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In the context of the rapid development of edge computing, optimizing data transmission and reducing latency is crucial for efficient collaborative processing among edge servers. Traditional TCP/IP protocols are hindered by high latency and low throughput, while RDMA (Remote Direct Memory Access) technology addresses these challenges by enabling direct memory access and bypassing the operating system kernel. However, the RDMA data transmission mechanism based on sliding windows requires frequent memory status exchanges in the order of memory blocks, which can limit its ability to handle multiple concurrent tasks within a single Queue Pair (QP). To address the limitations of the traditional sliding window transmission mechanism in multi-task environments, we propose a novel RDMA data transmission mechanism that utilizes status bytes to indicate memory block utilization, which utilizes stateless server connections, and multi-task shared QP transmission strategies. In the proposed mechanism, fine-grained control over memory blocks is achieved through the status byte, thereby enabling effective multi-task real-time video stream transmission. Experimental results show that, compared to the sliding window method, the proposed status-byte-assisted RDMA transmission mechanism provides higher throughput, lower latency, and reduced resource consumption, thus enhancing system scalability and reducing CPU utilization. Moreover, this mechanism achieves more stable throughput than the sliding window method when transmitting multiple real-time video streams in edge computing scenarios, making it particularly suitable for data transmission in such environments.

On the basis of the chaotic system proposed by Wang et al. in 2023, this paper constructs a 5D fractional-order memristive hyperchaotic system (FOMHS) with multiple coexisting attractors through coupling of magnetic control memristors and dimension expansion. Firstly, the divergence, Kaplan–Yorke dimension, and equilibrium stability of the chaotic model are studied. Subsequently, we explore the construction of the 5D FOMHS, introducing the definitions of the Caputo differential operator and the Riemann–Liouville integral operator and employing the Adomian resolving approach to decompose the linears, the nonlinears, and the constants of the system. The complex dynamic characteristics of the system are analyzed by phase diagrams, Lyapunov exponent spectra, time-domain diagrams, etc. Finally, the hardware circuit of the proposed 5D FOMHS is performed by FPGA, and its randomness is verified using the NIST tool.

Cybersecurity, as a crucial aspect of the information society, requires significant attention. Fortunately, the concept of trust, originating from the field of sociology, has been under extensive research in order to enhance cybersecurity by evaluating the trustworthiness of nodes with artificial intelligence (AI) techniques in distributed networks (DNs). However, the scalability issues faced by AI-enabled trust hinder its integration with the DNs. Currently, there is a lack of a comprehensive review article that explores the current state of AI-enabled trust development applications. This paper aims to address this gap by providing a review of the state-of-the-art AI-enabled trust in DNs. This review focuses on the concept of trust and how it can be facilitated through AI, particularly utilizing machine learning and deep learning methods. Additionally, the paper provides a comprehensive comparison and analysis of three key domains in the field of AI-enabled trust: trust management (TM), intrusion detection system (IDS), and recommender systems (RS). Some open problems and challenges that currently exist in the field are manifested, and some suggestions for future work are presented.

Due to the technical characteristics and application scenarios of distributed networks, their nodes can easily be invaded and compromised. It will result in information being forged or tampered without difficulty. An effective scheme to guarantee the authenticity and integrity of information is judging how trustworthy nodes are in terms of transmission. In D-S evidence theory (DST), the uncertainty can be expressed to solve the trust fusion issue for multiple nodes. In this paper, for reviewing the DST-based trust evaluation and decision and providing their future research directions systematically. Meanwhile, the DST is briefly reviewed, and two improvements in DST are categorically described. The role and mechanism in DST-based trust models are compared and analyzed. The valuable research directions in the near future are represented. Our contributions could solve the trust problem in resource constrained sensor nodes and improve the decision reliability of network.

Distance measurement is an important part of the wireless location technology.The ranging accuracy of millimeter wave based distance measurement depends on the frequency estimation accuracy of beat signals from the targets, but the existing frequency estimation algorithms have the shortcomings of either high complexity or low accuracy.In order to improve the ranging accuracy, the frequency compensation value and frequency estimation method based on all phase FFT amplitude was proposed to improve the performance of frequency estimation.Firstly, the signal amplitude was estimated based on the relationship between its all phase FFT and its conventional FFT spectrum.Moreover, using the relationship between the estimated signal amplitude, the signal spectrum amplitude and frequency compensation value, the frequency compensation value was solved by Newton iterative algorithm.Then, the frequency compensation value was used to shift the frequency of the signal, and the frequency compensation value was estimated again to obtain the final estimation value of the signal frequency, from which the distance of the target was calculated.Finally, simulation results were presented to show that the proposed method has higher distance measurement accuracy than the existing methods.

