Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.

     

Sun drying of cocoa with firebrick thermal storage materials

The thin-layer drying process of N38 cocoa beans using open-sun and a solar drying (SD) system with firebrick heat storage materials (FTSM) has been modeled. The 10 kg capacity force convective SD system was developed and used to carry out the experiments. The choice of the best model was based on a comparison of statistical indicators including determination coefficient (R²), reduced chi-square (χ²), root mean square error (RMSE), sum of square error (SSE), and normalized root mean square error (NRMSE) after fitting the experimental results to 11 common thin layer models in the literature. The results revealed that under open-sun drying and SD processes, the Midilli et al model provided the best drying characteristics of cocoa beans. Therefore, in the experimental context, this model can be assumed to reflect the solar/sun drying behavior of cocoa. The effective diffusivity values for the open-sun and SD of cocoa with FTSMA and FTSMB were 4.25× 10⁻¹¹, 6.64× 10⁻¹¹, and 5. 95 × 10⁻¹¹ m²/s respectively. The predicted pre-exponential factor and activation energy were 5.81 × 10⁻¹¹ m²/s and 22.79 kJ/mol respectively.     

Software-Defined Radio Enabled Cloud Radio Access Network Implementation Using OpenAirInterface

Wireless Personal Communications - Towards enabling 5G and beyond radio access technologies to meet the requirements for continuous dynamic and diverse services, flexibility and scalability of the...