Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate

A low-cost collaborative location scheme with GNSS and RFID for the Internet of Things

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities

Soft information for localization-of-things

Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.RYC-2016-1938

A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions

The Internet of Things (IoT) can enable seamless communication between millions of billions of objects. As IoT applications continue to grow, they face several challenges, including high latency, limited processing and storage capacity, and network failures. To address these stated challenges, the fog computing paradigm has been introduced, purpose is to integrate the cloud computing paradigm with IoT to bring the cloud resources closer to the IoT devices. Thus, it extends the computing, storage, and networking facilities toward the edge of the network. However, data processing and storage occur at the IoT devices themselves in the fog-based IoT network, eliminating the need to transmit the data to the cloud. Further, it also provides a faster response as compared to the cloud. Unfortunately, the characteristics of fog-based IoT networks arise traditional real-time security challenges, which may increase severe concern to the end-users. However, this paper aims to focus on fog-based IoT communication, targeting real-time security challenges. In this paper, we examine the layered architecture of fog-based IoT networks along working of IoT applications operating within the context of the fog computing paradigm. Moreover, we highlight real-time security challenges and explore several existing solutions proposed to tackle these challenges. In the end, we investigate the research challenges that need to be addressed and explore potential future research directions that should be followed by the research community.©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

TIC’s implicadas en el sector de la distribución de la alimentación

Las Tecnologías de la Información y la Comunicación nos rodean y forman parte de nuestro modo de vida. En un mundo en donde la mayoría de la población está conectada es necesario que la manera de llevar los negocios se adapten y en la mayoría de los casos eso requiere adoptar las mismas tecnologías que usan los potenciales clientes para poder así captar su atención. En otros casos, la adopción de nuevas herramientas tecnológicas viene dada por la necesidad de sobrevivir en mundo competitivo o de la oportunidad de crecer. El sector de la distribución de la alimentación es un sector que está creciendo y sobre el que recae una gran responsabilidad, la de ofrecer los mejores productos garantizando el respeto por el medio ambiente y salvaguardando las medidas de calidad. Para cumplir su misión requieren de las últimas innovaciones, analizaremos en este trabajo cuales son y que grandes empresas son las que las utilizan.Universidad de Sevilla. Grado en Finanzas y Contabilida

Sulautettu ohjelmistototeutus reaaliaikaiseen paikannusjärjestelmään

Asset tracking often necessitates wireless, radio-frequency identification (RFID). In practice, situations often arise where plain inventory operations are not sufficient, and methods to estimate movement trajectory are needed for making reliable observations, classification and report generation. In this thesis, an embedded software application for an industrial, resource-constrained off-the-shelf RFID reader device in the UHF frequency range is designed and implemented. The software is used to configure the reader and its air-interface operations, accumulate read reports and generate events to be reported over network connections. Integrating location estimation methods to the application facilitates the possibility to make deploying middleware RFID solutions more streamlined and robust while reducing network bandwidth requirements. The result of this thesis is a functional embedded software application running on top of an embedded Linux distribution on an ARM processor. The reader software is used commercially in industrial and logistics applications. Non-linear state estimation features are applied, and their performance is evaluated in empirical experiments.Tavaroiden seuranta edellyttää usein langatonta radiotaajuustunnistustekniikkaa (RFID). Käytännön sovelluksissa tulee monesti tilanteita joissa pelkkä inventointi ei riitä, vaan tarvitaan menetelmiä liikeradan estimointiin luotettavien havaintojen ja luokittelun tekemiseksi sekä raporttien generoimiseksi. Tässä työssä on suunniteltu ja toteutettu sulautettu ohjelmistosovellus teolliseen, resursseiltaan rajoitettuun ja kaupallisesti saatavaan UHF-taajuusalueen RFID-lukijalaitteeseen. Ohjelmistoa käytetään lukijalaitteen ja sen ilmarajapinnan toimintojen konfigurointiin, lukutapahtumien keräämiseen ja raporttien lähettämiseen verkkoyhteyksiä pitkin. Paikkatiedon estimointimenetelmien integroiminen ohjelmistoon mahdollistaa välitason RFID-sovellusten toteuttamisen aiempaa suoraviivaisemin ja luotettavammin, vähentäen samalla vaatimuksia tietoverkon kaistanleveydelle. Työn tuloksena on toimiva sulautettu ohjelmistosovellus, jota ajetaan sulautetussa Linux-käyttöjärjestelmässä ARM-arkkitehtuurilla. Lukijaohjelmistoa käytetään kaupallisesti teollisuuden ja logistiikan sovelluskohteissa. Epälineaarisia estimointiominaisuuksia hyödynnetään, ja niiden toimivuutta arvioidaan empiirisin kokein

