Due to the extremely important role of data in the industrial Internet of Things (IIoT) network, trust and security of data are among the major concerns. In this article, we develop a cloud-integrated 5G-IIoT network architecture enabled by a three-party authenticated key exchange (AKE) protocol with privacy-preserving to secure data exchanged via wireless communication, cope with unauthorized entities, and ensure data integrity. Moreover, we develop a trust model based on the Dempster–Shafer theory to check the trustworthiness of data collected by smart devices/sensor nodes. Security analysis performed on our scheme demonstrates that it can withstand different well known attacks in the IIoT environment. We also analyzed the validity of our scheme by using the automated validation of internet security protocols and applications tool. Additionally, the performance evaluation and experimental results prove the effectiveness of the proposed scheme compared to the existing works in terms of accuracy, delay, trust, and throughput.

In real-time medical monitoring systems, given the significance of medical data and disease symptoms, a secure and always-on connection with the medical centre over the public channels is essential. To this end, an edge-enabled Internet of Medical Things (IoMT) scheme is designed to improve flexibility and scalability of the network and provide seamless connectivity with minimum latency. The entities involved in such network are vulnerable to various attacks and can potentially be compromised. To address this issue, an authentication scheme comprised of digital signature and Authenticated Key Exchange (AKE) protocol is proposed which guarantees only authorized entities get access to the services available in the medical system. Moreover, to fulfill the privacy-preserving, each entity is mapped to a different pseudo-identity. The non-mathematical and performance analysis show that the proposed scheme is robust against various attacks such as impersonation and replay attacks.

Abstract Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non‐publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state‐of‐the‐art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.

Security and privacy of data-in-transit are critical issues in Industry 4.0, which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this article, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. In addition, we conduct a security analysis that proves that the scheme resists several attacks in the Industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

Abstract In recent years, due to significant evolution in adopting new technologies and development methodologies in the field of software engineering, the developers and researchers are striving to optimize the accuracy of software effort estimation (SEE). The overestimation and underestimation both are the key challenges for software progress; hence, there is a continuous need for an accurate SEE. This paper highlights a systematic review of studies associated with the best practices of use case point (UCP) and expert judgment–based software development effort estimation techniques. The primary aim and contribution of this paper are to support the researchers through an extensive review to ease to other researcher's search for effort estimation studies. We have performed state‐of‐the‐art review from five viewpoints of reference: (a) review of studies concerning UCPs and expert judgment–based effort estimation, (b) research contribution and future recommendation in different novelties, (c) usage of the dataset, (d) availability of accuracy metrics, and (e) findings of the studies. We have performed a systematic review of studies which are published in the period of 2000 to 2019. We have selected a total of 34 primary studies of UCP and expert judgment–based estimation techniques to report the research questions stated in this review.

Local government may face different challenges in adopting IT innovation due to its unique characteristic in terms of business functions which dealt directly with the citizens, organisational type, structure, and geographic location. To date, although numerous review studies on IT innovation adoption have been carried out, nevertheless there are obvious limitations in a local government context. Hence, this study aims to put forward an explanation for critical influential determinants of IT innovation adoption in local government contexts by reviewing related works from the year 2013 to 2017. This study has identified influential technological, organisational, individual, and environmental determinants of IT innovation adoption in a local government context. The most striking finding is that critical influential determinants of IT innovation in local government context were organisational (leadership, governance), technological (perceived benefits, cost), individual (personnel skills), and environmental (policy and regulations, citizen influence). The results from this study will help other researchers to understand the critical influential determinants of IT innovation adoption, particularly in a local government context.

Given the rapid development of Internet, social media opened up new opportunity for medical and health communications. Numbers of previous research conducted showed that social media shape up new chances to ensure an effective distribution of information in healthcare. Looking from the perspective of medical tourism, this study aimed to identify the availability of social media link in the medical tourism hospitals websites from Malaysia and evaluate their performance on every social media platform namely Facebook, Instagram, Twitter and YouTube. A list of 70 medical tourism hospitals gathered from the websites of Malaysian Health Travel Council (MHTC) http://www.mhtc.org.my. Each URL of the hospital identified and visited. The social media link availability on the website was observed and results showed that Facebook is the most preferable social media among the hospitals which is 51.4% followed by Instagram recorded as 14.3%. The Twitter recorded as 12.9% and YouTube only 11.4% hospitals. Most hospitals only provide one link to social media directly from their website. Astonishingly, almost half of the total medical tourism hospitals in Malaysia that is 44.2% does not have link to any social media on their websites. The top ten-hospital performance in Facebook rank gain more than 15000 “like” and “follows” at their page. Therefore, it was highly recommended for the medical tourism hospitals to actively engage with the social media and promote the link on their websites so that they can lead their current and prospective patients towards various information resources for a better distribution of medical and health information.

