Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.

Abstract

The sixth generation (6G) of cellular transmission is extremely adaptable. 6G will equip everyone with pervasive wireless connectivity and meet the needs of a fully connected world. It is anticipated that revolutionary ideas will increase in support for a fast-expanding variety of smart devices and applications. The sophisticated characteristics of 5G portable cellular network infrastructures produce novel threats and requirements. Comparing 6G cellular network technologies to 5G cellular networks, this paper discusses various security and privacy issues in 6G cellular networks based numerous security services. Proposes AA approach to evaluate the effectiveness of the methodology. Propose a new 6G wireless security architecture based on the analysis of secret key authentication and flexible position based identification, which serves as the foundation for an examination of identity management and flexible authentication. Demonstrates the benefits of the proposed architecture, analyzes BER against SINR and Measure Throughput Against various SINR values. Finally, Limitations and future recommendation for 6G cellular network security is presented.

Introduction

Over the past few years, wireless networks technologies have made significant technological advancement. The revolutionary changes brought about by the most recent 5G innovations have benefited a wide range of stakeholders, including commercial solution suppliers, university research organizations, standards bodies, and mobile-users [1,2]. In contrast, networks comprise a large number of Base Stations (BS) to enable high data transmission rates and adequate network capacity due to the low penetrability of high-frequency signals used in 5G [3,4]. The foundation of mobile network service continuity is multi-hop network technology, which allows users to seamlessly switch between source to destination that are outfitted with various access methods. The complexity of device changeover in 5G networks grows as a result of the diversity of service networks, increasing transmission times and affecting user experiences. As opposed to 5G networks, high-density access networks have a high transmission rate among tiny nodes. Existing wireless networks have been struggling to handle an increase in traffic due to the constant growth of data and devices in cellular networks [5,6]. In response, the 6G of cellular communication technologies has arisen to satisfy these high expectations. With many different communication networks, including Long Term Evolution, the Internet of Things, Mobile multi-hop Networks, wireless personal area networks and several versions of Wi-Fi, 6G is incredibly adaptable [7,8].

The 6G communication network is a network with several additional capabilities in addition to being an upgrade of the existing 5G network [9,10]. Research and development for 6G aims to provide a variety of advanced qualities, including greater bandwidth than existing 5G networks, higher mobile internet connection facilitating communication between devices among users as well as extensive technological communications [11,12]. Planned 6G networks also seeks to reduce energy consumption, latency and enhance security, for better wireless network deployment [13,14]. Additionally, a rise in connection density will lead to demands for higher energy efficiency, which 5G is not intended to provide. As a result, the research community has focused on solving the aforementioned key problems [15,16]. Accordingly, we assert that current work in the fields of terahertz band communications, intelligent surfaces and environments, and network automation. The general architecture of 6G wireless systems is shown in Fig. 1.

Conclusion

Cellular networks have been successful because of their emphasis on secure transmission. When everyone’s access to the internet is ensured in 6G, the networks will grow to enormous sizes. We have presented a thorough analysis of current advancements in 6G wireless security in this paper. We proposed a 6G wireless security architecture based on existing research. From the suggested security framework, an analysis of position based identification and adaptable authentication based on different security architectures has been provided. Demonstrated the benefits of the proposed architecture, Analyzed the performance of BER at the User Interface is 94 % improved than the CoMP, GKM and OSPR. Furthermore, Enhanced Throughput 96 % achieved for AA Approach due to integration of data plane and control plane in the proposed architecture. The efficacy of this class of solution is verified by using Riverbed Modeler 17.5 simulation tool.

Abstract

We study a radio resource allocation problem in mobile communication systems. As the distinct characteristic of this problem, a common data transmission rate is used on all channels allocated to a user. Because the channels differ in their quality, for each user the achievable rate varies by channel. Thus allocating more channels does not necessarily increase the total rate, as the common rate is constrained to be the lowest one supported by the allocated channels. Radio resource allocation subject to the common-rate constraint is of practical relevance, though little attention has been paid to modeling and solving the problem. We take a mathematical optimization perspective with focus on modeling. We first provide a complexity analysis. Next, several integer linear programming (ILP) formulations for the problem, including compact as well as non-compact models, are derived. The bulk of our analysis consists in a rigorous comparative study of their linear programming (LP) relaxations, to reveal the relationship between the formulations in terms of bounding. Computational experiments are presented to illustrate the numerical performance in bounding and LP-assisted problem solving. Our theoretical analysis and numerical results together serve the aim of setting a ground for the next step of developing model-based and tailored optimization methods.

