Renewable energy sources are an increasingly popular way to generate electrical energy for telecommunications systems. The use and effectiveness of remote monitoring systems in such systems has led to growing interests relatively to it. As with any new technological systems, new problems have arisen in the use of renewable energy sources in telecommunications systems and the use of IoT-based remote monitoring systems in them. In particular, one of these problems is the decentralization of remote monitoring systems for renewable power plants of telecommunication systems. The paper details the stages of development and modelling of open-source centralized monitoring systems for solar power station of telecommunication systems. In this paper, a real-time remote monitoring system of solar power sources was modelled and investigated by wireless sensor networks of telecommunications devices. Proteus software environment was obtained for modelling. Before modelling the system, a system structure and a block diagram were developed, each of the elements that are part of the system is described. Software for the system was created. The developed structure and software were tested by modelling. The results of the modelling were presented in a virtual terminal, an oscillography, and a local web browser. ABSTRAK: Sumber tenaga boleh baharu semakin meningkat popular dalam menghasilkan tenaga elektrik bagi sistem telekomunikasi. Penggunaan dan keberkesanan sistem pemonitoran jarak jauh dalam jaringan ini telah membawa kepada peningkatan minat kepada tenaga boleh baharu. Walau bagaimanapun, melalui sistem teknologi baru ini, masalah baru timbul dalam penggunaan punca tenaga boleh baharu dalam sistem telekomunikasi dan penggunaan bersama sistem pemonitoran berdasarkan IoT. Khususnya dalam desentralisasi sistem pemonitoran jarak jauh bagi loji kuasa sistem telekomunikasi. Kajian ini merincikan peringkat pembinaan dan model sumber terbuka berpusat sistem pemonitoran bagi sistem janakuasa solar sistem telekomunikasi. Kajian ini turut memodelkan sistem pemonitoran jarak jauh secara langsung bagi sumber kuasa solar dan dikaji dengan rangkaian sensor tanpa jaringan bagi alatan telekomunikasi. Perisian Proteus telah dihasilkan bagi model ini. Sebelum model sistem dibina, struktur sistem dan gambar rajah blok dibina. Setiap unsur dalam sistem ini diperihalkan. Perisian bagi sistem ini turut dibina. Struktur yang dibina dan perisian ini diuji melalui model. Hasil dapatan model dibentangkan dalam pangkalan maya, oscilograf dan pelayar web tempatan.

This paper proposes a priority-aware EP-ALOHA framework with predictive radio resource allocation for heterogeneous machine-to-machine (M2M) devices in 5G massive machine-type communication (mMTC) networks. The proposed framework extends conventional EP-ALOHA by introducing M2M priority classes, priority-dependent delay constraints, and Exploration Phase resource block (RB) allocation. The RB-allocation problem is formulated as an integer-constrained optimization problem, where the objective is to improve effective radio channel utilization while satisfying delay constraints for different priority classes. A Genetic Algorithm-based optimization procedure is used to generate optimization-derived RB-allocation targets under different traffic and system parameter settings. These targets are then used to train and evaluate predictive RB-allocation models, including Random Forest, Neural Network, Gradient Boosting, and Linear Regression. The simulation results show that the proposed priority-aware EP-ALOHA method achieves a higher successful access probability than the considered baseline schemes within the feasible operating region. For predictive RB allocation, the Neural Network achieved the best test-set performance, with MSE = 25.5002, RMSE = 5.0498 RBs, MAE = 3.2629 RBs, and R² = 0.9810. A separate computational evaluation showed that Random Forest inference reduced the mean allocation-decision time from 213.54 ms for GA-based optimization to 15.20 ms, corresponding to a 14.05-fold speed-up on the evaluated platform. In addition, M2M device activity probability forecasting is evaluated using Bayesian estimation, LSTM, moving average, and exponential smoothing. LSTM achieves the lowest forecasting error, while exponential smoothing provides a close and computationally simpler alternative. The results indicate that the proposed framework can support proactive and priority-aware resource management for heterogeneous M2M traffic, while the learning-based components are used as approximation and forecasting tools rather than as universally superior solutions.