Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes popular ML techniques used for fraud detection, the most popular fraud type, and evaluation metrics. The reviewed articles showed that support vector machine (SVM) and artificial neural network (ANN) are popular ML algorithms used for fraud detection, and credit card fraud is the most popular fraud type addressed using ML techniques. The paper finally presents main issues, gaps, and limitations in financial fraud detection areas and suggests possible areas for future research.

The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.

The energy consumption and coverage range of unmanned aerial vehicles (UAVs) are major challenges in UAV-based postdisaster communications. To address these challenges, energy harvesting is employed to power communication devices and prolong the lifetime of the wireless communication network during a disaster. In addition, clustering techniques and device-to-device (D2D) communication are needed to increase the overall network coverage and provide sustainable connectivity during the disaster and postdisaster phases. We have proposed a novel emergency communication system (ECS) using the optimal cluster head (CH) technique to improve the energy transfer efficiency for sustainable network connectivity. We have developed a UAV deployment model assisted by the clustering technique and D2D links that is capable of harvesting energy to increase the network lifetime. This new approach is expected to enhance the reliability of the network in disaster situations. The proposed methods have been evaluated by measuring the energy efficiency performance and the network outage probability. The simulation results demonstrate improved performance with the deployment of optimal CHs, while the outage probability has been effectively reduced. Moreover, the proposed approach has been proven to reduce the computational complexity. In conclusion, UAV deployment with the optimal CH algorithm is a suitable network design to recover from natural disasters and potentially save many lives.

Ensuring communication infrastructures always alive during disaster mitigation and recovery is of paramount importance. In this case, Unmanned Aerial vehicle (UAV) will be the ideal substitute for the malfunctioning ground base station due to disaster. Henceforth, we suggest a multi‐UAV and Search and Rescue (SAR) collaboration model in this letter. To improve its connectivity, we propose an optimal SAR elevation angle for the multi‐UAV and SARs to administer larger disaster areas effectively and efficiently. We evaluate the performance of the proposed model in terms of path loss, throughput, and coverage probability. The results demonstrate that the proposed model achieves a significant enhancement in serving the SARs.