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A Survey and Analysis of Mobility Models for Airborne Networks
Abstract
Mobility models serve as the foundation for evaluating and designing airborne networks (ANs). Due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the attributes of ANs. In this paper, we present a comprehensive survey and comparative analysis of mobility models that are either adapted to or developed for AN evaluation purposes. We evaluate these mobility models based on the following metrics: adaptability, networking performance, and ability to realistically capture the mobility attributes of ANs (including high mobility, mechanical and aerodynamic constraint, and safety requirements). To provide a deeper understanding and facilitate the selection and configuration of these mobility models, we also evaluate them based on randomness levels and associated applications.
... Mobility model describes the movement pattern of mobile nodes and with time how their direction, velocity and acceleration changes [7]. For MANETs, a wide variety of mobility models are present in literature from simple to complex models that mimic the node movements in MANET [8], [9]. ...
... FANETs, mobility models should control the high speed, velocity, acceleration, sudden change in direction, and maintain connectivity with time. Traditional MANET mobility models are used for a simulation scenarios [7], [8], [9] are classified in two categories i.e. Trace based mobility models and Synthetic mobility models. ...
... On reaching to boundary of the simulation area, node bounces back with pre determined angle and then continues on this path. RWMM is the widely used model and is also called Brownian Motion [9], [10]. -Random Waypoint Mobility Model (RWPMM): Mobile nodes in RWPMM moves randomly in a region with random speed to a destination point. ...
... The transmission radius of these UAVs is 500 m, and their initial energy is 100 Joules. Furthermore, the movement model of UAVs follows the three-dimensional Gauss Markov model (3D GM) [36,37]. In the simulation operation, the traffic model has a constant bit rate (CBR), and the size of data packets is 512 bytes. ...
A flying ad hoc network (FANET) is formed from a swarm of drones also known as unmanned aerial vehicles (UAVs) and is currently a popular research subject because of its ability to carry out complicated missions. However, the specific features of UAVs such as mobility, restricted energy, and dynamic topology have led to vital challenges for making reliable communications between drones, especially when designing routing methods. In this paper, a novel optimized link-state routing scheme with a greedy and perimeter forwarding capability called OLSR+GPSR is proposed in flying ad hoc networks. In OLSR+GPSR, optimized link-state routing (OLSR) and greedy perimeter stateless routing (GPSR) are merged together. The proposed method employs a fuzzy system to regulate the broadcast period of hello messages based on two inputs, namely the velocity of UAVs and position prediction error so that high-speed UAVs have a shorter hello broadcast period than low-speed UAVs. In OLSR+GPSR, unlike OLSR, MPR nodes are determined based on several metrics, especially neighbor degree, node stability (based on velocity, direction, and distance), the occupied buffer capacity, and residual energy. In the last step, the proposed method deletes two phases in OLSR, i.e., the TC message dissemination and the calculation of all routing paths to reduce routing overhead. Finally, OLSR+GPSR is run on an NS3 simulator, and its performance is evaluated in terms of delay, packet delivery ratio, throughput, and overhead in comparison with Gangopadhyay et al., P-OLSR, and OLSR-ETX. This evaluation shows the superiority of OLSR+GPSR.
... [3]). Additional examples of RMMs can be found in several survey papers [1,4]. With the increasing use of unmanned aerial vehicles (UAVs) as platforms for airborne wireless networks [5][6][7], RMMs are playing a more important role in modeling UAV's movements [8,9]. ...
Random mobility models (RMMs) capture the statistical movement characteristics of mobile agents and play an important role in the evaluation and design of mobile wireless networks. Particularly, RMMs are used to model the movement of unmanned aerial vehicles (UAVs) as the platforms for airborne communication networks. In many RMMs, the movement characteristics are captured as stochastic processes constructed using two types of independent random variables. The first type describes the movement characteristics for each maneuver and the second type describes how often the maneuvers are switched. We develop a generic method to estimate RMMs that are composed of these two types of random variables. Specifically, we formulate the dynamics of movement characteristics generated by the two types of random variables as a special Jump Markov System and develop an estimation method based on the Expectation–Maximization principle. Both off-line and on-line variants of the method are developed. We apply the estimation method to the Smooth–Turn RMM developed for fixed-wing UAVs. The simulation study validates the performance of the proposed estimation method. We further conduct a UAV experimental study and apply the estimation methods to real UAV trajectories.
