Network Economics and Utility Maximization

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 12873

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Interests: network modeling and optimization; IoT; cyber–physical systems; smart grid systems; network economics; wireless networks; social networks; cybersecurity; resource management; reinforcement learning; human behavior modeling; concentrated solar power systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece
Interests: complex networks; wireless systems; ad hoc and sensor networks; software-defined radios and software-defined networks; online social networks; network modeling and optimization; network economics; cyber–physical systems; internet of things; future internet research experimentation; resource orchestration; 5G/6G system design; system sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rise of 5G communications and the evolution of the Internet of Things era, mobile communications and computing solutions are tightly coupled and jointly studied with economic considerations. Any technology choice, wireless protocol selection, network architecture proposal, and virtualization approach should be made based on the understanding of the economic implications and the potential costs and benefits, both from the provider and the end-user perspective. Various network economics-based solutions have been identified in the literature and have already been applied in real-communication networking scenarios, as well as other interdependent systems. Usage-based pricing, inventive mechanisms, auction mechanisms, smart data pricing, and service providers’ reputation and trust models are only some indicative examples. The utmost goals of the network economics mechanisms are to improve resource management and decision-making processes, provide better services to the end-users via exploiting the available system resources, improve the fairness among the end-users, support the free-market operation among the service providers, enhance the available bandwidth usage, and provide energy-efficient solutions.

The concept of network economics is also tightly coupled with the Network Utility Maximization (NUM) theory, which aims to improve the end-users’ and the providers’ benefits from co-existing in the network by maximizing their perceived utility. Centralized methods have been identified as potential solutions, based on optimization techniques (convex and non-convex optimization), where the decision-making process lies on a centralized entity that has the global view of the network, such as software-defined networking (SDN). Decentralized methods have also been proposed based on game theory, reinforcement learning, and deep learning approaches, allowing the end-users to make optimal decisions about their strategies in the network based on limited information exchange.

Traffic on the Internet and wireless networks is continuously rising, with multimedia data being the dominant source. The explosive growth of ultra-high definition video services and bandwidth-expensive applications, create interesting challenges in content delivery networks (CDN), and storage/cloud costs. In this setting, the emerging tradeoffs between the quality of experience (QoE) and associated costs, for network, internet service, and cloud computing providers, motivate economic resource utilization approaches, and revenue management for all involved actors and players.

This Special Issue aims to present the advances in the research on network economics and utility maximization. We seek submissions that demonstrate how network economics and utility maximization can solve current problems in 5G networks and beyond, as well as challenges that arise in the Internet of Things era and emerging variations of the cloud computing paradigm. Topics of interest are any aspects of network economics and utility maximization, including but not limited to the following:

  • Energy-efficient networks and services
  • Foundations for economic multimedia communication
  • Network slicing design and optimization in 5G networks
  • Economic modeling of multimedia transmission over heterogeneous networks
  • Software-defined networking (SDN) architectures and networks
  • Software-defined networking (SDN) and network functions virtualization (NFV)
  • Future of IoT networks and cloud computing
  • Network economics for safe and smart cities
  • Network economics for content distribution networks (CDNs)
  • Metrics and models for quality of experience (QoE)
  • Smart data pricing
  • Quality of service in wireless and mobile networks
  • Cognitive network architectures
  • Smart grids communications and demand response techniques
  • Machine-learning and artificial intelligence for traffic/QoE management
  • Advanced content caching and dissemination techniques
  • Blockchain-based decentralization schemes for IoT
  • Mobile edge and fog computing systems
  • Vehicular cloud networks
  • Security, reliability, and trust in networks
  • Crowdsensing and crowdsourcing systems
  • Internet of Things (IoT) and Big Data applications
  • IoT platforms, integration, and services
  • Cyber-physical systems interacting with 5G networks and IoT

Dr. Eirini Eleni Tsiropoulou
Prof. Dr. Symeon Papavassiliou
Guest Editors

Manuscript Submission Information

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Keywords

  • Network economics
  • Network utility maximization
  • Internet of Things (IoT)
  • 5G communications
  • Multimedia communications
  • Software-defined networks (SDN)
  • Quality of experience
  • Smart cities
  • Mobile edge computing
  • Cyber-physical systems
  • Game theory
  • Blockchain
  • Tactile internet
  • Reinforcement learning

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Published Papers (3 papers)

