IoT is converting the agriculture sector by permitting peasants to gather and analyze data in real time to improve efficiency, productivity, and profitability. IoT equipment and detectors are used to monitor various aspects of agricultural operations, including soil moisture, temperature, nutrient levels, and weather conditions. By collecting and analyzing this data, farmers can make informed decisions approximately when and how to water crops, apply plant food, and make other adjustments to optimize crop yields. Cloud computing enables farmers to reserve and process massive quantities of information, which is crucial in agriculture where data can originate from different sources, such as sensors, and IoT devices. Smart farming is an information-informed agricultural strategy that employs information technologies to maximize the efficiency, productivity and sustainability of agricultural processes. This paper exhibits a survey of the contemporary advances in IoT in Agriculture Applications. Furthermore, the paper outlines the strengths and weaknesses of each methods and issues that remain to be resolved in the area. The primary findings of the study show that from 100 articles. IoT, Cloud Computing, Smart Farming, Blockchain, Precision Agriculture, Machine Learning, and Wireless Sensor Networks are the topics reviewed for the survey.

The Internet of Health Things (IoHT) is a precise adaptation of the Internet of Things (IoT) in the health domain that allows medical objects to be embedded with electronic and networking capabilities for real-time medical data exchange. Their increased adoption comes at the expense of widening cyberattack surfaces and other unintended cybersecurity consequences that require sophistication to address. This paper aims to employ AI and ML techniques to strengthen cybersecurity practices in the healthcare sector. For our methods, we used a streamlined and adapted ML pipeline on the Edith Cowan University (ECU) IoHT; a dataset developed primarily for the analysis and evaluation of network traffic. Furthermore, we compared several approaches employed in anomaly detection in IoHT environment and converged with four classification AI/ML techniques of Gradient Boosting (GB), Decision Trees (DT), Random Forest (RF) and Multi-Layer Perceptron (MLP). A comprehensive comparative analysis is conducted based on key performance metrics such as accuracy, precision, recall, and F1-score. The results showed impressive classification accuracy of more than 90

The imminent threat that phishing websites poses is a major concern for internet users worldwide. These fraudulent websites are crafted by cyber attackers to appear trustworthy and deceive vulnerable users into divulging confidential data like medical health records, credit card details, passwords, and Personal Identifiable information (PII). To bait their victims, cybercriminals employ tactics such as social engineering, spear-phishing attacks, and email phishing scams. As a result, unsuspecting individuals may be enticed to visit these websites, putting their sensitive information at risk. This work presents an application designed to predict phishing attacks after comparing polynomial and radial basis function of support vector machine (SVM). The proposed application leverages a dataset of known legitimate, suspicious and phishing attacks stored in a database and employs an SVM algorithm for classification based on user input. The application provides a user-friendly graphical user interface (GUI) that allows reporting of new phishing incidents based on the features that have strong relationship in determining if a website is phishing or not. The proposed application utilizes the inherent scalability of database technology to support record expansion whenever there is an instance of a user initiating phishing prediction thereby, making it suitable for use in a wide range of organizational settings.

The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond.

Network Intrusion Detection Systems (NIDS) are among the most dynamic cybersecurity assets in most organizations' cyber infrastructure because they provide a reliable means of protection against cyberattacks. In recent years, the expansion of Industry 5.0 technologies has resulted in more IoT devices being connected to the Internet, increasing attack surfaces and placing additional burden on NIDS. In the research world, ML-based solutions for IoT NIDS are evolving very quickly. However, the availability of high-quality publicly available datasets in this area diminishes the potential of this niche. This work compares three IoT-based datasets (ToN-IoT, X-IIoTID and UNSWNB15) through in-depth analysis of their features and architecture. The goal is to assess their readiness for a proposed cross-machine learning pipeline for the purpose of generalizability of the multi-label classification ML model. Using key ML performance metrics such as F1 score, precision, and recall, our results show that our model performed reasonably well in identifying different attack scenarios on each dataset.

The available telecommunication services nowadays make connecting users easier. In return, vast streams of data are generated every day. However, mining useful information in data requires relevant techniques and procedures. In this article, a novel study is proposed consisting of a multi-algorithm approach to understand, detect and predict anomalies in cellular networks. The study holds 37 million Call Detail Records data from a cellular network with deployment of the latest network generations including 5G. The research is divided into two phases. In the first phase, we outline the voice traffic profile, where utilizing certain algorithms are meant to target specific attributes and scenarios in the data to understand the typical voice traffic patterns. Gaussian Mixture model is used to define the regular groups of call duration and Mean Shift clustering algorithm is employed to detect the peak hours on a daily basis. In the second phase, we deseasonalize the data for higher accuracy followed by the distribution function to comprehend the patterns in the data. We introduce three algorithms to detect and predict anomalies in the cellular network. The performance evaluation shows that DBSCAN and Isolation Forest algorithms provide the highest accuracy with 98% compared to the Z-score algorithm.

