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

Objective
To examine the prevalence and trend of experimental research on the effectiveness of ICT use in supporting the management of mass casualty incidents.

Methods
A systematic review was conducted in five electronic databases between February and March 2022 following the PRISMA guidelines. The analysis included studies published since 2012.

Results
A total of fifteen studies were included in the final analysis. Three themes were identified: 1) the response of medical teams to mass casualty incidents, 2) the simulation of mass casualty incidents, and 3) the digitisation of mass casualty incident workflows.

Conclusion
The available evidence is limited due to the relatively small number of studies on the use of information and communication technology and the response of emergency rescue teams to mass casualty incidents. Therefore, further research is needed on the impact of information and communication technology on the efficiency, work, and decision-making of incident commanders during mass casualty incidents. Furthermore, more dynamism is needed to accumulate knowledge and methodological expertise to explore experimental ideas in the European Union, where such research is lacking.

Introduction

To ensure that Emergency Medical Teams (EMTs) are always prepared for Mass Casualty Incidents (MCIs), it is crucial to anticipate and plan for such situations, even though they may be rare. Although there have been several attempts to define an MCI quantitively, the best definition is that the number of casualties exceeds the resources generally available at the scene [1]. In this article, we refer to MCIs not caused by terrorist attacks. The rationale for excluding them is rooted in the significant differences in managing such MCIs, which stem from the deliberate intention behind these events and the often disproportionate number of casualties they cause [[2], [3], [4], [5], [6], [7], [8]]. Between 1982 and 2012, 290 mass casualty incidents (MCIs) were analysed in the study conducted by Turris, Lund, and Bowles [9]. Out of these MCIs, 71 (24 %) occurred in Asia, 69 (24 %) in Europe, 48 (17 %) in Africa, 48 (27 %) in North America, 27 (9 %) in South America, 25 (9 %) in the Middle East, and 2 (1 %) in Australasia. In Slovenia, an MCI occurs on average once a year [10].

Efficient and rapid deployment of rescue services is critical. In such circumstances, the approach and execution of rescue service operations are very specific. Through the proper use of available resources, every effort must be made to save the lives and health of as many people as possible. Such an event regularly exceeds the depleted capacities of local services and the nearest hospitals. It requires rapid reorganisation of work and support from additional rescue teams and other hospitals. The main goal of the disaster response organisation is to fulfil the necessary tasks and reduce the number of fatalities in the first hours after a disaster. To manage MCIs safely and effectively, EMTs need the appropriate knowledge and skills. The prehospital phase of an MCI is typically chaotic, dynamic and evolving in nature, requiring prehospital management decisions to be made under extreme time pressure. In an MCI, evacuating severely injured patients to an appropriate medical facility is critical.

Conclusions

ICT has the potential to improve crisis management, particularly in the context of EMTs actions during MCI. Further research is needed to determine if digital transformation optimises medical teamwork through advanced ICT, leading to more effective management decisions and increased chances of survival in MCIs. This study provides evidence for future researchers to conduct experimental research, especially within Europe.

In summary, these studies collectively unveil the multifaceted applications of ICT in mitigating the complex challenges inherent in MCI. From innovative triage protocols and aerial surveillance using drones to telehealth systems, advanced training methodologies such as AR/VR, AI, and machine learning for data analysis, and ethical considerations surrounding high-fidelity simulations and game-based training, these findings contribute to a comprehensive understanding of how ICT can augment preparedness, response, and training for MCI. As the academic community continues to delve into this domain, the integration of ICT remains pivotal in reshaping disaster response emergency response and management paradigms, ultimately bolstering societal resilience in the face of unforeseen calamities.

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

The lightweight Unmanned Aerial Vehicle (UAV) flight activities are constrained, particularly in the UAV range or activity span and perseverance, by the strategic correspondence link capabilities. This paper tends to the different overlap issue of trading off a set of mission prerequisites, the UAV execution parameters, and strategic credibility; thus compromising between the communication load characterized by a crucial, communication link transmitting power necessities, power accessibility onboard UAV as a weight-restricted parameter, and the UAV security.

Introduction

In current years, an outcome of decreased sizes and weights concentrated charges and amplified functionalities of various sensors and devices. The Unmanned Aerial Vehicles are attracting progressively general trendy varied variety of internal submissions (atmosphere monitoring, surveillance, photography, search-and-rescue, etc.) [1]. Through the marketplace develops and the diversity of applications increases, additional necessities are present-day being compulsory in spreading the flight variety and growing adaptability toward complex assignments of UAV structures [2].

UAV stands for Unmanned Aerial Vehicle, which is commonly considered to be a drone or an aircraft with no pilot on board. UAVs can be remote-controlled aircraft [3]. The unmanned aerial vehicle contains cameras, sensors, communication belonging as well as other payload devices [4]. It was created for military usage, and civilian usage to protect the border. UAVs are widely used in the military [5]. Unmanned aircraft system manufacturing was started by the United States department of defense (DOD) in 2005 [6]. Presently, leading UAV manufacturing countries are the USA, Israel, China, Iran, and Russia [7]. Rustom series of Indian UAVs are under development [8]. The Building of a UAV is easier, it is less expensive and it can be created at a reasonable price. UAVs are made of essential components.

Conclusion

In this paper, we reviewed UAV systems and communication design, and methods used to connect and send data to the ground station. Further, we discussed data processing via sensors and applications with security. We provide key definitions and concepts of UAV systems and details of the development of communication systems and GCS. We provide reviews and analyses of the security of UAVs and the types of attacks that are launched to disturb the communication of the UAV system. Finally, open research areas are given for further development of the UAV system and communication, which leads to better UAV technology for the industry.

