Network Simulator NS2

Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Softwarization of UAV Networks: A Survey of Applications and Future Trends

Current access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.

Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure

Demands for indoor positioning based services (IPS) in commercial and military fields have spurred many positioning systems and techniques. Complex electromagnetic environments (CEEs) may, however, degenerate the accuracy and robustness of some existing single systems and techniques. To overcome this drawback, fusion-based positioning of multiple systems and/or techniques have been proposed to revamp the positioning performance in CEEs. In this paper, we survey the fusion-based indoor positioning techniques and systems from seminal works to elicit the state of the art within our proposed unified fusion-based positioning framework, which consists of three fusion characteristics: source, algorithm, and weight spaces. Different from other surveys, this survey summarizes and analyzes the existing fusion-based positioning systems and techniques from three characteristics. Meanwhile, discussions in terms of lessons, challenges, and countermeasures are also presented. This survey is invaluable for researchers to acquire a clear concept of indoor fusion-based positioning systems and techniques and also to gain insights from this survey to further develop other advanced fusion-based positioning systems and techniques in the future.

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A Survey on Fusion-Based Indoor Positioning

Comprehensive survey of UAVs communication networks

FANET: Communication, mobility models and security issues

The need to locate objects and to be situated in the space, whether inside or outside, has long been the focus of a substantial amount of research. Especially in Wireless Sensor Networks, indoor localization has become an important issue in many fields of applications. In this paper, we propose an indoor location solution based on Support Vector Machine (SVM). SVM is a class of learning algorithms defined to resolve discrimination and regression problems. In fact, with many works, it turned out that it is very difficult to properly locate a target with only the RSSI measurements. Thus, the idea is to use multi-class SVM with RSSI measurements to propose a zoning localization approach. The performed experiments using different datasets, collected from two real world environments in both a hospital and a laboratory building, and the comparison with Artificial Neural Networks (ANN) confirm the effectiveness of our SVM-based localization proposal. Experimental results show that the system achieves a correct classification rate of around 90% with misclassification is in rooms where there is no wall separating them.

SVM-based indoor localization in Wireless Sensor Networks

The future internet will include a variety of heterogeneous wireless networks and all kinds of smart sensors connected over wireless links. The size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth which in turn limits the processing and communication capabilities of the sensor nodes. Named data networking (NDN) emerged as a promising new routing mechanism aimed to cope with the increasing number of heterogeneous networks and the need for efficient and robust data dissemination. But, one of the bottlenecks that hinders the full applicability of such a NDN-based approach to the future internet is the energy requirements. In this study, we propose a novel geographic interest forwarding scheme where we add support for push-based traffic and different forwarding techniques designed to balance the energy consumption across the network. Our simulation results show that our proposed approach is more scalable and outperforms the alternative methods in term of data retrieval delay and overall energy consumption.

Efficient forwarding strategy in a NDN-based internet of things

Visual surveillance systems have recently captured the attention of the research community. Most of the proposed surveillance systems deal with stationary cameras. Nevertheless, these systems may reflect minor applicability in anomaly detection when multiple cameras are required. Lately, under technological progress in electronic and avionics systems, Unmanned Aerial Vehicles (UAVs) are increasingly used in a wide variety of urban missions. Especially, in the surveillance context, UAVs can be used as mobile cameras to overcome weaknesses of stationary cameras. One of the principal advantages that makes UAVs attractive is their ability to provide a new aerial perspective. Despite their numerous advantages, there are many difficulties associated with automatic anomalies detection by an UAV, as there is a lack in the proposed contributions describing anomaly detection in videos recorded by a drone. In this paper, we propose new anomaly detection techniques for assisting UAV based surveillance mission where videos are acquired by a mobile camera. To extract robust features from UAV videos, three different features extraction methods were used, namely a pretrained Convolutional Neural Network (CNN) and two popular handcrafted methods (Histogram of Oriented Gradient (HOG) and HOG3D). One Class Support Vector Machine (OCSVM) has been then applied for the unsupervised classification. Extensive experiments carried on a dataset containing videos taken by an UAV monitoring a car parking, prove the efficiency of the proposed techniques. Specifically, the quantitative results obtained using the challenging Area Under Curve (AUC) evaluation metric show that, despite the variation among them, the proposed methods achieve good results in comparison to the existing technique with an AUC = 0.78 at worst and an AUC = 0.93 at best.

Deep learning and handcrafted features for one-class anomaly detection in UAV video

In recent years, a large number of researchers investigate the conception of systems that use a unique Unmanned Ariel Vehicles (UAV) or multiple independent UAVs to conduct civil or military missions, with minimal human intervention. In this paper we focus on using multiple UAVs to cooperatively monitor a crowded area. Communication in such UAVs network is an ongoing project. Due to the lack of proper communication standards and rules, designing a reliable communication model is essential for: (i) multi-UAV coordination, (ii) efficient bandwidth sharing according to data priority and urgency and (iii) avoiding useless transmission of the same data by multiple UAVs. To address the above challenges, we propose a centralized data-oriented communication architecture for crowd surveillance allocations using an UAV fleet. The Ground Control Station (GCS) is used as a central coordinator to manage bandwidth usage for the UAV fleet in its coverage area. To allow UAVs to send priority messages urgently to the GCS, we define two classes of urgent messages: critical state and important result. The class of the data as well as other relevant information about the detected event will be used by the GCS to authorize or not UAV data transmission and hence to optimize the bandwidth usage efficiency.

Haifa Touati

Papers

FANET: Communication, mobility models and security issues

SVM-based indoor localization in Wireless Sensor Networks

Efficient forwarding strategy in a NDN-based internet of things

Deep learning and handcrafted features for one-class anomaly detection in UAV video

UAV-GCS Centralized Data-Oriented Communication Architecture for Crowd Surveillance Applications