BIG DATA IN SMART CITIES: A SYSTEMATIC MAPPING REVIEW

Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain “Big Data in Smart Cities” by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored

A three-level ransomware detection and prevention mechanism

Ransomware encrypts victim's files or locks users out of the system. Victims will have to pay the attacker a ransom to decrypt and regain access to the user files. Petya targets individuals and companies through email attachments and download links. NotPetya has worm-like capabilities and exploits EternalBlue and EternalRomance vulnerabilities. Protection methods include vaccination, applying patches, et cetera. Challenges faced to combat ransomware include social engineering, outdated infrastructures, technological advancements, backup issues, and conflicts of standards. Three- Level Security (3LS) is a solution to ransomware that utilizes virtual machines along with browser extensions to perform a scan, on any files that the user wishes to download from the Internet. The downloaded files would be sent over a cloud server relay to a virtual machine by a browser extension. Any changes to the virtual machine after downloading the file would be observed, and if there were a malfunction in the virtual machine, the file would not be retrieved to the user's system

Protocol-specific and sensor network-inherited attack detection in IoT using machine learning

For networks with limited resources, such as IoT-enabled smart homes, smart industrial equipment, and urban infrastructures, the Routing Protocol for Low-power and Lossy Networks (RPL) was developed. Additionally, a number of optimizations have been suggested for its application in other contexts, such as smart hospitals, etc. Although these networks offer efficient routing, the lack of active security features in RPL makes them vulnerable to attacks. The types of attacks include protocol-specific ones and those inherited by wireless sensor networks. They have been addressed by a number of different proposals, many of which have achieved substantial prominence. However, concurrent handling of both types of attacks is not considered while developing a machine-learning-based attack detection model. Therefore, the ProSenAD model is proposed for addressing the identified gap. Multiclass classification has been used to optimize the light gradient boosting machine model for the detection of protocol-specific rank attacks and sensor network-inherited wormhole attacks. The proposed model is evaluated in two different scenarios considering the number of attacks and the benchmarks for comparison in each scenario. The evaluation results demonstrate that the proposed model outperforms with respect to the metrics including accuracy, precision, recall, Cohen’s Kappa, cross entropy, and the Matthews correlation coefficient

A secure communication protocol for unmanned aerial vehicles

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles. Multiple UAVs and autopilot systems support it, and it provides bi-directional communication between the UAV and Ground Control Station. The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS. In order to increase the transfer speed and efficiency, the Mavlink does not encrypt the messages. As a result, the protocol is vulnerable to various security attacks such as Eavesdropping, GPS Spoofing, and DDoS. In this study, we tackle the problem and secure the Mavlink communication protocol. By leveraging the Mavlink packet's vulnerabilities, this research work introduces an experiment in which, first, the Mavlink packets are compromised in terms of security requirements based on our threat model. The results show that the protocol is insecure and the attacks carried out are successful. To overcome Mavlink security, an additional security layer is added to encrypt and secure the protocol. An encryption technique is proposed that makes the communication between the UAV and GCS secure. The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency. The results are validated in terms of transfer speed, performance, and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol. Our achieved results have significant improvement over the literature and Mavlink in terms of security

Big data technology in education: Advantages, implementations, and challenges

This study provides an in-depth review of Big Data Technology (BDT) advantages, implementations, and challenges in the education sector. BDT plays an essential role in optimizing education intelligence by facilitating institutions, management, educators, and learners improved quality of education, enhanced learning experience, predictive teaching and assessment strategy, effective decision-making and better market analysis. Moreover, BDTs are used to analyze, detect and predict learners’ behaviors, risk failures and results to improve their learning outcomes and to ensure that the academic programmers undertaken are of high-quality standards. This study identified that some universities and governments had implemented BDTs for transferring traditional education to digital smart one. Despite BDT significant offerings for education still, there are several challenges regarding its full implementation such as security, privacy, ethics, lack of skilled professionals, data processing, storage, and interoperability

An experimental study to evaluate the performance of machine learning algorithms in ransomware detection

The research in the domain of ransomware is rapidly emerging, and the application of machine learning algorithms in ransomware detection is one of the recent breakthroughs. In this research, we constructed an experimental platform using ransomware datasets to compare the performance of various machine learning algorithms such as Random Forest, Gradient Boosting Decision Tree (GBDT), Neural Network using Multilayer Perceptron as well as three types of Support Vector Machine (SVM) kernels in ransomware detection. Our experiment is based on a combination of different methodologies reported in the existing literature. We used complete executable files in our experiment, analyzed the opcodes and measures their frequencies. The objective of this research was to discover the algorithms that are highly suitable to develop models as well as systems for ransomware detection. Consequently, we identified that Random Forest, GBDT and SVM (Linear) have shown optimal results in detection of ransomware

Performance of deep learning vs machine learning in plant leaf disease detection

Plants are recognized as essential as they are the primary source of humanity's energy production since they are having nutritious, medicinal, etc. values. At any time between crop farming, plant diseases can affect the leaf, resulting in enormous crop production damages and economic market value. Therefore, in the farming industry, identification of leaf disease plays a crucial role. It needs, however, enormous labor, greater preparation time, and comprehensive plant pathogen knowledge. For the identification of plant disease detection various machine learning (ML) as well as deep learning (DL) methods are developed & examined by various researchers, and many of the times they also got significant results in both cases. Motivated by those existing works, here in this article we are comparing the performance of ML (Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD)) & DL (Inception-v3, VGG-16, VGG-19) in terms of citrus plant disease detection. The disease classification accuracy (CA) we received by experimentation is quite impressive as DL methods perform better than that of ML methods in case of disease detection as follows: RF-76.8% > SGD-86.5% > SVM-87% > VGG-19–87.4% > Inception-v3–89% > VGG-16–89.5%. From the result, we can tell that RF is giving the least CA whereas VGG-16 is giving the best in terms of CA

Proposing an algorithm for UAVs interoperability: MAVLink to STANAG 4586 for securing communication

Recently, the use of unmanned aerial vehicles has become increased rapidly in both civilian and military applications. With the increased popularity and wide range of applications, these systems’ global manufacturer market has also been improved. UAVs play a vital role in modern warfares, and the country with this technology has many advantages over its enemies. A typical UAV interacts typically with a ground control station or a control station with different communication protocols. Among these protocols, an open-source protocol, MAVLink, is the most common and widely used protocol in the private sector. Despite being most commonly used, this protocol is weak, insecure communication. For military UAVs, the protocols and standards are generally different, and they are not openly available. NATO countries use such a protocol to agree on a standard protocol called STANAG (Standard and Agreement) 4586 for unmanned aerial vehicles. While other countries show interest in buying military UAVs, they need to upgrade their existing UAVs or ground control stations to be compatible with the standards. This paper proposes a communication bridge between MAVLink and STANAG 4586 to interoperate like AV Rodrigues et al. proposed. Additionally, this work will make the STANAG 4586 compliant GCSs operate with MAVLink supported UAVs more securely using our proposed algorithm to secure the MAVLink communication

Bargaining based design mechanism for delay sensitive tasks of mobile crowdsensing in IoT

Internet of Things (IoT) is getting growing interest to offer great opportunities in combination with Mobile Crowd Sensing for real-time applications. Existing approaches motivate mobile workers (MWs) for approaching the distant locations to receive attractive incentives for traveling. The main question addressed is that a number of tasks remain incomplete out of total al-located tasks. Moreover, the profitability and feasible budget constraints of the platform is also not considered. This paper presents Bargaining based Design Mechanism (BDM) to involve the nearest located MWs to improve the completion of tasks. The main method involves a bargaining based game model that increases the task completion ratio while considering the feasible budget constraint, platform profitability and social welfare. The proposed approach comprises of two algorithms: one for the selection of optimal MWs with low cost and less delay. Second is to organize bargaining for rewarding the platform on social welfare. Our work is validated by developing a testbed on Windows Azure cloud. Results prove that proposed BDM out-performs the counterparts in terms of decay coefficient, task completion ratio, participant's winning ratio, fraction of task incompletion and social welfare

Smart traffic monitoring system using Unmanned Aerial Vehicles (UAVs)

Road traffic accidents are one of the leading causes of deaths and injuries in the word resulting in the not only loss of precious human lives but also affect the economic resources. According to the World Health Organization (WHO), over 1.35 million people are killed, and over 50 million are injured due to road accidents throughout the world. Unfortunately, as compared to other developing countries with the same ratio of vehicle possession, in Saudi Arabia, the fatalities and injuries are much higher. Every year around 7000–9000 people die, and over 39000 serious injuries occur in road accidents. There is at least one accident happens every minute in Saudi Arabia. To decrease the road traffic accidents, fatalities, and injuries caused by them, the Saudi Ministry of Interior came up with new rules, regulations, and hefty fines. Also, they introduced a new traffic system called the SAHER system. Still, due to the static nature and other limitations of the system, the drivers found loopholes and ways to deceive the system to avoid the fines and not being caught by the system. The most common violation includes excess speed, abrupt deceleration, and distracted driving. In this paper, we propose a smart traffic surveillance system based on Unmanned Aerial Vehicle (UAV) using 5G technology. This traffic monitoring system covers the existing limitations of the SAHER system deployed in KSA. By overcoming the existing limitations and loopholes of the SAHER system, it is observed that the number of accidents and fatalities can be decreased. The projected results show that those violations when to overcome, the number of accidents per year falls to 299,317 leading to 4,868 deaths and 33,199 injuries for 1st year, and in the next five years the number of deaths and will be decreased to 3,745 and injuries to 16,600 based on the current data available. We aim the system will further reduce the number of accidents and fatalities and injuries caused by it