Performance Analysis of UAV Enabled Disaster Recovery Network: A Stochastic Geometric Framework based on Matern Cluster Processes

Drones will be employed by Facebook and Google for capacity off-loading in front/back hauling scenarios utilizing drone-empowered autonomous heterogeneous networks. But in another application, drone-based, post-disaster recovery of communication networks will also be of crucial importance in the design of future smart cities. So, in order to address the design issues of these latter networks, we present (from a stochastic geometric perspective) a comprehensive statistical framework for the spatial distribution of these hybrid user-centric drone/micro cellular networks. We introduce the novel idea of using a Stenien’s cell (with variable radius) to model the region over which the drones will be distributed and the drones will effectively form a Matern cluster process (MCP) across the original network space. We then employ this newly developed framework to investigate the impact of changing several parameters on the performance of the new drone small-cell clustered networks (DSCCNs) and we develop appropriate closed-form expressions that model the performance (later validated via Monte Carlo simulations)

Wireless Power Transfer System for Battery-Less Sensor Nodes

For the first time, the design and implementation of a fully-integrated wireless information and power transfer system, operating at 24 GHz and enabling battery-less sensor nodes, is presented in this paper. The system consists of an RF power source, a receiver antenna array, a rectifier, and a battery-less sensor node which communicates via backscatter modulation at 868 MHz. The rectifier circuits use commercially available Schottky diodes to convert the RF power to DC with a measured efficiency of up to 35%, an improvement of ten percentage points compared with previously reported results. The rectifiers and the receive antenna arrays were jointly designed and optimised, thereby reducing the overall circuit size. The battery-less sensor transmitted data to a base station realised as a GNU Radio flow running on a bladeRF Software Defined Radio module. The whole system was tested in free-space in laboratory conditions and was capable of providing sufficient energy to the sensor node in order to enable operation and wireless communication at a distance of 0.15 metres

Optimal Coverage and Rate in Downlink Cellular Networks: A SIR Meta-Distribution Based Approach

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study

HtrA2/Omi Terminates Cytomegalovirus Infection and Is Controlled by the Viral Mitochondrial Inhibitor of Apoptosis (vMIA)

Viruses encode suppressors of cell death to block intrinsic and extrinsic host-initiated death pathways that reduce viral yield as well as control the termination of infection. Cytomegalovirus (CMV) infection terminates by a caspase-independent cell fragmentation process after an extended period of continuous virus production. The viral mitochondria-localized inhibitor of apoptosis (vMIA; a product of the UL37x1 gene) controls this fragmentation process. UL37x1 mutant virus-infected cells fragment three to four days earlier than cells infected with wt virus. Here, we demonstrate that infected cell death is dependent on serine proteases. We identify mitochondrial serine protease HtrA2/Omi as the initiator of this caspase-independent death pathway. Infected fibroblasts develop susceptibility to death as levels of mitochondria-resident HtrA2/Omi protease increase. Cell death is suppressed by the serine protease inhibitor TLCK as well as by the HtrA2-specific inhibitor UCF-101. Experimental overexpression of HtrA2/Omi, but not a catalytic site mutant of the enzyme, sensitizes infected cells to death that can be blocked by vMIA or protease inhibitors. Uninfected cells are completely resistant to HtrA2/Omi induced death. Thus, in addition to suppression of apoptosis and autophagy, vMIA naturally controls a novel serine protease-dependent CMV-infected cell-specific programmed cell death (cmvPCD) pathway that terminates the CMV replication cycle

Unlocking Edge Intelligence through Tiny Machine Learning (TinyML)

Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and reduced latency for inference. At the core of this paradigm is TinyML, a framework allowing the execution of ML models on low-power embedded devices. TinyML allows importing pre-trained ML models on the edge for providing ML-as-a-Service (MLaaS) to IoT devices. This article presents a comprehensive overview of Tiny MLaaS (TMLaaS) architecture. The TMLaaS architecture inherently presents several design trade-offs in terms of energy consumption, security, privacy, and latency.We also present how TMLaaS architecture can be implemented, deployed, and maintained for large scale IoT deployment. The feasibility of implementation for the TMLaaS architecture has been demonstrated with the help of a case study

Game theoretic optimal user association in emergency networks

The availability of effective communications in post-disaster scenarios is key to implement emergency networks that enable the sharing of critical information and support the coordination of the emergency response. To deliver those levels of QoS suitable to these applications, it is vital to exploit the multiple communication opportunities made available by the progressive deployment of the 5G and Smart City paradigms, ranging from ad-hoc networks among smartphones and surviving IoT devices, to cellular networks but also drone-based and vehicle-based wireless access networks. Therefore, the user device should be able to opportunistically select the most convenient among them to satisfy the demands for QoS imposed by the applications and also minimize the power consumption. The driving idea of this paper is to leverage non-cooperative game theory to design such an opportunistic user association strategy in a post-disaster scenario using UAV ad-hoc networks. The adaptive game-theoretic scheme allows increasing of the QoS of the communication means by lowering the loss rate and also keeps moderate the energy consumption

Routing in Post-Disaster Scenarios

Current networks should provide disaster-resilience by coping with the possible failures and misbehaviours caused by massive natural or man-made disasters. This is necessary to keep a suitable level of Quality of Service after a disaster and to support the possible evacuation, rescue, assessment, and rescue operations within the affected area. Multiple possible methods and solutions can be put in place in a proactive and/or reactive manner to offer the required resilience degree. Among them, a proper routing algorithm can contribute to circumventing network elements damaged by the disaster or applying for spatial/temporal redundancy to guarantee effective communications. This chapter aims at presenting the main routing solutions to offer disaster-resilience communications, along with some related methods