Computing最新文献

The autonomous task success of an unmanned aerial vehiclel (UAV) or its military specialization called the unmanned combat aerial vehicle (UCAV) has a direct relationship with the planned path. However, planning a path for a UAV or UCAV system requires solving a challenging problem optimally by considering the different objectives about the enemy threats protecting the battlefield, fuel consumption or battery usage and kinematic constraints on the turning maneuvers. Because of the increasing demands to the UAV systems and game-changing roles played by them, developing new and versatile path planning algorithms become more critical and urgent. In this study, a greedy algorithm named as the Back-and-Forth (BaF) was designed and introduced for solving the path planning problem. The BaF algorithm gets its name from the main strategy where a heuristic approach is responsible to generate two predecessor paths, one of which is calculated from the start point to the target point, while the other is calculated in the reverse direction, and combines the generated paths for utilizing their advantageous line segments when obtaining more safe, short and maneuverable path candidates. The performance of the BaF was investigated over three battlefield scenarios and twelve test cases belonging to them. Moreover, the BaF was integrated into the workflow of a well-known meta-heuristic, artificial bee colony (ABC) algorithm, and detailed experiments were also carried out for evaluating the possible contribution of the BaF on the path planning capabilities of another technique. The results of the experiments showed that the BaF algorithm is able to plan at least promising or generally better paths with the exact consistency than other tested meta-heuristic techniques and runs nine or more times faster as validated through the comparison between the BaF and ABC algorithms. The results of the experiments further proved that the integration of the BaF boosts the performance of the ABC and helps it to outperform all of fifteen competitors for nine of twelve test cases.

In recent years, the significance of millimeter wave sensors has achieved a paramount role, especially in the non-invasive and ubiquitous analysis of various materials and objects. This paper introduces a novel IoT-based fake currency detection using millimeter wave (mmWave) that leverages machine and deep learning algorithms for the detection of fake and genuine currency based on their distinct sensor reflections. To gather these reflections or signatures from different currency notes, we utilize multiple receiving (RX) antennae of the radar sensor module. Our proposed framework encompasses three different approaches for genuine and fake currency detection, Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Transfer Learning Technique (TLT). After extensive experiments, the proposed framework exhibits impressive accuracy and obtained classification accuracy of 96%, 94%, and 98% for CNN, k-NN, and TLT in distinguishing 10 different currency notes using radar signals.

In recent years, containerization is becoming more and more popular for deploying applications and services and it has significantly contributed to the expansion of edge computing. The demand for effective and scalable container image management, however, increases as the number of containers deployed grows. One solution is to use a localized Docker registry at the edge, where the images are stored closer to the deployment site. This approach can considerably reduce the latency and bandwidth required to download images from a central registry. In addition, it acts as a proactive caching mechanism by optimizing the download delays and the network traffic. In this paper, we introduce an edge-enabled storage framework that incorporates a localized Docker registry. This framework aims to streamline the storage and distribution of container images, providing improved control, scalability, and optimized capabilities for edge deployment. Four demanding XR applications are employed as use cases to experiment with the proposed solution.

Wireless Body Area Networks (WBANs) are wireless sensor networks that monitor the physiological and contextual data of the human body. Nodes in a WBAN communicate using short-range and low-power transmissions to minimize any impact on the human body’s health and mobility. These transmissions thus become subject to failures caused by radiofrequency interference or body mobility. Additionally, WBAN applications typically have timing constraints and carry dynamic traffic, which can change depending on the physiological conditions of the human body. Several approaches for the Medium Access Control (MAC) sublayer have been proposed to improve the reliability and efficiency of the WBANs. This paper proposes and uses a systematic literature review (SLR) method to identify, classify, and statistically analyze the published works with MAC approaches for WBAN efficiency and reliability under dynamic network traffic, radiofrequency interference, and body mobility. In particular, we extend a traditional SLR method by adding a new step to select publications based on qualitative parameters. As a result, we identify the challenges and proposed solutions, highlight advantages and disadvantages, and suggest future works.

