IoT workflow is often composed following a set of procedures which makes it hard to self-adapt, self-configure to react to runtime environment changes. Therefore, declarative data-driven workflow composition wil
Three optional support sub functions of LBO are controlled by the operation and maintenance (O&M) system; namely, load reporting, adapting HO, and load balancing action based on HOs. One more sub-functions can be implemented, depending on an operator strategy [23], [24]. In 5G and six ...
A framework for self-assembly of workcells includes analyzing multiple constraints to determine configuration and movement of mobile robots and/or one or more robotic devices in the
IoTgraph transformationssensorssmart agricultureThe research described in this article aims to propose the creation of a framework that would enable the self-optimization of IoT device networks. The work is based on two foundations: distributed graph transformations and a flexible IoT network supported ...
Making adaptations to these models to address security issues faced in IoT networks, largely reduces cost and improves efficiency. These models can be simulated, analysed and supports architecture model adaptation; model changes are then reflected back to the real system. We propose a model-driven ...
It introduces the concept of capabilities, both at the individual device and network levels, which are used to describe the desired functions that will be performed by the given system. The network of distributed IoT devices is visualized as a graph, and graph transformations are used ...
Conversely, the LBO function assesses the traffic load in both serving and target cells to determine appropriate HCP values. Additionally, our algorithm considers the real-time dynamics of the network environment by continuously monitoring and adapting to changing conditions. It dynamically adjusts the ...
Current research trends also focus on MRO AI-based approaches such as Machine Learning (ML), Deep Learning (DL) and Reinforcement Learning (RL). The AI-based solution provides a better multi-objective output compared to the non-AI based solution. The AI-based solution can adapt and self-...
self-organization; self-optimization; self-healing; SON applications and architecture; SON design and dimensioning; machine learning (ML) and artificial intelligence (AI) for SON; massive Machine-Type Communication (mMTC); IoT; URLLC; backhauling; SON for 3G/4G; 5G and B5G networks...