This paper outlines a machine learning-enabled speaker-centric Emotion AI approach capable of predicting audience-affective engagement and vocal attractiveness in asynchronous video-based learning, relying solely on speaker-side affective expressions. Inspired by the demand for scalable, privacy-preserving affective computing applications, this speaker-centric Emotion AI approach incorporates two distinct regression models that leverage a massive corpus developed within Massive Open Online Courses (MOOCs) to enable affectively engaging experiences. The regression model predicting affective engagement is developed by assimilating emotional expressions emanating from facial dynamics, oculomotor features, prosody, and cognitive semantics, while incorporating a second regression model to predict vocal attractiveness based exclusively on speaker-side acoustic features. Notably, on speaker-independent test sets, both regression models yielded impressive predictive performance (R2 = 0.85 for affective engagement and R2 = 0.88 for vocal attractiveness), confirming that speaker-side affect can functionally represent aggregated audience feedback. This paper provides a speaker-centric Emotion AI approach substantiated by an empirical study discovering that speaker-side multimodal features, including acoustics, can prospectively forecast audience feedback without necessarily employing audience-side input information.

This paper investigates the irregular intelligent reflecting surface (IRS) topology with a focus on mitigating the mutual coupling effect on the IRS and aiming to improve the received signal of user equipment and IoT devices. A multi-objective genetic algorithm (MOGA)-based method is proposed to tackle the topology design and signal channel of an irregular IRS. The element topology of an irregular IRS is encoded into a chromosome, and the mechanism of crossover and mutation is designed to find the topology with lower mutual coupling and higher channel quality. To improve the efficiency of the designed MOGA, the probability of selecting elements is adjusted from high to low according to the main lobe direction from the base station (BS). Simulation results show that the achievable rate of users and IoT devices is improved by using the proposed MOGA compared to other heuristic selection strategies and existing works. In addition, the sum rate obtained by using a specific probability distribution is better at the later stage of the proposed MOGA, which validates that the probability modification based on BS’s main lobe has an improvement on MOGA efficiency and system performance for users and IoT devices.

In the Industrial Internet of Things (IIoT) environment, the integration of the Internet of Things (IoT) and Artificial Intelligence (AI) facilitates various applications. Sensors deployed in critical areas continuously collect diverse data, including real-time air quality monitoring, to provide accurate environmental insights. However, these systems are susceptible to adversarial examples (AEs), including physical AEs. These attacks can compromise the accuracy of predictions and lead to misinterpretation of carbon emission levels. For instance, malicious factory owners could exploit AEs to evade carbon emission inspections. To address this challenge, this study introduces a method that utilizes Poisson distribution-based statistical modeling to detect AEs by analyzing distinct neuron activation patterns. Furthermore, to address the complexity and variability of air quality data, we introduce a vector projection technique to enhance the alignment of probability vectors with the model’s actual output feature space. Experimental results demonstrate the effectiveness of this method in detecting attacks generated by FGSM, PGD, DeepFool, and C&W, achieving F1-scores of 0.993, 0.992, 0.972, and 0.961, respectively. This enhances the reliability and energy efficiency of IoT-based air quality monitoring systems within carbon-intelligent IIoT framework.

The development of artificial intelligence has led to the rapid growth of various industries. In addition to driving innovative applications, it has also brought significant changes to daily life. This transformation has accelerated the evolution of digital finance, enabling financial services to integrate AI and develop new applications. Among these services, smart investment has become one of the most popular. Due to the uncertainty of financial markets, achieving stable profits remains a challenging task. In this article, we propose a novel trading strategy that uses Search Economy Algorithms to analyze stock market trends. Our approach does not focus on a single stock; instead, it enables industry-wide investment decisions through our stock selection mechanism and fitness function, combined with the trading strategies provided by the Search Economy Algorithms. Compared with existing methodologies, our experimental environment is more complex. We benchmark our approach against ETF-0050 and demonstrate its effectiveness by evaluating capital growth rates and risk under specific parameters.