The single planar routing protocol has a slow convergence rate in the large-scale Wireless Sensor Network (WSN). Although the hierarchical routing protocol can effectively cope with large-scale application scenarios, how to elect a secure cluster head and balance the network load becomes an enormous challenge. In this paper, a Trust Management-based and ​Low Energy Adaptive Clustering Hierarchy protocol (LEACH-TM) is proposed. In LEACH-TM, by using the number of dynamic decision cluster head nodes, residual energy and density of neighbor nodes, the size of the cluster can be better constrained to improve energy efficiency, and avoid excessive energy consumption of a node. Simultaneously, the trust management scheme is introduced into LEACH-TM to defend against internal attacks. The simulation results show that, compared with LEACH-SWDN protocol and LEACH protocol, LEACH-TM outperforms in prolonging the network lifetime and balancing the energy consumption, and can effectively mitigate the influence of malicious nodes on cluster head selection, which can greatly guarantee the security of the overall network.

Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar.

Abstract For hierarchical wireless sensor network (WSN), the clustered routing protocol can effectively deal with large-scale application requirements, thereby, how to efficiently elect the secure cluster heads becomes very critical. Unfortunately, many current studies only focus on improving security while neglecting energy efficiency and transmission performance. In this paper, a lightweight trust management scheme (LTMS) is proposed based on binomial distribution for defending against the internal attacks. Simultaneously, distance domain, energy domain, security domain and environment domain are considered and introduced to propose a multidimensional secure clustered routing (MSCR) scheme by using dynamic dimension weight in hierarchical WSNs. The simulation results show that LTMS can effectively prevent a malicious node from being elected as a cluster head, and MSCR can achieve a balance between security, transmission performance and energy efficiency under the requirements of environmental applications.

Abstract Blockchain, as one of the most promising technology, has attracted tremendous attention. The interesting characteristics of blockchain are decentralized ledger and strong security, while non-repudiation is the important property of information security in blockchain. A digital signature scheme is an effective approach to achieve non-repudiation. In this paper, the characteristics of blockchain and the digital signature to guarantee information non-repudiation are firstly discussed. Secondly, the typical digital signature schemes in blockchain are classified and analyzed, and then the state-of-the-art digital signatures are investigated and compared in terms of application fields, methods, security, and performance. Lastly, the conclusions are given, and some future works are suggested to stir research efforts in this field. Our works will facilitate to design efficient and secure digital signature algorithms in blockchain.

In recent years, frequency-modulated continuous wave (FMCW) radar has been widely used in automatic driving, settlement monitoring and other fields. The range accuracy is determined by the estimation of the signal beat frequency. The existing algorithms are unable to distinguish between signal components with similar frequencies. To address this problem, this study proposed an enhanced root-MUSIC algorithm based on matrix reconstruction. Firstly, based on the sparsity of a singular value vector, a convex optimization problem was formulated to identify a singular value vector. Two algorithms were proposed to solve the convex optimization problem according to whether the standard deviation of noise needed to be estimated, from which an optimized singular value vector was obtained. Then, a signal matrix was reconstructed using an optimized singular value vector, and the Hankel structure of the signal matrix was restored by utilizing the properties of the Hankel matrix. Finally, the conventional root-MUSIC algorithm was utilized to estimate the signal beat frequency. The simulation results showed that the proposed algorithm improved the frequency resolution of multi-frequency signals in a noisy environment, which is beneficial to improve the multi-target range accuracy and resolution capabilities of FMCW radar.

Estimating the frequencies of multiple superimposed exponentials in noise is an important problem due to its various applications in engineering. In order to obtain good inhibition of spectral leakage and improve the estimation accuracy, an improved Chirp-Z transform (CZT) algorithm is proposed for high-precision frequency estimation. Firstly, the proposed algorithm analyzes the characteristics of the CZT spectrum and utilizes the CZT spectrum to construct a bias correction factor for frequency bias estimation. Then, an expression between the bias correction factor and the frequency estimation error is derived to obtain a more accurate estimate of the frequency bias. Finally, the frequency estimate of the CZT is corrected by the estimated frequency bias so as to obtain a higher frequency estimation accuracy. Compared with the conventional CZT algorithm, the proposed improved CZT algorithm achieves a higher frequency estimation accuracy by correcting the frequency estimate of the CZT method using the estimated frequency bias. The proposed improved CZT algorithm is verified using simulation studies and experimental results, and the results show that it has a higher accuracy and better robustness than the existing methods.