Introduction

Mobile communication systems have been evolving rapidly in the past decades. From an optimization standpoint, a key research topic for mobile networks is radio resource management for utilizing the radio resource efficiently, particularly because the amount of data traffic is constantly growing in the current 4th generation (4G) systems, and the trend will remain or even accelerate in the 5th generation (5G) networks. Along with this growth, 5G networks target a plethora of new services (e.g., vehicular communications), making resource management more challenging than before, as pointed out in Calabrese et al. (2018).

4G/5G networks deploy orthogonal frequency division multiple access (OFDMA). In essence, data transmission takes place in two dimensions, namely time and frequency that are divided into time slots and channels, respectively.1 Resource management for channel allocation in a time slot, as well as that across multiple time slots, is commonly called scheduling. In any time slot, a user may be scheduled to receive transmission from its base station (BS) on multiple channels, and a channel may be scheduled for at most one user of this BS. The latter is what the term “orthogonal” refers to in OFDMA. Among the BSs, the channels are reused, i.e., there is generally no dedicated (sub)set of channels for a BS. As a result, a channel is exposed to interference, if the channel is also scheduled in some nearby BSs in the same time slot. Note that even if interference is not present, the channels are still frequency-selective (Tse and Viswanath, 2005), meaning that they vary in quality and hence the achievable data rate from a user’s perspective.

Conclusions and future work

We have studied mathematical modeling of a resource optimization problem with common-rate channel allocation in mobile communication systems. We have demonstrated that the problem admits an array of integer programming formulations. The study shows that modeling does matter, analytically as well as numerically, in terms of performance in bounding and LP-based problem solving. Moreover, there is a clear correlation between the accuracy of LP and the quality of integer solution derived thereby.

As we have observed, straightforward schemes of constructing integer solutions are not practical in solution time, whereas for our resource allocation problem, the ultimate target is real-time optimization. To approach this goal with model-based optimization, we make several observations. First, to deliver a solution rapidly, the amount of computation has to be very small, and thus the LP relaxation may have to be solved approximately, followed by some primal heuristic (both admitting massive parallel computation). Second, non-compact models are also of interest, as restricting the number of columns or rows leads to approximation of the problem (and the LP relaxation). Finally, with presence of rate lower bound, obtaining solution feasibility is of clear significance, and one may need to trade solution quality against feasibility. Developing and implementing algorithmic notions along these lines form interesting topics for forthcoming research.

Abstract

     The automotive industry is undergoing a profound digital transformation to create autonomous vehicles. Vehicle-to-Everything (V2X) communications enable the provisioning of transportation use cases for road traffic and safety management. At the same time, during the past decade, Artificial Intelligence (AI) and Machine Learning (ML) have been in the spotlight because of their outstanding performance in various domains, including natural language processing, and computer vision. Considering also current standardization efforts, towards incorporating AI and ML as integral sub-systems of beyond 5G and 6G networks, these technologies are considered very promising to optimize user, control, and management network functions, but also to support road safety and even entertainment applications. This survey systematically reviews existing research at the intersection of AI/ML and V2X communications, focusing on handover management, proactive caching, physical and computation resources allocation, beam selection optimization, packet routing, and QoS prediction in vehicular environments. We extract the underlying AI/ML techniques, the training features, their architecture and discuss several aspects regarding the intricacies of vehicular environments and ML. These aspects include time complexity of the algorithms, quality of real-world vehicle traces, suitability of AI/ML techniques in relevance to the designated network operation and the underlying automotive use case, as well as velocity and positioning accuracy requirements towards the creation of more realistic and representative synthetic data.