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Research

17 pages, 504 KiB  
Article
User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
by Jie Zhao and Can Yan
Future Internet 2020, 12(12), 209; https://doi.org/10.3390/fi12120209 - 26 Nov 2020
Cited by 6 | Viewed by 3745
Abstract
It is of great significance for enterprises’ development to effectively use mobile Internet to carry out information feed advertising. This paper aims to study the influence factors and effect of the users’ acceptance intention of information feed advertising through empirical analysis to provide [...] Read more.
It is of great significance for enterprises’ development to effectively use mobile Internet to carry out information feed advertising. This paper aims to study the influence factors and effect of the users’ acceptance intention of information feed advertising through empirical analysis to provide references for further optimizing information feed advertising strategy. Traditional quantitative analysis methods, such as the Structural Equation Model (SEM), can only measure a single factor’s influence from an individual perspective. Therefore, we introduce the Qualitative Comparative Analysis (QCA) and present a two-stage hybrid method based on SEM and QCA. In the first stage, we analyze the influence of a single variable on the acceptance intention of information feed advertising by SEM. Then, in the second stage, we analyze the impact of different variable combinations by QCA. Based on the actual questionnaire data, we define six independent variables and use AMOS, SPSS, and fsQCA to carry out SEM analysis and the fuzzy-set-based QCA analysis, respectively. The SEM analysis results show that the four factors (including consistency, informativeness, sociability, and advertising reward) have a significant positive impact on user acceptance of information feed advertising. On the contrary, perceived advertising clustering has a significant negative impact. In addition, accuracy has no considerable effect. The QCA analysis reveals that seven constructs of six variables can all significantly enhance information feed ads’ acceptance intention. Full article
(This article belongs to the Special Issue Network Economics and Utility Maximization)
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17 pages, 380 KiB  
Article
Validating the Adoption of Heterogeneous Internet of Things with Blockchain
by Lulwah AlSuwaidan and Nuha Almegren
Future Internet 2020, 12(6), 107; https://doi.org/10.3390/fi12060107 - 21 Jun 2020
Cited by 16 | Viewed by 4062
Abstract
Emerging technologies such as Internet of Things (IoT) and blockchain have affected the digital transformation. Blockchain, on the one hand, was initially developed for the purpose of financial trading due to its robustness especially for fault tolerance and cryptographic security in addition to [...] Read more.
Emerging technologies such as Internet of Things (IoT) and blockchain have affected the digital transformation. Blockchain, on the one hand, was initially developed for the purpose of financial trading due to its robustness especially for fault tolerance and cryptographic security in addition to its decentralized architecture. IoT, on the other hand, is an open interconnected network of smart devices able to communicate simultaneously. This arises a challenge in privacy and security, specifically for the data being exchanged. To overcome this, studies have focused on the blockchain to resolve the security and privacy issues of IoT. Indeed, limited studies have proposed to assess blockchain’s viability for IoT and the associated challenges. In this paper, a conceptual model has proposed to identify the crucial factors affecting the adoption of blockchain in IoT. The model consists of four dimensions of factors that we assume will affect the adoption of the two technologies. The dimensions are: attitude-related factors, social influence related factors, data-related factors, and security-related factors. This model is validated through a survey that was distributed between professionals in blockchain and IoT. The findings show a significant impact of data-related factors on the adoption of blockchain in IoT and the intention to use them. The model can play an important role in the development of strategies, standards, and performance assessment. Full article
(This article belongs to the Special Issue Network Economics and Utility Maximization)
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13 pages, 548 KiB  
Article
Research on Factors Affecting Solvers’ Participation Time in Online Crowdsourcing Contests
by Keng Yang
Future Internet 2019, 11(8), 176; https://doi.org/10.3390/fi11080176 - 12 Aug 2019
Cited by 9 | Viewed by 3946
Abstract
A crowdsourcing contest is one of the most popular modes of crowdsourcing and is also an important tool for an enterprise to implement open innovation. The solvers’ active participation is one of the major reasons for the success of crowdsourcing contests. Research on [...] Read more.
A crowdsourcing contest is one of the most popular modes of crowdsourcing and is also an important tool for an enterprise to implement open innovation. The solvers’ active participation is one of the major reasons for the success of crowdsourcing contests. Research on solvers’ participation behavior is helpful in understanding the sustainability and incentives of solvers’ participation in the online crowdsourcing platform. So, how to attract more solvers to participate and put in more effort is the focus of researchers. In this regard, previous studies mainly used the submission quantity to measure solvers’ participation behavior and lacked an effective measure on the degree of participation effort expended by a solver. For the first time, we use solvers’ participation time as a dependent variable to measure their effort in a crowdsourcing contest. Thus, we incorporate participation time into the solver’s participation research. With the data from Taskcn.com, we analyze how participation time is affected four key factors including task design, task description, task process, and environment, respectively. We found that, first, for task design, higher task rewards will attract solvers to invest more time in the participation process and the relationship between participation time and task duration is inverted U-shaped. Second, for task description, the length of the task description has a negative impact on participation time and the task description attachment will positively influence the participation time. Third, for the task process, communication and supplementary explanations in a crowdsourcing process positively affect participation time. Fourth, for environmental factors, the task density of the crowdsourcing platform and the market price of all crowdsourcing contests have respectively negative and positive effects on participation time. Full article
(This article belongs to the Special Issue Network Economics and Utility Maximization)
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