In this paper, we develop a comprehensive statistical framework to characterize and model large-scale unmanned aerial vehicle-enabled post-disaster recovery cellular networks. the case of natural or man-made disasters, network is vulnerable destruction resulting in coverage voids holes. Drone-based small networks (DSCNs) can be rapidly deployed fill such voids. Due capacity back-hauling limitations on drone cells (DSCs), each hole requires multitude DSCs meet shortfall at desired...

Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system applications. The departure from traditional cloud-centric architecture means that deployments can be more power-efficient, provide better privacy reduced latency inference. At core this paradigm TinyML, framework allowing execution ML models low-power embedded devices. TinyML allows importing pre-trained providing ML-as-a-Service (MLaaS) to This article presents comprehensive overview Tiny MLaaS...

The advancement of sustainable energy sources necessitates the development robust forecasting tools for efficient management. A prominent player in this domain, solar power, heavily relies on accurate yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application tiny machine learning provide real-time, low-cost resource-constrained edge internet things devices, such as micro-controllers, improved residential industrial To...

Resilient communication networks, which can continue operations even after a calamity, will be central feature of future smart cities. Recent proliferation drones propelled by the availability cheap commodity hardware presents new avenue for provisioning such networks. In particular, with advent Google's Sky Bender and Facebook's internet drone, drone empowered small cellular networks (DSCNs) are no longer fantasy. DSCNs attractive solution public safety because swift deployment capability...

Abstract Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise informed practices. A tiny machine learning (TinyML)‐based framework is proposed unmanned aerial vehicle (UAV)‐assisted applications. The practical deployment such a on UAV bespoke internet things (IoT) sensors which measure soil moisture ambient environmental conditions demonstrated. key objective this to harness TinyML implementing transfer (TL) using deep neural...

The rapid proliferation of consumer UAVs, or drones, is reshaping the wireless communication landscape. These agile, autonomous devices find new life as UE in cellular networks. This paper explores their integration, emphasizing myriad applications, standardization efforts, challenges, and research community solutions. Key areas investigation include complexities 3D deployment, channel modelling, energy efficiency. Moreover, we highlight open questions opportunities these flying UEs present....

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the generation profiles from renewable energy sources, solar wind systems, determining locations sizes FACTS devices increase complexity OPF problems modern network models, such as loss, cost voltage deviation, a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces employs four new independent,...

Federated Learning (FL) presents a mechanism to allow decentralized training for machine learning (ML) models inherently enabling privacy preservation. The classical FL is implemented as client-server system, which known Centralised (CFL). There are challenges inherent in CFL since all participants need interact with central server resulting potential communication bottleneck and single point of failure. In addition, it difficult have some scenarios due the implementation cost complexity....

In this paper we present an optimal dimensioning for drone small cells. We extend the traditional models to include transmitter antenna gain patterns and wireless channel multi- path fading. show that enhanced model there exists height which ensures best performance in terms of various metrics. This analysis optimization is achieved by averaging metrics over all mobile user locations inside coverage area. Numerical simulation results confirm outage probability, bit-error rate capacity.

For the first time, design and implementation of a fully-integrated wireless information power transfer system, operating at 24 GHz enabling battery-less sensor nodes, is presented in this paper. The system consists an RF source, receiver antenna array, rectifier, node which communicates via backscatter modulation 868 MHz. rectifier circuits use commercially available Schottky diodes to convert DC with measured efficiency up 35%, improvement ten percentage points compared previously reported...

Grasp classification using data gloves can enable therapists to monitor patients efficiently by providing concise information about the activities performed these patients. Although, classical machine learning algorithms have been applied in grasp classification, they require manual feature extraction achieve high accuracy. In contrast, convolutional neural networks (CNNs) outperformed popular several scenarios because of their ability extract features automatically from raw data. However,...

Abstract Recently, smart grids introduce significant challenges to power system protection due the high integration with distributed energy resources (DERs) and communication systems. To effectively manage impact of DERs on networks, researchers are actively formulating adaptive strategies, requiring robust schemes. However, concerns remain over occurrence connection failures potential risks presented by cyber‐attacks. This work addresses these investigating cyber‐attacks different...

In recent years, the integration of Distributed Energy Resources (DERs) and communication networks has presented significant challenges to power system control protection, primarily as a result emergence smart grids cyber threats. As use grid-connected solar Photovoltaic (PV) systems continues increase with intelligent PV inverters, susceptibility these attacks their potential impact on grid stability emerges critical concern based inverter models. This study explores cyber-threat...

Tiny machine learning (TinyML) is a promising approach to enable intelligent applications relying on Human Activity Recognition (HAR) resource-limited and low-power internet of things (IoT) edge devices. However, designing efficient TinyML models for these devices remains challenging due computational resource constraints the need customisation unique use cases. To address this, we propose novel that utilises transfer (TL) techniques micro-controller units (MCUs) accelerate development. Our...

Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts various industrial domains, including smart farming. To increase efficiency farming operations and ensure ML accessibility for both small large-scale farming, need a low-cost ML-enabled framework more pressing. In this paper, we present an end-to-end solution that utilizes tiny (TinyML) adoption classification tasks with focus on post-harvest process olive fruits. We performed dataset...

In this paper, a new cooperative-diversity network with simple dual hop virtual diversity mixed decode-and-forward and amplify-and-forward best path selection is proposed. A closed-form expression for the average bit error rate of communication system, assuming phase shift keying modulation scheme over independent non-identical Rayleigh fading channels, derived. The results show that proposed model outperforms regular cooperative models sub-optimal allocation relay relative distance.

Rehabilitation of stroke survivors can be expedited by employing an exoskeleton. The exercises are designed such that both hands move in synergy. In this regard, often, motion capture data from the healthy hand is used to derive control behavior for Therefore, gloves provide a low-cost solution joints hand. However, current bulky, inaccurate, or inconsistent. These disadvantages inherited because conventional design glove involves external attachment degrades overtime and causes...

In this article, we develop a comprehensive framework to characterize the performance of drone assisted Backscatter communication based Internet things (IoT) sensor network. We consider scenario such where transmits RF carrier which is modulated by IoT node (SN) transmit its data. The SN implements load modulation results in amplitude shift keying (ASK) type for impinging carrier. order quantify considered network, coverage probability ground node. statistical developed explicitly...

The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been traditionally for equalisation. However, CSI intrinsically is a signature operational RF environment and can serve as proxy certain activities environment. For instance, gets influenced by scatterers therefore be an indicator how many or if there are mobile etc. mapping between whose encodes raw data not deterministic. Nevertheless, machine learning (ML)...

A new incremental-relaying cooperative-diversity technique with a simple dual transmit diversity, decode-and-forward and best relay selection scheme is presented. Closed-form expressions for the bit error rate, signal to noise ratio outage probability, average achievable rate are derived using binary phase shift keying modulation over independent non-identical Rayleigh fading channels. The results show that space time block coding cooperative diversity outperforms regular incremental fixed...

In this paper, we present a detailed analysis of the coverage and spectral efficiency downlink cellular network. Rather than relying on first order statistics received signal-to- interference-ratio (SIR) such as probability, focus characterizing its meta- distribution. Our is based alpha- beta-gamma (ABG) path-loss model which provides us with flexibility to analyze urban macro (UMa) micro (UMi) deployments. With help an analytical framework, demonstrate that selection underlying...