Introduction

     During the past years, car manufacturers have introduced driver assistance systems to their models, coupled with onboard intelligence, leading to a higher perception of their surroundings. This enables the possibility to achieve different levels of autonomous driving. Autonomous driving is considered critical in improving car safety, eliminating accidents due to human error, reducing traffic congestion, and improving passenger comfort. The Society of Automotive Engineers (SAE) has defined six driving automation levels, ranging from no automation to full automation [1]. Communications among vehicles, infrastructure and road users, collectively defined as Vehicle-to-Everything (V2X), are essential in realizing safety and non-safety-related applications, such as autonomous driving, car platooning, information sharing among vehicles and high data-rate infotainment.

     V2X may refer to Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N) or Vehicle-to-Pedestrian (V2P) communication. V2P refers to the communication among vehicles and pedestrians, cyclists or motorized two-wheeler operators, collectively called Vulnerable Road Users (VRUs) [2]. There are two leading technologies in V2X: i) the Cellular-V2X (C-V2X) based on cellular 4G/LTE [3] and 5G networks [4] and ii) the Dedicated Short-Range Communication (DSRC) [5] based on IEEE 802.11p [6]. These solutions will complement the sensor/camera/radar information and intelligently connect the car to its surroundings and the network.

Conclusion

     This survey presented recent advances in AI/ML applications in V2X communications. We classified the related literature in handover management, beam allocation, caching, radio network allocation, computations resources management, routing and QoS prediction considering only vehicular environments. For each category, we surveyed the ML technique, the training features, architecture, and optimization objectives, and extracted results and observations concerning time complexity, performance and suitability of learning techniques according to the designated problem

     g techniques according to the designated problem. Based on the surveyed publications, there is no “one size fits all” solution. Depending on the problem, different tasks require different formulations, including AI/ML algorithm selection, optimization objectives, architecture, and training features. Each family of AI/ML algorithms comes with their own advantages and disadvantages. The problem formulation must also consider the requirements of the underlying use case, which is largely affected by the training and response times of the selected AI/ML model. In addition, it is vital to explore approaches that reduce these times, while preserving the robustness of the AI/ML algorithm. AI/ML in V2X has already shown potential in optimizing network operations, but there are still open issues that need to be addressed due to the intricacies of both AI/ML and the highly dynamic vehicular networks. Based on the surveyed papers, vehicular networks, empowered by AI/ML and V2X communications as cooperative technologies, can be transformed into autonomous networks with self-configuration/optimization/healing capabilities.

Abstract

This article presents a study with feasibility and performance analysis of machine learning (ML) techniques using supervised techniques for anomaly detection problems in a 5G communication network. The proposed ML models (Multilayer Perceptron, Decision Tree, and Support Vector Machine) were used to classify data into anomaly or non-anomaly based on two 5G Open Radio Access Network (O-RAN) datasets with various key performance indicators (KPIs). Furthermore, we propose a strategy that devotes to labeling anomalous situations, leveraging the t-Distributed Stochastic Neighbor Embedding (tSNE) technique atop datasets enclosing multiple KPIs. The results were significant, with an accuracy above 90% for all use cases considered.

IntroductionTo support new 5G cellular network requirements (e.g., data rates exceeding 10 Gbps, network latency under 1 ms, capacity expansion by a factor of 1,000, and energy efficiency gains), vendors have begun investigating new radio access network (RAN) architectures [17, 13, 25, 5, 23].

Open Radio Access Network (O-RAN), suggested by the O-RAN Alliance [15], stands as a promising radio technology that has gained worldwide acceptance. O-RAN is a worldwide community of operators, manufacturers,technology that has gained worldwide acceptance. O-RAN is a worldwide community of operators, manufacturers, and academic institutes [18, 1]. The vision is to rewrite the RAN industry towards establishing an open, adaptable, and intelligent RAN [15]. Artificial intelligence (AI) in machine learning (ML) will play a crucial role in the 5G networkwith particular emphasis on the O-RAN. For example, ML use can drive more efficient enhancements in 5G network planning, automation of network operations (e.g., provisioning, optimization, and fault prediction), network slicing, service quality prediction, and other applications and services [8, 3, 14, 20].

