ARMS-EGR—Adaptive ranking and mobile sink-enabled energy-efficient geographic routing protocol in flying ad hoc networks

Unmanned aerial vehicles (UAVs) have become widespread because of their involvement in a variety of applications. The task of designing the energy-efficient routing between UAVs has been considered a matter of great interest due to the inherent challenges of controlling the dynamics exhibited by UAVs. Energy limitations are considered the main limitations of UAVs. This research paper proposes a novel routing protocol, adaptive ranking and mobile sink (MS)-enabled energy-efficient geographic routing (ARMS-EGR) for flying ad hoc networks. In ARMS-EGR, the whole network is partitioned into cells. The cell contains cell members (CM) and cell heads (CHs). The CH works as a cluster head. Additionally, two MSs have been used to collect data captured by CM. Multihop communication on the network leads to an increase in traffic and consumes the energy of the UAVs located near the base station (BS). MSs are used for power distribution and load balancing across the network. Adaptive ranking of forwarder UAVs and CHs is performed during intracell and intercell multihop communication, respectively, using adaptive ranking. A cell with one-hop communication can directly send packets to the MS, but the ARMS-EGR routing protocol has been proposed for multihop communication. The proposed approach is simulated in NS-2.35 software. The results show that end-to-end latency and power consumption during packet transmission are greatly minimized. ARMS-EGR also demonstrates improvements in message success rates, number of alive nodes, and packet delivery ratio, making ARMS-EGR particularly suitable for flying ad hoc networks (FANETs).     

A novel method of improving energy efficient QoS in FANET using Correlation And Confidence Based Gradient Clustering Metric approach

Flying Ad-hoc Networks (FANETs) being the most popular wing of the research in recent time had many evolutions to cope up with the growing demands in defense and other civil applications. Limited number of nodes, limited source of energy and increased mobility of the drones were the major problems to be addressed while building an effective FANET. Drones were aerial-nodes that are driver-less; their actions were prescribed by means of algorithms devoid of some human relations and could be easily deployed in the system. Hence this article aims to propose a novel framework to improvise the energy efficiency of FANET through Correlation and Confidence based Gradient Clustering Metric (CC-GCM) approach in the clustering algorithm. The results obtained through the proposed model are proven to be outstanding by producing an improvised performance efficiency and augmented energy efficiency. This article also involves a comparative study of the performance attributes with the existing models with tabulated values and graphs in terms of energy consumption, average end-to-end delay, cluster building time, average cluster life time, and Packet Delivery Ratio (PDR).     

UAV-based LoRaWAN flying gateway for the internet of flying things

The Internet of Things (IoT) is one of the paradigms related to the evolution of telecommunication networks which is contributing to the evolution of numerous use cases, such as smart city and smart agriculture. However, the current communication infrastructure and wireless communication technologies are not always able to guarantee a proper service for these IoT scenarios. Smart solutions are needed to overcome current terrestrial network limitations offering a cost-effective way to extend the current terrestrial network coverage. For example, temporary extensions “on-request” of the terrestrial infrastructure may be a viable solution to allow collecting data generated by nodes outside the current network coverage. Flying objects can help achieve this goal. Various studies supported the use of unmanned aerial vehicles (UAVs) as intermediate nodes between IoT devices and the network. However, such solutions have not been exhaustively tested yet in real-case scenarios. This paper proposes an efficient solution to collect data from multiple IoT sensors in rural and remote areas based on UAVs. It describes the implementation of the proposed UAV-based Long RangeWide Area Network (LoRaWAN) flying gateway able to collect data directly from LoRaWAN sensors during its flight, keep them stored in an onboard memory, and forward them at the end of its flying path to a platform where the authorized users can access them. A prototype of the gateway has been developed to assess the proposed solution through both indoor and outdoor tests aiming to test its feasibility both in terms of communication performance and UAV-required hardware resources.     

Investigation on the cavitation bubble collapse and the movement characteristics near spherical particles based on Weiss theorem

In this paper, the cavitation bubble dynamics near two spherical particles of the same size are investigated theoretically and experimentally. According to the Weiss theorem, the flow characteristics and the Kelvin impulse are obtained and supported by the sufficient experimental data. In terms of the initial bubble position, the bubble size and the distance between the two particles, the collapse morphology and the movement characteristics of the bubble are revealed in detail. The main findings include: (1) Based on a large number of experimental results, it is found that the Kelvin impulse theoretical model established in this paper can effectively predict the movement characteristics of the cavitation bubble near two particles of the same size. (2) When the initial bubble position is gradually away from the particles along the horizontal symmetry axis near two particles of the same size, the movement distance of the bubble centroid in the first period increases first and then decreases. (3) When the initial position of the bubble centroid is at the asymmetric position near the two particles, the movement direction of the bubble centroid is biased towards the particle closer to the bubble, but not towards the center of this particle.     

Intelligent deep learning-aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle-assisted anti-jamming Terahertz communication system

Wireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti-jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.     

UAV-IoT collaboration: Energy and time-saving task scheduling scheme

UAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV-IoT collaborative system has become a current hot research topic. However, other concerns require in-depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision-making, energy harvesting, and so forth. Here, we suggest a task-scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA-WS in terms of energy use (67%) and delays (36%).     

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP-hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state-of-the-art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.