Ozgur Koray Sahingoz

Flying Ad-Hoc Networks (FANETs)

One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

Survey Flying Ad-Hoc Networks (FANETs): A survey

One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

Generation of Bezier Curve-Based Flyable Trajectories for Multi-UAV Systems with Parallel Genetic Algorithm

In recent years, Unmanned Aerial Vehicles (UAVs) have been used in many military and civil application areas, due to their increased endurance, performance, portability, and their larger payload-carrying, computing and communication capabilities. Because of UAVs’ complex operation areas and complicated constraints related to the assigned task, they have to fly on a path, which is calculated online and/or offline to satisfy these constraints and to check some control points in the operation theatre. If the number of control points and constraints increases, finding a feasible solution takes up too much time in this large operation area. In this case, the use of multi-UAVs decreases operation completion time; however, this usage increases the complexity of finding a feasible path problem. This problem is typically NP-hard and genetic algorithms have been successfully utilized for solving it in the last few decades. This paper presents how a flyable trajectory can be constructed for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment and at a constant altitude. A GA is implemented parallel in a multi-core environment to increase the performance of the system. First, a feasible path is calculated by using a parallel GA, and then the path is smoothed by using Bezier curves to convert it flyable. Preliminary results show that the proposed method provides an effective and feasible path for each UAV in an Unmanned Aerial System with multi-UAVs. The proposed system is realized in Java with a GUI for showing results. This paper also outlines future work that can be conducted on the multi-UAV path planning.

Mobile networking with UAVs: Opportunities and challenges

With the advances in computation, sensor, communication and networking technologies, utilization of Unmanned Aerial Vehicles (UAVs) for military and civilian areas has become extremely popular for the last two decades. Since small UAVs are relatively cheap, the focus is changing, and usage of several small UAVs is preferred rather than one large UAV. This change in orientation is dramatic, and it is resulting to develop new networking technologies between UAVs, which can constitute swarm UAV teams for executing specific tasks with different levels of intra and inter vehicle communication especially for coordination and control of the system. Setting up a UAV network not only extends operational scope and range but also enables quick and reliable response time. Because UAVs are highly mobile nodes for networking, setting up an ad-hoc network is a challenging issue, and this networking has some requirements, which differ from traditional networks, mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) in terms of connectivity, routing process, services, applications, etc. In this paper, it is aimed to point out the challenges in the usage of UAVs as mobile nodes in an ad-hoc network and to depict open research issues with analyzing the opportunities and future works.

Large scale wireless sensor networks with multi-level dynamic key management scheme

Cyber-Physical Systems (CPSs) have emerged as a promising approach to facilitate the integration of the cyber and physical worlds in highly interconnected and complex ways. CPSs consist of several components, such as sensors, actuators, controllers, etc., and their structures are being complicated, and their scales are increasing day by day. Therefore, the data reliability and security have emerged as critical challenges between physical and virtual components of these systems. Wireless Sensor Networks (WSNs) are accepted as one of the most crucial technologies for building future CPSs. Because of their wireless and dynamic nature, WSNs are more vulnerable to security attacks than wired networks. The main solution for this problem is the usage of signed messages with symmetric or asymmetric key cryptography. Although, asymmetric key cryptography increases network security, it also causes severe computational, memory, and energy overhead for sensor nodes. On the other hand, symmetric key cryptography has the difficulty of providing high-level security and efficient key management scheme; however, it is better in terms of speed and low energy cost. In this paper, it is aimed to build a multi-level dynamic key management system for WSNs with the aid of an Unmanned Aerial Vehicle (UAV), which is a key distribution and coordination center for asymmetric keys. After that, each sensor node constructs different symmetric keys with its neighbors, and communication security is achieved by data encryption and mutual authentication with these keys. Evaluation results show the proposed system is scalable, and its performance is significantly better than asymmetric key management systems.

Adapting the GA approach to solve Traveling Salesman Problems on CUDA architecture

The vehicle routing problem (VRP) is one of the most challenging combinatorial optimization problems, which has been studied for several decades. The number of solutions for VRP increases exponentially while the number of points, which must be visited increases. There are 3.0×10^64 different solutions for 50 visiting points in a direct solution, and it is practically impossible to try out all these permutations. Some approaches like evolutionary algorithms allow finding feasible solutions in an acceptable time. However, if the number of visiting points increases, these algorithms require high performance computing, and they remain insufficient for finding a feasible solution quickly. Graphics Processing Units (GPUs) have tremendous computational power by allowing parallel processing over lots of computing grids, and they can lead to significant performance gains compared with typical CPU implementations. In this paper, it is aimed to present efficient implementation of Genetic Algorithm, which is an evolutionary algorithm that is inspired by processes observed in the biological evolution of living organisms to find approximate solutions for optimization problems such as Traveling Salesman Problem, on GPU. A 1-Thread in 1-Position (1T1P) approach is developed to improve the performance through maximizing efficiency, which then yielded a significant acceleration by using GPUs. Performance of implemented system is measured with the different parameters and the corresponding CPU implementation.