Conclusion

In this work, we present results and analyses of three ML/AI supervised approaches applied to anomaly detection: Multilayer Perceptron, Decision Tree, and Support Vector Machine. The tests were conducted on an emulation testbed concerning a network environment dataset. The unsupervised ML/AI strategy, based on t-Distributed Stochastic Neighbor Embedding (tSNE), was used to create data labels. Results associated with the accuracy of the ML/AI algorithms were obtained, suggesting an excellent performance with an accuracy of above 90% for all cases.

Abstract

In recent years artificial intelligence (AI) has been seen as a technology with tremendous potential for enabling companies to gain an operational and competitive advantage. However, despite the use of AI, businesses continue to face challenges and are unable to immediately realize performance gains. Furthermore, firms need to introduce robust AI systems and mitigate AI risks, which emphasizes the importance of creating suitable AI governance practices. This study, explores how AI governance is applied to promote the development of robust AI applications that do not introduce negative effects, based on a comparative case analysis of three firms in the energy sector. The study illustrates which practices are placed to produce knowledge that assists with decision making while at the same time overcoming barriers with recommended actions leading to desired outcomes. The study contributes by exploring the main dimensions relevant to AI’s governance in organizations and by uncovering the practices that underpin them.

Introduction

As businesses adopt Artificial Intelligence (AI), they are faced with new value propositions, but they also have to deal with new challenges, such as reducing the gap between intent and action(Amershi et al., 2019; Enholm et al., 2021; Mishra & Pani, 2020). Artificial intelligence has been perceived as a tool with which we can layer many different functions or as a solution to problems that are beyond the ability of traditional applications to solve. (Smuha, 2019). In order to gain a competitive advantage over their competitors (Raisch & Krakowski, 2021), businesses have implemented and deployed AI solutions to automate their processes, increase efficiency and reduce costs (Frank et al., 2019; Gregory et al., 2020). To achieve these goals, AI governance is essential. According to Butcher and Beridze (2019), AI governance “can be characterized as a variety of tools, solutions, and levers that influence AI development and applications”. Yet, further research is needed to better determine how AI Governance can be introduced into a company and whether AI governance can assist a company in achieving its objectives.

While AI has the potential to generate business value in terms of performance, productivity and effectiveness, it is not autonomous, as it works in concert with human capabilities (Zhang et al., 2021). Consequently, organizational capabilities are the results of combining and deploying multiple complementary resources within a firm to achieve competitive advantage (Mikalef & Gupta, 2021). When a firm optimizes its firm-level resources and adopts AI technological innovations, it can enhance its transformed projects’ business value which drives business value and impacts performance (Wamba-Taguimdje et al., 2020). Simultaneously, the AI algorithms can be considered performative in the sense that they assist in decision-making, the extent to which their use can form organizational processes, or even take autonomous decisions (Faraj et al., 2018; Grønsund & Aanestad, 2020) that leads to new organization capabilities through AI. The use of AI, for instance, could create more substantial customer acquisition or higher customer lifetime value and lower operating costs or reduce credit risk.

Discussion

In this study, we set out to explore the underlying activities that comprise an organization’s AI governance. Specifcally, we built on the prior distinction between structural, relational, and procedural dimensions of governance in order to understand how organizations are planning around their AI deployments. Through a multi-case study of three organizations that have been using AI for several years, we conducted a series of interviews with key respondents and identifed a set of activities that were relevant under each of the three dimensions, as well as challenges they faced during deployments of AI and how they managed to overcome them. Our analysis essentially points out the various obstacles that AI governance is oriented to overcoming, and the mechanisms employed to operationalize them.

Specifcally, we fnd that the obstacles that are identifed during the process of deploying AI are observable at diferent phases and concern diferent job roles. When it comes to difcult management responsibilities that a business owner must do, AI solutions can always provide a variety of responses and probabilities for each of these alternatives. However, AI lacks the ability to make decisions in specifc contexts. To make the ultimate decision, a business owner or manager must employ intuition to reconcile the choices provided by AI (Kar & Kushwaha, 2021). In addition, they span various levels of analysis, from the personal, such as fear of AI and reluctance of employees to adopt it, to organizational-level ones, such as organizational directives on how to comply with laws and regulations. What is more, the study reveals not only that AI governance is a multi-faceted issue for organizations but that it spans multiple levels, therefore requiring a structured approach when it is deployed. In addition, different concerns emerge at diferent phases of AI projects, so AI governance also encapsulates a temporal angle in its formation and deployment.

