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In audio transduction applications, virtualization can be defined as the task of digitally altering the acoustic behavior of an audio sensor or actuator with the aim of mimicking that of a target transducer. Recently, a digital signal preprocessing method for the virtualization of loudspeakers based on inverse equivalent circuit modeling has been proposed. The method applies Leuciuc’s inversion theorem to obtain the inverse circuital model of the physical actuator, which is then exploited to impose a target behavior through the so called Direct–Inverse–Direct Chain. The inverse model is designed by properly augmenting the direct model with a theoretical two-port circuit element called nullor. Drawing on this promising results, in this manuscript, we aim at describing the virtualization task in a broader sense, including both actuator and sensor virtualizations. We provide ready-to-use schemes and block diagrams which apply to all the possible combinations of input and output variables. We then analyze and formalize different versions of the Direct–Inverse–Direct Chain describing how the method changes when applied to sensors and actuators. Finally, we provide examples of applications considering the virtualization of a capacitive microphone and a nonlinear compression driver.

Although network function virtualization has provided a breakthrough in realizing the agility and flexibility for network resource management, virtual network functions (VNF) embedding is a challenging task in the Internet of Things (IoT) environments due to their specific requirements such as Quality of Service (QoS) and energy awareness. While there is a lot of research on VNF embedding on its own, there is very little on the load‐balanced, energy‐aware solutions. In this paper, we propose a load‐balanced energy and delay aware deep reinforcement learning framework based on the advantage actor–critic learning strategy for efficient embedding of VNFs by considering the QoS and energy requirements of the IoT applications. Compared with the deep deterministic policy gradient method and the so‐called DDRA (deep Q‐learning‐based dynamic resource allocation) method, our proposed approach shows outstanding performance in the successful acceptance rate of requests. The proposed algorithm is a load‐balanced and energy‐delay aware reinforcement learning approach accompanied by the parallel actor–critic learning strategy for embedding virtual network functions in Internet of Things (IoT) architectures. It handles the dynamic and real‐time nature of the IoT service requests.

With the proliferation of mobile demands and increasingly multifarious services and applications, mobile Internet has been an irreversible trend. Unfortunately, the current mobile and wireless network (MWN) faces a series of pressing challenges caused by the inherent design. In this paper, we extend two latest and promising innovations of Internet, software-defined networking and network virtualization, to mobile and wireless scenarios. We first describe the challenges and expectations of MWN, and analyze the opportunities provided by the software-defined wireless network (SDWN) and wireless network virtualization (WNV). Then, this paper focuses on SDWN and WNV by presenting the main ideas, advantages, ongoing researches and key technologies, and open issues respectively. Moreover, we interpret that these two technologies highly complement each other, and further investigate efficient joint design between them. This paper confirms that SDWN and WNV may efficiently address the crucial challenges of MWN and significantly benefit the future mobile and wireless network.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Originality value: The following groups should be interested in the research results: 1) musical arts creators to develop or structure their perception of the creative process; 2) musical art managers to develop or structure their perception of the complex nature of the creative process of musical art groups they manage; 3) musical art institutions' managers to develop or structure their perception of the complex nature of the creative process of musical artists involved in the institutions they manage.

A KVM (Kernel-based Virtual Machine) is subject to the complexity of the Linux kernel and the difficulty and cost of safety certification; thus, it is not popularized in embedded high-reliability scenarios. This paper proposes a KVM-based Heterogeneous Virtualization (KHV), which is independent of hardware virtualization (KVM mandatory virtualization), follows the principle of static partitioning, localizes the hypervisor, and inherits the KVM software ecosystem. KHV balances the demands of static partitioning and flexible sharing in the embedded system. The paper implemented KHV on the RISC-V Xuantie C910 CPU-based SoC and conducted a performance comparison with KVM. The experiment shows that KHV is 50% smaller than KVM in terms of fluctuation, and KHV makes the guest OS have the same performance as the bare-metal OS in scheduler benchmarks, whereas KVM dropped an average of 28%.

The growing number of cyber-crimes is affecting all industries worldwide, as there is no business or industry that has maximum protection in this domain. This problem can produce minimal damage if an organization has information security audits periodically. The process of an audit includes several steps, such as penetration testing, vulnerability scans, and network assessments. After the audit is conducted, a report that contains the vulnerabilities is generated to help the organization to understand the current situation from this perspective. Risk exposure should be as low as possible because in cases of an attack, the entire business is damaged. In this article, we present the process of an in-depth security audit on a distributed firewall, with different approaches for the best results. The research of our distributed firewall involves the detection and remediation of system vulnerabilities by various means. In our research, we aim to solve the weaknesses that have not been solved to date. The feedback of our study is revealed with the help of a risk report in the scope of providing a top-level view of the security of a distributed firewall. To provide a high security level for the distributed firewall, we will address the security flaws uncovered in firewalls as part of our research.