New Challenges of Networking Technologies and IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 2885

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Department of Electrical Engineering, University of Moncton, Moncton, NB, Canada
Interests: diffractive elements; optical interconnection; waveguide therapy; optics and multimedia; optical fiber/wireless hybrid systems; optical networks; optical design; optical aberration; biomedical engineering; electronic network learning
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Special Issue Information

Dear Colleagues,

Traditionally, mobile data have referred to data from phones, tablets, access points, connected cars, etc. However, NarrowBand IoT (NB-IoT) presents a novelty. NB-IoT allows for low cost, low energy connections of mobile devices to an existing wireless infrastructure. You can take your Apple Watch for a run and maintain full connectivity while leaving your phone at home. In smart cities, traffic lights can react intelligently to the presence of connected cars in real time, or waste bins can even tell you when they are full without the need for a human to check. There are already devices on the market using similar technology, such as the Bigbelly solar compactor, Bluesmart suitcases, or the Whistle activity tracker for dogs. This enables a low-cost ecosystem of independently connected devices. As this technology matures, we will see a whole range of devices becoming connected, from smartwatches to cameras, activity trackers, and more. The richness of IoT that has become commonplace in the home will soon be just as ubiquitous in the mobile world as well.

In addition to a more robust network capacity and an increasing number of connected devices, changing how these systems are maintained will have a dramatic effect on our mobile experience as well. Future network users will have an increasing number of connected devices, which means that there will be even greater amounts of data from which to derive preferences and likely actions. Combine that with the capability to deliver enormous amounts of information with no perceptible delay and the user will experience nearly instantaneous responsiveness from their experiences.

As Artificial Intelligence, including machine learning and deep learning, become more prevalent in network operations, this technology will augment the capabilities of human engineers and help them to perform their jobs better and on a larger scale. Artificial Intelligence can observe how engineers identify and solve problems and can learn how to spot potential service issues before they develop. Artificial Intelligence can also alert engineers to impending problems with a range of viable solutions to select from.

Topics of interest for submission include but are not limited to the following:

  • Internetworking
    • Next-generation networks;
    • Networking technologies;
    • Network architectures;
    • High-speed networks;
    • Routing, switching, and addressing techniques;
    • State-of-the-art of network operations and management;
    • Network performance, QoS, and resource management;
    • Peer-to-peer and overlay networks.
  • Wireless Networks
    • Ad hoc and sensor networks;
    • WiFi and LiFi network technologies;
    • Ubiquitous networks;
    • Mobile networks and wireless networks;
    • Recent trends in WiMedia and multimedia networks.
  • Reconfigurable Networks
    • Heterogeneous networks;
    • Self-organizing/reconfigurable networks and networked systems;
    • Interconnection network on chip (NoC);
    • Software-defined networks (SDNs);
    • Virtualization and network traffic balancer;
    • Data center design;
    • Data center networks.
  • Ultrahigh-Speed Networks
    • Optical communications and networks;
    • Photonic networks;
    • Satellite and space communications;
    • ICT and intelligent transportation systems;
    • Internet of Things (IoT).
  • Intelligent and Advanced Systems
    • Next-generation Internet;
    • Next-generation web architectures;
    • Parallel and distributed computing networks;
    • Mobile/wireless computing;
    • Intelligent computing systems;
    • Cloud computing and Big Data analytics;
    • Real-time and embedded systems;
    • Modelling and simulation;
    • Network security;
    • Wireless network attacks;
    • Cloud security;
    • Cloud document integrity and privacy.
  • Intelligent Data Sensing
    • Intelligent data sensing and collection;
    • Intelligent data processing and mining;
    • Scalable data and resource management;
    • Mobile computing-based intelligent sensing and data collection, processing, mining, and communication of data;
    • Complexity of mobile computing of intelligent sensing and data collection and communication;
    • Mobile-computing-based intelligent sensing and data collection and communication in the cloud;
    • Large-scale data analysis in mobile computing-based intelligent sensing and data collection and communication;
    • Knowledge and service discovery in mobile computing-based sensing and data collection and communication with intelligence;
    • Business and societal applications of intelligent sensing and data collection and communication in mobile computing;
    • Big Data-based technologies and algorithms for data acquisition, processing, and mining;
    • Security and privacy issues;
    • IoT-based data sensing, processing, mining, and communication;
    • Intelligent integration and exploration of biomedical and industrial data;
    • Artificial Intelligence-driven analytics of data;
    • Data acquisition techniques (RFID, sensors, etc.);
    • Communication, networking, optimization, and performance measuring of trustworthy systems.
  • Artificial Intelligence
    • Machine learning;
    • Deep learning;
    • Smart cities;
    • Data mining.
  • New Network Protocols
    • Named data networking (NDN);
    • Recursive InterNetwork Architecture (RINA);
    • Enhanced IP;
    • Easy IP (EZIP).

