2026· EPJ Web of Conferences· Vol 383, pp. 07002· 0 citations· 7 references
TL;DR
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.
Abstract
The rapid expansion of Internet of Things (IoT) devices requires middleware capable of handling heterogeneous traffic while satisfying strict Quality of Service (QoS) targets. ITU-T Recommendation Y.1541 defines well-established performance thresholds for IP networks; however, baseline oneM2M deployments frequently fail to meet these targets under mixed workloads. This paper evaluates the open-source OM2M platform against ITU-T Y.1541 using eight QoS metrics spanning application and network layers, making the compliance gap explicit and quantifiable. Under the default configuration, the platform achieved only 20% overall compliance. To close this gap, an autonomic control architecture based on the Monitor-Analyze-Plan-Execute with Knowledge (MAPE-K) loop is integrated with a Random Forest (RF) classifier that predicts four discrete QoS operational states with 91.9% accuracy. The optimized configuration improves ITU-T compliance from 20% to 60%, achieving latency reductions of 53 to 71%, jitter mitigation of 93 to 97%, and transaction failure rate decreases of 36 to 64%, all measured during steady-state operation. The paper identifies the mechanisms responsible for the remaining non-compliant metrics and proposes a cross-layer roadmap for achieving full ITU-T compliance.
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
This paper presents the design and evaluation of a network slicing implementation in a simulated 5G Standalone (SA) mobile network deployed as a nomadic edge node, where “nomadic” refers to the physical portability and ease of redeployment of a self-contained, containerized 5G testbed suitable for university teaching and experimentation. The platform integrates Open5GS, UERANSIM, Kamailio, and Prometheus/Grafana to emulate a sliced 5G core and access network supporting differentiated service requirements typical of heterogeneous traffic classes and latency-sensitive applications. Slice provisioning is fully configurable, and Docker-based resource constraints are applied to enforce Quality of Service (QoS) differentiation. Performance was assessed through bandwidth and traffic-quality measurements, demonstrating measurable improvements in packet loss and jitter for high-priority slices, with corresponding degradation for lower-priority slices. Although the laboratory environment limits replication of distributed real-world deployments, the results confirm the effectiveness of network slicing for traffic isolation and service prioritization in 5G SA systems. These findings highlight the practical boundaries of container-based slicing enforcement in a single-host nomadic 5G SA node, and inform the design of future multi-host deployments.
Elena-Ramona Modroiu, Jian-Wei Cheng, Damian Atlaß et al.· International Conference on...· 0 citations
Maintaining a stable Quality of Service (QoS) in oneM2M deployments is challenging because edge-to-cloud traffic in IoT systems is bursty and resource demand changes rapidly. We propose a fuzzy-logic QoS controller, integrated into a MAPE-K autonomic loop, that adaptively decides the share of traffic offloaded from the local oneM2M platform to the cloud as a function of CPU usage, Round-Trip Time (RTT), and incoming traffic rate. The controller uses a 27-rule Mamdani inference engine, formally defined trapezoidal membership functions, and centroid defuzzification, and is integrated with the open-source Mobius platform. Compared with an unmanaged baseline under peak load, our approach reduces operating cost by 43.5%, RTT by 55.9%, and increases the request success rate by 19.4%, while keeping CPU and RAM usage in the 40–50% range. A qualitative comparison with static-threshold and recent fuzzy/learning-based offloading methods, together with a discussion of scalability to hundreds of edge nodes, positions the controller as a practical and cost-effective option for oneM2M-compliant IoT platforms.
A. Zyane, Jamal Et-Tousy· International Conference on...· 0 citations
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.
João Luiz Pereira de Araújo, Elen Priscila de Souza Lobato, Wellington da Silva Fonseca et al.· IEEE Access· 0 citations
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
MQTT is widely used in Internet of Things (IoT) systems because of its lightweight publish–subscribe architecture and efficient support for resource-constrained devices. Although wildcard subscriptions simplify topic management, their impact on broker performance, stability, and security under authenticated high-load conditions has not been comprehensively investigated. Existing studies typically evaluate routing performance, security mechanisms, or broker scalability independently, leaving a limited understanding of their combined effects. This paper presents a systematic experimental evaluation of wildcard subscription behavior in an authenticated MQTT v5 environment. A controlled testbed employing TLS-based authentication and role-based access control was used to compare exact-topic subscriptions with single-level (+) and multi-level (#) wildcard subscriptions under progressively increasing workloads. Performance was evaluated using end-to-end latency, CPU utilization, throughput, delivery success rate, broker stability, and authorization exposure. The experimental results demonstrate that increasing wildcard-subscription complexity significantly increases routing overhead, resulting in higher latency and CPU utilization while reducing throughput and broker service capacity. Multi-level wildcard subscriptions consistently exhibited the greatest performance degradation and reached broker saturation at lower workload levels than exact-topic subscriptions, demonstrating that wildcard density compresses the broker’s operational stability region. The experiments also show that broad wildcard-based access control policies increase the risk of authorization leakage when improperly configured. These findings demonstrate that wildcard-subscription complexity is a critical determinant of MQTT scalability, broker stability, and security, and provide practical guidance for designing efficient and secure IoT messaging infrastructures.
N. Radwan, Frederick T. Sheldon, T. Soule· Electronics· 0 citations
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