Genetic Algorithm and Differential Evolution are widely utilized and emulated in the field of metaheuristic algorithms. Species achieve population evolution through crossover and mutation with a small number of individuals. However, this paper argues that the continuity of species should be based on the phenomenon of species reproduction. This phenomenon applies to various species, with typically more dominant individuals having greater mate selection priority, and vice versa. This approach not only preserves the essence of GA and DE but also imparts a more diverse search capability. Experimental results demonstrate that our proposed method not only incorporates some concepts from GA and DE but also ensures the preservation of solution structures, preventing easy entrapment in local optimum in high-dimensional problems.

Hierarchical federated learning (HFL) allows edge devices aggregate trained parameters at local before global aggregation. However, data distribution as well as the service or application types of edge devices should be considered at the same time. In this paper, we propose a selective HFL (SHFL) algorithm that not only aims at accelerating model convergence but also at improving model robustness for independent and identically distributed (IID) data and non-IID data. The SHFL firstly performs clustering based on the data distribution of each client, then each cluster performs their training for local FL. After that, an extra FL training is performed as finetuning the aggregated model. The simulation results demonstrate that SHFL algorithm is superior to classic FL algorithms and an existing FL algorithm with hierarchical clustering scheme, especially for non-IID data.

In the intelligent reflecting surface (IRS)-aided wireless communication system, prior works showed that mutual coupling caused by the IRS elements is negative for signal transmission and cannot be ignored on performance. This paper studies the selection of elements on IRS from the perspective on mutual coupling, and aims to improve receiver's signal by obtaining an irregular IRS element topology with lower mutual coupling. On the basis of genetic algorithm (GA), we encoded an IRS element topology into a chromosome. GA's operations such as crossover and mutation are proposed to find the better element topology under a limitation in the number of selected elements. Simulation results show that the proposed GA-based approach converges quickly and the achievable rate of user equipment is improved compared to other heuristic selection strategies.

Unmanned aerial vehicle (UAV)-assisted mobile communication has been studied in recent years. UAVs can be used as aerial base stations (BSs) to improve the performance of terrestrial mobile network. In this article, mobile data offloading with UAV trajectory optimization is investigated. To tackle with the delay of requesting data and the immediacy of requested data at the same time, a new metric named delanalty is newly proposed. The delanalty metric jointly considers the delay of user requesting data, the immediacy of requested data file, and the quantity of residual requesting data. A find max delanalty user mechanism is proposed to eliminate the user who has the largest delay time. Furthermore, an actor–critic (AC)-based deep reinforcement learning (DRL) algorithm called AC -based delanalty trajectory optimization (ACDTO) algorithm is proposed to solve UAV’s trajectory optimization problem. Simulation results show that the proposed ACDTO algorithm can find an optimal flight trajectory with minimal delanalty for all users.

Due to the development of technologies, such as the Internet and mobile communication, news production is increasing day by day. Proper news delivery can lead to a thriving economy and disseminate knowledge. However, in addition to disrupting the existing order, fake news may create incorrect values and even beliefs. Therefore, detecting the authenticity of news is an extremely important issue. At present, many scholars have used artificial intelligence (AI) to detect fake news, achieving excellent results. However, once humans become dependent on AI, adversarial examples (AEs) can deceive the AI model and allow humans to receive false information. We have discovered that samples from different categories result in distinct and independent activation state distributions for each neuron. Therefore, this study proposes a method that detects adversarial samples of fake news by observing the activation states of neurons and modeling them as a Poisson distribution. The results of the experiment showed that our method can effectively detect AEs mixed in normal data and remove them, thereby improving the classification accuracy of the model by about 17%. The experimental results show that the method proposed in this article can improve the detection accuracy of fake news AEs.

