CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Analysis of handoff in distributed mobile communications system based on remote antenna unit selection

A remote antenna unit （RAU） selection model is presented, and two kinds of handoffs, intra-cell handoff （HO） and inter-cell HO, are defined in distributed mobile communications systems （DAS）. After that, an inter-cell HO model is proposed, in which the average power of the active set （AS） is used to predict the position of the mobile station （MS）. The total power of the AS and the handoff set （HOS） are utilized to determine whether an inter-cell HO is necessary. Furthermore, the relationship between HO parameters and performance metrics is studied in detail based on RAU selection. Simulation results show that both the intra-cell HO and the inter-cell HO can achieve oerfect performance by aoprooriate settings of HO parameters.

Finite sensor selection algorithm in distributed MIMO radar for joint target tracking and detection

Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

Distributed Secure Relay Selection Approach over Wireless Cooperative Networks

Cloud computing is always adopted to enhance the computing capability of mobile systems, especially when the mobile users prefer to use some computation intensive applications. Consequently, the distributed wireless relay infrastructure should be deployed to aid the traffic transmission. To further enhance the QoS provisioning goals of wireless cooperative network, this paper puts forward a multi-objective approach for distributed optimal relay selection, which takes Bit Error Rate (BER) and Secrecy Capacity (SC) into account simultaneously. Firstly, our proposal partitions the channel state into several levels according to the received signal-to-noise ratio (SNR) and describes the time-varying Rayleigh fading channel characteristics by using first order finite-state Markov model. Secondly, we model the relay selection as Restless Multi-armed Bandit optimal solution problem with respect to the channel state and the state transition probability. Finally, simulation results demonstrate the efficiency of the proposed approach which outperforms the existing ones.

Joint Antenna Selection and Robust Beamforming Design in Multi-cell Distributed Antenna System

Most of studies on Distributed Antenna System(DAS) focus on maximizing the sum capacity and perfect channel state information at transmitter(CSIT).However,CSI is inevitable imperfect in practical wireless networks.Based on the sources of error,there are two models.One assumes error lies in a bounded region,the other assumes random error.Accordingly,we propose two joint antenna selection(AS) and robustbeamforming schemes aiming to minimize the total transmit power at antenna nodes subject to quality of service(QoS) guarantee for all the mobile users(MUs) in multicell DAS.This problem is mathematically intractable.For the bounded error model,we cast it into a semidefinite program(SDP) using semidefinite relaxation(SDR) and S-procedure.For the second,we first design outage constrained robust beamforming and then formulate it as an SDP based on the Bernstein-type inequality,which we generalize it to the multi-cell DAS.Simulation results verify the effectiveness of the proposed methods.

Systematic-bit-selection distributed with selective-relaying

An improved distributed turbo coding （DTC） scheme, namely, systematic-bit-selection DTC, is proposed for a two-hop relay network implementing selective-relaying （SR）. In the scheme, source broadcasts a punctured turbo code in order to increase the successful decoding rate of relay. The relay forwards systematic-bit to the destination in case of successful cyclic redundancy check （CRC）. From the two versions of systematic-bit coming from relay and source respectively, the des- tination selects the one with higher signal-to-noise ratio （SNR） for turbo decoding. Simulation re- suits show the improved bit error rate （BER） performance of our scheme.

Symbol Error Probability of Incremental Relaying with Distributed/Centralized Relay Selection

In this paper,we derive the Symbol Error Probability(SEP)of cooperative systems with incremental relaying and Distributed Relay Selection(DRS).The relays remain idle when the Signal to Noise Ratio(SNR)between the source and destination is larger than T.Otherwise,we activate a relay using DRS.Relay nodes transmit only if their SNR is larger than thresholdβ.If the SNRs of more than two relays is larger thanβ,there is a collision and the destination uses only the received signal from the source.If all relays have SNR less thanβ,no relay is chosen.Thresholdβis optimized to yield the lowest SEP at the destination.Our results are compared to centralized relay selection using opportunistic Amplify and Forward(OAF),Partial and Reactive Relay Selection(PRS and RRS).We compare our results to computer simulations for Rayleigh fading channels.

Adaptive path selection for the improved distributed mobility management

Current mobility management schemes usually represent centralized or hierarchical architectures,which force data traffic to be processed by a centralized mobility anchor.This allows the mobile node(MN)to be reachable anywhere and provides an efficient method for seamless session continuity.However,all of the signal messages and data traffic converge on particular mobility anchor,which causes excessive signaling and traffic at the centralized mobility anchor and single point of failure issues as data traffic increases.To overcome these limitations and handle increasing data traffic,the distributed mobility management(DMM)scheme has emerged as an alternative solution.Although previous researches have been conducted on DMM support,because their schemes employ an unconditional way to make direct paths after handover,they have some drawbacks,such as several signaling and chain of tunneling problems.Therefore,this paper introduces a new DMM scheme which adaptively creates a direct path.To support it,we present the path selection algorithm,which selects the most efficient path between a direct path and no direct path based on routing hops and traffic load.Through the performance analysis and results,we confirm that the proposed scheme is superior in terms of signaling and packet delivery costs.