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.

6.1 Introduction

6.2 Fifth Generation of Network Technology

6.3 Internet of Things (IoT)

6.4  Industrial Internet of Things

6.5 IoT in the Automotive Industry

6.6 IoT in Agriculture

6.7 AI, IoT, and 5G Security

6.8 Conclusion

References

6.1 Introduction 

The world of Artificial Intelligence (AI), fifth-generation data networks (5G), and the rapidly growing Internet of Things (IoT) devices can be beneficial to us, but also present numerous flaws as they are all new and rapidly developing technologies. It is important to note that the 5G network has better security than the third generation (3G) and forth generation (4G) networks. Still, it has been said that some of the classic vulnerabilities and security flaws from 3G and 4G networks were directly carried over to the developing 5G network, thus presenting additional security flaws right out of the gate. Both AI and the IoT will benefit from the development of 5G networks where businesses can use such devices, which will be tied to the growing 5G network, and can serve several purposes throughout the business market, among other areas.

When it comes to consumer-based electronic devices, they are certainly on the rise. As technology improves, these devices will consume more bandwidth and data than ever before. The increase in data and bandwidth consumption from newly emerging technologies could include the ability to interpret human speech on-the-fly and identify various patterns throughout data or documents from a mobile device. This data can even adjust business efficiency, increasing overall profits with endless possibilities for automation in manufacturing. This is where the development of 5G data networks come into play. 5G networks will have the bandwidth needs for current and future data-hungry devices in a growing technological world.

AI technology is growing from ordinary scientists’ simple tools to as far as the use within the professional development community for higher intelligence use. These various organizations can use AI technology to fill the current gap in the data science area, a big gamechanger for data science! This also always gives end-consumers the ability to take their business and personal data wherever they go. This is a big deal when making end-consumers happy while retaining their privacy with their online data.

When it comes to data processing, with the help of 5G, AI, and the IoT, modern technology demands that data management and processing capabilities should have the data possessors brought to the data itself rather than sending the collected data processing elsewhere. This may also include the data processing and distributed data stores included within the data management process in order to guarantee that support is offered where AI, 5G, and IoT: Driving Forces Towards the Industry Technology Trends 118 6 AI, 5G, and IoT: Driving Forces Towards the Industry Technology Trends the received critical data is being stored. 5G network technology will be revolutionary to end-consumers, businesses, and data processing centers worldwide.

Abstract

     Small- and medium-sized enterprises (SMEs) play a crucial role within a country’s economy considering that they provide a source of skills and innovation for entrepreneurship and their capacity for introducing, integrating and disseminating new technologies is incomparable. However, the world may be looking at an impending devastating recession delivered by the coronavirus pandemic. As governments intensify restrictions on business to halt the spread of the pandemic, the fear of the virus also reconfigures the very concept of business and the workplace. Therefore, there is a need for a greater focus on digital transformation considering that physical distancing requirements to curb the spread of this virus may become a cultural orientation for businesses and the workplace. A real digital transition is anticipated for small businesses with the 5G network technology. 5G network will not just be a technological connection but will affect different socio-economic sectors and will stimulate innovation in small companies. The goal of this paper was to carry out a literature review on the potential ways small businesses can leverage on 5G network for digital innovation. The paper proposed that small and medium businesses can leverage the 5G Mobile Technology through enhanced interpersonal communication, expanded remote work opportunities, innovative digital tools and supply chain efficiency. Finally, while many individuals and small business owners will profit from 5G technology some will work to undermine it. As a result, companies must keep this in mind when implementing 5G, or other new technology, and adjust their security protocols accordingly to remain secure.

Introduction

     Small businesses can expect a true digital transition with 5G (fifth generation of broadband data-networking digital communications technology) network technology (Tullberg et al., 2016). With an estimated annual growth rate of 97% and approximately 40% of the global population using IoT apps on a 5G network, the new mobile technology has the potential to generate a $251 trillion economic impact by 2025. This growth can be attributed to the fact that the 5G network will not only provide a technological connection but will also have an impact on various socioeconomic sectors and encourage small business innovation (Webb, 2016). The new technological link will make it possible to implement well-known initiatives, such as Smart City, Smart Company, and artificial intelligence in business (Zhu et al., 2016). Because of increased data transfer speed and reduced time between signal transmission and reception, the 5G network will redefine the role of connectivity and connections in global society, providing a new foundation for business innovation, particularly in small businesses. The goal of this paper is to conduct a review of the literature on the various ways small businesses can use the 5G network for digital innovation.

Conclusion

     The goal of this article was to establish a link between 5G technology and digital innovation, particularly in SMEs. The article began by establishing the context of the paper by summarising current knowledge and background information on the subject. This was accomplished by stating the purpose of the paper, the research question/problem, briefly explaining the methodological approach, and emphasising the study’s potential outcomes. Conceptual framework and synthesis were created to demonstrate the key relationships between the focal constructs (5G technology and digital innovation). This was done to aid comprehension of the conceptual framework. This paper contends that the implementation of 5G technology creates a platform for digital innovation in SMEs’ communication, remote working, supply chains, and marketing activities. This can simplify and improve the efficiency of work processes and operations within SMEs, resulting in increased productivity. 5G technology will create enormous opportunities for new revenue streams and will serve as the foundation for business innovation and development on a scale that companies in almost every industry have yet to fully explore.

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.