Reinforcement-Learning-Enabled Massive Internet of Things for 6G Wireless Communications

Recently, extensive research efforts have been devoted to developing beyond fifth generation (B5G), also referred to as sixth generation (6G) wireless networks aimed at bringing ultra-reli-able low-latency communication services. 6G is expected to extend 5G capabilities to higher communication levels where numerous connected devices and sensors can operate seamlessly. One of the major research focuses of 6G is to enable massive Internet of Things (mIoT) applications. Like Wi-Fi 6 (IEEE 802.11ax), forthcoming wireless communication networks are likely to meet massively deployed devices and extremely new smart applications such as smart cities for mIoT. However, channel scarcity is still present due to a massive number of connected devices accessing the common spectrum resources. With this expectation, next-generation Wi-Fi 6 and beyond for mIoT are anticipated to have inherent machine intelligence capabilities to access the optimum channel resources for their performance optimization. Unfortunately, current wireless communication network standards do not support the ensuing needs of machine learning (ML)-aware frameworks in terms of resource allocation optimization. Keeping such an issue in mind, we propose a reinforcement-learning-based, one of the ML techniques, a framework for a wireless channel access mechanism for IEEE 802.11 standards (i.e., Wi-Fi) in mIoT. The proposed mechanism suggests exploiting a practically measured channel collision probability as a collected dataset from the wireless environment to select optimal resource allocation in mIoT for upcoming 6G wireless communications

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5{th} generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3{rd} generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR

Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill

A federated reinforcement learning framework for incumbent technologies in beyond 5G networks

Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G) networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and low-latency communication services with massively connected devices. Amalgamating WiFi networks with 5G/B5G networks has attracted strong researcher interest over the past two decades, because over 70 percent of mobile data traffic is generated by WiFi devices. However, WiFi channel resource scarcity for 5G/B5G is becoming ever more critical. One current problem regarding channel resource allocation is channel collision handling due to increased user densities. Reinforcement learning (RL) algorithms have recently helped develop prominent behaviorist learning techniques for resource allocation in 5G/B5G networks. An agent optimizes its behavior in an RL-based algorithm based on reward and accumulated value. However, densely deployed WiFi environments are distributed and dynamic, with frequent changes. Thus, relying on individual local estimations leads to higher error variance. Therefore, this article proposes a federated RL-based channel resource allocation framework for 5G/B5G networks, and suggests collaborating learning estimates for faster learning convergence. Experimental results verify that the proposed approach optimizes WiFi performance in terms of throughput by collaborative channel access parameter selection. This study also highlights six potential applications for the proposed framework

Medical diagnosis using machine learning: a statistical review

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results

An anonymous device to device access control based on secure certificate for internet of medical things systems: an anonymous D2D access control scheme for IoMT

The Internet of Medical Things (IoMT) is structured upon both the sensing and communication infrastructure and computation facilities. The IoMT provides the convenient and cheapest ways for healthcare by aiding the remote access to the patients’ physiological data and using machine learning techniques for help in diagnosis. The communication delays in IoMT can be very harmful to healthcare. Device to device (D2D) secure communication is a vital area that can reduce communication delays; otherwise, caused due to the mediation of a third party. To substantiate a secure D2D communication framework, some schemes were recently proposed to secure D2D based communication infrastructure suitable for IoMT-based environments. However, the insecurities of some schemes against device physical capture attack and non-provision of anonymity along with related attacks are evident from the literature. This calls for a D2D secure access control system for realizing sustainable smart healthcare. In this article, using elliptic curve cryptography, a certificate based D2D access control scheme for IoMT systems (D2DAC-IoMT) is proposed. The security of the proposed D2DAC-IoMT is substantiated through formal and informal methods. Moreover, the performance analysis affirms that the proposed scheme provides a good trade-off between security and efficiency compared with some recent schemes

A secure and lightweight drones-access protocol for smart city surveillance

The rising popularity of ICT and the Internet has enabled Unmanned Aerial Vehicle (UAV) to offer advantageous assistance to Vehicular Ad-hoc Network (VANET), realizing a relay node's role among the disconnected segments in the road. In this scenario, the communication is done between Vehicles to UAVs (V2U), subsequently transforming into a UAV-assisted VANET. UAV-assisted VANET allows users to access real-time data, especially the monitoring data in smart cities using current mobile networks. Nevertheless, due to the open nature of communication infrastructure, the high mobility of vehicles along with the security and privacy constraints are the significant concerns of UAV-assisted VANET. In these scenarios, Deep Learning Algorithms (DLA) could play an effective role in the security, privacy, and routing issues of UAV-assisted VANET. Keeping this in mind, we have devised a DLA-based key-exchange protocol for UAV-assisted VANET. The proposed protocol extends the scalability and uses secure bitwise XOR operations, one-way hash functions, including user's biometric verification when users and drones are mutually authenticated. The proposed protocol can resist many well-known security attacks and provides formal and informal security under the Random Oracle Model (ROM). The security comparison shows that the proposed protocol outperforms the security performance in terms of running time cost and communication cost and has effective security features compared to other related protocols

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

© 1998-2012 IEEE. The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies

A three-dimensional clustered peer-to-peer overlay protocol for mobile ad hoc networks

Peer-to-peer (P2P) computing involves exchanging resources and files by computers connected through a network rather than a central server. In P2P, over mobile ad hoc networks (MANETs), the fundamental necessity is the linkage of the overlay participating peers (OPPs) for the efficient operation of the DHT-based P2P overlay protocol over MANETs. This necessity becomes more critical in a high mobility environment. Due to the high mobility of OPPs, the topology of the P2P overlay is altered continuously. Consequently, the efficiency of DHT-based P2P overlay protocol over MANETs greatly decreases due to the increased lookup latency, maintenance, computational, and control overheads. In the current research, a novel three-dimensional clustered overlay P2P protocol, i.e., 3DCOP, is suggested to cope with the identified issues. Simulation results depict that 3DCOP performs better in routing overhead, false-negative ratio, path-stretch ratio, and file discovery delay in the high mobility environment over MANETs

Localizing pedestrians in indoor environments using magnetic field data with term frequency paradigm and deep neural networks

Indoor environments are challenging for global navigation satellite systems and cripple its performance. Magnetic field data-based positioning and localization has emerged as a potential solution for ubiquitous indoor positioning and localization. The availability of embedded magnetic sensors in the smartphone simplifies the positioning without the additional cost of infrastructure. However, the data divergence due to smartphone heterogeneity circumscribes the wide applicability of magnetic field-based positioning approaches. This research proposes the use of term frequency (TF) extracted from the magnetic field data to alleviate the impact of smartphone heterogeneity. For this purpose, the magnetic field data are transformed into terms (words) and documents. Extracted TF vectors are used to train long short term memory and gated recurrent unit networks. A voting scheme is contrived to incorporate the predictions from these networks. Experiment results with three different smartphones like LG G6, Galaxy S8, and LG Q6 demonstrate that the use of TF mitigates the impact of the smartphones’ variability. Performance comparison with state-of-the-art approaches reveals that the proposed approach performs better than those of other approaches in alleviating the influence of using various smartphones for magnetic field-based indoor localization. Furthermore, the localization performance of the proposed is better than those of other approaches, even using a smaller amount of magnetic field data