Abstract

     A quad-element multiple-input-multiple-output (MIMO) antenna with fractional bandwidth (FBW) of 52.42% (3.35–5.73 GHz) is proposed for LTE, WLAN (4.9/5 GHz), and 5G (sub-6 GHz) applications. The bandwidth is improved by introducing a tapered feed line and rectangular stubs in the partial ground plane. The maximum isolation of the proposed MIMO antenna is 27 dB. The diversity performance characteristics of the proposed antenna are studied in terms of the envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), total active reflection coefficient (TARC), isolation between the ports, and channel capacity loss (CCL) and the values obtained are 0.003, 9.98 dB, ±3 dB, −4 dB, −10 dB, and 0.10 bits/s/Hz respectively. A model of the proposed antenna is fabricated on the FR-4 substrate having a dielectric constant of 4.4 and a loss tangent of 0.02 with an electrical dimension of 0.45λ0 × 0.45λ0. The measured results demonstrate a decent likeness to simulated ones in the entire operating frequency range.

Introduction

     The current scenario for mobile communication is to achieve a higher data rate, capacity, low latency, and high resolution. The fifth-generation (5G) mobile communication has been deployed around the world to fulfill the above advantage [1,2]. It has been shown [3,4] that to improve the information throughput in a multipath environment for 5G operations, the MIMO antenna system should be adopted. MIMO antenna systems should have a minimal inter-element distance, low correlation values, and better isolation between inter elements [5]. The primary advantage of adopting an MIMO network over a normal one is that it can enhance wireless connection capacity while consuming less spectrum. The system’s link dependability and data rate can be enhanced by increasing the number of antennas on the transmitter/receiver, which results in more signal routes and thus higher performance. Hence MIMO techniques become very vital for wireless communication systems which are suffering from frequency constraints [6,7].

Conclusion

     A compact quad-element MIMO antenna of 40 × 40 × 1.6 mm3 for 5G, LTE, and WLAN frequency bands is designed, fabricated, and measured. The measured results demonstrate a decent likeness to simulated ones in the whole frequency range. The proposed design’s performance was evaluated and described in terms of impedance bandwidth, surface current distribution, reflection coefficient, gain, efficiency, and radiation characteristics. The MIMO provides good diversity performance with a low envelope correlation coefficient and a better diversity gain. Furthermore, within the operating frequency band, the proposed antenna obtained a maximum channel capacity of > 20 bits/s/Hz. The antenna has a total efficiency of 85.12% and a peak gain of 2.74 dBi. As a result, it is recommended as a candidate for 5G, WLAN, and LTE applications.

Abstract

     The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device’s reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs.

Introduction

     The further progress of the Internet of Things (IoT) reveals it is a fundamental piece of different applications, for instance, keen home, smart healthcare, savvy agriculture, brilliant city, and so forth. The remarkable improvement of IoT devices encounters accepting various benchmarks and progressions. Another significant issue in IoT gadgets is interoperability among particular gadgets and organizations. The specific devices should be versatile in accepting the situation in passing on information with less human commitment (Xu et al. 2004). The human free and human-driven are two kinds of unavoidable perspectives on account of human affiliation. The energy effectiveness and resource management in IoT acquired huge examination interests. The key necessity of IoT empowered WSNs is that they ought to work steadily and economically for a more drawn-out period with no requirement for human mediation (He et al. 2004). Devices in such IoT systems will ordinarily work dependent on battery power sources, and subsequently, energy proficiency has normally of most extreme significance in a gadget the executives (Nadeem et al. 2015; Arifin and Ohtsuki 2014). Thinking about the WSN area, energy productivity for battery worked sensor hubs and lifetime upgrades have been researched issues for a long time (Riggio and Sicari 2009; Sohrabi et al. 2000). Medium Access Control (MAC) layer conventions center around changing the obligation cycle for sensor hubs and steering layer conventions have intended for information accumulation and many-to-one transmission. Likewise, since IoT gadgets working in the IoT arrange worldview are additionally battery worked, battery utilization ought to be remembered during IoT organize organization. The acknowledgment of cost decreases to accomplish green systems administration is the exploration goal of this research. Numerous energy productive plans for WSN have been proposed in the ongoing past, for example, a chain of command (Sohrabi et al. 2000) and careful (Mikhail et al. 2017a, b) ones, anyway there are numerous gaps identified with effective information total and spread strategies.