The Intelligent Reflecting Surface (IRS) is an emerging technology in mobile communication networks. It is regarded as a potential solution for the path fading problem and obstacle issue while using millimeter wave communications. Most existing researches focus on deploying few IRSs within the coverage of a macrocell. In the paper, the deployment problem of multiple macro cells and IRSs is considered. The Macrocell-first and IRS-first algorithms are proposed, and the Ring-based algorithm is further proposed to achieve the highest average utility rate of all served users. It divides a macrocell's coverage into$K$rings, and deploys IRSs from outer ring to inner ring. Simulation-based results showed that the proposed Ring-based algorithm is superior to the Macrocell-first and IRS-first algorithms in terms of higher average utility rate with slightly fewer number of served users.

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Abstract Intelligent reflecting surface (IRS) composed of a large number of low‐cost, phase‐adjustable passive reflecting elements, which is an attractive solution of overcoming the signal attenuation and interference. IRS can be used as a low‐power passive system with signal enhancement and interference suppression. However, the planning problem of a large‐scale network contains a great numbers of base stations (BSs) and IRSs is challenging. The network planning problem considers limited deployment cost and signal enhancement at the same time is vital. Here, the 6G wireless signal coverage (WSC) problem with the consideration of both BS and IRS is considered. More specifically, the 6G WSC problem is first formulated through the integer linear programming. Due to its obvious NP‐hardness, two efficient heuristic algorithms that can reach a near‐optimal solution, that is, the WSC algorithm and the tree‐based WSC (TBWSC) algorithm is then proposed. Simulation‐based results showed that the proposed WSC algorithm achieves lower total cost than that of the TBWSC algorithm but also results in lower signal quality. The proposed TBWSC algorithm obtains the planning result with the highest signal quality and slightly higher total cost. Both proposed algorithms can find better planning results of multiple BSs and IRSs for 6G wireless communications.

A wireless rechargeable sensor network was proposed to extend the lifetime of the wireless sensor network. In this article, a charger is combined together with a self-propelled vehicle to provide a more flexible result of charger deployment. The dynamic chargers path selection problem is defined and mapped into the traveling salesman problem. Four metaheuristic algorithms for Internet-of-Things (IoT) applications are designed, and the higher fitness value between the charging path and the number of dead IoT devices is achieved. However, metaheuristic approaches may spend more time on searching solutions so that many IoT devices overuse limited power and fail to be charged for a long time, leading to power exhaustion. In this article, the edge computing technique is applied to accelerate the obtainment of charging paths with the well-defined edge/centralized unit switching. Moreover, to assure the calculated path trustworthy and will not be tampered with, the blockchain technology is adopted. The proposed architecture maintains high-level information credibility while transmitting the information of charging paths within the cloud and edge. The simulation results showed that the proposed method is capable of achieving better charging efficiency and less deployment cost.

Data center benefits cloud applications in providing high scalability and ensuring service availability. However, virtual machine (VM) placement in data center poses new challenges for service provisioning. For many cloud services such as storage and video streaming, present placement approaches are unable to support network-demanding services due to overwhelming communication traffic and time. Therefore VM placement concerning link capacity is vital to cloud data centers. In this paper, we define the network-aware VM placement optimization (NAVMPO) problem based on integer linear programming. The objective function of NAVMPO problem aims to minimize communication time for VMs of the same service type. Then we propose the service-oriented physical machine (PM) selection (SOPMS) algorithm and link-aware VM placement (LAVMP) algorithm. The SOPMS algorithm selects the most appropriate PM based on service-oriented architecture, and then the LAVMP algorithm deploys the most suitable VM to target PM regarding to the link capacity between them. Simulation results show that the proposed placement approach significantly decreases communication time compared to existing non-service-oriented and service-oriented VM placement algorithms, and also improves the average utility rate of PMs with lower power consumption.

