Tactile Based Intelligence Touch Technology in IoT Configured WCN in B5G/6G-A Survey

Touch-enabled sensation and actuation are expected to be the most promising, straightforward, and important uses of the B5G/6G communication networks. In light of the next generation (6G) systems' prerequisite for low latency, the infrastructure should be reconfigurable, intelligent, and interoperable in the real-time existing wireless network. It has a drastic impact on society due to its high precision, accuracy, reliability, and efficiency, combined with the ability to connect a user from remote areas. Hence, the touch-enabled interaction is primarily concerned with the real-time transmission of tactile-based haptic information over the internet, in addition to the usual audio, visual, and data traffic, thus enabling a paradigm shift towards a real-time control and steering communication system. The existing system latency and overhead often have delays and limitations on the application's usability. In light of the aforementioned concerns, the study proposes an intelligent touch-enabled system for B5G/6G and an IoT-based wireless communication network, incorporating AR/VR technologies. The tactile internet and network-slicing serve as the backbone of touch technology and incorporates intelligence from techniques such as artificial intelligence and machine/deep learning. The survey also introduces a layered and interfacing architecture with its E2E solution for the intelligent touch-based wireless communication system. It is anticipated for the upcoming 6G system to provide numerous opportunities for various sectors to utilize AR/VR technology in robotics and healthcare facilities to help in addressing several problems faced by society. Conclusively the article presents a few use cases concerning the deployment of touch infrastructure in automation, robotics, and intelligent healthcare systems, assisting in the diagnosis and treatment of the prevailing Covid-19 cases. The paper concludes with some considerable future research aspects of the proposed system with a few ongoing projects concerning the development and incorporation of the 6G wireless communication system.

Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.

PRISC: Privacy-Preserved Pandemic Infection Risk Computation through Cellular Enabled IoT Devices

The pandemics such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this paper, we develop a novel privacy-preserved pandemic tracing system, PRISC, to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties: a mobile network operator, a social network provider, and the health department. The physical contact records between users are obtained by the mobile network operator from the users’cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for datasets with 100,000 users. IEEE

NATIVE: Network Aggregation based Tiled Live Video Streaming

The COVID-19 pandemic has forced most interactions to move to online space, starting from online lectures, conferences in hybrid mode, and work-from-home office works. Hence, it is essential for live video streaming to be reliable and provide a good quality of experience (QoE) to users. However, in large sections of the world, the cellular network is not reliable enough to be used for online participation in such events. To quantify this problem, we first measure the QoE in terms of lag, video resolution, and dropped calls on a popular video conferencing platform Google Meet over three different cellular ISPs in New Delhi, India. We observe significantly worse quality of experience metrics compared to a study recently performed in the US. To mitigate this problem, we propose NATIVE (Network Aggregation-based Tiled lIve Video strEaming), a system of aggregating the cellular network connectivity using a secondary or helper device in the vicinity trusted by the user. The implementation of NATIVE uses tiled encoding of video, where the video frames are divided into rectangular units known as tiles. All the tiles are divided into two subsets which are scheduled independently via the available network interfaces depending on its importance. The receiver device receives video segments from the two network paths and stitches back the tiles in them to play. We show the demo of NATIVE using two laptops and a cloud server where the server acts as a streamer. © 2023 IEEE.

Towards Future Internet: The Metaverse Perspective for Diverse Industrial Applications

The Metaverse allows the integration of physical and digital versions of users, processes, and environments where entities communicate, transact, and socialize. With the shift towards Extended Reality (XR) technologies, the Metaverse is envisioned to support a wide range of applicative verticals. It will support a seamless mix of physical and virtual worlds (realities) and, thus, will be a game changer for the Future Internet, built on the Semantic Web framework. The Metaverse will be ably assisted by the convergence of emerging wireless communication networks (such as Fifth-Generation and Beyond networks) or Sixth-Generation (6G) networks, Blockchain (BC), Web 3.0, Artificial Intelligence (AI), and Non-Fungible Tokens (NFTs). It has the potential for convergence in diverse industrial applications such as digital twins, telehealth care, connected vehicles, virtual education, social networks, and financial applications. Recent studies on the Metaverse have focused on explaining its key components, but a systematic study of the Metaverse in terms of industrial applications has not yet been performed. Owing to this gap, this survey presents the salient features and assistive Metaverse technologies. We discuss a high-level and generic Metaverse framework for modern industrial cyberspace and discuss the potential challenges and future directions of the Metaverse's realization. A case study on Metaverse-assisted Real Estate Management (REM) is presented, where the Metaverse governs a Buyer–Broker–Seller (BBS) architecture for land registrations. We discuss the performance evaluation of the current land registration ecosystem in terms of cost evaluation, trust probability, and mining cost on the BC network. The obtained results show the viability of the Metaverse in REM setups.

Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was "Networking Data and Intelligent Management in the Post-COVID19 Era.”. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Improving epidemic risk maps using mobility information from mobile network data

In this paper we propose a method for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We apply our methods to a dataset from more than 2 million cellphones, collected by a mobile network provider located in London, UK. The approach yields a 98.6% agreement with released public records of patients admitted to NHS hospitals. Analyzing the mobility pattern of these individuals prior to their potential hospitalization, we present a series of risk maps. Compared with census-based maps, our risk maps indicate that the areas of highest risk are not necessarily the most densely populated ones and may change from day to day. Finally, we observe that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones. © 2022 Owner/Author.

Designing Smart Restaurant for Reopening During the Relaxation of Lockdown in the Time of Corona Pandemic

Considering the changes that the world is going through, since the emergence of the Coronavirus 'COVID 19' and the following versions of the same virus which has fast spreading behavior among all countries and sometimes worse health effects on the human. It seems that its economic and social repercussions will be significant and influential at the global and national levels, as the outbreak imposed by taking a set of precautionary measures and measures represented in 'isolation' with quarantine or social distancing and restaurant closures. These procedures cause poverty among workers in malls and restaurants and economical losses, so countries start to relax the lockdown. This work aims to propose some procedures in designing restaurants for reopening during the relaxation of lockdown to reduce human contact through designing a waiter robot moves by line follower approach in a restaurant to serve meals to customers and also designing a wireless network to send the customizers orders from the tables to the kitchen. © 2022 IEEE.

Using mobile network data to color epidemic risk maps

In this paper we propose a method for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We apply our methods to a dataset from more than 2 million cellphones, collected over the months of March and April in 2020 by a British mobile network provider. The method consists of different algorithms, including detection, filtering, validation and fine-tuning. The approach detected over 2,800 potentially hospitalized individuals, yielding a 98.6% agreement with released public records of patients admitted to NHS hospitals. Analyzing the mobility pattern of these individuals prior to their potential hospitalization, we present a series of risk maps. Compared with census-based maps, our risk maps indicate that the areas of highest risk are not necessarily the most densely populated ones. We also show that the areas of highest risk may change from day to day. Finally, we observe that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones. Overall, we conclude that the rich spatio-temporal information extracted from mobile network data may benefit both the mobile-based technologies and the policies that are being developed against existing and future epidemics. © 2022 ACM.

Low Cost System on IOT Technology, for remote control of Biomedical Equipment

The effects of the pandemic produced by COVID-19 have caused many changes in the protocols of care and operation of medical equipment, due to the need to be able to have less contact with the patient, while ensuring patient care, In the present work, a solution is implemented to be able to control medical equipment remotely, avoiding contact with patients to a minimum, the proposed methodology makes use of a power outlet that has the ability to connect to a wireless network and thus allow the on and off by means of an application installed on any mobile device, the results demonstrate the applicability in various equipment, as well as being able to work in many connections, each equipment connected to a specific power outlet. One of the necessary recommendations is security in the wireless network, because no unauthorized user can access the network and be able to connect, in this way you have to have a specific wireless network for the use of these devices, the proposal is Applicable and scalable according to the needs and uses that may be given to it. © 2022 IEEE.

