Internet of things for the hotel industry: a review

The Internet of Things (IoT) represents an opportunity for the hotel in-dustry to increase customer satisfaction while simultaneously reducing operational costs. This paper analyses the existing knowledge on this subject, through a re-view of the relevant publications indexed by Scopus and/or ISI Web of Science, concluding that, despite the existence of many relevant patents, registered in the past few years, the published research is very limited on this topic. The restriction to publication prior to the conclusion of the patent registration process may be a justification for this fact, and, if so, the near future will bring many novelties that will help the development of the hotel industry. It is also possible to conclude from this work that the potential of IoT is not yet well explored in the hotel indus-Try, once authors frequently theorize on the use of IoT for applications that could easily be of interest for the hotel industry, but fail to identify that opportunity as a major market.info:eu-repo/semantics/publishedVersio

Investigating Security for Ubiquitous Sensor Networks

The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network paradigm which is providing various types of solutions to the community and the individual user in various sectors including environmental monitoring, entertainment, transportation, security, and healthcare. We explore and compare the features of wireless sensor networks and ubiquitous sensor networks and based on the differences between these two types of systems, we classify the security-related challenges of ubiquitous sensor networks. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges that need to be addressed to design more secure ubiquitous sensor networks in the future

Latency Optimization in Large-Scale Cloud-Sensor Systems

With the advent of the Internet of Things and smart city applications, massive cyber-physical interactions between the applications hosted in the cloud and a huge number of external physical sensors and devices is an inevitable situation. This raises two main challenges: cloud cost affordability as the smart city grows (referred to as economical cloud scalability) and the energy-efficient operation of sensor hardware. We have developed Cloud-Edge-Beneath (CEB), a multi-tier architecture for large-scale IoT deployments, embodying distributed optimizations, which address these two major challenges. In this article, we summarize our prior work on CEB to set context for presenting a third major challenge for cloud sensor-systems, which is latency. Prolonged latency can potentially arise in servicing requests from cloud applications, especially given our primary focus on optimizing energy and cloud scalability. Latency, however, is an important factor to optimize for real-time and cyber-physical applications with limited tolerance to delays. Also, improving the responsiveness of any IoT application is bound to improve the user experience and hence the acceptability and adoption of smart city solutions by the city citizens. In this article, we aim to give a formal definition and formulation for the latency optimization problem under CEB. We propose a Prioritized Application Fragment Caching Algorithm (PAFCA) to selectively cache application fragments from the cloud to lower layers of CEB, as a key measure to optimize latency. The algorithm itself is an extension of one of the existing optimization algorithms of CEB (AFCA-1). As will be shown, PAFCA takes into account the expectations of cloud applications on real-timeliness of responses. Through experiments, we measure and validate the effect of PAFCA on latency and cloud scalability. We also introduce and discuss the trade-off between latency and sensor energy in this given context

Desarrollo de arquitecturas especializadas para Sistemas de Conducción Inteligente

El trabajo consiste en el desarrollo de un prototipo que permita clasificar modelos de conducción a partir de datos obtenidos a través de una interfaz del vehículo OBD-II. El trabajo está estructurado en tres partes. En primer lugar, se trata la problemática de la selección y recolección de datos. Se eligen que datos pueden ser de utilidad para el sistema para determinar si una conducción se está realizando de forma normal o ineficiente. Para ello se elaboran siete circuitos (divididos en callejeo, vía rápida y mixto) y se recogen datos conduciendo sobre los mismos. Acto seguido, se desarrolla una solución que permita tratar los datos obtenidos y posibilite extraer conclusiones. En esta parte se implementa una red neuronal denominada SOM, la cual permite clasificar los datos reduciendo su dimensionalidad. Finalmente, una vez disponemos de los resultados de las redes SOM se elabora una clasificación. Se muestra como las redes SOM encuentran patrones entre circuitos probados según los datos obtenidos de las variables posición del acelerador, rpm, velocidad del vehículo, temperatura en la toma de aire, temperatura del refrigerante y carga del motor

Transfer Learning for Improved Audio-Based Human Activity Recognition

Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes

The digital twin of discrete dynamic systems: Initial approaches and future challenges