Small cell aims at improving the cell coverage and capacity of macrocell. Network operators investigate cell planning for improving system performance and for satisfying user requirements with minimal construction cost and least unserved users. Most of the existing literature merely considered a single objective function or investigated cell planning for small‐scale and homogeneous networks. This study aims to optimise multiple objective functions of a large‐scale and heterogeneous wireless network with multiple macrocells and multiple small cells. The authors formulate the multi‐objective optimisation problem of cell planning for heterogeneous cellular networks. The three objects include construction cost, number of unserved users and network capacity. Then they propose the large‐scale cell planning genetic algorithm (LSCPGA) to find a planning result with the lowest fitness value. Simulation results show that LSCPGA economises 9–20% construction cost, eliminates the number of unserved users from 4 to 10% and enhances 2–15% in network capacity compared to other planning algorithms.

Fog computing provides a more flexible service environment than cloud computing. The lightweight fog environment is suitable for industrial applications. In order to strengthen service scalability, container virtualization has been proposed and studied in recent years. It is vital to explore the tradeoff between service scalability and operating expenses. This paper integrates the hypervisor technique with container virtualization, and constructs an integrated virtualization (IV) fog platform for deploying industrial applications based on the virtual network function. This paper presents a fuzzy-based real-time autoscaling (FRAS) mechanism and implements it in the IV fog platform. The FRAS mechanism provides a dynamic, rapid, lightweight, and low-cost solution to the service autoscaling problem. Experimental results showed that the proposed FRAS mechanism yields a better service scale with lower average delay, error rate, and operating expenses compared to other autoscaling schemes.

The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning to small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network Function Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically. In addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro Operator (μO). Telecom operators can provide regional network services through public buildings, shopping malls, or industrial sites. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed architecture ofμO tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the micro data center ofμO. The service model ofμO is capable of reducing network latency in response to the low‐latency applications for future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision Tree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism allows differentμOs to share network resources and speed up the development of edge computing in the future.

The demand for satisfying service requests, effectively allocating computing resources, and providing service on‐demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway‐based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on‐demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud‐edge computing environment for 5G and beyond.

Abstract In this paper, the planning problem of multiple evolved Node Bs (eNBs) and two types of relay nodes is defined base on linear programming. Type 1 relay nodes are placed in the center of eNB, and Type 1a relay nodes are deployed at cell edge. Three algorithms are proposed to investigate the Type 1a relay node placement and communication interference. All algorithms are designed on the basis of graph theory and analyzed in planning case and simulation results. The ultimate goal is to maximize the average throughput of all served users with minimum communication interference. Results showed that the proposed interference coordination algorithm not only provides the lowest construction cost with slightly fewer numbers of served users but also eliminates the communication interference with the highest average throughput. Most importantly, it achieves the best communication quality for next generation mobile networks. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, the coverage problem of network planning in mobile multi-hop relay networks is defined on the basis of integer linear programming. In order to provide desired utilities and also meet deployment limitations for network planning, we propose a supergraph tree algorithm to place base stations and relay stations at the lowest cost position. Furthermore, another algorithm for avoiding the interference between base stations, which is called interference aware tree algorithm is also proposed. Both the proposed algorithms are formulated on the basis of a graph theoretic technique and analyzed in the simulation results. The results show that the supergraph tree algorithm provides the lowest construction cost with different network scenarios, and the interference aware tree algorithm provides the highest communication quality for mobile multi-hop relay infrastructure-based communication network planning. Copyright © 2013 John Wiley & Sons, Ltd.

In recent years new research studies have appeared that concern the issue of network planning in LTE-Advanced. In this paper, the coverage problem in LTE-Advanced relay networks is formulated based on integer linear programming (ILP). We propose the Enhanced tree (E-Tree) algorithm to place the evolved Node B (eNB) and relay station (RS) at the location which has the lowest construction cost. The goal is not only satisfy the minimum requirement but also meet the two hop relaying limitation in LTE-Advanced networks. The E-Tree algorithm is proposed based on graph theoretic technique, and analyzed with the simulation results. The simulation results show that the proposed algorithm provides a rapid planning method and the lowest construction cost with various network environment.