... By contrast, the UAV communication channels dominated by LoS links may improve the link capacity [2]. Third, UAVs can move quickly in the three-dimensional (3D) free space, providing degree of freedom for performance enhancement via trajectory design [3]. Due to the above advantages, UAV-assisted wireless communications have been used in a variety of scenarios, such as mobile relaying [4,5], data collection and information dissemination [6][7][8]. ...
This paper investigates an optimization problem corresponding to energy efficiency maximization of an unmanned aerial vehicle (UAV)-enabled relaying system, where a fixed-wing UAV acts as an amplify-and-forward mobile relay to assist data transmission between a source node and a destination node. On the premise of satisfying the speed and acceleration constraints of the UAV, the energy efficiency (EE) of the relaying system is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay. The initial joint optimization problem is non-convex and cannot be solved directly. Therefore, the joint optimization problem is decomposed into two sub-problems which are solved by applying the successive convex approximation technique and the Dinkelbach’s algorithm. On this basis, an efficient iterative algorithm is proposed to tackle the joint optimization problem through the block coordinate descent technique. Simulation results demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm contributes to the higher EE compared with the benchmark schemes.
To ensure reliable data transmission in flying ad hoc networks (FANETs), efficient routing protocols are necessary to establish communication paths in FANETs. Recently, reinforcement learning (RL), particularly Q-learning, has become a promising approach for overcoming challenges faced by traditional routing protocols due to its capacity for autonomous adaptation and self-learning. This study presents a Q-learning-based routing strategy, enhanced by an innovative cylindrical filtering technique, named QRCF in FANETs. In QRCF, the dissemination interval of hello packets is adaptively adjusted based on the connection status of nearby UAVs. Then, this routing process leverages Q-learning to discover reliable and stable routes, using a state set refined by the cylindrical filtering technique to accelerate the search for the optimal path in the network. Afterward, the reward value is computed using metrics such as relative speed, connection time, residual energy, and movement path. Finally, QRCF is deployed in the network simulator 2 (NS2), and its performance is evaluated against three routing schemes, QRF, QFAN, and QTAR. These evaluations are presented based on the number of UAVs and their speed. In general, when changing the number of nodes, QRCF improves energy usage (about 5.01%), data delivery ratio (approximately 1.20%), delay (17.71%), and network longevity (about 3.21%). However, it has a higher overhead (approximately 10.91%) than QRF. Moreover, when changing the speed of UAVs in the network, QRCF improves energy usage (about 4.94%), data delivery ratio (approximately 2.36%), delay (about 17.5%), and network lifetime (approximately 8.75%). However, it increases routing overhead (approximately 15.47%) in comparison with QRF.
Recently, the rapid development of wireless technologies, low-priced equipment, advances in networking protocols, and access to modern communication, electrical, and sensing technologies have led to the evolution of flying ad hoc networks (FANETs). However, the high movement of unmanned aerial vehicles (UAVs) in these networks causes iterated failures of communication links and constant changes in network topology. These features challenge the design of a proper routing protocol in FANETs. Today, computational intelligence (CI) techniques are rapidly developing as a mighty and intelligent computing model. This promising technology can be used to improve various applied areas, especially routing in FANETs. This paper examines and assesses various CI-based routing techniques in FANETs. Accordingly, this paper introduces a classification of CI-based routing protocols for FANETs. This categorization includes three groups: learning system-based routing methods (including artificial neural networks, reinforcement learning, and deep reinforcement learning), fuzzy-based routing schemes, and bio-inspired routing schemes (evolutionary algorithms and swarm intelligence). Subsequently, based on the offered classification, the most recent CI-based routing methods and their key features are outlined. Ultimately, the opportunities and challenges in this area have been mentioned to help researchers familiarize themselves with future research directions in CI-based routing algorithms for FANETs and work toward improving these methods in such networks.
