Computing software plays an essential role in almost every sector of the digital age, but the process of efficient software development still faces several challenges. Effective software development methodology can be the difference between the success and failure of a software project. This research aims to evaluate the overall impact of Agile Software Development (ASD) on the individual, organizational, software development, and project management dimensions. For this purpose, we surveyed several software development professionals from a variety of backgrounds (experience, location, and job ranks) to explore the impact of ASD on the IT industry of Pakistan. Our analysis of the collected information is two folds. First, we summarized the findings from our surveys graphically clearly show the opinions of our survey respondents regarding the effectiveness of the Agile methodology for software development. Secondly, we utilized quantitative measures to analyze the same data statistically. A comparison is drawn between the graphical and statistical analysis to verify the reliability of our findings. Our findings suggest the existence of a strong relationship between effective software development and the use of Agile processes. Our analysis shows that the job fit of software development professionals and ASD are critical factors for software development project success in terms of cost, quality, stakeholders satisfaction, and time. Although the study focuses on the IT industry of Pakistan, the findings can be generalized easily to other developing IT industries worldwide.

The fifth generation of cellular technologies, 5G, promises to deliver a wide variety of services. However, the first release of 5G (3GPP Rel-15) did not consider multicast capabilities in the 5G system including both access and core networks. This article fills the gap by describing solutions for 5G mobile core network architecture incorporating multicast and broadcast capabilities. Two innovative solutions to provision multicast and broadcast, based on 3GPP Rel-15 arechitecture, have been proposed and analyzed. One is 5G architecture friendly with low imprint over Rel-15 while the other is based on the evolution of LTE Broadcast architecture. The architectural solutions feature two new main transmission modes: transparent multicast transport and point-to-multipoint services. The former enables the 5G network as a network pipe supporting multicast traffic providing a cost-effective delivery mode for multimedia while the latter enables multicast and broadcast as a service. The paper also presents a convergent fixed wireline and mobile network architecture based on the proposed mobile network architecture altenatives. The proposed architecture alternatives are suitable for different verticals and applications such as Digital Terrestrial Television, Public Warning, Internet of Things, V2X (vehicle to everything) and mission critical communications (MCC).

This work presents eight demonstrators and one showcase developed within the 5G-Xcast project. They experimentally demonstrate and validate key technical enablers for the future of media delivery, associated with multicast and broadcast communication capabilities in 5 ^th Generation (5G). In 5G-Xcast, three existing testbeds: IRT in Munich (Germany), 5GIC in Surrey (U.K.), and TUAS in Turku (Finland), have been developed into 5G broadcast and multicast testing networks, which enables us to demonstrate our vision of a converged 5G infrastructure with fixed and mobile accesses and terrestrial broadcast, delivering immersive audio-visual media content. Built upon the improved testing networks, the demonstrators and showcase developed in 5G-Xcast show the impact of the technology developed in the project. Our demonstrations predominantly cover use cases belonging to two verticals: Media & Entertainment and Public Warning, which are future 5G scenarios relevant to multicast and broadcast delivery. In this paper, we present the development of these demonstrators, the showcase, and the testbeds. We also provide key findings from the experiments and demonstrations, which not only validate the technical solutions developed in the project, but also illustrate the potential technical impact of these solutions for broadcasters, content providers, operators, and other industries interested in the future immersive media delivery.

The main purpose of mathematical model building while employing statistical data analysis is to obtain high accuracy of approximation within the range of observed data and sufficient predictive properties. One of the methods for creating mathematical models is to use the techniques of regression analysis. Regression analysis usually applies single polynomial functions of higher order as approximating curves. Such an approach provides high accuracy; however, in many cases, it does not match the geometrical structure of the observed data, which results in unsatisfactory predictive properties. Another approach is associated with the use of segmented functions as approximating curves. Such an approach has the problem of estimating the coordinates of the breakpoint between adjacent segments. This article proposes a new method for determining abscissas of the breakpoint for segmented regression, minimizing the standard deviation based on multidimensional paraboloid usage. The proposed method is explained by calculation examples obtained using statistical simulation and real data observation.

The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

This paper contains approach to security key indicators assessment in modern cellular networks. To improve the security of cellular networks it is necessary first to analyze the existing protection level and create a solution that will improve data transmission security. The current assessment tools do not separate key quality and performance indicators for specific services provided by mobile operators. It limits the ability to control the security of the provided services and cellular networks modernization. Based on the key quality and performance indicators methodology the summary table that contains formulas for calculating these indicators was developed and presented. This table provides more flexibility to assess the security of provided services and highlight the main issues based on statistical data gathered from specific users. Overall, this method allows continuous control over network information security and alerts about security violation in the network.