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The proliferation of connected devices in the Internet of Things (IoT) presents a connectivity challenge. The future internet will require a paradigm shift in which content is evaluated on the basis of “What” it is rather than “Where” it originated. ICN’s goal is to provide the benefits of name-based content addressing in order to facilitate scalable content distribution, security, mobility, and trust. NDN is a new internet architecture that evolved from Content-Centric Networking (CCN). NDN is viewed as a solution to the IoT’s challenges, as well as a way to transcend the IP paradigm. With IoT systems that had a number of challenging characteristics to satisfy, including heterogeneous devices, resource constraints, and energy efficiency. Due to the fact that NDN native features deliver data via hierarchically structured names, it offer promising solutions for current research integrating NDN into IoT. The review discusses the significance of naming, its influence, and security factor. Additionally, research challenges in the areas of naming and security will be discussed. The primary objective of this review is to give a new facelift to a new integrating naming convention for NDN.

The 21st century is a high-tech information era in which our lives are closely linked by computer networks. Hence, how to effectively supervise networks and reduce the frequency of network security incidents has now become a research hotspot in cyberspace. Specifically, researchers have shown an increased interest in predicting the network traffic before any untoward incident happens. Optimization and decomposition technologies are the core components of network traffic prediction model which plays an important role in network management. This article discusses past network traffic prediction research and critically examines the optimization and decomposition technologies used in the model, lists the model parameter structure based on the research methodology, the data set used, the evaluation criteria and so on. By comparison, digging out the Particle Swarm Optimization (PSO) algorithm and the Variational Mode Decomposition (VMD) decomposition technique will effectively solve the network traffic model predictive difficulties that have proven to be crucial to improving predictive accuracy and convergence speed strategy.The comprehensive review reveals that PSO and VMD are the most suitable optimization algorithm and decomposition technology for network traffic prediction modeling.

The vision of the Internet of Things (IoT) covers not only the well-regulated processes of specific applications in different areas but also includes ubiquitous connectivity of more generic objects (or things and devices) in the physical world and the related information in the virtual world. For example, a typical IoT application, such as a smart city, includes smarter urban transport networks, upgraded water supply, and waste-disposal facilities, along with more efficient ways to light and heat buildings. For smart city applications and others, we require unique naming of every object and a secure, scalable, and efficient name resolution which can provide access to any object's inherent attributes with its name. Based on different motivations, many naming principles and name resolution schemes have been proposed. Some of them are based on the well-known domain name system (DNS), which is the most important infrastructure in the current Internet, while others are based on novel designing principles to evolve the Internet. Although the DNS is evolving in its functionality and performance, it was not originally designed for the IoT applications. Then, a fundamental question that arises is: can current DNS adequately provide the name service support for IoT in the future? To address this question, we analyze the strengths and challenges of DNS when it is used to support ubiquitous IoT. First, we analyze the requirements of the IoT name service by using five characteristics, namely security, mobility, infrastructure independence, localization, and efficiency, which we collectively refer to as SMILE. Then, we discuss the pros and cons of the DNS in satisfying SMILE in the context of the future evolution of the IoT environment.

Two of the most promising candidate solutions for realizing the next-generation all-IP mobile networks are Mobile IPv6 (MIPv6), which is the host-based and global mobility supporting protocol, and Proxy MIPv6 (PMIPv6), which is the network-based and localized mobility supporting protocol. However, the unprecedented growth of mobile Internet traffic has resulted in the development of distributed mobility management (DMM) architecture by the Internet engineering task force DMM working group. The extension of the basic MIPv6 and PMIPv6 to support their distributed and scalable deployment in the future is one of the major goals of the DMM working group. We propose an all-IP-based mobility management architecture that leverages the concept of Named Data Networking (NDN), which is a distributed content management and addressing architecture. In the proposed solution, mobility support services are distributed among multiple anchor points at the edge of the network, thereby enabling a flat architecture that exploits name-based routing in NDN. Our approach overcomes some of the major limitations of centralized IP mobility management solutions, by extending existing routing protocol and mobility management architecture, to distribute the mobility management function of anchor points in the IP network and optimize the transmission path of mobile traffic.

Internet Engineering Task Force (IETF) has proposed an extension based on Mobile IPv6 (MIPv6), named as network mobility basic support protocol (NEMO-BSP), to support NEMO in IPv6 networks. However, NEMO-BSP inherits all the drawbacks of MIPv6, such as inefficient routing path, high handover latency, and packet encapsulation overhead. To address these drawbacks of NEMO-BSP, this paper proposes an NEMO supporting scheme based on a novel Locator/ID Separation (LIDS) architecture, namely LIDS-NEMO. In LIDS-NEMO, Multiple Virtual Mapping (MVM) scheme is proposed to differentiate the intra-NEMO and inter-NEMO mobility. Besides, packets are transmitted through the most optimized route in LIDS-NEMO. The simulation results show that LIDS-NEMO reduces the signaling cost significantly when compared with NEMO-BSP and it will be a promising scheme to provide NEMO support in the LIDS context.

