Mobile IoT-Edge-Cloud Continuum Based and DevOps Enabled Software Framework

Judvaitis, Jānis; Balašs, Rihards; Greitāns, Modris

doi:10.3390/jsan10040062

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Dandelion [24] 2019 NA Yes Indoor and outdoor DUTs, sub-GHz LoRa DUTs Open CLORO [25] 2019 NA No DUTs include moving robot, Android app for robot movement control FIST [26] 2012 NA No Wirelessly managed DUTs, the same radio channel for control and experimentation, tree topology City of Things [27] 2016 NA Yes Urban deployment, integrated big data analysis, includes wearables, data visualization AssIUT IoT [28] Update [29] 2018 4 No DUTs supporting LoRa, WiFi, ZigBee and Cellular communication, web interface TWECIS [30] Abstract [31] 2014 4 No GPIO event timestamping, support of gdb, network-wide power monitoring Rover [32] 2016 8 No Open source, cheap to implement Ocean-TUNE UCONN testbed [33] 2014 [41] 2011 27 Yes Mobile and static DUTs, a large variety of sensors, time synchronization, indoor positioning, robot movement control CaBIUs [42] 2020 30 No Custom designed programming language, web interface SDNWisebed [43] 2019 40 No SDN networking support, traffic statistics for DUTs I3ASensorBed [44] 2013 46 No Wide range of sensors, including CO2, presence, smoke, etc. OpenTestBed [45] 2019 80 Yes Fully open-source FlockLab [46] Demo [47] 2013 106 Yes GPIO tracing and actuation, power monitoring, time synchronization EDI TestBed [23] Updates [48][49][50][51] 2015 110 Yes GPIO interaction, power monitoring, ADC and DAC interaction, versatile deployment options, CLI RT Lab [52] 2015 115 Yes Indoor and outdoor DUTs, online code editing, parameterized control, digital multimeter for power monitoring Indriya [53] 2012 127 Yes Small maintenance costs, distributed in three floors NetEye [54] 2012 130 Yes Topology control, health monitoring, policy-based scheduling USN testbed [55] 2020 142 No Indoor and outdoor DUTs, management GUI LinkLab [56] 2020 155 Yes Web interface, online compilation, self-inspection module SmartCampus [57] Demo …”

Section: Resultsmentioning

confidence: 99%

Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review

Judvaitis

Abolins

Elkenawy

et al. 2023

JSAN

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As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a systematic review of the availability and usage of testbed facilities published in scientific literature between 2011 and 2021, including 359 articles about testbeds and identifying 32 testbed facilities. The results of the review revealed what testbed facilities are available and identified several challenges and limitations in the use of the testbed facilities, including a lack of supportive materials and limited focus on debugging capabilities. The main contribution of this article is the description of how different metrics impact the uasge of testbed facilities, the review also highlights the importance of continued research and development in this field to ensure that testbed facilities continue to meet the changing needs of the ever-evolving IoT and WSN domains.

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Section: Resultsmentioning

confidence: 99%

Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review

Judvaitis

Abolins

Elkenawy

et al. 2023

JSAN

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“…presented the Flower open-source FL framework. Comparing heterogeneous client support (clients with different hardware), ML framework agnosticism (different ML library support), communication agnosticism (different communication protocol support), language agnosticism (different programming language support), and many other features with other frameworks [133,135].…”

Section: Toolsmentioning

confidence: 99%

Distributed Learning in the IoT–Edge–Cloud Continuum

Arzovs,

Judvaitis,

Nesenbergs

et al. 2024

MAKE

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The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.

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“…EDI TestBed [33] is an infrastructure as a service-based module that provides the user with remote access to WSN/IoT hardware distributed across the EDI office 7-floor building and outside of it located in Riga, Latvia. Although the hardware is located in EDI building remote access is provided for users to interact with the infrastructure.…”

Section: Predictive Maintenance With Iotmentioning

confidence: 99%

Technology Modules Providing Solutions for Agile Manufacturing

Deniša,

Ude,

Simonič

et al. 2023

Machines

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In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape.

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Mobile IoT-Edge-Cloud Continuum Based and DevOps Enabled Software Framework

Cited by 9 publications

References 21 publications

Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review

Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review

Distributed Learning in the IoT–Edge–Cloud Continuum

Technology Modules Providing Solutions for Agile Manufacturing

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