Multi-Unmanned Aerial Vehicle (UAV) enabled Wireless Sensor Networks (WSNs) provide a wide range of applications, covering civilian and military expeditions along with geographical navigation, control, and reconnaissance. The coordinated networks formed between the UAVs and the WSNs help in enhancing the issues related to quality as well as coverage. The overall coverage issues result in starvation as an effect of long waiting time for the nodes, while forwarding the traffic. The coverage problem can be resolved by an intelligent choice of UAV way-points. Therefore, a specialized UAV mobility model is required which takes into account the topological structure as well as the importance of strategic locations to fix UAV way-points and decide the data transmission paradigm. To resolve this problem, a novel mobility model is proposed, which takes into account the attraction factor for setting up the way-points for UAV movements. The model is capable of deciding between the locations which result in more coverage, increased throughput with lesser number of UAVs employed, as justified by the simulation results and comparative evaluations.

The recent developments in collaborative search, acquisition, and tracking have hoisted the geographical barrier. The network between unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) is one such collaboration, which comprises battery-powered static sensor nodes that act as sources and sinks and UAVs that act as relays. This collaborative network presents with opportunities and advantages, but at the same time, configuration of such networks is an arduous task. The WSN nodes are characterized by constant depleting power. Their network itself requires constant management and reconfiguration. These requisites can be slaked through the formation of an efficient data dissemination algorithm, which acclimates according to the network state. Considering this, a data dissemination approach is presented in this paper, which constructs a virtual topology predicated on the charge of WSN nodes utilizing software-defined networks (SDNs) through UAVs. The topology is constantly monitored and reconfigured when required. The aerial nodes are equipped with multiple-input multiple-output (MIMO) antennas in order to facilitate simultaneous communication with the ground nodes, the base station, and the SDN controller. An efficient sleep timer and backoff counter strategies are also utilized by the proposed approach. The SDN controller facilitates the topology formation and maintenance of a sleep timer and a backoff counter. The proposed model is compared with clustered hierarchical layouts and hexagonal cell layouts through the network simulations. The results suggest significant improvements in the proposed model for various metrics, such as lifetime, delay, latency, delivery ratio, and throughput in comparison with the existing solutions.High radio frequency (RF) coverage, high altitude, high throughput, heavy payload, less operating time, ease of use, and low cost are the basic requirements of UAV-CONOPS. Chemical, Biological, Radiological, and Nuclear Reconnaissance (CBRN)-CONOPS whose major purpose is the containment of hazards are also turning towards UAV networks. Cellular network-based air-to-ground links and unmanned aircraft system (UAS)-backbone systems, communication aware sensor distribution, etc are some of the promising aspects of the UAV systems. 2,3 The crucial step towards UAV networks is the development of sustainable multi-UAV environments. National regulations, routing, path planning, quality of service (QoS), integration with Global Information Grid (GIG), mobility control, coordination, and standardization are the steps required for efficient migration towards flying networks (multi-UAV). 4,5 The complexity of the multi-UAV ad hoc networks makes data dissemination also a challenging prospect. Latency, delay, antenna type, timing, etc come into play when we are talking about a network that is so fast moving and dynamic. 6 Multi-UAVs collaborate to achieve common objectives. It is evident that the collaborative UAV and the ground networks together can perform complex tasks. Some of these tasks...

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing centralized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software defined 5G. In this paper, we propose an intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture. It flexibly combines security function modules which are adaptively invoked under centralized management and control with a globle view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent intrusion detection system achieves a better performance. 1 Introduction Software Defined 5G architecture will be a crucial tendency in the development of future 5G networks [1]. It takes the advantage of Software Defined Network (SDN) [2] and Network Functions Vir-tualization (NFV) [3] through centralized management and dynamic resource allocation to meet the demands of 5G networks. Besides, the separation of the control and execution planes also facilitate the supervision of network status and the collection of information. With the uprising of novel technologies and attacks, it will also be faced with various challenges and severe security situations. As a result, new network security systems and architectures are desperately needed to enhance the security of Software Defined 5G networks [4]. As an essential technology in network security, intrusion detection systems have received more and more concerns in efficiently detecting malicious attacks. Existing IDS with separate functions are usually deployed locally within restricted areas which are hard to cooperate with each other. Moreover, they are usually signature-based by matching behaviors of incoming intrusions with historical knowledge and predefined rules, which are unable to detect novel attacks intelligently. To overcome the limitation of traditional IDS, Artificial Intelligence (AI) has been employed for intelligent detection. They classify abnormal traffic using machine learning techniques with a self-learning ability [5]. At present, there have been a few researches in the combinations of IDS and AI. However, they are still inadequate for coordinated detection considering the evolution and development of network systems. In this paper, we propose an intelligent intrusion detection system for Software Defined 5G networks. Benefit from the Software Defined technology, it integrates relevant security function modules into a unified platform which are dynamically invoked under centralized management and control. Besides, it implements machine learning to intelligently learn rules fro...

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The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs.