2026· EPJ Web of Conferences· 1 citation· 4 references
TL;DR
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.
Abstract
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.
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.
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