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

Smart IoTs based urine measurement system.

Urine Measurement is one of the most important processes for diagnosis in the hospitals nowadays. Acute Kidney Injury (AKI) is usually diagnosed by taking patient’s urine samples for a specific period of time. It has been suggested that the average Urine Output of a patient depends upon his weight. As we are all aware that currently the means to monitor the major vital signs of the human body in the ICU (Intensive Care Unit) or various clinical settings such as Heart Rate, Blood Pressure, Central Pressure etc. is done by the means of a continuous recording of impulses and its digital display. It is utmost necessary to record and continuously monitor a patients’ fluid input, administered mostly by electronic devices (e.g. Syringe infusion pumps). At the same time, it is also important to monitor patients’ fluid outputs, in which, urine volume is one of the major components. Currently, it is obtained intermittently (per hour) from urine meters and urine collection bags, and a visual assessment is made and recorded manually relying heavily on the nurse's capability and skills. Therefore, even after so much technological advancement the measurement of urine output is literally the only critical parameter constantly recorded and monitored non-electronically by the medical staff. The references from Medical Professionals at Royal Bournemouth Hospital clearly indicate a need for automated Urine Measurement System for efficient diagnosis process. There are automated devices for urine measurement, that are discussed in the Literature Review section, but none of them is available commercially. Some have cost issues whereas others are too complex to implement. We have found approx. 15 systems which have been patented by the inventors but none of them made it to the market. Cost-efficiency, complexity, and reliability are the issues we need to address, and we have tried to address in our project. In this project, an integrated prototype based on IoT, that measures urine volume in real time for both high and low flow is developed. The system measures the urine coming from the patient through two different sensors, Photo Interrupter Module and Hall-Effect based liquid sensor, and transmits that data to a cloud-based application via WiFi. The Arduino Yun micro-controller was used because of its built-in WiFi chip and more robust performance as compared to other options. The measurement of both high and low flow of liquid makes our system unique from the existing systems. The application at Cloud analyze the data from the sensors for visualizations as mentioned by the doctors. MATLAB analytics facilities will be used because it provides extended options for multiple real-time visualizations. The data is sent in real-time, every 20 seconds and visualizations are updated accordingly. The data is also available to view on an Android App. The real-time stream of data on cloud and ease of data accessibility distinguishes our system to those described in the literature. Series of experimentation was carried out for the prototype. Firstly, due to a problem in Photo Interrupter sensor for drop by drop measurement, the error was huge. Then, we developed an algorithm that solved the problem of object detection and then the error came to below 10% for both the high and low-flow measurement combined. This algorithm can be used to improve the working of photo interrupter sensor in other scenarios and it is one of the contributions of our project. This system decreases the workload of the nursing staff as well as that of the doctors. The human-error is minimized. The Data Analytics application enables the doctors to have an in-depth understanding of the condition of a patient at several different intervals of time. Hence, our system is expected to benefit the medical industry and especially the staff at the hospitals. Lastly, we have also found our concept to be helpful in process industry also where the liquid measurement is used and we presented this concept at EPSRC conference in Glasgow