Conclusion and future work

     Progressions in the 5G standard and its integration with emerging Industry 4.0 and AI innovations were studied in this paper. The fast-expanding savvy devices and IoT modules in volume produce a huge measure of information traffic, putting extra weight on existing portable remote sensor networks. This paper presents an itemized review on the combination of 5G, Massive MIMO, and AI for improving IoT performance. The wide range of recent studies reviewed throughout this paper and based on it the research gaps for future roadmap discussed. We further have designed the novel model for smart healthcare monitoring using the methods of 5G and IoT together. The model was implemented and evaluated using the recent 5G methods for resource and interference management. The simulation results reveal the research gaps for further development.

Abstract

     An individual’s health is one of the essential aspects of life, but due to the limited technology at health care centers, many people face various restrictions during their treatment. These days, Internet-of-Things (IoT) is the most fascinating topic that provides solutions to these limitations in various ways. The IoT is utilized in various healthcare conducts that include detection, treatment, and monitoring of diseases. Wearable devices are a part of IoT that is proposed for helping patients to get the correct treatment. The conventional communication networks developed for humans-based applications face many issues like stringent latency, restricted computing capability, and short battery life. On the other hand, the onset of 5G has developed a new set of technologies that offer the vital “backbone” for connecting to the billions of devices for the upcoming IoT that would completely modify our professional and private lives. Due to the data capabilities, intelligent management, and superfast connectivity of 5G, this network has enabled new health care opportunities that include treatment, data analytics, diagnostics, and imaging. In the current review, a systematic literature survey of IoT, IoT-based wearable devices, and the role of 5G in IoT for healthcare is described in detail. Furthermore, we have explained the usage of wearable devices in detecting the issue in terms of healthcare, such as curing, monitoring, and detection of disease. Nevertheless, this review article also emphasizes the employment of IoT architecture and its wearable devices in addition to the upcoming research challenges related to this area.

Introduction

     With the increase in the advancement of technology, the demand for comfort and accessibility is also increasing, leading to an increase in the development of funding programs. Usually, it has been observed that many city hospitals lack to fulfil certain medical requirements of the patients, and therefore, patients have to search in other cities/move to other countries’ hospitals to complete their desired medical needs (Surantha et al., 2021). These days, the Internet-of-Things (IoT) in healthcare has become one of the latest trending subjects and has been found as a promising characteristic to improve the current technologies that lower its cost in each aspect of healthcare. Moreover, the IoT in biomedical is considered as the need of an hour that is required to provide medical aid to humans for monitoring, managing, detecting, and taking action from the system after receiving data, thereby effectively reducing the cost of health expenses (Khan et al., 2018). IoT is the sub-category of a computer having sensors, microcontrollers, and transceivers that are interconnected to provide information to aid the user with detailed information. It is applied in various healthcare means, including detection of disease as a precaution, monitoring disease as a self-healing route, and treating disease as the healing solution (YIN et al., 2016). Wearable devices are a part of IoT that have been developed for helping people to get the correct treatment. The IoT-based wearables are smart devices that could be worn with external accessories, tattooed or even adhered on the skin, implanted in the body, or embedded in garments and clothing.

Conclusion and prospects

     With the advancement in science and technology, the accessibility of healthcare has now become easy and affordable. But, many city hospitals lack the desired medical requirements, and therefore, the introduction of biosensors has eased these problems. Moreover, these days, the Internet-of-Things (IoT) in healthcare has become one of the latest trending subjects and a promising characteristic to improve the current technologies that lower its cost in each aspect of healthcare. IoT has become the need of the hour as it is useful for monitoring, managing, detecting, and taking action from the system after receiving data, thereby effectively reducing the excess cost of health expenses. Wearable devices are a part of IoT that have been developed to help patients get the correct treatment. These IoT-based wearables devices are considered smart devices that could be worn with external accessories tattooed or even adhered to on the skin, implanted in the body, or embedded in garments and clothing. These smart devices can link with the internet for collecting, sending data, and receiving information for making smart decisions. These wearables have become a vital fragment of IoT technology, and their growth is shifting towards more specialized and practical utility from simple accessories.