Systematic Literature Review (SLR) is a structured way of conducting a review that can assist the researcher in analyzing the progress of a specific stream of research. Although there are a number of SLR guidelines proposed by existing researchers, there is still too little attention paid to the approach taken and the tools that could be applied throughout the SLR process. Therefore, this paper attempted to fill the gap by presenting the application of Data Analytics approach in the SLR with the utilization of data mining and text mining technique. It proposed a step-by-step procedure using data analytics approach, and recommended tools to be used in the SLR process. To demonstrate the applicability of the approach, the authors selected a Master Data Management research domain as a case study. Based on this particular application, it can be concluded that this approach is a replicable, effective and output-based approach in conducting an SLR. It is hoped that other researchers would be able to replicate this approach in doing SLR for other research domains such as software engineering and information technology fields of study.

Software development mostly adopts two kinds of methodologies; Traditional and Agile. In both methodologies, software changes are inevitable due to the dynamic nature of the software development project itself. One of the factors that influences the effectiveness of the change acceptance decision is the accuracy of the change effort estimation. There are two current models that have been widely used to estimate change effort which are algorithmic and non-algorithmic models. The algorithmic model is known for its formal and structural way of estimation and best suited for Traditional methodology. While non-algorithmic model is widely adopted for Agile methodology of software projects due to its easiness and requires less work in term of effort predictability. Nevertheless, none of the existing change effort estimation models are proven to suit both, Traditional and Agile methodology. Thus, this paper proposes an algorithmic-based change effort estimation model that uses change impact analysis method which is applicable for both Traditional and Agile methodologies. The proposed model uses a current selected change impact analysis method for software development phase which is the SDP-CIAF (Software Development Phase-Change Impact Analysis Framework). The proposed model is evaluated through an extensive experimental validation using case scenarios of six real Traditional and Agile methodologies software projects. The evaluation results confirmed the applicability for both Traditional and Agile methodologies.

It is important for software project manager to make effective decisions when managing software changes during software development. One type of information that helps to make the decision is the estimation of the change effort produced by the changes. One of the inputs that help to decide whether to accept or reject the changes is by having reliable estimation on the change effort. From software development perspective, the estimation has to take into account the inconsistent states of software artifacts across project lifecycle i.e., fully developed and partially developed. This research introduces a new change effort estimation tool (Constructive Change Cost Model or COCHCOMO) that is able to take into account the inconsistent states of software artifacts in its estimation process. This tool was developed based on our extended version of static and dynamic impact analysis techniques. Extensive experiments using several case studies have been conducted in which the results show acceptable error rates have been achieved.

Software undergoes changes at all stages of the software development process. Accepting too many changes will cause expense and delay and rejecting the changes may cause customer dissatisfaction. One of the inputs that help the software project management to decide whether to accept or reject the changes is by having reliable predictions of the impact of the changes. Change impact analysis is one of the methods that can be used to provide the predictive information. Many current impact analysis approaches have been developed for the software maintenance phase. These approaches assume that all classes in the class artifact are completely developed and the class artifact is used as a source of analysis since it represents the final user requirements. However, these assumptions are not practical for impact analysis in the software development phase as some classes in the class artifact are still under development or partially developed. This leads to inaccuracy. This paper presents a novel impact analysis approach to be used in the software development phase. The significant achievements of the approach are demonstrated through an extensive experimental validation using several case studies. The experimental analysis shows improvement in the accuracy over current impact analysis results.

Impact analysis is an activity of assessing the effect of making a set of changes to a software system. Many approaches have been developed include performing impact analysis on a high level model that reflects to low level analysis using class interaction prediction. However, analysis from the model contains false results due to not all interactions between classes have impact to one another. In this paper we introduce a new impact analysis approach that is able to filter some false results using a set of impact prediction filters. The contributions of the paper are: (1) a new impact analysis approach; (2) a new set of impact prediction filters and; (3) evaluation results that show the new impact analysis approach improves the accuracy of the prediction results.