Prof. Dr. Habib Hamam
Guest Editor

Manuscript Submission Information

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Keywords

  • internetworking
  • wireless networks
  • reconfigurable networks
  • ultrahigh-speed networks
  • intelligent and advanced systems
  • intelligent data sensing

Published Papers (1 paper)

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Research

20 pages, 1499 KiB  
Article
Human–Computer Interaction and Participation in Software Crowdsourcing
by Habib Ullah Khan, Farhad Ali, Yazeed Yasin Ghadi, Shah Nazir, Inam Ullah and Heba G. Mohamed
Electronics 2023, 12(4), 934; https://doi.org/10.3390/electronics12040934 - 13 Feb 2023
Cited by 2 | Viewed by 2398
Abstract
Improvements in communication and networking technologies have transformed people’s lives and organizations’ activities. Web 2.0 innovation has provided a variety of hybridized applications and tools that have changed enterprises’ functional and communication processes. People use numerous platforms to broaden their social contacts, select [...] Read more.
Improvements in communication and networking technologies have transformed people’s lives and organizations’ activities. Web 2.0 innovation has provided a variety of hybridized applications and tools that have changed enterprises’ functional and communication processes. People use numerous platforms to broaden their social contacts, select items, execute duties, and learn new things. Context: Crowdsourcing is an internet-enabled problem-solving strategy that utilizes human–computer interaction to leverage the expertise of people to achieve business goals. In crowdsourcing approaches, three main entities work in collaboration to solve various problems. These entities are requestors (job providers), platforms, and online users. Tasks are announced by requestors on crowdsourcing platforms, and online users, after passing initial screening, are allowed to work on these tasks. Crowds participate to achieve various rewards. Motivation: Crowdsourcing is gaining importance as an alternate outsourcing approach in the software engineering industry. Crowdsourcing application development involves complicated tasks that vary considerably from the micro-tasks available on platforms such as Amazon Mechanical Turk. To obtain the tangible opportunities of crowdsourcing in the realm of software development, corporations should first grasp how this technique works, what problems occur, and what factors might influence community involvement and co-creation. Online communities have become more popular recently with the rise in crowdsourcing platforms. These communities concentrate on specific problems and help people with solving and managing these problems. Objectives: We set three main goals to research crowd interaction: (1) find the appropriate characteristics of social crowd utilized for effective software crowdsourcing, (2) highlight the motivation of a crowd for virtual tasks, and (3) evaluate primary participation reasons by assessing various crowds using Fuzzy AHP and TOPSIS method. Conclusion: We developed a decision support system to examine the appropriate reasons of crowd participation in crowdsourcing. Rewards and employments were evaluated as the primary motives of crowds for accomplishing tasks on crowdsourcing platforms, knowledge sharing was evaluated as the third reason, ranking was the fourth, competency was the fifth, socialization was sixth, and source of inspiration was the seventh. Full article
(This article belongs to the Special Issue New Challenges of Networking Technologies and IoT)
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