Aug 2026· Journal of Sensor and Actuator Networks· Vol 15, pp. 63· 0 citations· 62 references
TL;DR
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.
Abstract
This paper introduces the Sensor Traffic Generator (STGen), a lightweight, pure-software testbed for evaluating IoT application- and transport-layer protocols at scale. Relative to existing software-based IoT evaluation platforms, STGen combines three design decisions that, to the best of our knowledge, no prior testbed offers together. Every emulated sensor node runs as an independent operating-system process using a real transport stack rather than a discrete-event model or a container. Sensor workloads are generated using physically grounded stochastic models calibrated against real deployment data. Experiments are specified in three independent tiers, IoT Protocols (N), Scenarios (M), and Networks (L), reducing configuration effort from a combinatorial N×M×L problem to a linear N+M+L workflow, with new protocols integrated by overriding a four-method abstract interface. STGen operates above OSI Layer 4 and therefore does not model PHY- or MAC-layer behavior, such as RF interference, CSMA/CA collision avoidance, or duty cycling. The sensor models are calibrated using 1,826,223 real-world readings from the Intel Berkeley Research Laboratory; for temperature, the synthetic stream matches the 37-day measurements of 54 Mica2Dot motes with a Kolmogorov–Smirnov D of 0.071 and a Jensen–Shannon divergence of 0.018, showing that STGen reproduces the statistical structure of real sensor data rather than only plausible values. By inverting these calibrated models, STGen also synthesizes labeled false-data-injection anomalies that are separable from normal traffic, with a receiver operating characteristic AUC of 0.898 for stealthy drift and 1.0 for hard physical range violations. In our experiments, STGen instantiates 6000 concurrently emulated sensor nodes on a commodity workstation in 1.02 s using 0.62 GB of memory (approximately 99 KB per node), which is more than two orders of magnitude below the per-node memory costs of container- and VM-based testbeds. STGen also exposes deployment-relevant behavior that controlled emulation alone may hide. Under live wide-area jitter, MQTT and CoAP exhibit different loss and latency patterns than those observed under uniformly degraded NetEm conditions, including MQTT reconnection storms. These results show that STGen provides a scalable and reproducible bridge between lightweight protocol emulation and practical deployment-oriented IoT protocol evaluation.
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
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
In the last years, the Internet of Things (IoT) has become a key part of today’s digital infrastructure. From industrial automation to smart home devices, IoT systems connect physical sensors to cloud analytics and change how we interact with our environment. As these systems become larger and more complex, there is a growing need for communication methods that can manage unpredictable network errors, hardware limits, and frequent failures. Right now, MQTT and CoAP are the main protocols for IoT-to-cloud communication. But most systems use a fixed protocol, which makes it hard to adapt when problems like network traffic congestion or backend outages happen. In this paper, we introduce an adaptive edge gateway for ZigBee-based IoT networks that can switch between MQTT and CoAP as needed. The gateway monitors real-time performance, such as latency, packet loss, and backend availability, and chooses the best protocol and mode automatically. We tested our approach with in-depth simulations in ns-3, covering 180 scenarios including different types of failures, like link problems and broker outages. Our results show that adaptive switching improves packet delivery by 15–20% during faults in comparison to static setups and reduces recovery time without much extra overhead. This suggests that adapting protocols at the gateway is an effective way to make IoT systems more reliable.
Ali H. BenHusein, Mohamed Buker· Comprehensive Journal of Sci...· 0 citations
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.
João Luiz Pontes de Araújo, Elen Priscila de Souza Lobato, Wellington da Silva Fonseca et al.· IEEE Access· 0 citations
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
Smart microgrids combining photovoltaic arrays, wind turbines, and battery storage generate telemetry that existing open-source monitoring tools cannot process with per-mechanism energy loss visibility in real time. This paper presents the design, implementation, and evaluation of an IoT monitoring framework. The framework incorporates a physics-based microgrid simulator, a hierarchical MQTT communication architecture, and a React-based web-based user interface that supports WebSocket-based real-time data visualization. The framework consists of ten containerized microservices that can be started with a single command: docker compose up -d. All stack performance testing was conducted using a simulated 1 h test case based on a 100 kWp PV system, 10 kW wind turbine, and 50 kWh battery-powered campus microgrid. Median P50 publisher-to-subscriber latency was 27.2 ms and 99th percentile (P99) latency was 48.3 ms, with 100% message delivery across 5840 test messages, with per-topic analysis revealing a 25 ms serialization-order effect in sequential MQTT publishing. Comparative analysis against nine existing platforms including OpenEMS, VOLTTRON, Eclipse Ditto, and pymgrid confirms that, among the platforms surveyed, none unifies physics-based loss telemetry, IoT communication, time-series storage, and real-time visualization in a single reproducible deployment.
Elton Boshnjaku, Galia Marinova, Edmond Hajrizi et al.· Telecom· 0 citations