This book includes selected papers from the 5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2021).

The rapid proliferation of the Internet of Things (IoT) underscores the need for robust network security, especially in sectors like healthcare. While numerous datasets support cyber-attack detection in the IoT space, there remains a challenge due to the limited availability of publicly accessible data specific to certain sectors, like healthcare. This study delves into Cross-Machine Learning (Cross-ML), a novel approach to leveraging data from multiple sources to enhance Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS). We introduce a well-defined framework for Cross-ML and demonstrate its application across three key datasets: ToN- IoT, X-IIoTID, and UNSWNB15. Preliminary results, when juxtaposed with analogous studies, reveal significant insights. The study also indicates that relying on a small number of features might be limiting for ML-based NIDS, suggesting an exploration of more comprehensive datasets, like those with NetFlow features, for more conclusive results. This research not only underscores the potential of CrossML in IoT network security but also emphasizes areas of further exploration to solidify its application.

Utilization of Artificial Intelligence in mobile networks has seen significant growth in the last decade. It spans from mobile phone applications to various aspects of mobile network operations, including planning and optimization. Understanding and modeling users’ mobility patterns enhance mobility-based operations such as handover, scheduling, paging, and caching. In the literature, numerous models exist for grouping users’ mobility patterns in mobile networks based on their regularity. In our paper, we employ machine learning techniques to achieve this task, prioritizing low prediction time and high accuracy. The dataset used in our work is provided by an Internet Service Provider in Shanghai city, covering a span of six months. User records represent the sequence of serving base stations over four consecutive weeks. We employ various Machine Learning classification algorithms. The results demonstrate that our proposed coding method and the procedure of unifying the number of records, followed by the use of the Random Forest algorithm, yield the best results.

There are still many manual administrative processes involved in clinical clerkships. However, digital technologies and software solutions can significantly improve medical education. This paper aims to utilize extreme programming (XP) as a methodology for developing the information system. A total of 23 user stories have been planned for completion. The priority of each requirement stated in the user stories is determined using the MoSCoW prioritization technique. The results indicate that the software successfully passed all tests among the 23 black box testing scenarios. Developing an integrated information system to streamline clinical clerkships has been successfully achieved. The user experience questionnaire (UEQ) is utilized to gather the perspectives of hospital management and medical faculty members regarding the information system. The average scores for each category are as follows: 1.93 for attractiveness, 2.04 for perspicuity, 2.20 for efficiency, 2.06 for dependability, 2.14 for stimulation, and 1.85 for novelty. Therefore, it can be concluded that all existing user experience scales exhibit an outstanding degree of user satisfaction.

Mobile networks technologies are evolving rapidly in parallel with smart mobile devices wide spreading. On other hand, utilization of Artificial Intelligence in mobile networks has been increasing widely. It starts from mobile phones applications to mobile network operations, planning, optimization, etc. In this paper, an overview of using signalling data from radio interface in cooperation with machine learning techniques is introduced. The main machine learning types and models are summarized, as well as some of previous related works mainly depended on applying Machine learning on radio signalling. Benefits of those Machine learning-Signaling combinations vary from enhancing network key performance indicators to predicting user's specifications as trajectory, location, work, gender, etc. Moreover, mobile network planning, coverage evaluation, path loss prediction and channel modeling can be enhanced by using machine learning.

Mobile network users’ movements analysis has increasingly importance with the mobile networks evolution. Time-spatial information about current and previous locations can contribute in optimizing mobile network performance. Extracting features from this information is the most common method in analyzing users’ mobility. In this paper, a new position-time pattern based method is proposed to analyze the mobility by translating time-spatial records of each user to target-oriented image. Dataset provided from an ISP in Shanghai city for one month is used in our work. The proposed method is deducing user’ mobility behavior by visualizing serving base stations positions vs time according to unified rules for all users. Mobility pattern ambiguity resulting from neighboring base stations is processed. Moreover, K-Nearest Neighbors based method is suggested to predict base stations neighborhood. Results shows that visualizing time-spatial dataset, with proper processing of neighboring base stations issue, provide better understanding of users’ mobility data analysis.

In this paper, global disaster databases and their implementation standards are discussed. The set of standards followed by each disaster database, the scope of different set of databases and its relevance in global disaster area are analyzed and explained. Knowledge obtained from a disaster database can be used for the disaster risk reduction planning and for comparing the effectiveness of active disaster risk reduction plan. The disaster risk reduction plan plays a vital role in the mitigation and resilience phase; a well-constructed plan should address these two factors. This paper aims to study representative disaster databases and to establish the correlation between the disaster database and disaster reduction plan.