In this paper, we first present a simple seven-term 4D hyperchaotic system based on the classical Sprott-C 3D chaotic system. This novel system is inspired by the simple 4D hyperchaotic system based on Sprott-B proposed by A. T. Sheet (2022). We discuss the phenomenon of premature divergence brought about by the improper choice of coupling parameters in that paper and describe the basic properties of the new system with phase diagrams, Lyapunov exponential spectra and bifurcation diagrams. Then, we find that the dynamical behaviors of the system suffer from the limitation of the control parameters and cannot represent the process of motion in detail. To improve the system, we expand the dimensionality and add the control parameters and memristors. A 5D memristive hyperchaotic system with hidden attractors is proposed, and the basic dynamical properties of the system, such as its dissipation, equilibrium point, stability, Lyapunov exponential spectra and bifurcation diagram, are analyzed. Finally, the hardware circuits of the 4D Sprott-C system and the 5D memristive hyperchaotic system were realized by a field programmable gate array (FPGA) and verified by an experiment. The experimental results are consistent with the numerical simulation results obtained in MATLAB, which demonstrates the feasibility and potential of the system.

As an innovative networking paradigm in the Internet of Things, information-centric networking (ICN) has improved security than the traditional IP networks. However, it still faces a large number of security threats, especially those threats from internal attacks. The trust management technology is an effective approach for defending against internal attacks. In this paper, our contributions lie in our investigating the requirements of cybersecurity in ICN and analyzing the typical attack behaviors and defense schemes. Then, we propose a fast and efficient trust management scheme (FETMS) for defending against the on-off attack, which is an intelligent internal attack. The simulation results show that FETMS can efficiently detect and remove the malicious node in a short period of time; meanwhile, it can achieve lower latency and add its mobility.

Rician distribution has been widely utilized to describe wireless fading channel. In the non-stationary temporal fading channel like industrial scenarios, both the specular and scattered components of the multi-path fading channel will be time varying. As a result, the online estimation of Rician parameters is necessary to provide stable wireless service. The traditional estimation approaches of Rician parameters are designed for channel measurement usage and therefore have to work in the data-aided mode for online estimation with modulated I/Q samples. To solve this problem, some non-data-aided algorithms have been proposed in recent years, but only valid in specific scenarios. In this paper, we formulate the estimation of Rician parameters from modulated I/Q samples as a two-dimensional Gaussian mixture model to provide a general non-data-aided Rician parameter estimation method. By involving a priori information of modulation scheme and the motivation of optimized gradient searching, the independent parameters in the maximum likelihood estimation can be significantly decreased to three, which leads to fast convergence of the modified expectation-maximization algorithm with high accuracy. The combination of these modifications has been finally formulated as a Rician mixture model. The numerical results and field measurements illustrate the feasibility of this methodology.

As an efficient approach, the trust policy is implemented to defend against insider attacks in complex networks. However, the imperfection of trust relationships directly hinders the effort to quantitatively calculate trust value, especially in choosing a cooperative partner. Fortunately, the game theory is gradually concerned with addressing the above issue to further enhance security. In this paper, the game theory and the trust policy are reviewed briefly. Then, the research roadmap on game-based trust in complex networks is discussed and analysed deeply. Furthermore, some research directions in the near future are given. It is worth mentioning that our contributions not only describe the evolution of the game-based trust clearly but also suggest the trust mechanism based on the bounded rational game more suitable to uncertain information.

Frequency estimation of a sinusoidal signal is a fundamental problem in signal processing for the Internet of Things. The frequency interpolation estimation algorithm based on the fast Fourier transform is susceptible to being disturbed by noise, which leads to estimation error. In order to improve the accuracy of frequency estimation, an improved Rife frequency estimation algorithm based on phase angle interpolation is proposed in this paper, namely the PAI–Rife algorithm. We changed the existing frequency deviation factor of the Rife algorithm using phase angle interpolation. Then, by setting the frequency shift threshold, the frequency that is not within the threshold range is shifted to the optimal estimation space. The simulation results show that the proposed algorithm has a wider valid estimation range, and the estimated standard deviation is closer to the Cramer–Rao lower bound. Compared with the Rife algorithm and some recently proposed advanced algorithms, the proposed algorithm has less computational complexity, lower misjudgment rate, and more stable performance.