Risk Area Alert Through Heterogeneous Mobile Networks: A New Approach to Fight COVID-19 and Beyond

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this paper, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID- 19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end. IEEE

A NS-3-based Cloud Wireless Network's Emulator for Undergraduate Teaching

The COVID-19 pandemic limited the availability of in-classroom laboratory experiences with physical wireless equipment. Existing simulation tools, while adequate for performance analysis as required for research, fail to provide the real-world feeling and user interface of a physical equipment. Due to this lack of realism, they do not fulfill the educational objectives of undergraduate courses. Indeed, under a competencies-based curricula, it is expected that a student interacts with the equipment, configures it, run her experiments, evaluates the results, decides in any changes, and then implements and validates them. This paper reports our development of a cloud-based wireless network emulator that intends to give the students the same user experience as the one obtained with our testbed of (physical) Wireless Access Points running OpenWRT. We also discuss students' experiences using our emulator on a Wireless Networking undergrad course in the second half of 2020.

Research on mobile network online education under the background of post epidemic Era – Based on the perspective of Chinese Art Curriculum

The development of mobile network and intelligent terminal technology has laid the foundation for online learning. After the 2020 COVID-19, online learning mode of art courses has become an upsurge and trend in China's schools and training institutions. This paper discusses the strategies to further improve the online learning of art courses from three aspects: strengthening teachers' guidance to students' online learning, strengthening the cultivation of students' online learning concept and building a mixed learning model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

Investigating the effect of High-altitude Platform Positioning on Latency and Coverage of 4G Cellular Systems

Wireless communication technologies are rapidly being adopted and developed by countries all over the world as a strategy for sustaining a digital economy. This has proven very useful for economic recovery from the crises brought about by the COVID-19 pandemic of the year 2020. The latency and coverage area of a wireless network are two major areas that are always seeking improvement. The High-Altitude Platform communication technology can provide improvement in speed and coverage area for 4G cellular systems. This work investigated the effect of positioning High Altitude Platforms on the latency and coverage of 4G cellular Systems. A quantitative approach was used in the methodology of this paper. A HAP model showing a single platform flying in a circular trajectory over Base Transceiver Stations BTSs and serving as a relay mobile station was presented. A detailed simulation algorithm for the HAP and results for the simulation were given. Results showed that using the HAP as a relay mobile station in a network can give a latency reduction of up to 58.9%. Also, the altitude of the HAP directly affects the angle of reception which was found to improve the coverage.

Improving city infrastructure resilience capacity: resilience management guidelines in mobile network

In Republic of Croatia ways of rescue and protection of citizens, materials, and other goods in catastrophes such as earthquakes, flooding or ongoing global pandemic of coronavirus disease are regulated by Law on Protection and Rescue. Some of the most important parts are ways of controlling, handling, and coordinating tasks, constitutions of bodies for administration and ways of alarming and informing in activities of protection and rescue. Water supply network is very important part of the city infrastructure. Hence, it is necessary to ensure high-quality and reliable water supply network management process. In our previous research network model and relevant real-time data are integrated into the complete system that will cover all city infrastructure, acting as infrastructure data interchange portal usable especially in the case of disasters. All citizens are communicated with this platform through different user roles or services using telecommunications infrastructure. Main goal of research in this paper is to improve ways of controlling, coordinating tasks and informing citizens. Platform is used to collect whole relevant data from different sources such as water supply network operating center, electricity network operating center, gas transmission network operating center, telecommunication network operating center, citizens, 112 service and so on in emergency. Different behavioral scenarios have been implemented in the platform. According to the collected relevant data, the platform activates a certain scenario that best suits the current situation. According to the scenario, the platform automatically generates tasks to relevant people via a mobile application. In addition, other citizens receive information about acting through social networks notifications. © 2022 Croatian Society MIPRO.

Phone Sharing and Cash Transfers in Togo: Quantitative Evidence from Mobile Phone Data

Phone sharing is pervasive in many low- and middle-income countries, affecting how millions of people interact with technology and each other. Yet there is very little quantitative evidence available on the extent or nature of phone sharing in resource-constrained contexts. This paper provides a comprehensive quantitative analysis of demographic variation in phone sharing patterns in Togo, and documents how a large cash transfer program during the COVID-19 pandemic impacted sharing. We analyze mobile phone records from the entire Togolese mobile network to measure the movement of SIM cards between SIM card slots (often on different mobile devices). By matching phone sharing measures derived from SIM reshuffling to demographic data from a government-run cash transfer program covering hundreds of thousands of individuals, we find that phone sharing is most common among women, young people, and people in rural areas. We also leverage randomization in the cash transfer program to find that the delivery of cash aid via mobile money significantly increases phone sharing among beneficiaries. We discuss the limitations of measuring phone sharing with mobile network data and the implications of our results for future aid programs delivered via mobile money. © 2022 Owner/Author.

