Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities

[EN] The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.Rehman, A.; Haseeb, K.; Saba, T.; Lloret, J.; Ahmed, Z. (2021). Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities. Sustainability. 13(16):1-16. https://doi.org/10.3390/su13169092S116131

A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring

There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm2, with an average error in accuracy of 18% compared to manual sweat rate readings

A Framework for Service-Oriented Architecture (SOA)-Based IoT Application Development

Funding: This research was partially supported by funds provided by the European Commission in the scope of FoF/H2020-723710 vf-OS, ICT/H2020-825631 ZDMP projects, and by the FCT— Fundação para a Ciência e a Tecnologia in the scope of UIDB/00066/2020 related to CTS—Centro de Tecnologia e Sistemas research unit.In the last decades, the increasing complexity of industrial information technology has led to the emergence of new trends in manufacturing. Factories are using multiple Internet of Things (IoT) platforms to harvest sensor information to improve production. Such a transformation contributes to efficiency growth and reduced production costs. To deal with the heterogeneity of the services within an IoT system, Service-Oriented Architecture (SOA) is referred to in the literature as being advantageous for the design and development of software to support IoT-based production processes.The aim of SOA-based design is to provide the leverage to use and reuse loosely coupled IoT services at the middleware layer to minimise system integration problems. We propose a system architecture that follows the SOA architectural pattern and enables developers and business process designers to dynamically add, query or use instances of existing modular software in the IoT context. Furthermore, an analysis of utilization of modular software that presents some challenges and limitations of this approach is also in the scope of this workpublishersversionpublishe

Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity

Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them

Desarrollo de servicios de IoT seguros: una revisión de las plataformas de IoT orientada a la seguridad

Undoubtedly, the adoption of the Internet of Things (IoT) paradigm has impacted on our every-day life, surrounding us with smart objects. Thus, the potentialities of this new market attracted the industry, so that many enterprises developed their own IoT platforms aiming at helping IoT services’ developers. In the multitude of possible platforms, selecting the most suitable to implement a specific service is not straightforward, especially from a security perspective. This paper analyzes some of the most prominent proposals in the IoT platforms market-place, performing an in-depth security comparison using five common criteria. These criteria are detailed in sub-criteria, so that they can be used as a baseline for the development of a secure IoT service. Leveraging the knowledge gathered from our in-depth study, both researchers and developers may select the IoT platform which best fits their needs. Additionally, an IoT service for monitoring commercial flights is implemented in two previously analyzed IoT platforms, giving an adequate detail level to represent a solid guideline for future IoT developer

Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity

Autonomous Wireless Sensors (AWSs) are at the core of every Wireless Sensor Network (WSN). Current AWS technology allows the development of many IoT-based applications, ranging from military to bioengineering and from industry to education. The energy optimization of AWSs depends mainly on: Structural, functional, and application specifications. The holistic design methodology addresses all the factors mentioned above. In this sense, we propose an original solution based on a novel architecture that duplicates the transceivers and also the power source using a hybrid storage system. By identifying the consumption needs of the transceivers, an appropriate methodology for sizing and controlling the power flow for the power source is proposed. The paper emphasizes the fusion between information, communication, and energy consumption of the AWS in terms of spectrum information through a set of transceiver testing scenarios, identifying the main factors that influence the sensor node design and their inter-dependencies. Optimization of the system considers all these factors obtaining an energy efficient AWS, paving the way towards autonomous sensors by adding an energy harvesting element to them