Computer system resilience refers to the ability of a computer system to continue functioning even in the face of unexpected events or disruptions. These disruptions can be caused by a variety of factors, such as hardware failures, software glitches, cyber attacks, or even natural disasters. Modern computational environments need applications that can recover quickly from major disruptions while also being environmentally sustainable. Balancing system resilience with energy efficiency is challenging, as efforts to improve one can harm the other. This paper presents a method to enhance disaster survivability in microservice architectures, particularly those using Kubernetes in cloud-based environments, focusing on optimizing electrical energy use. Aiming to save energy, our work adopt the consolidation strategy that means grouping multiple microservices on a single host. Our aproach uses a widely adopted analytical model, the Generalized Stochastic Petri Net (GSPN). GSPN are a powerful modeling technique that is widely used in various fields, including engineering, computer science, and operations research. One of the primary advantages of GSPN is its ability to model complex systems with a high degree of accuracy. Additionally, GSPN allows for the modeling of both logical and stochastic behavior, making it ideal for systems that involve a combination of both. Our GSPN models compute a number of metrics such as: recovery time, system availability, reliability, Mean Time to Failure, and the configuration of cloud-based microservices. We compared our approach against others focusing on survivability or efficiency. Our approach aligns with Recovery Time Objectives during sudden disasters and offers the fastest recovery, requiring 9% less warning time to fully recover in cases of disaster with alert when compared to strategies with similar electrical consumption. It also saves about 27% energy compared to low consolidation strategies and 5% against high consolidation under static conditions.

We present a simple and quick method to approximate network centrality indexes. Our approach, called QuickCent, is inspired by so-called fast and frugal heuristics, which are heuristics initially proposed to model some human decision and inference processes. The centrality index that we estimate is the harmonic centrality, which is a measure based on shortest-path distances, so infeasible to compute on large networks. We compare QuickCent with known machine learning algorithms on synthetic network datasets, and some empirical networks. Our experiments show that QuickCent can make estimates that are competitive in accuracy with the best alternative methods tested, either on synthetic scale-free networks or empirical networks. QuickCent has the feature of achieving low error variance estimates, even with a small training set. Moreover, QuickCent is comparable in efficiency—accuracy and time cost—to more complex methods. We discuss and provide some insight into how QuickCent exploits the fact that in some networks, such as those generated by preferential attachment, local density measures such as the in-degree, can be a good proxy for the size of the network region to which a node has access, opening up the possibility of approximating expensive indices based on size such as the harmonic centrality. This same fact may explain some evidence we provide that QuickCent would have a superior performance on empirical information networks, such as citations or the internet. Our initial results show that simple heuristics are a promising line of research in the context of network measure estimations.

Software requirements play a vital role in ensuring a software product’s success. However, it remains a challenging task to implement all of the user requirements, especially in a resource-constrained development environment. To deal with this situation, a requirements prioritization (RP) process can help determine the sequence for the user requirements to be implemented. However, existing RP techniques are suffered from some major challenges such as lack of automation, excessive effort, and reliance on stakeholders’ involvement to initiate the process. This study intends to propose an automated requirements prioritization approach called association rule mining-oriented (ARMO) to address these challenges. The automation process of the ARMO approach incorporates activities to first pre-process the requirements description and extract features. The features are then examined and analyzed through the applied rule mining technique to prioritize the requirements automatically and efficiently without the involvement of stakeholders. In this work, an evaluation model was further developed to assess the effectiveness of the proposed ARMO approach. To validate the efficacy of ARMO approach, a case study was conducted on real-world software projects grounded on the accuracy, precision, recall, and f1-score measures. The promising experimental results demonstrate the ability of the proposed approach to prioritize user requirements. The proposed approach can successfully prioritize user requirements automatically without requiring a significant amount of effort and stakeholders’ involvement to initiate the RP process.