Impact of COVID-19 Pandemic Measures and Restrictions on Cellular Network Traffic in Malaysia

Due to the COVID-19 pandemic, intensive controls were put in place to prevent the pandemic from spreading. People's habits have been altered by the COVID-19 measures and restrictions imposed such as social distance and lockdown measures. These unexpected changes created a significant impact on cellular networks, such as increased use of online services and content streaming, which increased the burden on wireless networks. This research work is basically a case study that aims to examine and investigate cellular network performance, including upload speed, download speed, and latency, during two periods (MCO, CMCO) in three different regions, including Kuala Lumpur, Selangor (Cheras), and Johor Bahru, to observe the effects of lockdown enforcement and other restrictions in Malaysia on cellular network traffic. We used the phone application Speedtest™ as a tool for data collection within different times during the day, considering the peak times, including morning, evening, and night times. The research findings show how COVID-19 has affected internet traffic in Malaysia significantly. This research would help perspective developers and companies to better understand and be prepared for tough times and higher load on cellular networks in future pandemics such as COVID-19 © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Multiband Handset Antenna System for UMTS/LTE/WLAN/Sub-6 5G and mmWave 5G Future Smartphones

In this paper, a new antenna system for rapidly emerging multifunction devices is presented. The proposed antenna system consists of four antenna components each one operating at different frequency bands separately. The designed antennas are isolated and integrated on a single substrate. The first antenna is designed to operate at 1920–2170 MHz covering the UMTS band, whereas the second antenna is proposed for the lower band 5G systems and WiMAX operating within the frequency range of 3.4–4.2 GHz. Furthermore, another antenna is designed to cover the higher band 5G system and the IEEE 820.11a WLAN within the frequency range of 5.1–5.85 GHz. Finally, a 28 GHz bowtie-based MIMO antenna array is designed and simulated for the mmWave future 5G mobile networks. The proposed antennas were designed and simulated by using CST microwave studio. The results showed that all of the proposed antennas exhibited excellent reflection characteristics below −20 dB at the resonant frequency and achieved high radiation efficiency reached 99% in some cases with a peak gain ranging between 4–6 dBi. The proposed antenna system helps smartphones to perform multitasks and achieve a better-quality operation especially with the enormous growth of IoT techniques.

Scope of machine learning applications for addressing the challenges in next-generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID-19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real-time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next-generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever-increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

Evaluation Index of Basketball Teaching Environment Based on the Wireless Communication Network and Genetic Algorithm

With the advent of the new era, all fields in China are full of vitality. In particular with the vigorous development of network science and technology, many fields are going through a huge change or in the near future. As an important part of the development goal of China’s “sports power,” physical education is also undergoing changes. With the successful conclusion of the Winter Olympics in China, this grand event has added an important stroke to the development of sports in China. In the process of physical education and teaching, the environment of physical education is extremely important. Compared with nonphysical education subjects, it has the characteristics of direct contact between teachers and students and the time of the course teaching process. This paper studies the evaluation index of basketball teaching environment. As a part of physical education environment, it has the general characteristics of physical education environment, but it has its own characteristics. People in the environment are constantly experiencing the influence of the environment on their own individual. Therefore, the research on the evaluation indicators of the basketball teaching environment will play an important role for the students involved. For the research of the mentioned basketball teaching environment evaluation index, this paper proposes the application of genetic algorithm and wireless network technology;the purpose is to make a more scientific and comprehensive target evaluation system for the basketball environment evaluation index. In the simulation experiment of the teaching environment evaluation index, this paper selected a total of 164 people of different grades. The final result showed that 85.16% of the students gave positive comments to this evaluation system. This result confirms the research of this paper.