This paper employs a discrete damped dynamic system to investigate the emerging concept of a digital twin. Dynamic systems are well understood across engineering and science domains, and represent a familiar and convenient platform for exploring the various aspects of a digital twin design. The aim is to create a framework accessible to engineering sciences related to the aerospace, electrical, mechanical and computational area. The virtual model of the physical system is expressed as a differential equation in two-time scales, with the concept of a slow time being used to separate the evolution of the system properties from the instantaneous time. Cases involving stiffness variation and mass variation are considered, individually and together. It is assumed that the damped natural frequency and the time response are measured through sensors placed on the physical system. Issues of errors and reduced sampling rate in sensor measurements on the digital twin are investigated. The digital twin is expressed as an analytical solution through closed-form expressions and the effect of sensor errors is brought out through the simulations. Several key concepts introduced in this paper are summarized and ideas for urgent future research needs are proposed. The current work breaks free from the qualitative description of digital twins pervading the literature and can be used as benchmark solutions to validate digital twin of experimental dynamic systems and their implementation using sensors, the internet of things and deployment on the cloud computing systems

Location-aware scalable service composition.

The problem of service composition is the process of assigning resources to services from a pool of available ones in the shortest possible time so that the overall quality of service is maximized. This article provides solutions for the composition problem that takes into account its scalability, services’ locations, and users’ restrictions, which are key for the management of applications using state-of-the-art technologies. The provided solutions use different techniques, including genetic algorithms and heuristics. We provide an extensive experimental evaluation, which shows the pros and cons of each of them, and allows us to characterize the preferred option for each specific problem. Since no solution dominates the others, we propose a decision tree, based on our results, to select the best composition algorithm in each situation.Spanish Government projects TED2021-130666B-I00 and PID2021-125527NB-I00. Funding for open access charge: Universidad de Málaga / CBUA

IoT DEVICE MANAGEMENT AND CONFIGURATION

As the number of IoT devices grows, the management and configuration of IoT devices becomes crucial in resource constraint networks. It is hard to manage and configure a large amount of heterogeneous resource constraint IoT devices because people need to know how they connect to each other, what internet-enabled services are available to provide, and how people interact with things through the internet. The thing-centric approach focuses on user experience when engaging things, but the cloud- centric approach switch the focus to IoT services that can process data streams collected from things and applications that help get people joined in the IoT world. To manage IoT populations effectively in a centralized manner, not only does it mean that moving computational power closer to the edge is a way to reduce bandwidth and latency, but it also implies that it is necessary to build an architecture which can scale and manage tons of connected devices by a uniform interface. In particular, RESTful Web services can provide a uniform interface that operates resources by HTTP methods. For example, users can read and write data by a uniform interface, and a flowerpot can write data and be triggered to water plants by a uniform interface. Thus, in the scope of IoT, embedded middleware can implement uniform interface by REST model. Virtualizing physical things has emerged as a design pattern to build IoT systems. Resource less constraint devices are capable of being virtualized with enough CPU power, memory, networking, but they are more expensive and power consuming. However, resource highly constraint devices take advantage of low energy consumption and cheaper price, but they cannot be virtualized because they do not have ability to even run a single multi-threaded program. Therefore, it is very important to select the right platforms for the right roles. In our case, we use Raspberry Pi 3 as a middleware and Nordic nRF52832 as a BLE endpoint. In this thesis, a REST-based IoT management system based on Service-Oriented Architecture is built, and the performance of the system has been tested, including the response time of HTTP GET and POST requests of the centralized server in a Fog domain and a script engine onto a BLE-enabled endpoint

IoT-DDL—Device Description Language for the “T” in IoT

We argue that the success of the Internet of Things (IoT) vision will greatly depend on how its main ingredient—the “thing”—is architected and prepared to engage. The IoT’s fragmented and wide-varying nature introduces the need for additional effort to homogenize these things so they may blend together with the surrounding space to create opportunities for powerful and unprecedented IoT applications. We introduce the IoT Device Description Language (IoT-DDL), a machine- and human-readable descriptive language for things, seeking to achieve such integration and homogenization. IoT-DDL explicitly tools things to self-discover and securely share their own capabilities, entities, and services, including the various cloudbased accessories that may be attached to them. We also present the Atlas thing architecture—a lightweight architecture for things that fully exploits IoT-DDL and its specifications. Our architecture provides new OS layers, services, and capabilities we believe a thing must have in order to be prepared to engage in IoT scenarios and applications. The architecture and IoT-DDL enable things to generate their offered services and self-formulate APIs for such services, on the fly, at power-on or whenever a thing description changes. The architecture takes advantage of widely used device management, micro-services, security, and communication standards and protocols. We present details of IoT-DDL and corresponding parts of the thing architecture. We demonstrate some features of IoT-DDL and the architecture through proof-of-concept implementations. Finally, we present a benchmarking study to measure and assess time performance and energy consumption characteristics of our architecture and IoT-DDL on real hardware platforms