Minimum Number Of Unmanned Aerial Vehicles Research Articles

Power-Efficient Wireless Coverage Using Minimum Number of UAVs.

Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.

Exploring Biological Robustness for Reliable Multi-UAV Networks

Unmanned Aerial Vehicles (UAVs), as aerial base stations, is a promising solution for providing end-to-end wireless communications to ground users, thanks to its positioning, flexibility, and autonomy. However, to provide end-to-end wireless communication services, all UAVs must ensure a reliable multi-UAV network topology, even when one or more UAVs are knocked off the network due to hardware/software faults, unreliable wireless connections, etc. Hence, how to design a reliable Multi-UAV network with a minimum number of UAVs becomes a key design issue, which is largely unaddressed in the literature. In this paper, we propose exploring biological robustness to design a reliable MuLtI-UAV NetworK, termed, bio-LINK, which is resilient against the UAV node failures and thus, ensures reliable end-to-end communication services to ground users. We first formulate the above bio-LINK problem as an integer linear programming (ILP) optimization problem and show it is NP-Hard. Next, we propose a polynomial-time heuristic that employs an iterative UAV positioning inspired by Markov Chain Monte Carlo (MCMC) random sampling approach. Our extensive simulation study shows that the proposed algorithm outperforms three baseline algorithms in terms of several considered robustness metrics (e.g., motif count, network efficiency, etc.) and ground user coverage, notwithstanding the random and targeted failure of UAV nodes. When compared with a baseline algorithm with the same number of UAVs, the proposed algorithm retains the motif count by 5-6 folds and improves network efficiency by 39 - 95% and ground user coverage by 2-18%.

Persistent Covering With Latency and Energy Constraints

Most consumer-level low-cost unmanned aerial vehicles (UAVs) have limited battery power and long charging time. Thus, they cannot accomplish some practical tasks such as providing service to cover an area for an extended time, also known as persistent covering. Algorithmic approaches are limited mostly due to the computational complexity and intractability of the problem. Approximation algorithms that assume unlimited energy have been considered to segment a large area into smaller cells that require periodic visits under the latency constraints. In this letter, we explore geometric and topological properties that allow us to significantly reduce the size of the optimization problem. Consequently, the proposed method can efficiently determine the minimum number of UAVs needed and schedule their routes to cover an area persistently. We experimentally demonstrate that the proposed algorithm has better performance than the baseline.

Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks

Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

Determination of Minimum Number of UAVs for Local Area Networks of Drones

There are many applications of Unmanned Aerial Vehicle (UAV) networks where all UAVs belong to one organization or a person. In such applications, the number of UAVs that can be employed is limited due to the cost factor. Local Area Networks of Drones (LoDs) have been introduced recently for such purposes. This paper presents methods of determining the minimum number of UAVs to perform tasks in four basic categories of LoD applications: completing a single task at a time, completing multiple tasks simultaneously, monitoring the entire area using a single UAV branch and monitoring the entire area simultaneously using multiple branches of UAVs. Completing one task at a time always requires a minimum number of UAVs and increasing the number of simultaneous tasks performed always increases the number of UAVs required for any application.

Signaling Protocols for Local Area Networks of Drones

Unmanned Aerial Vehicles (UAVs) or drones have become extremely popular and are used in various commercial applications. When multiple UAVs communicate and work together, they form a UAV network. A private UAV network or Local Area Network of Drones (LoDs) is a special type of UAV network which has the minimum number of UAVs to carry out a certain task. All UAVs in a LoD use the wireless medium to send and receive the data as well as the control signals. An organization or a single owner will be more interested in this type of network, where they want multiple UAVs to scan an area, communicate with each other, and send all the images and live video streams to a single ground station. The currently available commercial UAVs Can send the video signals to and receive control signals only from their own ground station controllers. However, in an LoD network where UAVs are connected in tandem, the UAVs that are in the middle of the network have to carry the control and video signals of other UAVs. Given the limited processing power and dynamic memory capacity of UAVs, this would increase the queuing delays and performance.In this paper, we study the frame formats of existing control, feedback, and data messages of commercial AR UAVs and propose a new approach to construct the payloads of control and feedback frames that are suitable for an LoDs. We compare the performance of our approach of single control and feedback frame for all UAVs in a LoD branch with that of separate control and feedback frames for each UAV. We calculate and compare the UAV node processing delay in both types of signaling mechanisms and show that the single control and feedback frame signaling has less delay on the average.

