Strategic Deployment of Swarm of UAVs for Secure IoT Networks

Security provisioning for low-complex and constrained devices in the Internet of Things (IoT) is exacerbating the concerns for the design of future wireless networks. To unveil the full potential of the sixth generation (6G), it is becoming even more evident that security measurements should be considered at all layers of the network. This work aims to contribute in this direction by investigating the employment of unmanned aerial vehicles (UAVs) for providing secure transmissions in ground IoT networks. Toward this purpose, it is considered that a set of UAVs acting as aerial base stations provide secure connectivity between the network and multiple ground nodes. Then, the association of IoT nodes, the 3D positioning of the UAVs and the power allocation of the UAVs are obtained by leveraging game theoretic and convex optimization-based tools with the goal of improving the secrecy of the system. It is shown that the proposed framework obtains better and more efficient secrecy performance over an IoT network than state-of-the-art greedy algorithms for positioning and association

Multi UAV-enabled Distributed Sensing: Cooperation Orchestration and Detection Protocol

This paper proposes an unmanned aerial vehicle (UAV)-based distributed sensing framework that uses orthogonal frequency-division multiplexing (OFDM) waveforms to detect the position of a ground target, and UAVs operate in half-duplex mode. A spatial grid approach is proposed, where an specific area in the ground is divided into cells of equal size, then the radar cross-section (RCS) of each cell is jointly estimated by a network of dual-function UAVs. For this purpose, three estimation algorithms are proposed employing the maximum likelihood criterion, and digital beamforming is used for the local signal acquisition at the receive UAVs. It is also considered that the coordination, fusion of sensing data, and central estimation is performed at a certain UAV acting as a fusion center (FC). Monte Carlo simulations are performed to obtain the absolute estimation error of the proposed framework. The results show an improved accuracy and resolution by the proposed framework, if compared to a single monostatic UAV benchmark, due to the distributed approach among the UAVs. It is also evidenced that a reduced overhead is obtained when compared to a general compressive sensing (CS) approach

Strategic learning-based approaches for secure deployment of swarm of UAVs in IoT networks

Abstract. In next generation wireless networks such as 5G and 6G, security is a key concern, for which physical layer security (PLS) emerges as an interesting solution which allows the provisioning of security to low-complexity devices. This work investigates the employment of unmanned aerial vehicles (UAVs) for providing secure transmissions in ground networks relying on information-theoretic security in internet of things (IoT) settings. To this goal, two scenarios are investigated, namely secure communications with UAVs as friendly jammers and secure communications in massive IoT networks served by a swarm of UAVs. For the former scenario, it is considered that two UAVs are deployed to act as friendly jammers to improve the secrecy in the communication between a pair of legitimate nodes in the presence of an eavesdropper of unknown location. For this scenario reinforcement learning techniques are leveraged for 3D positioning of UAVs in order to improve the secrecy performance of the system via friendly jamming. For the second scenario, it is considered that a set of UAVs acting as aerial base stations provide secure connectivity between the network and multiple ground nodes. Therein, both the association of the nodes and the 3D positioning of the UAVs are obtained by leveraging game theoretic tools and greedy algorithms with the goal of improving the secrecy of the system. In both scenarios, it is shown that the proposed solutions enhance the secrecy performance of the systems. Namely, in the first scenario it was shown that the proposed solution enhanced the secrecy of the system through friendly jamming, and in the second scenario it was found that game-theoretic approaches in both association of the IoT nodes and positioning of the UAVs presents a remarkable increase in secrecy performance over a random positioning, strongest channel association benchmark

Estudio comparativo sobre las capacidades físicas del adulto mayor

El presente trabajo busca exponer un estudio comparativo entre dos centros de atención al adulto mayor, con relación al deterioro de sus capacidades físicas (sarcopenia), provocada por el desgaste natural de su edad, que afectan directamente en sus capacidades funcionales, que otorgan al adulto mayor una calidad de vida que responde directamente al tratamiento que se brinde sobre ella. Se utilizó una muestra de10 personas en un rango de edad entre 57 y 88 años para valoración mediante aplicación de test físicos, y 18 personas en un rango de edad entre 80 y 100 años para valoración mediante cuestionario de actividad física, entre los centros: “Hogar de Ancianos Mi Amigo Divino” y “Hogar del Adulto Mayor San Ignacio de Loyola”, mediante los cuales se determinó su nivel físico a través de pruebas funcionales considerando las capacidades físicas de fuerza, flexibilidad y equilibrio estático tanto del tren superior e inferior, valorando de manera funcional el grado de movilidad del adulto mayor, totalizando datos reales de sujetos con distintas características de índole social, por lo que se describe a una población mayoritariamente sedentaria desde un parámetro cuantitativo. Los datos presentados en esta investigación brindan una referencia de gran importancia sobre la situación actual del adulto mayor en relación a la pérdida progresiva tanto del volumen muscular como de la fuerza de los mismos

Positioning and power optimisation for UAV-assisted networks in the presence of eavesdroppers:a multi-armed bandit approach

Abstract Unmanned aerial vehicles (UAVs) are becoming increasingly attractive for the ambitious expectations for 5G and beyond networks due to their several benefits. Indeed, UAV-assisted communications introduce a new range of challenges and opportunities regarding the security of these networks. Thus, in this paper we explore the opportunities that UAVs can provide for physical layer security solutions. Particularly, we analyse the secrecy performance of a ground wireless communication network assisted by N friendly UAV jammers in the presence of an eavesdropper. To tackle the secrecy performance of this system, we introduce a new area-based metric, the weighted secrecy coverage (WSC), that measures the improvement on the secrecy performance of a system over a certain physical area given by the introduction of friendly jamming. Herein, the optimal 3D positioning of the UAVs and the power allocation is addressed in order to maximise the WSC. For that purpose, we provide a reinforcement learning-based solution by modelling the positioning problem as a multi-armed bandit problem over three positioning variables for the UAVs: angle, height and orbit radius. Our results show that the proposed algorithm improves the secrecy of the system over time in terms of the WSC, and it converges into a stable state close to the exhaustive search solution for discretised actions, where there is a trade-off between expediency of the positioning of the UAVs to positions of better secrecy outcome and energy consumption

A Fast and Accurate Approximation of IEEE 802.11 Physical Layer Models for Network Simulators

Network simulators are used for the research and development of several types of networks. However, one of the limitations of these simulators is the usage of simplified theoretical models of the Packet Error Rate (PER) at the Physical Layer (PHY) of the IEEE 802.11 family of wireless standards. Although the simplified PHY model can significantly reduce the simulation time, the resulting PER can differ considerably from other more realistic results. In this work, we first study several PER theoretical models. Then, we propose a curve fitting algorithm, which is able to obtain a fast and accurate approximation of other PER models. The curve fitting algorithm uses simulated data as input and outputs the coefficients of a simple model that offers a very accurate approximation of the original PER. Finally, we implemented this approximation in the ns-3 network simulator, thus obtaining high realism since now we can select several theoretical PER models or even a more realistic scenario with the effect of the high Peak-to-Average Power Ratio (PAPR) in the signal. The ns-3 results show how the selection of the PER model at the PHY can significantly impact the Packet Loss Rate (PLR) of a scenario composed of a linear chain of several nodes, one of the simplest multi-hop scenarios