Quality of Service Aware Orchestration for Cloud-Edge Continuum Applications

The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.This work has been financially supported by the European Commission through the ELASTIC project (H2020 grant agreement 825473), by the Spanish Ministry of Science, Innovation and Universities (project RTI2018-096116-B-I00 (MCIU/AEI/FEDER, UE)), and by the Basque Government through the Qualyfamm project (Elkartek KK-2020/00042). It has also been financed by the Basque Government under Grant IT1324-19

A Privacy Impact Assessment Method for Organizations Implementing IoT for Occupational Health and Safety

Internet of Things (IoT) technologies are increasingly being integrated into occupational health and safety (OHS) practices; however, their adoption raises significant privacy concerns. The General Data Protection Regulation (GDPR) has established the requirement for organizations to conduct Privacy Impact Assessments (PIAs) prior to processing personal data, emphasizing the need for privacy safeguards in the workplace. Despite this, the GDPR provisions related to the IoT, particularly in the area of OHS, lack clarity and specificity. This research aims to bridge this gap by proposing a tailored method for conducting PIAs in the OHS context, with a particular focus on addressing the how to aspect of the assessment process. The proposed method integrates insights from domain experts, relevant literature sources, and GDPR regulations, ultimately leading to the development of an online PIA tool

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

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Towards Seamless IoT Device-Edge-Cloud Continuum:

In this paper we revisit a taxonomy of client-side IoT software architectures that we presented a few years ago. We note that the emergence of inexpensive AI/ML hardware and new communication technologies are broadening the architectural options for IoT devices even further. These options can have a significant impact on the overall end-to-end architecture and topology of IoT systems, e.g., in determining how much computation can be performed on the edge of the network. We study the implications of the IoT device architecture choices in light of the new observations, as well as make some new predictions about future directions. Additionally, we make a case for isomorphic IoT systems in which development complexity is alleviated with consistent use of technologies across the entire stack, providing a seamless continuum from edge devices all the way to the cloud.Peer reviewe

Creating the Internet of Augmented Things: An Open-Source Framework to Make IoT Devices and Augmented and Mixed Reality Systems Talk to Each Other

[Abstract] Augmented Reality (AR) and Mixed Reality (MR) devices have evolved significantly in the last years, providing immersive AR/MR experiences that allow users to interact with virtual elements placed on the real-world. However, to make AR/MR devices reach their full potential, it is necessary to go further and let them collaborate with the physical elements around them, including the objects that belong to the Internet of Things (IoT). Unfortunately, AR/MR and IoT devices usually make use of heterogeneous technologies that complicate their intercommunication. Moreover, the implementation of the intercommunication mechanisms requires involving specialized developers with have experience on the necessary technologies. To tackle such problems, this article proposes the use of a framework that makes it easy to integrate AR/MR and IoT devices, allowing them to communicate dynamically and in real time. The presented AR/MR-IoT framework makes use of standard and open-source protocols and tools like MQTT, HTTPS or Node-RED. After detailing the inner workings of the framework, it is illustrated its potential through a practical use case: a smart power socket that can be monitored and controlled through Microsoft HoloLens AR/MR glasses. The performance of such a practical use case is evaluated and it is demonstrated that the proposed framework, under normal operation conditions, enables to respond in less than 100 ms to interaction and data update requests.Xunta de Galicia; IN853B-2018/0

Outdoor node localization using random neural networks for large-scale urban IoT LoRa networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m

Low-cost Soil Moisture Sensors Based on Inductive Coils Tested on Different Sorts of Soils

[EN] The use of precision agriculture and the Internet of Things has improved the efficiency of many cultures. Nevertheless, there are a few low-cost options to monitor soil moisture. Moreover, those options depend on the specific characteristics of the soil. In this paper, we attempt to find a sensor, based on mutual inductance, that could be used for more than one sort of soil. We study three prototypes, one of them with casing. The sensors are powered with a voltage of 10 peak to peak volts. One of the soils has a high content of organic matter and sand while the other is rich in sand and silt. The best prototype for the soil with high levels of organic matter has 10 turns on the powered coil and 5 on the induced coil. The best frequency for this sensor is 1340 kHz. For the soil with a significant quantity of silt, the best prototype has 80 turns on the powered coil and 40 on the induced coil. The frequency at which this sensor works best is 229 kHz, which happens to be its peak frequency. With those characteristics regressions lines with R2 values higher than 0.75 can be modeledThis work is partially found by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR, and by the European Union with the Fondo Europeo Agrícola de Desarrollo Rural (FEADER) Europa invierte en zonas rurales, the MAPAMA, and Comunidad de Madrid with the IMIDRA, under the mark of the PDR-CM 2014-2020 project number PDR18-XEROCESPED.Parra-Boronat, M.; Parra-Boronat, L.; Lloret, J.; Mauri, PV.; Llinares Palacios, JV. (2019). Low-cost Soil Moisture Sensors Based on Inductive Coils Tested on Different Sorts of Soils. IEEE. 616-622. https://doi.org/10.1109/IOTSMS48152.2019.8939258S61662

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors