Modern transportation networks, with their complexity and dynamic nature, have a substantial demand for intelligent vehicles. Developing effective production strategies for smart vehicles is essential to reducing both production costs and energy consumption. Traditional vehicle production planning has largely depended on heuristic algorithms and solvers, which lack scalability and are susceptible to local optima. Furthermore, existing solutions do not concurrently address both dynamic and regular vehicle production planning. To overcome these limitations, this paper proposes an effective optimizing method for large-scale smart manufacturing within intelligent transportation networks using Federated Reinforcement Learning. In our proposal, the Gated Recurrent Unit and Asynchronous Advantage Actor Critic (A3C) reinforcement algorithms are employed to develop a Dynamic Optimizing Planning Module(DOPM), which can output an excellent solution of 1000 vehicles within 5 seconds. A High-Quality Processing Module(HQPM) is constructed by the Transformer with A3C, significantly enhancing the production plan’s quality. Finally, the proposed methods will integrate with Federated Learning (FL) to establish a scalable, privacy-preserving intelligent manufacturing scheduling framework for transportation networks. Experimental results demonstrate that our work significantly outperforms traditional solutions, achieving over a 93% improvement in solving speed and reducing constraint violations by more than 95%.

With the advancement of Big Data technology, deep learning automatic modulation recognition (DLAMR) has undergone new improvements. Existing DLAMR methods focus mostly on the primary matching of the model itself or ubiquitous big communications data, which lack interpretability and ignore deep representations for the modulation mechanism of the communication signals; thus, difficulties in further improving the recognition accuracy and multiquadrant amplitude modulation (MQAM) discriminability in complex communication environments are encountered. In response to these challenges, this article proposes an innovative communication signal graph mapping method to address the uncertainty in the modulation mechanisms. Specifically, it models sampling points as nodes; connects inter- and intrasymbol points with edges to represent modulation mechanisms and propagation uncertainty; and maps amplitude, phase, in-phase, and quadrature values as node features. A deep graphical and temporal neuro-fuzzy methodology (GT-DNFS) that integrates graph attention networks and bidirectional long short-term memory networks is subsequently proposed for DLAMR. The numerical results show that GT-DNFS achieves a significantly higher recognition accuracy of 93.01%, and an MQAM (M=16, 64) discrimination of 94.5%. This research offers valuable insights for neuro-fuzzy networks and efficient DLAMR algorithm design.

This paper addresses long-term energy efficiency in a wireless power transfer-enabled mobile-edge computing (MEC) system, facing challenges from time-varying channels and stochastic task arrivals. We formulate the problem to optimize offloading, power transfer duration, and energy consumption while ensuring queue stability. We propose a novel Lyapunov-guided deep reinforcement learning (LyCNN-DRL) algorithm to efficiently solve the long-term mixed integer non-linear programming problem without prior knowledge of future conditions. The approach decomposes the problem into resource allocation and binary offloading components, using a convolutional neural network for near-optimal offloading decisions and the Lagrange dual function for optimal resource allocation. Extensive simulations show that LyCNN-DRL outperforms benchmark algorithms in energy efficiency and latency, achieving over 97% of the optimal utility while reducing execution latency to approximately 50 milliseconds in ten-WD networks. Additionally, we derive the trade-off between energy efficiency and queue length as [O(1/V),O(V)], where V is the Lyapunov control parameter.

Abstract The advancement in the field of artificial intelligence (AI) has revolutionized every field of life, including the learning of second languages. These intelligent devices are capable of effectively and efficiently utilizing the time and energy of both learners and teachers. Students can learn at their own pace and at their own skill level. They can learn and practice in an interactive and fruitful environment thanks to intelligent chatbots and voice assistants. Students no longer require humanized teachers as a result of the use of these new methodologies; instead, they can learn more effectively by interacting with computer‐assisted systems. With the integration of information and communication technology (ICT) and AI, new technologies like computer‐assisted language learning (CALL) and mobile‐assisted language learning (MALL) are playing a very crucial role in the learning of the English language. Due to the various AI‐based applications and technologies available, learners are unable to use the most valuable and effective ones. This paper focuses on the role of AI in the learning of the English language. The study will help learners in the selection of efficient and effective AI‐powered paradigms for the teaching and learning process of the English language. Various features have been selected from the identified ones, and then, on the basis of these features, different AI‐grounded paradigms for English learning are ranked using analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The alternative with the highest performance value is ranked at the top of all available alternatives, while the one with the lowest performance score is placed at the last.

