Unmanned Aerial Vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services. During ...the last decade, there has been a growing focus of research in the UAV's assistance paradigm as a fundamental concept resulting in the constant improvement between different kinds of ground networks and the hovering UAVs in the sky. Recently, the wide availability of embedded wireless interfaces in the communicating entities has allowed the deployment of such a paradigm simpler and easiest. Moreover, due to UAVs' controlled mobility and adjustable altitudes, they can be considered as the most appropriate candidate to enhance the performance and overcome the restrictions of ground networks. This comprehensive survey both studies and summarizes the existing UAV-assisted research, such as routing, data gathering, cellular communications, Internet of Things (IoT) networks, and disaster management that supports existing enabling technologies. Descriptions, classifications, and comparative studies related to different UAV-assisted proposals are presented throughout the paper. By pointing out numerous future challenges, it is expected to simulate research in this emerging and hot research area. To the best of our knowledge, there are many survey papers on the topic from a technology perspective. Nevertheless, this survey can be considered as the first attempt at a comprehensive analysis of different types of existing UAV-assisted networks and describes the state-of-the-art in UAV-assisted research.
Search and find mission in ocean environment is a none trivial operation given the amount of random parameters associated with it. The uncertain and dynamic aspects related to ocean current movement ...make the trajectory prediction of drifting lost object onto sea water a very complicated task. In this work we present a novel lost target searching algorithm based on Recursive Area Clustering and target trajectory predication in ocean environment. Based on the widely known GlobCurrent v2 dataset which model the drifting of ocean surface current using satellite sensory data combined with mathematical and simulation modeling, we propose a regression algorithm based on our Recursive Area Clustering algorithm that we have developed previously to determine the strategic zones (weight centers) characterizing the high density areas extracted from drifting target history. Given those weight centers, we predict the object trajectory through refined regression. The predicted lost object trajectory is used to plan the path of UAV search mission. The model developed has a significant impact as we have tested our strategy in a scenario for searching an area covering 68517 km2, we have shown that 78% of the time, the lost object can be found within 32 km distance of the predicted trajectories limiting the significant search area to be about 5% of the whole searched area.
•In this work we presented a novel lost target searching algorithm based on Recursive Area Clustering and target trajectory predication in ocean environment.•We used the widely known GlobCurrent v2 dataset which model the drifting of ocean surface current using satellite sensory data combined with mathematical and simulation modeling.•We proposed a regression algorithm based on our Recursive Area Clustering algorithm that we have developed previously to determine the strategic zones (weight centers) characterizing the high density areas extracted from drifting target history.•Given those weight centers, we predict the object trajectory through refined regression. The predicted lost object trajectory is used to plan the path of UAV search mission.•We obtained encouraging results with a significant impact as we have tested our strategy in a scenario for searching an area covering 68517 km2, we have shown that 78% of the time, the lost object can be found within 32 km distance of the predicted trajectories limiting the significant search area to be about 5% of the whole searched area.
As a complementary solution for seamless and ubiquitous coverage, satellite communications will play crucial roles in future global Internet of Things (IoT). Focusing on enhancing access efficiency ...and resource utilization of massive machine-type devices (MTDs), we propose an efficient collision detection scheme at the first step of random access (RA) procedure for satellite-based IoT. By leveraging a single root Zadoff-Chu (ZC) sequence with an elaborate set of cyclic shift offsets to generate all the available preamble sequences, the proposed scheme can achieve rapid collision detection and load estimation in one-shot correlation operation, while having the robustness to the non-orthogonal interference. The preamble detection probability, collision detection probability, and load monitoring accuracy, are mathematically analyzed, and an optimal set of preamble selection probabilities is given to maximize the overall load monitoring accuracy. Simulation results exhibit the remarkable performance improvement of our scheme in typical satellite line-of-sight (LOS) scenario, by compared to the state-of-the-art collision detection schemes.
Malware Propagation in Large-Scale Networks Yu, Shui; Gu, Guofei; Barnawi, Ahmed ...
IEEE transactions on knowledge and data engineering,
2015-Jan.-1, 2015-1-1, Letnik:
27, Številka:
1
Journal Article
Recenzirano
Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate ...how malware propagates in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model for malware propagation from network to network. Based on the proposed model, our analysis indicates that the distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed through two real-world global scale malware data sets, and the results confirm our theoretical findings.
With the rollout of sixth generation (6G)–enabled massive internet of things (IoT) networks, the amount of data generated by IoT devices is expected to grow tremendously. Therefore, existing ...intrusion detection system (IDS) technology may not be sufficient to meet the scale and accuracy requirements for securing massive IoT. To overcome this shortcoming, edge intelligence–empowered next-generation (NextGen) IDSs utilize federated learning (FL) architectures. Nonetheless, to explore the full potential of federated IDSs, convergence and intercommunication overhead require some attention. One solution is softwarization and virtualization, which can increase network scalability and provide innovative security infrastructure for NextGen IDSs, offering holistic coverage and visibility to the entire network. In this line, we propose IDSoft, a novel softwarized solution that resides across the network infrastructure and leverages 6G enabling technologies, such as network function virtualization, mobile edge computing, and software-defined networking, to support FL-based IDSs. IDSoft is a scalable solution aimed at providing rapid and accurate detection combined with adaptive mitigation of large-scale cyberattacks while optimizing network resources. In this work, we analyze existing anomaly-based IDS approaches in IoT networks and focus on designing a hierarchical FL (HFL) framework for intrusion detection in IDSoft with synchronous and asynchronous aggregation and an additional offloading mechanism to enhance its performance. The numerical results demonstrate that the proposed HFL solution significantly reduces communication overhead, accelerates convergence, and promises greater scalability. Finally, future research trends are discussed.
