The rapid adoption of technology led to the rapid growth of various fields, including Unnamed Aerial Vehicles (UAV).Digital Twin (DT) became a popular concept to facilitate this progress, serving as a virtual replica of the physical drones to support run-time compliance checking, coordination, and analysis in trustworthy UAV design and operation.Nevertheless, the DT technology in UAV often lacks a precise specification and clear explanation of its characteristics, parameters, and functionalities.To address this gap, this paper investigates current research in DT applications for autonomous drones and compiles the findings towards the design of a DT to support the UAV sector.To this end, we extract the DT characteristics from existing papers and leverage these insights to propose a DT design for autonomous drones.The resulting DT is foundational in facilitating seamless collaboration and decision-making among collaborating autonomous drones in autonomous ecosystems to ensure safe and trustworthy operation, as demonstrated in a proof of concept, demonstrated through a case study of logistics shipment, showcasing the DT application for autonomous drones' collaboration in autonomous ecosystems.

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Nowadays, software projects are a vital element in any organization’s success. It plays the highest role in the organization’s success in most cases. Hence, its main focus is to earn more money on a project and minimize the developing time cost. Sometimes due to unavailability of experts may decrease the profit of the organization. Thus, the organization comes with these types of issues. They want to increase the profit and decrease the development time and on fewer budgets, hire the more expert people to achieve the organization’s goal. For this purpose, they are applying the new development approach called GSD (global software development). Through global software development, they develop their project on a reasonable budget and maximize profit. However, in GSD, other challenges of team communication, coordination, geographical location, and cultural and time zone differences increase the project’s effort. This paper shows the more critical factors in GSD and the more challenging and shows their impact. They are more challenging on which project manager or team leader more focus on these factors, which can be more helpful for the project’s success. This paper is more helpful for both industries and researchers in that they can easily estimate the effort in the context of GSD.

The present time in the industry is a time where Android Applications are in a wide range with its widespread of the users also. With the increased use of Android applications, the defects in the Android context have also been increasing. The malware of defective software can be any pernicious program with malignant effects. Many techniques based on static, dynamic, and hybrid approaches have been proposed with the combination of Machine learning (ML) or Artificial Intelligence (AI) techniques. In this regard. Scientifically, it is complicated to examine the malignant effects. A single approach cannot predict defects alone, so multiple approaches must be used simultaneously. However, the proposed techniques do not describe the types of defects they address. The paper aims to propose a framework that classifies the defects. The Artificial Intelligence (AI) techniques are described, and the different defects are mapped to them. The mapping of defects to AI techniques is based on the types of defects found in the Android Context. The accuracy of the techniques and the working criteria has been set as the mapping metrics. This will significantly improve the quality and testing of the product. However, the appropriate technique for a particular type of defect could be easily selected. This will reduce the cost and time efforts put into predicting defects.

The 21st century is the age of automation. The automotive industry is converging towards deployment of complete automation by 2030. But are humans ready for it, or will they be hesitant to adopt it due to the lack of trust? To safeguard future autonomous mobility, robust run-time trust assurance and assessment is necessary. One strategy that is so far under-explored is rooted in involving the intelligence inside the autonomous agents, which could be directed towards detection of trust-breaking behaviour in other agents so that problematic vehicles are reported before they can engage in harmful behaviour.To support the progress in this direction, we propose a peer-to-peer model-based run-time trust assessment method, employing the model in terms of a Digital Twin for an autonomous vehicle (drone in our case) to ensure the trusted execution of intelligent agents. In this research, we examine the role of the Digital Twin in the trust-building scenario, and propose the characteristics of the intended Digital Twin model. To illustrate the approach, we present a case study of an autonomous-drone food delivery system and use formal approaches such as Petri Nets and Finite State Machines (FSM) to evaluate the scenario and demonstrate how trust could be built among autonomous drones or other vehicles.

The world is facing an energy crisis. The smart building concept is presented by many researchers using IoT devices that do not have sufficient computational power to compute the data to decide about the results. According to an estimation, there will be 50 billion IoT devices in 2020. Most IoT devices send data to the cloud for processing. Latency and network usage will be increased in cloud servers because the cloud servers will not be able to handle millions of requests spontaneously. In this paper, we have proposed a framework that minimizes latency and energy consumption in cloud computing. The proposed framework uses edge computing and most of the processing is performed on fog nodes. The framework contributes significantly to energy saving by supporting behavioral and physical changes in the cloud network.

Software fault prediction is a process to detect bugs in software projects. Fault prediction in software engineering has attracted much attention from the last decade. The early prognostication of faults in software minimize the cost and effort of errors that come at later stages. Different machine learning techniques have been utilized for fault prediction, that is proven to be utilizable. Despite, the significance of fault prediction most of the companies do not consider fault prediction in practice and do not build useful models due to lack of data or lack of enough data to strengthen the power of fault predictors. However, models trained and tested on less amount of data are difficult to generalize, because they do not consider project size, project differences, and features selection. To overcome these issues, we proposed an instance-based transfer learning through data leveraging using logistic linear regression as a base proposed statistical methodology. In our study, we considered three software projects within the same domain. Finally, we performed a comparative analysis of three different experiments for building models (targeted project). The experimental results of the proposed approach show promising improvements in (SFP).