The next generation of wireless communication systems will rely on advantageous sixth-generation wireless network (6G) features and sophisticated edge Internet-of-Things technology to provide continuous service delegation and resource allocation. Network slicing and virtualization are common in these scenarios to meet user demands and application services. This article introduces a Normalized Slicing-assigned Virtualization Method for satisfying the 6G features in future generation systems. The proposed method relies on available resource roots and time intervals for replications. Based on the availability and Accessibility, the resource virtualization and network slicing processes are forwarded. The proposed method exploits federated learning for determining availability and accessibility models in detecting slicing, virtualization, or both the requirements throughout the resource sharing process. This improves the resource sharing rate, with less latency and high processing despite the user and application demands. The learning models are trained to balance replication and network slicing for confining complexity across different resources. The proposed method's performance is validated using the above metrics for varying users and intervals.

Abstract Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences. The study also highlights crowdsourcing usage in the field of software engineering. All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed. The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing.

Agricultural food production is projected to be 70% higher by 2050 than it is today, with the world population rising to more than 9 billion, 34% higher than it is now. The farmers have been forced to produce more with the same resources. This pressure means that optimizing productivity is one of the main objectives of the producers but also in a sustainable way. Not only does agriculture face a decline in production, but it has also had to face limitations in data collection, storing, securing, and sharing, climate change, increases in input prices, traditional food supply chain systems where there is no direct connection between the farmer and the buyer, and limitations on energy use. Existing IoT‐based agriculture systems have a centralized format and operate in isolation, leaving room for unresolved issues and major concerns, including data security, manipulation, and single failure points. This paper proposes a futuristic IoT with a blockchain model to meet these challenges. Further, this paper also proposes and novel energy‐efficient clustering IoT‐based agriculture protocol for lower energy consumption and network stability and compares its results with its counterpart low‐energy adoptive clustering hierarchy (LEACH) protocol. The simulation results show that the proposed protocol network stability is 23% higher as compared to LEACH as first node of LEACH dies at 168 rounds while IoT‐based agriculture first node dies after 463 rounds. Similarly, IoT‐based agriculture protocol energy consumption is 68% lower than that of LEACH. The proposed protocol also extends the network life to more rounds and demonstrates an increase of 112%.

Components are the significant part of a system which plays an important role in the functionality of the system. Components are the reusable part of a system which are already tested, debugged, and experienced based on the previous practices. A new system is developed based on the reusable components, as reusability of components is recommended to save time, effort, and resources as such components are already made. Security of components is a significant constituent of the system to maintain the existence of the component as well as the system to function smoothly. Component security can protect a component from illegal access and changing its contents. Considering the developments in information security, protecting the components becomes a fundamental issue. In order to tackle such issues, a comprehensive study report is needed which can help practitioners to protect their system. The current study is an endeavor to report some of the existing studies regarding component security evaluation based on multicriteria decision and machine learning algorithms in the popular searching libraries.

Proliferation of electronics and their increasing connectivity pose formidable challenges for information security. At the most fundamental level, nanostructures and nanomaterials offer an unprecedented opportunity to introduce new approaches to securing electronic devices. First, we discuss engineering nanomaterials, (e.g., carbon nanotubes (CNTs), graphene, and layered transition metal dichalcogenides (TMDs)) to make unclonable cryptographic primitives. These security primitives not only can supplement existing solutions in silicon integrated circuits (ICs) but can also be used for emerging applications in flexible and wearable electronics. Second, we discuss security engineering of advanced nanostructures such as reactive materials.