Abstract

     The rollout of 5G wireless service has been much anticipated by consumers across the U.S. since it will bring faster data download speeds and expanded service; however, the impact to aviation and air travel are as a major issue and the potential impact to aviation has not been estimated or assessed. This paper fills that gap and provides the first analysis of the potential number of flights impacted by 5G at ten of the busiest airports in the U.S. Using detailed historical weather and flight data in 2019 at 10 large hub airports, we find that nearly 15,700 flight arrivals could be directly impacted annually, at the ten airports considered. The geographical and seasonal impact varies significantly with more flights affected by the potential for 5G interference at airports in Seattle and Dallas Fort-Worth and relatively few flights affected at airports in Phoenix and Las Vegas. A case study examining ten years of data at Chicago O’Hare International, a hub for two major airlines, suggests that over 2,400 flights every year could be impacted by 5G interference. Fortunately, there has been increasing cooperation between the cell phone industry and aviation stakeholders to ensure a safe rollout of 5G C-band; this paper provides a significant contribution by documenting the issues and context for aviation operations.

Introduction

     The rollout of 5G wireless service has been much anticipated by consumers across the U.S. as data download speeds will be increased and service expanded. 5G C-band enables data intensive technology for personal and business use, ranging from entertainment such as video games to critical communications for public services (Pham et al, 2020), and provides more consistent, faster speeds and increased capacity. The communications band or “C” band was originally designated for industrial applications and satellite communications and were repurposed for 5G wireless between 3.7 and 3.98 GHz. C-band is one component of the mid-band frequency between 3.3 and 4.2 GHz. The benefits of the new 5G C-band service have been contraindicated near airports by the aviation industry due to the potential for negative operational and safety impacts. Since the 5G C-band frequencies is very close to the Aeronautical Radionavigation Service (ARS) spectrum (as shown in Fig. 1), there is a potential for signal interference which could potentially disrupt the operation of the radio/radar altimeter equipment on many transport aircraft. This equipment is necessary to conduct certain low visibility procedures when weather conditions do not permit use of standard precision or non-precision procedures. The inability of aircraft to safely perform these procedures could generate flight disruptions throughout the system. This fact was underscored by the acknowledgement that over 100 pilot reports of potential 5G interference have been made in the first few weeks of 5G rollout (Levin and Shields, 2022). Additionally, the U.S. Transportation Secretary noted that these issues will not be fixed by the summer flying season (Reardon, 2022).

Conclusions

     As the U.S. and other countries introduce 5G C-band, the need to understand the potential disruption to air travel is apparent. This paper provides the first estimates of the number of flights that could be potentially impacted at ten of the busiest airports in the U.S. These flights conducting low visibility procedures could be delayed, diverted, or cancelled. The number of flights conducting these low visibility approaches varies across seasons and location. Some airports, such as Seattle-Tacoma International could experience a significant impact to flight operations while others such as Harry Reid International in Las Vegas could be minimally impacted. Dallas Fort-Worth International, which area serves as several major airline hubs and headquarters, experiences numerous low visibility flight operations with the greatest monthly variation. The systemic impact from disruptions out of these ten busy hub airports presents a significant risk to travelers and airline operations. These risks include delays and cancelations as well as their direct and indirect economic costs. Further, to highlight the significant potential impact we determined that Chicago O’Hare International had over 27,000 flights conduct low visibility operations in the decade from 2009 to 2019.

Abstract

     The expedite to network economy during the COVID-19 pandemic has raised the question of how to induce and sustain a societal and industrial transformation towards a more networked world. Among the driving forces behind network economy, the commercialization of 5G, the fifth generation of mobile technologies, is especially noteworthy. How does 5G induce such a transition? How do countries respond? are questions deserving more investigation. However, most discussions of 5G have been confined to standardization or standard-setting. To take into accounts interactions between technology and economy, we adopt Geels’ (2002) multi-level perspective to put 5G transition in the social-technical context. We choose China as an influential case and deploy mixed methods to analyze a variety of data sources. The results show a rich picture of technological transition, including: 1) 5G standard-setting as a transition trigger in the global level; 2) IoT incubation in the niche level; and 3) regime configuration in the national level. We help transcend the limitation of standardization studies, extend the scope of transition studies into network economy, and introduce more industrial dynamics.

Introduction

     The expedite to network economy during the COVID-19 pandemic has raised the question of how to induce and sustain a societal and industrial transformation towards a more networked world. Network economy is not a new thing since the world has been deeply embedded in a fabric interwoven by network infrastructure (Zammuto et al., 2007). What is new is that the pandemic has triggered transformations not only in a global scale, but also in a massive scope on every aspect of political, economic and social life. We have seen the rise of pandemic information systems, the move to on-line education, the increase in flexible work arrangements, and the intensification of network economy across many sectors (Whitelaw et al., 2020). The pandemic will end eventually, but the momentum it generated might have long-lasting effects. Network economy will continue making new ways of organizing and working possible (Brynjolfsson and McAfee, 2014), connecting humans and machines via a vast array of internet of things (IoT) applications (Shafique et al., 2020), and incorporating artificial intelligence (AI) as a component to augment and automate increasingly more functions and processes within as well as across organizations (Kellogg et al., 2020). It is therefore foreseeable a transition towards a more networked economy and society.

Conclusion and discussion

     Through the case study, we analyze changes in the three levels during 5G transition, which suggest: 1) 5G standard-setting as a transition trigger in the global level; 2) IoT incubation in the niche level; and 3) regime reconfiguration in the national level. These changes, however, must be put under the scrutiny of alignment to make linkages between them. According to Kemp et al. (2001), “It is the alignment of developments (successful processes within the niche reinforced by changes at regime level and at the level of the sociotechnical landscape) which determine if a regime shift will occur”. The alignment is important for stability and sustainability of both regime reconfiguration and actors’ construction and response. Therefore, we can observe how this alignment helps reinforce regime reconfiguration and stabilize a new regime.

Abstract

     Physical layer security (PLS) in Internet-of-Things (IoT) has attracted great attention in recent years. As an important and intrinsic property in an IoT system, node mobility is not fully investigated when designing a PLS scheme. Existing PLS works with mobility are designed with typical random mobile models, which cannot represent a real scenario. Considering this challenge, we propose a friendly jamming handover scheme to protect the downlink transmission from a controller to an actuator, with the help of multiple cooperative jammers to fight against a mobile conscious eavesdropper. In particular, we first introduce a conscious eavesdropping model (CEM) to characterize a real mobile wiretapping scenario. Second, we present a handover scheme to enhance security in a mobile scenario and then define handover efficiency to discuss the handover performance. Finally, numerical results are provided to verify the effectiveness of the proposed handover scheme, which demonstrates that our scheme can efficiently fight against a conscious mobile eavesdropper.

Introduction

     Internet of Things (IoT) is expected as a promising technique to achieve ubiquitous connectivity and intelligent information processing for the intelligent future life [1, 2]. Since the IoT is penetrating our daily life, the security and privacy are of the utmost importance [3–5]. Physical layer security (PLS) techniques have been attracted growing interests for the scenarios with resource-constrained IoT sensors and actuators [6–8]. A PLS scheme taking advantage of the unique properties of wireless channels can guarantee the information-theoretic communication security, regardless of the computing capability of eavesdroppers [9]. Various PLS schemes have been investigated to enhance secrecy in IoT networks, including multi-antenna beamforming and precoding [10, 11], artificial noise (AN) [12], intermittent cooperative jamming [13], and sociability-enabled PLS [8, 14, 15].

Conclusion

     This work investigated secure downlink transmmission in an IoT system with a mobile eavesdropper, and proposed a handover scheme to enhance PLS with the help of multiple cooperative jammers. In order to reflect the real situation, the conscious eavesdropping model was proposed to compare with the typical RWP model. Moreover, we introduced a metric called handover efficiency to reflect the handover performance. The numerical results verified that the handover scheme could efficiently improve the secrecy performance and effectively confront the conscious eavesdropper. The proposed scheme is feasible to provide security enhancement in many real mobile eavesdropping scenarios.