The perception and interaction technology between IoT devices is one of the necessary foundations for the development of the Internet of things.With the growing popularity of multimedia devices,such as smart phones,tablet,and home audio,the speakers and microphones are ubiquitous on these devices which make it possible to the low cost interaction between devices using acoustic signal.The low speed and low frequency physical properties of sound waves make the acoustic-based sensing technology have higher accuracy and lower computation.A phase based measurement method using acoustic signals was proposed,which could be used to measure high precision distance between different mobile devices and sound sources.In a certain range,firstly,the mobile device receives the single frequency acoustic signal transmitted by a sound source.The mobile device uses the sound signal to synchronize the phase between the mobile devices the sound source,and then calculates the distance between the mobile device and the sound source by calculating the phase difference during movement.

The Industrial Internet of Things is cited as the latest means for making manufacturing more flexible, cost effective, and responsive to changes in customer demands. In this paper, we present a mobile cloud based scheduling strategy for the industrial Internet of Things. Several computing paradigms, such as mobile cloud computing, fog computing, and edge computing can be integrated to the industrial Internet of Things, which allow to offload tasks to the cloud for execution. We model the task scheduling problem as an energy consumption optimization problem, while taking into account task dependency, data transmission, and some constraint conditions, such as response time deadline and cost, and further solve it by genetic algorithms. A series of simulation experiments are conducted to evaluate the performance of the algorithm and the results have shown that our proposal is more efficient than the baseline approach.

Traffic flow modeling plays an important role in routing，MAC algorithm and protocol designs in vehicular Ad Hoc networks （VANET）. An accurate traffic flow model is crucial to traffic management of an intelligent transportation system （ITS）and information safety in a VANET. Based on Shanghai’s traffic flow data，the performance of the two different models was compared using auto regressive（AR）model and back-propagation（BP） network model，and the corresponding prediction result was given. Research finds that both of the two models can efficiently predict the traffic data，but they have different prediction accuracy for the data of different periods. The research result will provide support for future research on ITS and VANET.

To satisfy the requirement of high wireless transmission rate from user terminals, the multi-cell cooperative communication is an important solution for cellular networks. The selection of cooperative cells is usually depended on every wireless single cellular coverage boundary in cellular networks. In this paper, we analyze the characteristics of wireless single cellular coverage boundary based on real measured data. Moreover, the alpha-stable processes are first proposed to model the wireless single cellular coverage boundary. Simulation results indicate that probability density functions of wireless single cellular coverage boundary can be fitted by alpha-stable processes with 95% confidence interval based on the real measured data. This result provides a basis for cooperative communications considering the anisotropic fading characteristic of wireless signal propagation in wireless single cellular coverage region.

The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility.

Fog-based MANET (Mobile Ad hoc networks) is a novel paradigm of a mobile ad hoc network with the advantages of both mobility and fog computing. Meanwhile, as traditional routing protocol, ad hoc on-demand distance vector (AODV) routing protocol has been applied widely in fog-based MANET. Currently, how to improve the transmission performance and enhance security are the two major aspects in AODV’s research field. However, the researches on joint energy efficiency and security seem to be seldom considered. In this paper, we propose a source anonymity-based lightweight secure AODV (SAL-SAODV) routing protocol to meet the above requirements. In SAL-SAODV protocol, source anonymous and secure transmitting schemes are proposed and applied. The scheme involves the following three parts: the source anonymity algorithm is employed to achieve the source node, without being tracked and located; the improved secure scheme based on the polynomial of CRC-4 is applied to substitute the RSA digital signature of SAODV and guarantee the data integrity, in addition to reducing the computation and energy consumption; the random delayed transmitting scheme (RDTM) is implemented to separate the check code and transmitted data, and achieve tamper-proof results. The simulation results show that the comprehensive performance of the proposed SAL-SAODV is a trade-off of the transmission performance, energy efficiency, and security, and better than AODV and SAODV.