Studying customer behavior and anticipating future trends is a challenging task, as customer behavior is complex and constantly evolving. To effectively anticipate future trends, businesses need to ...analyze large amounts of data, use sophisticated analytical techniques, and stay up-to-date with the latest research and industry trends. In this paper, we propose a comprehensive framework to identify trends in consumer behavior using multiple layers of processing, including clustering, classification, and association rule learning. The aim is to help a major retailer in Saudi Arabia better understand customer behavior by utilizing the power of big data analysis. The proposed framework is presented as being generalized to gain insight into the generated big data and enable data-driven decision-making in other relevant domains. We developed this framework in collaboration with a large supermarket chain in Saudi Arabia, which provided us with over 1,000,000 sales transaction records belonging to around 30,000 of their loyal customers. In this study, we apply our proposed framework to those data as a case study and present our initial results of consumer clustering and association rules for each cluster. Moreover, we analyze our findings to figure out how we can further utilize intelligence to predict customer behavior in clustered groups.
The auscultation of heart sounds has proven advantageous for the early diagnosis of cardiovascular conditions. Various methods have been proposed for the automatic analysis of heart sounds to reduce ...subjectivity in diagnosis and alleviate physicians' workload. However, the effectiveness of these methods heavily depends on the amount and quality of the heart sound datasets used and the availability of publicly accessible datasets that include the most common and difficult classes. In this study, we introduce the HeartWave dataset, a comprehensive heart sound dataset comprising recordings from nine distinct classes of the most common heart sounds from all classes and subclasses of cardiovascular diseases, documented, with enough samples, good quality, and well labelled, with a focus on the hard and difficult cases of diagnosis. The dataset includes a total of 1353 recordings of heart sounds. Notably, this dataset includes extremely rare and difficult-to-diagnose classes. In order to establish a reliable reference standard, a team of experienced cardiologists actively participated in the entire annotation process. The length of audio recordings is substantial, allowing for the extraction of multiple heartbeats from a single recording through the use of segmentation techniques. Moreover, our dataset takes into consideration the standard cardiologist practices to enable the capture of specific heart sounds associated with corresponding clinical locations. According to our post analysis of the dataset, the average signal-to-noise ratio of our proposed dataset surpasses that of the widely known PhysioNet/CinC 2016 public dataset by about two folds, ensuring a cleaner acoustic signal. Our proposed dataset provides a valuable resource for training and evaluating machine learning models aimed at automated heart sound classification and diagnosis.
Landmine contamination is a significant problem that has devastating consequences worldwide. Unmanned aerial vehicles (UAVs) can play an important role in solving this problem. The technology has the ...potential to expedite, simplify, and improve the safety and efficacy of the landmine detection process prior to physical intervention. Although the process of detecting landmines in contaminated environments is systematic, it is proven to be rather costly and overwhelming, especially if prior information about the location of the lethal objects is unknown. Therefore, automation of the process to orchestrate the search for landmines has become necessary to utilize the full potential of system components, particularly the UAV, which is the enabling technology used to airborne the sensors required in the discovery stage. UAVs have a limited amount of power at their disposal. Due to the complexity of target locations, the coverage route for UAV-based surveys must be meticulously designed to optimize resource usage and accomplish complete coverage. This study presents a framework for autonomous UAV-based landmine detection to determine the coverage route for scanning the target area. It is performed by extracting the area of interest using segmentation based on deep learning and then constructing the coverage route plan for the aerial survey. Multiple coverage path patterns are used to identify the ideal UAV route. The effectiveness of the suggested framework is evaluated using several target areas of differing sizes and complexities.
The World Health Organization (WHO) highlights that cardiovascular diseases (CVDs) are one of the leading causes of death globally, with an estimated rise to over 23.6 million deaths by 2030. This ...alarming trend can be attributed to our unhealthy lifestyles and lack of attention towards early CVD diagnosis. Traditional cardiac auscultation, where a highly qualified cardiologist listens to the heart sounds, is a crucial diagnostic method, but not always feasible or affordable. Therefore, developing accessible and user-friendly CVD recognition solutions can encourage individuals to integrate regular heart screenings into their routine. Although many automatic CVD screening methods have been proposed, most of them rely on complex prepocessing steps and heart cycle segmentation processes. In this work, we introduce a simple and efficient approach for recognizing normal and abnormal PCG signals using Physionet data. We employ data selection techniques such as kernel density estimation (KDE) for signal duration extraction, signal-to-noise Ratio (SNR), and GMM clustering to improve the performance of 17 pretrained Keras CNN models. Our results indicate that using KDE to select the appropriate signal duration and fine-tuning the VGG19 model results in excellent classification performance with an overall accuracy of 0.97, sensitivity of 0.946, precision of 0.944, and specificity of 0.946.
In recent years, artificial intelligence has been widely used in the field of robotics. However, these robot-related tasks are difficult to migrate from the cloud to edge nodes due to the large ...computing and storage resource requirements. In this project, we develop a platform for a heterogeneous robotic system. The platform is built to facilitate the development of advanced robotic applications with minimal human interactions, where search and find system traversal is the main application. To support robots performing tasks in near field in real time, in this paper we introduce traversal and task division algorithms which are introduced to perform a cooperative search mission by a group of robotic agents to achieve intelligent search and find as an edge service. We evaluate the performance against the algorithm's parameters using data obtained in controlled field experiments. The aim was to identify and study some key performance parameters impacting the traversal function in the application.
PDF
