In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

A backscatter-assisted relaying network (BRN) has been recently proposed to improve data rate, transmission range, and energy efficiency of D2D communications. In the BRN, as a D2D transmitter actively transmits data to a receiver in its time slot, other D2D transmitters can act as relays, i.e., helpers, through backscattering signal from the D2D transmitter to the receiver. This passive relay method has shown to be effective in terms of diversity gain. However, this impairs energy harvesting by the helpers and thus degrades their active data transmission performance. Therefore, the problem in the BRN is to optimize backscatter relaying policies, i.e., reflection coefficients, for the helpers to maximize the total network throughput over time slots. Finding the optimal decisions is generally challenging as energy in batteries, i.e., energy states, of the helpers and communication channels are dynamic and uncertain. In this letter, we propose to adopt the Deep Deterministic Policy Gradient (DDPG) algorithm to determine the optimal reflection coefficients of the helpers. The simulation results show that the proposed DRL scheme significantly improves the throughput performance.

In this paper, we develop a new framework called blockchain-based Radio Frequency (RF)-powered backscatter cognitive radio network. In the framework, IoT devices as secondary transmitters transmit their sensing data to a secondary gateway by using the RF-powered backscatter cognitive radio technology. The data collected at the gateway is then sent to a blockchain network for further verification, storage and processing. As such, the framework enables the IoT system to simultaneously optimize the spectrum usage and maximize the energy efficiency. Moreover, the framework ensures that the data collected from the IoT devices is verified, stored and processed in a decentralized but in a trusted manner. To achieve the goal, we formulate a stochastic optimization problem for the gateway under the dynamics of the primary channel, the uncertainty of the IoT devices, and the unpredictability of the blockchain environment. In the problem, the gateway jointly decides (i) the time scheduling, i.e., the energy harvesting time, backscatter time, and transmission time, among the IoT devices, (ii) the blockchain network, and (iii) the transaction fee rate to maximize the network throughput while minimizing the cost. To solve the stochastic optimization problem, we then propose to employ, evaluate, and assess the Deep Reinforcement Learning (DRL) with Dueling Double Deep Q-Networks (D3QN) to derive the optimal policy for the gateway. The simulation results clearly show that the proposed solution outperforms the conventional baseline approaches such as the conventional Q-Learning algorithm and non-learning algorithms in terms of network throughput and convergence speed. Furthermore, the proposed solution guarantees that the data is stored in the blockchain network at a reasonable cost.

Curriculum learning shows promise in improving machine learning tasks like spoken language identification (LID) in challenging scenarios. We propose an end-to-end language identification framework, the x-vector Transformer-based End-to-End Language Identification system, leveraging curriculum learning. The x-vector Transformer (x-Trans) combines x-vector and transformer-based language modeling to capture rich speaker embeddings and extract high-level linguistic features from raw audio signals, enabling robust language identification. Evaluation on the MLS14 set of NIST LRE 2017 demonstrates significant improvements, achieving equal error rates of 8.91 %, 4.83%, and 3.89% on 3s, 10s, and 30s test speech, which is respectively 34.96%, 47.50% and 52.03% reduction compared to the referred work's performance using x-vector and BLSTM. To assess noise robustness, the clean LRE data undergoes full corruption with radio channel noises extracted from the RATS corpus at various Signal-to-Noise Ratio levels. The subse-quent phase employs curriculum learning, facilitating progressive learning from simple to complex data, resulting in improved performance in both clean and noisy conditions. Finally, we conduct multilingual Asia dataset experiments, achieving over 97.5 % accuracy for in-domain languages, even under highly noisy conditions.