The recent technological enhancement in the communication and computing paradigm paves way for the development of next-generation ubiquitous IoT systems and applications. Moreover, the 5G and technology beyond are making huge differences on both computing and communication services. As the connectivity increases, the sheer volume of things connected through wireless mode for various real-time applications will not be efficiently handled by the existing technologies. The next-generation ubiquitous clouds and communication networks will gain the ability to leverage on-demand access through internet to utilize a shared pool of configurable communication resources. The ubiquitous cloud and communication platforms are widely used as services for data management, processing, and storage, whereas it can also enhance its reliability, flexibility, and efficiency by reducing the complexities. The research papers for this special issue were selected from among all the accepted papers by the special issue guest editors Dr. V. Suma, Dr. Ram Palanisamy, Dr. Xavier Fernando, and Dr. Robert Bestak based on the scope and objective of the journal. We appreciate the willingness of the authors to help in organizing this special issue. This special issue contains a collection state-of-the-art research articles with the objective to share research ideas and techniques in the areas of ubiquitous clouds and communication with the underlying technologies and applications, which include internet architecture and protocol, data analysis, information management, and its security. In particular, the articles included in the special issue analyzes the emerging state-of-the-art research areas like big data, cloud, cyber physical systems, and IoT to develop innovative solutions in order to overcome the existing challenges in computing and communication domains. This remains more essential to comply with the necessities of ubiquitous computing and communication applications. As one of the intended goals, the research articles aim to propose various network optimization algorithms, cloud/edge computing architectures, network routing protocols and task scheduling, self-organized networks, heterogeneous 5G networks, and Artificial Intelligence (AI) frameworks to propose the diverse range of features of reliability, flexibility, security, privacy, trust in ubiquitous clouds, and communication networks. Furthermore, this special issue also examines the significant network theories, formulates significant communication applications, and devises innovative methods to overcome the significant challenges that this research area poses.

The increase in the number of emitters operating at a frequency close to that of the modern radar systems impose a major challenge on the rapidly developing market for communication systems and the increasing demand for electromagnetic spectrum. Interference problems occur when multiple radar systems of the same type operate in the same environment with limited bandwidth availability and leads to frequency regions overlap. The radar systems incur significant performance losses due to the inferences caused under such circumstances. The concept of Cognitive Radar can provide solutions for such issues. This paper provides an improved Cognitive Radar architecture to address these challenges. The strong interference signals are suppressed using adaptive processing technique. The significant market advantages and application overview of this system is also presented in this paper.

Wireless sensor networks consist of unattended small sensor nodes having low energy and low range of communication. It has been observed that if there is any system to periodically start and stop the sensors sensing activities, then it saves some energy, and thus, the network lifetime gets extended. According to the current literature, security and energy efficiency are the two main concerns to improve the quality of service during transmission of data in wireless sensor networks. Machine learning has proved its efficiency in developing efficient processes to handle complex problems in various network aspects. Routing in wireless sensor network is the process of finding the route for transmitting data among different sensor nodes according to the requirement. Machine learning has been used in a broad way for designing energy efficient routing protocols, and this chapter reviews the existing works in the said domain, which can be the guide to someone who wants to explore the area further.

Augmented reality (AR) is the newest technology that can be applied to computer vision, audio, video and other sensor-based input projects into 3D vision. It is the backbone for all specialisation of science, medical and engineering concepts. Currently, the reading and learning method through AR-based approaches are quite highly intensive than the existing methods such as papers, books and magazines. This strategy is more expensive but it is more interactive to the user in understanding the root concepts in an effective manner. This paper explores the experiment on solar system revolution pattern along with 3D audio effect in spatial dimension. This novel idea inculcates more vibrancy in the current generation of students to understand the concepts with the clear illustrations and demonstrations.

In recent years, the monitoring and prediction of users' mobility in a mobile network has become a helpful tool to optimize the network's operation. However, the conventional techniques being used in this regard have several drawbacks, such as their dependence on user/device cooperation in a way or another, or on a specific HW/SW implementation in devices and/or networks. In this paper, we discuss a possible user/device-independent technique which is based on the radio interface signaling messages that result in user mobility data stored in the network. These data can also be used, to a certain extent, in obtaining information about user mobility. Although this technique, known as the Cell-ID location technique, does not provide a precise location, a careful analysis of these data, detecting the occurrence of the “cell oscillation” phenomenon in the mobility data, combined with a good knowledge of the coverage topology, can help detecting the status of the mobile station and improving the location accuracy. Additionally, this also allows the reduction of the mobility data to process.