FMF-DP). First, we design a federated matrix factorization framework where each user is a participant and only uploads their gradients
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Cyber Pandemics IEEE Internet Comput. (IF 3.2) Pub Date : 2023-03-22 Barbara Carminati, Leila Bahri
The focus of this special issue is on studying the consequences of pandemics and cyber pandemics on privacy and trust both in the digital and the real worlds. In the aftermath of the recent COVID-19 pandemic that has shaken several aspects of our lives for almost two years of time, preliminary research indicates that the technological capabilities and the data that have been deployed and exploited
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Toward an Ethical Framework for Smart Cities and the Internet of Things IEEE Internet Comput. (IF 3.2) Pub Date : 2023-03-22 Munindar P. Singh, Pradeep K. Murukannaiah
As smart cities increasingly become real, an ethical framework for them becomes increasingly necessary. Surprisingly, current approaches largely disregard such a framework and concentrate primarily on challenges pertaining to the data lifecycle. However, a smart city involves much more than data gathering: it involves the interactions of residents, businesses, and government agencies with respect to
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Healthful Connected Living: Vision and Challenges for the Case of Obesity IEEE Internet Comput. (IF 3.2) Pub Date : 2023-03-17 Munindar P. Singh, Min Chi, Veena Misra
We envision a new integrated suite of multimodal sensing and artificial intelligence techniques that can incorporate advances in health psychology to produce effective solutions for long-term healthful living. We discuss challenges and opportunities arising in realizing this vision.
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Model Ensemble for Predicting Heart and Respiration Rate From Speech IEEE Internet Comput. (IF 3.2) Pub Date : 2023-03-17 Stavros Ntalampiras
Stress levels are a significant source of information in assessing human well-being, including both mental and physical health. Interestingly, speech signals can be indicative of stress and may be used to infer related physiological markers, such as heart rate and respiration cycles. To this end, this article proposes a nonintrusive, low-cost, and automatic stress monitoring framework facilitating
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Applications of Knowledge Graphs in Telecommunication Systems Management IEEE Internet Comput. (IF 3.2) Pub Date : 2023-03-08 F. Javier Zorzano Mier, Carlos Á. Iglesias
Telecommunications systems management represents a technical challenge with profound economic implications. Many different sources of information must be integrated, and complex decisions must be automatically taken and executed. In this article, we review the usage of knowledge graphs in this domain since they are a powerful tool for dealing with data integration and complex knowledge representation
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Toward Building Edge Learning Pipelines IEEE Internet Comput. (IF 3.2) Pub Date : 2023-02-14 Anastasios Gounaris, Anna-Valentini Michailidou, Schahram Dustdar
From a bird's eye point of view, large-scale data analytics workflows, e.g., those executed in popular tools, such as Apache Spark and Flink, are typically represented by directed acyclic graphs. Also, they are in a large scale in two dimensions: first, they are capable of processing big data (e.g., both in terms of volume and velocity) mainly through employing massive parallelism, and second, they
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On the Development of AI Governance Frameworks IEEE Internet Comput. (IF 3.2) Pub Date : 2023-02-14 Virgílio Almeida, Laura Schertel Mendes, Danilo Doneda
There is growing demand to frame artificial intelligence (AI) regulations to minimize the risks to public safety and preserve human rights while at the same time enabling a flexible and innovative environment. This article explores new frameworks, institutional arrangements, and different types of regulation to devise and implement governance of artificial intelligence technologies, services, and devices
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Democratic Wireless Channel Assignment: Fair Resource Allocation in Wi-Fi Networks IEEE Internet Comput. (IF 3.2) Pub Date : 2023-02-14 Ivan Marsa-Maestre, Jose Manuel Gimenez-Guzman, Marino Tejedor-Romero, Enrique de la Hoz, Pradeep Murukannaiah
User experience is the ultimate quality of service criterion for modern WLAN networks. However, network configuration approaches are mainly network-centric. We envision a paradigm shift, empowering users in network management. We study how automated negotiation and collective intelligence can support the democratic configuration of a wireless network, leveraging client and provider interests. This
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Knowledge Graph Empowered Machine Learning Pipelines for Improved Efficiency, Reusability, and Explainability IEEE Internet Comput. (IF 3.2) Pub Date : 2023-02-14 Revathy Venkataramanan, Aalap Tripathy, Martin Foltin, Hong Yung Yip, Annmary Justine, Amit Sheth
Artificial intelligence (AI) pipelines are complex, heavily parameterized, and expensive to execute in terms of time and computational resources. Consequently, it is onerous to run experiments with all possible parameter combinations to achieve an optimal solution. However, these AI experiments can be optimized by recommending relevant parameters to commence the experiments, reducing search space significantly
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Service as a Commodity: Promoting Platform-Based Retailing of E-Services IEEE Internet Comput. (IF 3.2) Pub Date : 2023-02-01 Xinyue Zhou, Zhiyong Feng, Jianmao Xiao, Shizhan Chen, Xiao Xue, Hongyue Wu
With the comprehensive interconnection in human-cyber-physical systems, e-services are growing rapidly in platform retailing. E-services as the main body of transactions have begun to dominate the construction of retail platforms. In this article, we innovatively call such study views “Service as a Commodity (SaaC).” The variability of e-services and the dynamics of retail markets make it face challenges