Among these applications, emotion AI has emerged as a transformative tool in the educational sector, enabling personalized learning experiences by adapting content and teaching methods based on students’ emotional states. This often involves integrating multimodal data, including text, speech, and facial expressions, to accurately interpret and respond to students’ needs. However, despite its potential, deep learning models are highly vulnerable to adversarial example attacks (AE). These malicious inputs cause incorrect decisions while remaining imperceptible to humans. The risks posed by AEs are particularly concerning in education, where misinterpreting emotional cues may disrupt adaptive learning processes and hinder student engagement. Effectively addressing these challenges is essential. Existing research detects attacks by comparing normal and AEs, but this method struggles with the variability of normal data and the complexity of high-dimensional features, resulting in low detection accuracy and high computational costs. Furthermore, AEs often exhibit unique morphological characteristics, making it difficult to develop a universal detection mechanism. This article proposes a novel method using Poisson distribution to analyze neuron output differences, enhancing model robustness against adversarial attacks and ensuring cross-model generality. By improving adversarial robustness, this method aims to protect emotion AI applications in education, ensuring accurate multimodal data interpretation and supporting adaptive learning systems. Experimental results show that our method performs robustly in multimodal data processing, encompassing text, speech, and facial expressions. Additionally, its strong performance across various attack scenarios highlights the propose method’s generalization capabilities.

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.

In recent years, the exponential increase in mobile and Internet of Things (IoT) data traffic has placed substantial demands on infrastructure for Internet Service Providers (ISPs). To meet these demands sustainably, it is critical to enhance energy efficiency, resource utilization, and cost-effectiveness while reducing the carbon footprint. Simply adding hardware is not a viable solution. This study introduces an innovative approach to 3D cellular deployment in sustainable B5G/6G networks, designed to optimize Quality of Service (QoS) for users and IoT devices. Although 5G/B5G utilizes millimeter waves for high data rate transmission, their limited coverage and susceptibility to interference from buildings pose unique deployment challenges. To address these, we formulate the 3D cellular deployment problem as a Multi-Objective Optimization (MOO) problem and propose an advanced deployment strategy using VA-NSGA-II, a metaheuristic-based algorithm. By factoring in building interference, Received Signal Strength Indicator (RSSI), coverage, deployment cost, and a balance between performance and sustainability, VA-NSGA-II provides an optimal deployment solution. Simulation results demonstrate that VA-NSGA-II achieves effective deployment performance across various building materials, highlighting its adaptability and effectiveness in different environmental scenarios.

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.

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.

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.

Abstract Recently, the 5G as the next‐generation network is a popular research and discussed widely. The architecture of 5G is a heterogeneous network, and it can support more networked types, like the ultra‐dense network, traditional cellular network, and Machine to Machine communication. Although the high frequency and larger bandwidth have been using in 5G, resource allocation is still a critical issue that needs to be discussed and solved. Consider the spectrum resource is limited, but almost all users hope that equipment can get a better quality of services. Therefore, how to manage the spectrum resource and allocation is a big problem. Consider the fast‐growing devices and traffic in the future; hence, task scheduling for UEs to reduce energy consumption will be focused on. To solve resource allocation and minimise energy consumption, the Markov decision chain is proposed to be used to predict the channel state. The modified particle swarm optimization (MPSO) is also used in this paper to find the best task scheduling. The simulation will be used to verify the performance of the mechanism that is used and compare it with PSO and first‐in‐first‐service (FIFS). The result shows the method used can be scheduled for the task efficiently.

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.

The constant rapid growth and development of modern medical technology has resulted in an ever-growing demand for higher quality health monitoring systems. This is especially true for the development of the Internet of Things: as the Internet of Things becomes more ubiquitous in dally life, so does the possibility of, and demand for, the ability to remotely monitor the health of patients at anytime, anywhere, using a wide variety of biometrie information. Thus e-Healthcare has become a significant trend in the medical field. In addition to monitoring a patient's health remotely, data can also be relayed to doctors or hospitals in real-time, in order to assist in correct medical decision-making. Hospitals have begun to implement this technology, and patients therefore have immediate access to required diagnoses and care. However, the use of a large number of remote medical sensors requires significant bandwidth, especially when relaying real-time information. Hospital networks thus become susceptible to problems arising from network congestion. This study proposes a load-balancing mechanism based on SDN-SFC for the optimization of hospital remote-monitoring network planning, which simultaneously eliminates the need for large amounts of hardware.

The wireless rechargeable sensor network is a technology to solve lifetime problem for wireless sensor network. Most researches discussed about the outdoor scenario but there are few studies focused on the indoor scenario. Actually, wireless sensor network is very important for the indoor scenario. The reason is that sensors help factory control production for better quality as well as both of disaster relief and prevention are also need to deploy sensors in the indoor scenario. These instances have the concept of sustainable development because sensors cannot be suddenly interrupted so that the expected benefits cannot be realized. As in the example just mentioned, quality of production is no longer to be guaranteed and disaster will not be prevented. That is why the wireless rechargeable sensor network becomes more popular. There are many low cost charger deployment algorithms have been proposed. However, these works almost have opportunity to fall into local optimum. This paper will present a layoff algorithm to combine simulated annealing-based algorithm to achieve optimization for wireless rechargeable sensor network. The simulation results show that the proposed method can reduce the number of chargers efficiently but just need same time with simulated annealing-based algorithm.

The limited energy of sensor can be regarded as an optimization problem aimed at finding some useful deployment strategies for a set of chargers, but it typically might spend a lot of computation costs. In order to provide a good solution for reducing a large number of chargers caused by all the sensor nodes need to be covered, add the motor to some particular chargers is an alternative solution. That is why some recent studies attempted to develop the movable charger based algorithms to enhance transmission range. However, most movable charger-based algorithms are the greedy or rule-based algorithms, they will easy fall into local optimum at early iteration and consequently the end results will far away to global optimum. That is why it still has rooms for improvement. This paper will present a simulated annealing-based algorithm to improve the deployment result of wireless sensor network. The simulation results show that the proposed method can reduce the number of chargers significantly for full coverage.

Internet technology is advancing with each passing day, the user's demand is also increasing. Of course the users will more concern to quality of service. The vendors must find out a win win method of resource allocation to meet users and itself. Therefore, the resource allocation of cloud computing has become one of hottest topics. In literatures, some researchers have proposed resource allocation methods which include allocation of virtual machines and service classification, etc. However, these methods are based on subjective observations that lead to the overall cloud architecture becomes imbalance. In order to prevent such situation happened, we use the Data Envelopment Analysis (DEA) to solve the imbalance problem. In this paper, our analysis is start from the user's requests, and use the DEA to evaluate the whole cloud parameters. Then we can find out the resource allocation policy which is the most suitable between the users and vendors.

Wireless Sensor Network (WSN) is still a hottest research topic which can be applied to a lot of new research field such as robotic and smart grid. Its not only used for detecting the new environment but also monitoring any situation which has dynamic variation. However, the sensor node is relied on the battery energy that limited the lifetime of entire network. In this paper, we proposed a novel scheme called Dynamic Duty cycle and Dynamic Schedule Assignment (DDDSA) which not only can reduce number of RTS/CTS (Request to Send / Clear to Send) packets but also dynamically update the Duty Cycle value. Furthermore, the proposed scheme does not calculate the traffic information repeatedly in overlapping areas. This scheme will reduce more idle nodes to achieve energy efficiency.