Improving service scalability in IoT platform business

Abstract. This thesis aims to improve the scalability of several case companies’ business which offer their services through their own IoT platforms. The case companies are still in the early stages of their lifecycle, and their aim is to grow their businesses significantly in the future. Thus, enabling high scalability in service production is important for them. A literature review was conducted to find the most critical factors that affect scalability of services that are provided through an IoT platform. Interviews with open-ended questions were used to determine the current state of the case companies regarding the factors that were presented by the literature review. Based on the literature review and the current state analysis, two productization models were created including commercial and technical portfolios. Resource drivers were also included in the models. The created productization models for IoT service offerings are suggested to ease sales item management and to clarify the service offerings for both the provider and the buyer. Further, linking the resource drivers to the processes needed to offer the services illustrates the needed resources in different service production processes. The presented productized service models are one step that the case companies can take to improve their service scalability, but the models are not a solution to all scalability problems. However, similar models could be used in other companies that provide their service offerings through an IoT platform to improve their service scalability as well.Palvelutuotannon skaalautuvuuden parantaminen alustan kautta toimivissa yrityksissä. Tiivistelmä. Tämän opinnäytetyön tavoitteena on parantaa alustatalouden kautta palveluitaan tarjoavien case yritysten skaalautuvuutta. Case-yritykset ovat vielä elinkaarensa alkuvaiheessa ja niiden tavoitteena on kasvattaa liiketoimintaa merkittävästi tulevaisuudessa. Tämän johdosta korkean skaalautuvuuden mahdollistaminen yrityksien palvelutuotannossa on tärkeää. Kirjallisuuskatsauksessa pyritään löytämään merkittävimmät tekijät, jotka vaikuttavat skaalautuvuuteen alustatalouden kautta tehtävässä palveluntarjonnassa. Case yritysten nykytila analysoidaan avoimin kysymyksin suoritettavilla haastatteluilla, joilla pyritään selvittämään tekijät, joissa case yrityksillä olisi parantamisen varaa. Kirjallisuuskatsauksen ja yritysten nykytila-analyysin pohjalta luodaan kaksi tuotteistusmallia, joissa kaupallinen ja tekninen tuoteportfolio on eroteltu toisistaan, lisäksi resurssiajurit on kuvattu mukaan malleihin. Tuotteistusmalli helpottaa eri tuotenimikkeiden hallintaa ja lisää palvelun selkeyttä niin myyjän kuin ostajankin puolella, lisäksi resurssiajureiden ottaminen mukaan malliin havainnollistaa tarjoajayritykselle sen tarvitsemia resursseja eri palveluprosessin vaiheissa. Työn loppupäätelmänä luodut tuotteistusmallit toimivat yksinä toimenpiteinä, joidenka voidaan nähdä parantavan case-yrityksien skaalautuvuutta, mutta ne eivät ole ratkaisu kaikkiin skaalautuvuuden ongelmiin. Samankaltaisia malleja voitaisiin kuitenkin hyödyntää muissakin yrityksissä, jotka tarjoavat palveluitaan alustatalouden kautta toimialasta riippumatta

Integration of Multiscale Sensing Data for Phenomics Applications

Sensing technologies can be a powerful tool for phenotyping in breeding programs. Plant phenotypes can be assessed non-invasively and repeatedly across the whole population and throughout the plant development period utilizing advanced sensors and remote sensing platforms. In this study, multiscale sensing platforms—satellite, unmanned aerial vehicle (UAV), proximal sensing system, and Internet of Things (IoT) based sensing systems—equipped with sensors such as visible/RGB, multispectral, and hyperspectral systems were utilized for field-based phenomics applications. The applicability of a suitable sensing technology depends on the area of study, specific phenomics application, sensor specification, and data acquisition conditions. Three main phenomics applications were explored: (i) pasture crop health status evaluation, (ii) above-ground biomass quantity and quality evaluation in the field pea, and (iii) evaluating wheat yield potential in winter and spring wheat. The first study demonstrates the reliability of using a high-resolution satellite (ground sampling distance, GSD = 3 m) and UAV imagery for pasture management. The data from multiscale sensing data showed that the grazing density significantly affected pasture biomass (p < 0.05) only in 2019, and the vegetation index (VI) data from the two imagery types were highly correlated (r ≥ 0.78, p < 0.001, 2019). In the second study, the above-ground biomass (AGBM) and biomass quality (12 quality traits) were evaluated using UAV-based RGB and multispectral imaging, and hyperspectral sensing, respectively, in the winter pea breeding program (2019 and 2020 seasons). Three image processing approaches were evaluated for AGBM estimation, where the best results were acquired using the 3D point cloud model at 1.5 alpha shape technique showing high correlation with harvested fresh (r = 0.78–0.81, p < 0.001) and dry (r = 0.70–0.81, p < 0.001) AGBM. Similarly, the selected features from the normalized difference spectral indices and the ratio spectral indices extracted from hyperspectral data with the random forest model provided high predictive accuracy for all 12 biomass quality traits (0.81 < R2 < 0. 93; 0.05 < RMSE (%) < 1.80; 0.03 < MAE (%) < 1.32).In the wheat study, the vegetation indies were highly correlated between satellite (GSD = 0.31 m) and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) from winter and spring wheat breeding trials (2020 and 2021). The yield prediction using such VIs with the high-resolution satellite imagery (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) was also high. In addition to these two platforms, an intelligent and compact IoT-based sensor system was developed for independent and automated phenomics applications to measure and monitor plant responses in real-time. The sensor development, improvisation, and implementation encompassed three field seasons (2020, 2021, and 2022 seasons). The developed IoT-based sensor system could be successfully implemented to monitor multiple trials for timely crop management and increased resource efficiency. The system shows a high potential for supporting plant breeding programs for in-field phenotyping applications. All studies demonstrated promising results in monitoring and estimating crop performance and phenotypic traits using multiscale sensing systems