The current ns-3 implementation of the 3D Gauss-Markov mobility model (3D-GMMM) allows mobile nodes to reach and bounce off the simulation boundaries. This causes sudden and unnatural movement of the nodes in the vicinity of the simulation boundaries. In this paper, we present a modification to the current ns-3 implementation of the 3D-GMMM. We follow an approach in which mobile nodes are directed toward the center of the simulation region at a random angle if they are within a certain distance from the simulation boundaries. As the simulation results show, the improved ns-3 implementation of 3D-GMMM prevents mobile nodes from reaching the simulation boundaries while resulting in smooth movement.
The random waypoint model is a commonly used mobility model for simulations of wireless communication networks. In this paper, we present analytical derivations of some fundamental stochastic properties of this model with respect to: (a) the length and duration of a movement epoch, (b) the chosen direction angle at the beginning of a movement epoch, and (c) the cell change rate of the random waypoint mobility model when used within the context of cellular networks. Our results and methods can be used to compare the random waypoint model with other mobility models. The results on the movement epoch duration as well as on the cell change rate enable us to make a statement about the 'degree of mobility' of a certain simulation scenario. The direction distribution explains in an analytical manner the effect that nodes tend to move back to the middle of the system area.
Realistic random mobility models for airborne networks (ANs) are highly desirable to evaluate and design airborne networking. In this paper, we develop a comprehensive model-ing framework for ANs, including two 3-D random mobility models and the associated boundary models. These simple random mobility models obey physical laws governing aerial maneuvers, and thus capture the smoothness of aerial trajectories.
The random waypoint model is a commonly used mobility model for simulations of wireless communication networks. By giving a formal description of this model in terms of a discrete-time stochastic process, we investigate some of its fundamental stochastic properties with respect to: (a) the transition length and time of a mobile node between two waypoints, (b) the spatial distribution of nodes, (c) the direction angle at the beginning of a movement transition, and (d) the cell change rate if the model is used in a cellular-structured system area.The results of this paper are of practical value for performance analysis of mobile networks and give a deeper understanding of the behavior of this mobility model. Such understanding is necessary to avoid misinterpretation of simulation results. The movement duration and the cell change rate enable us to make a statement about the “degree of mobility” of a certain simulation scenario. Knowledge of the spatial node distribution is essential for all investigations in which the relative location of the mobile nodes is important. Finally, the direction distribution explains in an analytical manner the effect that nodes tend to move back to the middle of the system area.
Unmanned Aerial Vehicles (UAVs) are widely used in modern warfare for surveillance, reconnaissance sensing and attacking. However, the communication among UAVs always suffers packet loss due to the highly dynamic environment. It means that the data routing of UAVs faces series challenge different from low mobility environment. In this paper, we propose an efficient and effective geographic based routing protocol. Our proposal uses Gauss-Markov mobility model to predict the movement of UAVs to eliminate the impact of highly dynamic movement. Then it selects the next hop according to the mobility relationship in addition to Euclidean distance to make more accuracy decision. The experiment result shows that our approach can provide effective and accuracy data forwarding. 1553-9105/
The simulation and evaluation of networking protocols for airborne networks (ANs) require accurate random mobility models. In our previous studies, we developed a 3D smooth-turn (ST) random mobility model that captures the smoothness of aerial trajectories. The model follows physical laws that govern the spatiotemporal correlation of aerial turns. In this paper, we suggest an estimation procedure to extract parameters in the model from real flight test data. The good match between the estimated trajectories and real flight trajectories also validates the suitability of this model for ANs. The 3D ST random mobility model and the estimation procedure lead to the creation of "realistic" simulation and evaluation environment for airborne networks.
We consider a structural approach to the consensus building problem in multi‐group multi‐layer (MGML) distributed sensor networks (DSNs) common in many natural and engineering applications. From among the possible network structures, we focus on bipartite graph structure as it represents a typical MGML structure and has a wide applicability in the real world. We establish exact conditions for consensus and derive a precise relationship between the consensus value and the degree distribution of nodes in a bipartite MGML DSN. We also demonstrate that for subclasses of connectivity patterns, convergence time and simple characteristics of network topology can be captured by explicit algebra. Direct inference of the convergence behavior of consensus strategies from MGML DSN structure is the main contribution of this paper. The insights gained from our analysis facilitate the design and development of large‐scale DSNs that meet specific performance criteria. Copyright © 2012 John Wiley & Sons, Ltd.