The most efficient routing approach for data collection in WSNs is the cluster-based approach. The sensor nodes of WSNs are discriminated by several multi-criteria. Hence, a number of energy-efficient cluster-based routing protocols that comprehensively consider these mutually contradictory multi-criteria have been developed.

The typical algorithms which were used to comprehensively consider multi-criteria include fuzzy logic, MCDM, meta-heuristic optimization algorithm, and the combination of these approaches.

MCDM approach is mainly used to evaluate the completed alternatives defined by several criteria or factors. Thus, recently, there has been active research effort to exploit MCDM approaches for cluster-based routing.

The goal of cluster-based routing optimization is to maximize energy consumption balancing among nodes by taking into account various criteria in the whole process of clustering routing while maintaining stability, reliability and connectivity of network, and thus to extend network lifetime as much as possible. However, existing cluster-based routing protocols exploit either individual MCDM approaches or fuzzy logic or meta-heuristic optimization algorithms in the cluster head (CH) node selection of clustering stage.

Ri Man Gun, an institute head at the Faculty of Communication, proposed a novel clustering scheme using adaptive fuzzy C-means (AFCM) and an improved ant-lion optimization (ALO) approach.

This scheme first divides the whole network into k clusters using the AFCM algorithm. After that, an improved ALO is applied to each cluster to select the optimal CH nodes. The improved ALO prescribes a new fitness function by the multi-criteria based on the weights assigned by FCNP-VWA.

The simulation results revealed that the proposed scheme achieves superior energy consumption balance.

For more information, you can refer to his paper “An Energy Efficient Routing Scheme using a Hybrid MCDM and Meta-Heuristic Algorithm in WSNs” in “Proceedings of KUTIC-2025”.

The ball and plate system is a typical multivariable, nonlinear system, which is widely used in control engineering experiments and scientific research. As a two-dimensional extension of a ball and beam system, it not only stabilizes the ball at a desired position on the plate but also realizes random tracking control, which has involved a lot of research.

Model predictive control, which was mostly used for delay systems like chemical processes, has been used a lot in the control of mechanical systems recently. Prediction models are of different types, and recently, state space models have been used as a prediction model for many predictive controls. In this case, a state observer is necessary because it is impossible to measure all of the system states in practice. However, there are some problems with using a state observer. One of its drawbacks is robustness. And whether a state observer is included or not is also a problem for ball and plate systems.

Kim Hun Chol, a researcher at the Faculty of Automatics, presented a non-minimal state space (NMSS) model of ball and plate system, a typical multivariable, nonlinear system, and realized position control of the ball on the plate using NMSS model predictive control (MPC).

In conformity with the characteristics of MPC and the configuration of the proposed control system, he introduced the NMSS model without the need for a state observer to design MPC.

He verified the effectiveness and performance through MATLAB simulation and real experiments. The results validated that the proposed NMSS-MPC is a suitable control method for ball and plate systems.

You can find the details in his paper “Study on Stabilization and Tracking Control of Ball and Plate System using Non-Minimal State Space Model Predictive Control” in “Proceedings of KUTIC-2025”.

The particle swarm optimization (PSO) algorithm is a kind of optimization method based on the theory of cluster intelligence. The greatest advantage of PSO algorithm is its fast convergence speed, simplicity of evolution operation, low computational cost and few parameters needed.

Kim Yong Su, a researcher at the Faculty of Automatics, proposed a method of designing a PID controller using the PSO algorithm and the dominant pole placement.

He used the PSO algorithm to determine the position of dominant pole for minimizing the judge function consisting of the absolute error integral index (IAE) and overshoot value, and used Fuzzy Neural Network (FNN) to determine the parameters of PID controller.

The proposed method makes it possible to get both the optimal dominant pole and the exchanging frequency at the same time and to get good control results as the overshoot value is small. The simulation results demonstrated that the proposed method is effective.

For more information, you can refer to his paper “Self-Tuning Method of PID Controller using PSO and Fuzzy Neural Network” in “Proceedings of KUTIC-2025”.