Recently, drones (or unmanned aerial vehicles (UAVs)) are getting increasing attention and taking mobile computing to a new era. Due to the support of highly advanced technologies, soon they might be ubiquitous and networks of drones might be used in providing civilian drone services. In this paper, we provide a survey of drone services and applications, data management for drones, data services using drones, distributed computing trends fuelled by drones and a range of human-drone interaction research which is useful if drones are to regularly serve non-technical users, while highlighting the specific concerns in data management and airborne Internet-of-Things (IoT) computing infrastructure. We present concepts such as drones-as-a-service and fly-in, fly-out computing infrastructure, and note data management and system design issues that arise in these situations. Issues of Big Data arising from such applications, optimising the configuration of airborne and ground infrastructure to provide the best QoS and QoE, situation-awareness, scalability, reliability, scheduling for efficiency, interaction with users and drones using different methods are noted.

Drones are rapidly finding their way into civilian applications, and are mostly networked, enabling their remote programming, and connectivity with humans. However, drones are limited by the weight they can carry and battery power resulting in limited resources. Moreover, some applications require utilising multiple drones to act in coordination. The combination of utilising nearby devices (i.e. with additional resources beyond the drone capability) and controlling multiple drones in a more convenient way has the potential to overcome these limitations. This paper proposes and examines programmable crowd-powered drones, involving two key concepts for combining drones and smartphones as a crowd-powered resource cloud. In particular, we focus on crowd-sourcing for drone computations, and multi-drone service management using a new scripting language for coordinated flight paths of multiple drones. We describe our underlying model and experimentation with these concepts. We then extensively discuss the prospect of drones servicing communities within IoT ecosystems, as a future direction.

In the age of social media, the spread of rumors is becoming easier due to the proliferation of communication and information dissemination platforms. Detecting rumors is a major problem with significant consequences for the economy, democracy, and public safety. Deep learning approaches were used to classify rumors and have yielded state-of-the-art results. Nevertheless, the majority of techniques do not attempt to explain why or how decisions are made. This paper introduces a hybrid attention neural network (HANN) to identify rumors from social media. The advantage of HANN is that it will allow the main user to capture the relative and important features between different classes as well as provide an explanation of the model’s decisions. Two deep neural networks are included in the proposal: CNNs and Bidirectional Long Short Term Memory (Bi-LSTM) networks with attention modules. In this paper, the model is trained using a benchmark dataset containing 3612 distinct tweets crawled from Twitter include several types of rumors related to COVID-19. Each subset of data has a balanced label distribution with 1480 rumors tweets (46.87% ) and 1677 non-rumors tweets (53.12% ). The experimental results demonstrate that the new approach (HANN model) performs better results in terms of performance and accuracy (about 0.915% ) than many contemporary models (AraBERT, MARBEART, PCNN, LSTM, LSTM-PCNN and Attention LSTM). Moreover, a number of software engineering features such as followers, friends, and registration age are used to enhance the model’s accuracy.

There is a crucial need to process patient’s data immediately to make a sound decision rapidly; this data has a very large size and excessive features. Recently, many cloud-based IoT healthcare systems are proposed in the literature. However, there are still several challenges associated with the processing time and overall system efficiency concerning big healthcare data. This paper introduces a novel approach for processing healthcare data and predicts useful information with the support of the use of minimum computational cost. The main objective is to accept several types of data and improve accuracy and reduce the processing time. The proposed approach uses a hybrid algorithm which will consist of two phases. The first phase aims to minimize the number of features for big data by using the Whale Optimization Algorithm as a feature selection technique. After that, the second phase performs real-time data classification by using Naïve Bayes Classifier. The proposed approach is based on fog Computing for better business agility, better security, deeper insights with privacy, and reduced operation cost. The experimental results demonstrate that the proposed approach can reduce the number of datasets features, improve the accuracy and reduce the processing time. Accuracy enhanced by average rate: 3.6% (3.34 for Diabetes, 2.94 for Heart disease, 3.77 for Heart attack prediction, and 4.15 for Sonar). Besides, it enhances the processing speed by reducing the processing time by an average rate: 8.7% (28.96 for Diabetes, 1.07 for Heart disease, 3.31 for Heart attack prediction, and 1.4 for Sonar).

Crimes have clearly had a detrimental impact on a nation’s development, prosperity, reputation, and economy. The issue of crime has become one of the most pressing concerns in societies, thus reducing the crime rate has become an increasingly critical task. Recently, several studies have been proposed to identify the causes and occurrences of crime in order to identify ways to reduce crime rates. However, few studies have been conducted in Saudi Arabia technological solutions based on crime analysis. The analysis of crime can help governments identify hotspots of crime and monitor crime distribution. This study aims to investigate which Saudi Arabian areas will experience increased crime rates in the coming years. This research helps law enforcement agencies to effectively utilize available resources in order to reduce crime rates. This paper proposes SARIMA model which focuses on identifying factors that affect crimes in Saudi Arabia, estimating a reasonable crime rate, and identifying the likelihood of crime distribution based on various locations. The dataset used in this study is obtained from Saudi Arabian official government channels. There is detailed information related to time and place along with crime statistics pertaining to different types of crimes. Furthermore, the new proposed method performs better than other traditional classifiers such as Linear Regression, XGB, and Random Forest. Finally, SARIMA model has an MAE score of 0.066559, which is higher than the other models.

Although drones are currently used in many outdoor and indoor applications, still there are numerous opportunities to be explored by combining drones with other accessible, adaptable and emerging technologies, for example, cell phones, smartwatches, and Internet-of-Things appliances. In this paper, we propose and investigate an approach to using drones to provide services, one where drones act as fly-in-fly-out servers (edge computing style) in order to collect data, provide Internet access, and process data for mobile users. Our study simulates drones providing services, not restricted to edge computing services. We provide anecdotal experiments on factors affecting drone service delivery.