Requirement changes occur frequently during the product development process owing to diverse and personalized customer needs. This necessitates multiple reworks of the development task, causing delays. Therefore, it is necessary to analyze the impact of requirement change on the progress of product development. First, the development task is divided into three phases: concept development, detail design, and pilot production. Reworks that are a result of development error or changes in information are analyzed along with the reworks caused by requirement changes, which are the focus of analysis. Then, development task activities and relationships are quantitatively described, and a simulation model is built based on the system dynamics method. This is combined with a practice case of product development, and the impact of requirement change on the progress of the development task is analyzed. The analysis shows that requirement changes greatly increase the development task duration and uncertainty of the development task schedule. The development task schedule is most sensitive to the deadline for requirement change, and the impact of a requirement change occurring at the later phase is greater.

Anomaly detection in videos is the task of identifying frames from a video sequence that depict events that do not conform to expected behavior, which is an extremely challenging task due to the ambiguous and unbounded properties of anomalies. With the development of deep learning, video anomaly detection methods based on deep neural networks have made great progress. The existing methods mainly follow two routes, namely, frame reconstruction and frame prediction. Due to the powerful generalization ability of neural networks, the application of reconstruction-based methods is limited. Recently, anomaly detection methods based on prediction have achieved advanced performance. However, their performance suffers when they cannot guarantee lower prediction errors for normal events. In this paper, we propose a novel future frame prediction model based on a bidirectional retrospective generation adversarial network (BR-GAN) for anomaly detection. To predict a future frame with higher quality for normal events, first, we propose a bidirectional prediction combined with a retrospective prediction method to fully mine the bidirectional temporal information between the predicted frame and the input frame sequence. Then, the intensity and gradient loss between the predicted frame and the actual frame together with an adversarial loss are used for appearance (spatial) constraints. In addition, we propose a sequence discriminator composed of a 3-dimensional (3D) convolutional neural network to capture the long-term temporal relationships between frame sequences composed of predicted frames and input frames; this network plays a crucial role in maintaining the motion (temporal) consistency of the predicted frames for normal events. Such appearance and motion constraints further facilitate future frame prediction for normal events, and thus, the prediction network can be highly capable of distinguishing normal and abnormal patterns. Extensive experiments on benchmark datasets demonstrate that our method outperforms most existing state-of-the-art methods, validating the effectiveness of our method for anomaly detection.

At present, the GNSS interference mode has developed from single interference source to multi-interference source cooperative work, which brings new challenges to the positioning of interference source. Due to the mutual influence caused by multi-interference source working together, the traditional methods of locating interference source cannot accurately extract the characteristics of each interference signal, and thus cannot correctly locate. In this paper, an innovative multi-interference source centroid localization method based on clustering centroid convergence is proposed. In order to eliminate the influence of the receivers which are not interfered, the method selects GNSS receivers participating in the interference location by setting the threshold of carrier noise power density rate (C/N0). Then, the algorithm based on clustering centroid convergence is used to realize the accurate estimation of the number of interference sources and cluster the selected receivers. The algorithm is suitable for any clustering algorithm that needs to give the number of clusters in advance. Finally, the centroid weight based on C/N0 is applied to each clustering data set to locate multiple GNSS interference sources. Simulation results demonstrate that the method is accurate and reliable for GNSS multi-interference source location.

Currently, the new operational tactic of the swarming unmanned aerial vehicles (UAVs) combat is becoming a hotspot in military research, and evaluating the combat efficiency and the roles of UAVs is of vital importance for the future development of UAVs. However, most of the recent studies on the swarming UAVs combat are merely qualitative analyses. This paper proposes an operational effectiveness evaluation method of the swarming UAVs combat system based on a system dynamics (SD) model. The weapons in the combat process are divided into nine subsystems and we build corresponding nine in-trees models using the rate-variable in-trees modeling method. The final SD model is established based on the nine in-trees and the characteristics of swarming UAVs are considered. Taking the surviving rate of UAVs and task completion degree as the evaluation indicators, the model simulation shows that over 50% of the enemy ground targets can be destroyed in 15-time units, although some UAVs may be damaged. It is also confirmed through comparison experiments with other combat patterns that the swarming UAVs play a crucial role in improving the system combat efficiency and can decompose the function of traditional high-value weapon platforms.

This paper focuses on anti-synchronization and synchronization of a stochastic multi-coupled chaotic system with ring connection and control schemes. First, the system is simplified by means of the formula deformation technique and the controller. Based on the Lyapunov method and stochastic differential theory, some sufficient conditions are obtained by some control methods. At last, the Chen system, Lorenz system, and Lü system with stochastic perturbations are used to verify the correctness of the conclusions.

Increasing congestion has spurred investigations into more efficient utilization of the radio frequency (RF) spectrum while minimizing the volume and weight of the RF equipment. Considering the similarity of radar and communication in both software and hardware, this paper presents a co-design integrated waveform of radar and communication based on the multiantenna system. In this paper, communication data is first transported by utilizing space-frequency modulation on a fixed transmitting frequency set. By using digital beamforming technology in the subarray level, directional communication with cooperative targets in the main lobe can be achieved. However, modulation in space-frequency domain results in high sidelobes in the autocorrelation function. Thus, to acquire an acceptable radar performance, the mismatched filter (MMF) is proposed to realize information defusion and sidelobe suppression. Aiming at reducing the signal-to-noise ratio loss of the MMF process, the filter response is further modified. Finally, numerical simulations assess the effectiveness of the proposed MMF and the Doppler coherency between pulses for clutter cancellation. Simulation results demonstrate the capability and potential of the proposed co-design signal for radar-communication system.