Proxy Network Intrusion Detection System for cloud computing

Cloud computing is the state of the art approach of information technologies. While migrating from conventional data centers to cloud computing paradigm, administrators have to consider costs. Security is one of the biggest problems for all information technologies and also in cloud computing. Some mechanisms are used such as antivirus software, firewall, guard systems or intrusion detection/prevention systems to increase the system security. The main handicap of these mechanisms is extensive usage of hardware, especially CPU and memory. In this paper, we examine the hardware usage of Network Intrusion Detection Systems (NIDSs) with different network architectures. Although the virtualization is preferred by the most of the providers, we chose a virtualized environment as our test bed because of its easier implementation structure from other cloud technologies such as grid and blade architectures. We focused on a proxy NIDS architecture, which is a gateway-based approach (intrusion detection assignment carried out by an outer entity, so why we called it proxy) and has less hardware requirements than other positioning options. It is aimed to show an effective location to IDSs in a virtualized environment. As a result, both providers and customers can easily decide where to locate their defense mechanism.

A UAV path planning with parallel ACO algorithm on CUDA platform

Solving the path planning problem of a UAV is a challenging issue especially if there are too many checkpoints to visit. Mainly, the brute force approach is used to find the shortest path in the mission area, which requires too many times to find a solution. Therefore, evolutionary algorithms and swarm intelligence techniques are used to find a feasible solution in an acceptable time. In this study, path planning problem of a UAV is solved by using a highly parallelized Ant Colony Optimization (ACO) algorithm on CUDA platform. The UAV path is constructed for disseminating keys and collecting data from a Wireless Sensor Network, which is previously defined. Due to its simplicity and effectiveness, ACO is selected as a path planning algorithm. However, ACO is not satisfactory if the mission area becomes large and there are an excessive number of checkpoints and/or additional constraints. In order to increase the performance, some parallelization techniques must be used in high performance computing platforms. GPU architecture has emerged as a powerful and low cost architecture for enabling impressive speedups for scientific calculations. Therefore, the parallel structure is constructed on CUDA architecture. The experimental results are compared with the CPU performance of the serial algorithm, and they clearly show that the proposed approach have a great potential for acceleration of ACO and allow to solve many complex tasks such as UAV path planning problem. We also present the execution results with different parameter values to expose the results for the researchers.

Flyable path planning for a multi-UAV system with Genetic Algorithms and Bezier curves

Unmanned Aerial Vehicles (UAVs) are used in numerous military and civil application areas, and they have gained prominence in the research community. A UAV has to operate in a complex environment and checks the control points in the mission area by satisfying different constraints of the assigned task. Therefore, the path planning problem is one of the important areas in UAV researches. If the number of control points increases in the Unmanned Aerial System (UAS), finding a feasible solution in this large search space takes up a great deal of time. Nowadays low-cost UAVs are available, and this enables the use of multi-UAV systems to perform different tasks more efficiently and quickly. This usage increases the complexity of effective path planning and task allocation problem. This paper presents a flyable path planning for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment at a constant altitude. A feasible path is firstly calculated by GAs, and then this path is smoothed by using Bezier curves. Experimental results indicate that the proposed approach produces effective and feasible paths for each UAV in a multi-UAV system. System is implemented in Java with a GUI for presenting results. The paper also draws future works that can be done on this topic.

Optimal UAV path planning in a 3D threat environment by using parallel evolutionary algorithms

In recent years, unmanned aerial vehicles-UAVs represent one of the most demanding technologies in aeronautics, and they have tremendous appeal because of their operability with considerable autonomy (by using minimal human intervention). UAVs have to operate in complex environments with different constraints such as obstacles, threatening zones, UAV kinematics, etc. In this technology, path planning plays a crucial role for high autonomy operations, although absolute autonomy is still an open question. In this paper, we tried to discuss, how a feasible path planning for a UAV can be done in the 3-dimensional environment by avoiding threats such as a radar network which contains several radars with different detection ranges. The proposed methodology is implemented with using genetic algorithms, and a parallel approach is used for reducing path planning calculations. The environment is represented as 3 dimensional structure by using World Wind, which is an open-source and accurate 3D environment browser. The developed methodology can provide fast and safe routes for autonomous single UAVs or operator-assisted flight.