A comparative performance evaluation of three containerized Internet of Things middleware platforms, Dojot, FIWARE, and Node-RED, deployed to support a photovoltaic Digital Twin within the EMOB-AMAZON research project provides complementary evidence for understanding the relationship between middleware architecture, resource utilization, and application-level performance.
Abstract
This paper presents a comparative performance evaluation of three containerized Internet of Things (IoT) middleware platforms, Dojot, FIWARE, and Node-RED, deployed to support a photovoltaic Digital Twin within the EMOB-AMAZON research project. Moving beyond descriptive resource monitoring, the study combines descriptive metrics with inferential statistical analysis based on pooled experimental observations. Pearson’s correlation coefficient was used to investigate the relationships between infrastructure resource utilization and application-level performance under nominal and stress workloads. The results indicate that the evaluated platforms exhibit distinct resource utilization and scalability profiles. FIWARE maintained the highest realized throughput together with stable sub-millisecond latency under the evaluated workloads, while requiring higher CPU utilization than the other platforms. Node-RED consistently exhibited the lowest infrastructure resource consumption, making it well suited for resource-constrained Edge deployments, although its scalability decreased under the highest evaluated workload. In contrast, the evaluated Dojot deployment showed higher idle resource consumption together with reduced throughput, increased latency, and elevated request timeout rates during workload execution. Overall, the combined descriptive and inferential analyses provide complementary evidence for understanding the relationship between middleware architecture, resource utilization, and application-level performance, supporting middleware selection according to the computational requirements of photovoltaic Digital Twin applications.
A systematic review evaluating a final cohort of n=86 contemporary energy management frameworks published between 2020 and 2026 highlights how the strategic integration of cohesive, cross-layer hybrid optimization strategies can mitigate active energy dissipation, providing actionable technical recommendations for future IoT-based WSN architectures.
David Ochola, Okuthe P. Kogeda· Digital· 0 citations
An autonomic control architecture based on the Monitor-Analyze-Plan-Execute with Knowledge loop is integrated with a Random Forest classifier that predicts four discrete QoS operational states with 91.9% accuracy, making the compliance gap explicit and quantifiable.
Jamal Et-Tousy, A. Zyane· EPJ Web of Conferences· 0 citations
Environmental monitoring facilitates solutions to major worldwide challenges, including air pollution, climate change, and water resource degradation. Yet, conventional cloud-based IoT systems are unable to provide real-time solutions because of issues like latency, increased energy consumption, and limited scalability. This paper aims to present a positive environmental impact of edge computing for real-time environmental monitoring and provide a sustainable, energy-efficient, low-latency environmental monitoring solution. The Edge Computing Real-Time Environmental Monitoring (ECRM) framework of the paper achieves local data processing and decision-making through the integration of edge intelligence, collective, and low-power machine learning models at edge gateways. The framework achieves system responsiveness and low energy consumption through the integration of energy-aware task scheduling and adaptive data transmission strategies. Processed data for air quality (AQI), CO₂, humidity, and temperature levels substantially reduce the framework's reliance on cloud computing. The framework provides a 39% reduction in energy consumption and a 40% reduction in latency in comparison to established cloud system models. The reductions improve real-time system responsiveness and reduce network traffic. The research demonstrates that combining edge architecture and green computing is a potential solution for sustainable environmental monitoring. The proposed systems align with the goals of computing sustainability and future smart city solutions.
Dawakit Lepcha, Kanchan Thakur· 2026 4th International Confe...· 0 citations
Results show that STGen provides a scalable and reproducible bridge between lightweight protocol emulation and practical deployment-oriented IoT protocol evaluation, and exposes deployment-relevant behavior that controlled emulation alone may hide.
H. Islam, M. M. Maharaz, M. Georgiades et al.· Journal of Sensor and Actuat...· 0 citations
The proposed IoTScal-CoM middleware 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.
S. Abourriche, A. Zyane, A. Ghammaz· EPJ Web of Conferences· 1 citation
The rapid proliferation of IoT devices and ecosystems creates significant challenges in managing increasing data traffic and service requests while maintaining system performance [1]– [3]. In oneM2M-based IoT systems, overloaded Common Service Entities (CSEs) can become bottlenecks, leading to resource saturation, higher latency, and request loss [4]. To address these challenges, this paper proposes IoTScal-2CoM-ALO, an adaptive load orchestration framework that introduces a two-level collaboration model (2CoM) enabling distributed CSEs to cooperate within and across domains. The framework incorporates an Adaptive Load Orchestration (ALO) mechanism that continuously monitors key performance indicators, including CPU utilization, memory consumption, round-trip time (RTT), and packet loss, to detect overload conditions and dynamically redirect traffic to suitable neighboring CSEs. The proposed approach is evaluated in a simulated distributed oneM2M environment under heterogeneous traffic conditions. Experimental results demonstrate significant performance improvements compared with non-collaborative and static collaboration approaches, achieving up to 73% reduction in memory consumption, RTT peak reductions of up to 4750 ms, and success rate improvements of approximately 4.8%. These results highlight the effectiveness of IoTScal-2CoM-ALO in improving resource utilization and maintaining service continuity in scalable IoT systems.
S. Abourriche, A. Zyane, A. Ghammaz· International Conference on...· 0 citations
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