This research aims to provide a high-level software framework for IoT-Edge-Cloud computational continuum-based applications with support for mobile IoT and DevOps integration utilizing the Edge computing paradigms. This is achieved by dividing the system in a modular fashion and providing a loosely coupled service and module descriptions for usage in the respective system layers for flexible and yet trustworthy implementation. The article describes the software architecture for a DevOps-enabled Edge computing solution in the IoT-Edge-Cloud computational continuum with the support for flexible and mobile IoT solutions. The proposed framework is validated on an intelligent transport system use case in the rolling stock domain and showcases the improvements gained by using the proposed IoT-Edge-Cloud continuum framework.

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.

Background The Internet of Things, similar to wireless sensor networks, has been integrated into the daily life of almost everyone. These wearable, stationary, or mobile devices are in multiple locations, collecting data or monitoring and executing certain tasks. Some can monitor environmental values and interact with the environment, while others are used for data collection, entertainment, or even lifesaving. To achieve the wireless part of the system, the majority of sensor nodes are designed to be battery-powered. While battery power has become increasingly ubiquitous, it tends to increase the global carbon footprint of electronic devices. This issue can be mitigated by employing some form of energy harvesting so that batteries can be refilled and the gadget lasts longer, but this does not alter the reality that batteries are still used and eventually discarded. Methods In this paper, the authors emphasize the significance of power consumption in battery-powered devices. To be able to monitor devices’ power consumption, one of the measurable parameters is current. When users know the exact current consumption, they can decrease it by polishing the program or tweaking the duty cycle, making radio transmit fewer data or less frequently, thus decreasing overall power draw. Results In order to simplify current consumption monitoring, the authors have developed a testbed facility that provides real-time current consumption measurements, which may be used to enhance the duty cycle and battery life of the aforementioned devices. Conclusions While minimizing total current consumption is a great way to extend the battery life and, thus, the carbon footprint, the primary culprit in the Internet of Things is radio communications. This transmission is the primary source of current consumption. By determining the exact amount of current drawn during transmission and adjusting it, users can significantly extend battery life.

Developing and managing complex IoT–Edge–Cloud Continuum (IECC) systems are challenging due to the system complexity and diversity. Internet of Things (IoT), Edge, and Cloud components combined with artificial intelligence (AI) in data processing systems must ensure strong security and privacy for data sources. The approach of the IECC Data Management Framework (DMF) introduces a novel combination of multiple easy-to-configure plugin environments using data visualization features. These contributions collectively address the critical challenges inherent in heterogeneous environments such as scalability, data privacy, and configuration management by standardizing data flow configurations and increasing stakeholder trust in sensitive applications, particularly in critical infrastructure monitoring.

The research community has been working on sensor networks for more that seven decades and altogether more than a million research articles on sensor networks have been published, with this number growing every day. In this article, we try to provide a thorough and complete systematic review on the sensor network field resulting from representative subset of more than 3000 articles which include actual sensor network deployments. After a thorough analysis of these data, we provide a definitive overview of the trends in sensor network deployment described in published research articles.

Background: The Internet of Things, similar to wireless sensor networks, has been integrated into daily life of almost everyone. These wearable, stationary, or mobile devices are in multiple locations, collecting data or monitoring and executing certain tasks. Some can monitor environmental values and interact with the environment, while others are used for data collection, entertainment, or even life-saving. To achieve the wireless part of the system, the majority of sensor nodes are designed to be battery-powered. While battery power has become increasingly ubiquitous, it tends to increase the global carbon footprint of electronic devices. This issue can be mitigated by employing some form of energy harvesting so that batteries can be refilled and the gadget lasts longer, but this does not alter the reality that batteries are still used and eventually discarded. Methods: In this paper, the authors emphasise the significance of power consumption in battery powered devices. To be able to monitor devices power consumption, one of the measurable parameters is current. When users know the exact current consumption, they can decrease it by polishing the program or tweaking the duty cycle, making radio transmit less data or less frequently, thus decreasing overall power draw. Results: In order to simplify current consumption monitoring, the authors have developed a testbed facility that provides real-time current consumption measurements, which may be used to enhance the duty cycle and battery life of the afore- mentioned devices. Conclusions: While minimising total current consumption is a great way to extend the battery life and, thus, the carbon footprint, the primary culprit in the Internet of Things is the radio communications. This transmission is the primary source of current consumption. By determining the exact amount of current drawn during transmission and adjusting it, users can significantly extend battery life.