The results show that heterogeneous IoT services can be hosted on modest local infrastructure while maintaining logical separation between Living Lab data and services, and offers a practical model for higher-education institutions and community-oriented renewable-energy initiatives in resource-constrained environments.
Abstract
IoT cloud platforms support the monitoring and management of distributed renewable-energy systems, but sustaining them can be challenging in academic and community initiatives with limited resources. Recurring cloud-service fees, dependence on proprietary platforms, and limited local control may become major obstacles, particularly when long-term service operation must be combined with technical capacity-building within local institutions and surrounding communities. This paper presents an open-source, multi-tenant IoT cloud architecture developed within the GREATER Erasmus+ framework and deployed in renewable-energy Living Labs in Rwanda. The architecture is built using widely adopted open-source tools, including Docker, Node-RED, MySQL, Nginx, MQTT, and HTTPS APIs. The main novelty is an open-source middleware layer that transforms standard components into a shared, tenant-aware platform by coordinating authentication, role-based access, tenant-aware routing, controlled database access, separate Node-RED workspaces, and dashboard visibility for different Living Labs and user roles. The platform has been deployed in real settings, supporting photovoltaic monitoring, solar-powered irrigation, community energy services, domestic energy monitoring, and educational activities. The platform is evaluated in terms of cost, resource usage, communication delay, reliability mechanisms, and access-control behavior. The results show that heterogeneous IoT services can be hosted on modest local infrastructure while maintaining logical separation between Living Lab data and services. By combining open-source technologies, multi-tenant management, and field deployment, the proposed architecture offers a practical model for higher-education institutions and community-oriented renewable-energy initiatives in resource-constrained environments.
The rapid growth of smart cities has highlighted the need for robust, flexible, and cost-effective platforms that can support the integration and management of Internet of Things (IoT) devices, data streams, and analytics pipelines. However, recent studies have revealed a significant gap in the availability of open-source IoT platforms tailored for smart cities, creating a barrier for researchers and developers seeking to experiment, innovate, and validate connected urban applications. This article proposes a lightweight yet comprehensive open-source smart city platform architecture to address this gap. By integrating essential components such as real-time data ingestion, big data storage, messaging protocols, and data analytics, the platform offers a solid foundation for developing and testing smart city solutions. The architecture focuses on modularity, interoperability, and scalability, ensuring that the system can grow alongside the demands of urban environments. We aim to foster an open, collaborative ecosystem where researchers and developers can freely access, modify, and enhance the platform, driving forward the next generation of smart city innovations. The proposed solution serves as a bridge between theoretical research and practical deployment, reducing barriers to entry and accelerating the development of smarter, more sustainable urban systems.
Nikolaos Monios, P. Papageorgas, Dimitrios D. Piromalis et al.· Electronics· 0 citations
Recently, the Internet of Things (IoT) has expanded rapidly thanks to significant achievements in multiple fields. Each new device adds more data requests, demand, and network pressure [1]. Handling scale and QoS for IoT middleware platforms gets harder under high or irregular traffic loads. In most oneM2M-based middleware platforms, there are no external resource-usage mechanisms for overload conditions. Our new approach, IoTScal-CoM, presents a collaborative middleware architecture that enables QoS-based request redirection among independent oneM2M systems with the help of performance monitoring. In contrast to current techniques, the proposed solution employs only native oneM2M capabilities such as RTT, packet loss rate, CPU, and memory usage in order to guarantee the SLA conformity without changing the main standard specifications. The IoTScal-CoM middleware is deployed and tested in a simulated oneM2M environment by conducting a comparative analysis of both collaborative and non-collaborative scenarios. Experimental results demonstrate that the collaboration leads to increased stability and successful request processing as well as to improved system scalability.
S. Abourriche, A. Zyane, A. Ghammaz· EPJ Web of Conferences· 1 citation
The rapid growth of Internet of Things (IoT) ecosystems has transformed modern industrial, commercial, and operational infrastructures into highly distributed computational environments. Edge devices continuously generate large volumes of real-time data, while cloud platforms provide scalable processing, long-term analytics, and predictive intelligence capabilities. Traditional edge-to-cloud architectures are typically designed around a hierarchical data flow model in which information is collected at the edge, transmitted to centralized platforms, and processed to support operational decision-making. However, large-scale distributed IoT systems increasingly face challenges related not only to latency, scalability, and synchronization, but also to the consistency and evolution of decisions themselves. Edge systems frequently make rapid local decisions under conditions of limited visibility, while cloud systems generate more informed decisions based on broader contextual analysis. Treating these outputs as isolated and final decisions often creates inconsistencies, duplicated actions, and operational fragmentation across distributed environments. This paper introduces the concept of Decision Continuity Architecture (DCA) as a new systems abstraction for distributed edge-to-cloud environments. Within this framework, decisions are modeled not as isolated events but as evolving operational entities that progressively gain context, confidence, and refinement as they move through distributed computational layers. The study explores how decision continuity improves resilience, synchronization tolerance, predictive operations, and operational governance in real-time IoT systems. It further examines how distributed architectures can balance rapid edge responsiveness with deeper cloud intelligence without relying on rigid synchronization or centralized decision authority. By reframing distributed decision-making as a continuous and evolving process rather than a collection of disconnected outputs, this work proposes a scalable architectural model for intelligent IoT systems operating under uncertainty, partial visibility, and dynamic real-world conditions.
Ilker Kanatli· International Journal of Res...· 0 citations
The advent of 6G networks and the rapid growth of Internet of Things (IoT) are revolutionizing the telecommunication sector, integrating edge-cloud systems with vast amounts of data from IoT and AI techniques. These advancements make these systems essential in managing and delivering a multitude of services, offered by smart cities and industrial domains, being just a few among the many possible application scenarios. Specifically, this shift introduces complexities in orchestrating services and managing available resources while addressing challenges such as reducing latency, growing bandwidth, ensuring trust, and integrating different technologies. In this sense, this review explores the recent approaches of the state of the art focused on service orchestration in IoT edge-cloud environments, concentrating on architectures and methodologies that enable resource allocation and service management. Furthermore, it examines platforms for orchestration development, highlighting their characteristics and contributions. Finally, emerging concerns such as network topology and trust, which previous surveys have often overlooked, are discussed together with the most relevant research directions.
An LLM-assisted design-time programming workflow for generating Arduino-compatible firmware templates for the proposed modular gateway architecture, a flexible and extensible solution built on Arduino-compatible hardware to support the integration of wireless sensors, communication technologies, and cloud platforms.
Antti Martikkala, A. Lobov, I. F. Ituarte· Discover Internet of Things· 0 citations
The Energy-Aware Hierarchical Green Fog framework is presented, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity, and task deadlines into hierarchical fog-cloud decision-making.
M. Harandi, Afshin Yaghoobi· Scientific Reports· 0 citations