A Molecular Force Field-Based Optimal Deployment Algorithm for UAV Swarm Coverage Maximization in Mobile Wireless Sensor Network

In the mobile wireless sensor network (MWSN) field, there exists an important problem—how can we quickly form an MWSN to cover a designated working area on the ground using an unmanned aerial vehicle (UAV) swarm? This problem is of significance in many military and civilian applications. In this paper, inspired by intermolecular forces, a novel molecular force field-based optimal deployment algorithm for a UAV swarm is proposed to solve this problem. A multi-rotor UAV swarm is used to carry sensors and quickly build an MWSN in a designated working area. The necessary minimum number of UAVs is determined according to the principle that the coverage area of any three UAVs has the smallest overlap. Based on the geometric properties of a convex polygon, two initialization methods are proposed to make the initial deployment more uniform, following which, the positions of all UAVs are subsequently optimized by the proposed molecular force field-based deployment algorithm. Simulation experiment results show that the proposed algorithm, when compared with three existing algorithms, can obtain the maximum coverage ratio for the designated working area thanks to the proposed initialization methods. The probability of falling into a local optimum and the computational complexity are reduced, while the convergence rate is improved.

Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

This work investigates the deployment of unmanned aerial vehicles (UAVs) as access points to provide wireless services to users in a green field. Three fundamental deployment designs are studied under practical air-to-ground (ATG) channel models, namely the minimum number of UAVs, their optimal deployment locations and the optimal transmit power allocation. Since these three design goals are coupled, a particle swarm optimization (PSO)-based scheme is proposed in conjunction with the balanced Signal to Interference plus Noise Ratio (SINR) transmit power allocation. By exploiting the closed-form expressions of the SINR-balanced optimal power allocation and the resulting SINR, the proposed PSO-based scheme iteratively optimizes the UAV number and subsequently, their locations until the resulting SINR of each user meets its required minimum value. To improve the convergence behavior of the proposed scheme, two schemes are devised to provide an initial estimate on the minimum number of UAVs by analyzing the system sum-rate capacity before using a K-means clustering technique to initialize the UAV locations. Finally, a power fine-tuning scheme is developed to further reduce the total transmit power. Extensive simulation is performed to confirm the good performance of the proposed schemes.

Optimizing UAV-based radiation sensor systems for aerial surveys

In this study, we qualitatively and quantitatively analyzed unmanned aerial vehicle (UAV)-based radiation sensor systems suitable for specific survey missions. We examined a variety of UAVs, radiation sensors, and radiological survey missions; after reviewing previous studies that developed and conducted field tests, we categorized them by mission and suggested suitable types of UAVs and radiation sensors for each mission. To quantitatively analyze various system designs previously suggested, we proposed a new figure of merit (FOM) formula that can explain the mutual effects of parameters of both radiation sensors and UAVs on system performance. After targeting a radiological survey mission, we selected UAV and radiation sensor types qualitatively. Then, the quantitative FOM formula can be employed to efficiently assess whether the system achieves the required minimum detectable activity (MDA) without field test, while keeping the error of the MDA values in the order of 100. After defining the constraints including the proposed FOM formula, we replicated a severe nuclear power plant accident scenario. We found that one fixed-wing UAV and multiple rotary-wing UAVs are the minimum number of UAVs, and thus the optimal system for timely achievement of a satisfactory MDA and estimation of the three-dimensional radiation distribution contributed by both ground radiation and an atmospheric radioactive plume.

Centralized Unmanned Aerial Vehicle Mesh Network Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach.

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in military operations to prevent pilot losses. Nowadays, the fast technological evolution has enabled the production of a class of cost-effective UAVs that can service a plethora of public and civilian applications, especially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is a challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, the elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

In this paper, the efficient deployment of multiple unmanned aerial vehicles (UAVs) with directional antennas acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the three-dimensional locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate the various tradeoffs involved in various UAV deployment scenarios.