Dynamic Relay Selection Method for Distributed MIMO Relay System

The research on distributed MIMO relay system has been attracting much attention. In this paper, a decode-and-forward scheme distributed MIMO relay system is examined. For upper bound of channel capacity, the distance between transceivers is optimized when the propagation loss is brought close to actuality. Additionally, the number of relay is optimized whether total antenna element is fixed or not. When the number of relay is assumed to be infinite, the dynamic relay selection method based on the transmission rate is proposed. We represent that with the proposed method, the transmit power and the number of relays are saving.

Antenna selection based on large-scale fading for distributed MIMO systems

An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.

Distributed Optimal Variational GNE Seeking in Merely Monotone Games

In this paper, the optimal variational generalized Nash equilibrium(v-GNE) seeking problem in merely monotone games with linearly coupled cost functions is investigated, in which the feasible strategy domain of each agent is coupled through an affine constraint. A distributed algorithm based on the hybrid steepest descent method is first proposed to seek the optimal v-GNE. Then, an accelerated algorithm with relaxation is proposed and analyzed, which has the potential to further improve the convergence speed to the optimal v-GNE. Some sufficient conditions in both algorithms are obtained to ensure the global convergence towards the optimal v-GNE. To illustrate the performance of the algorithms, numerical simulation is conducted based on a networked Nash-Cournot game with bounded market capacities.

Fault Line Selection Method Considering Grounding Fault Angle for Distribution Network

In the distribution network system with its neutral point grounding via arc suppression coil, when single-phase grounding fault occurred near zero-crossing point of the phase voltage, the inaccuracy of the line selection always existed in existing methods. According to the characteristics that transient current was different between the fault feeder and other faultless feeders, wavelet transformation was performed on data of the transient current within a power frequency cycle after the fault occurred. Based on different fault angles, wavelet energy in corresponding frequency band was chosen to compare. The result was that wavelet energy in fault feeder was the largest of all, and it was larger than sum of those in other faultless feeders, when the bus broke down, the disparity between each wavelet energy was not significant. Fault line could be selected out by the criterion above. The results of MATLAB/simulink simulation experiment indicated that this method had anti-interference capacity and was feasible.

Comparison of Block Design Nonparametric Subset Selection Rules Based on Alternative Scoring Rules

This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.

Mode Selection Strategy of Dual Distribution Channel Based on Revenue Analysis

Within the scope of dual distribution channel(DDC)modes—ME&T-C and M-T&E-C,a game model designed for channel members was proposed.Based on this game model,the game equilibrium under both centralized and decentralized decisionmaking situations was analyzed,the channel members' and overall revenues of two modes under the same decision-making situation are compared,and the influence of demand shift coefficient to the overall and members' revenue was also studied through example analysis.Based on the comparison and analysis of the revenue yielded from the two DDC modes,it's discovered that within a certain hypothetical range,the M-T&E-C mode seems to be a better option for the manufacturer than the ME&T-C mode.Therefore,this discovery can be served as a theoretical reference for manufacturers when choosing the optimal DDC mode in real life.

Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature selection.This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values.Further,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating scheme.This strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI repository.IGNDO is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected features.Since the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best.

Fabrication of 32 Gb/s Electroabsorption Modulated Distributed Feedback Lasers by Selective Area Growth Technology

A 32 Gb/s monolithically integrated electroabsorption modulated laser is fabricated by selective area growth technology. The threshold current of the device is below 13mA. The output power exceeds 10mW at 0V bias when the injection current of the distributed feedback laser is 100mA at 25℃. The side mode suppression ratio is over 50 dB. A 32Gb/s eye diagram is measured with a 3.SVpp nonreturn-to-zero pseudorandom modulation signal at -2.3 V bias. A clearly opening eyediagram with a dynamic extinction ratio of 8.01 dB is obtained.

A Fast Global Node Selection Algorithm for Bearings-only Target Localization

In the target tracking, the nodes aggregate their observations of the directions of arrival of the target. The network then uses an extended Kalman filter （EKF） to combine the measurements from multiple snapshots to track the target. In order to rapidly select the best subset of nodes to localize the target with the minimum mean square position error and low power consumption, this paper proposes a simple algorithm, which uses the location information of the target and the network. The lower botmd of localization error is utilized according to the distances between the target and the selected active nodes. Furthermore, the direction likelihoods of the active nodes is predicted by way of the node/target bearing distributing relationships.

Protection of Distributed Generation: Challenges and Solutions

Distributed generation (DG) is the future of energy. This technology allows the bidirectional flow of power within an electrical network. Researchers are faced with many challenges to the accurate implementation of protection schemes for DG-connected distribution network. The schemes designed must satisfy the performance requirements of selectivity, reliability, and sensitivity. Most researchers opine that conventional protection schemes based on over current detection are insufficient to completely and accurately protect a DG-connected distributed power system. There are many challenges?that?need to be tackled before embarking upon the journey to successfully implement these schemes. This paper summarizes the major challenges which one can encounter while designing protection schemes for DG-connected distribution networks. Some possible solutions from the literature are also mentioned. Moreover, a suggested solution for protecting future active distribution networks is provided. It is expected that this paper will act as a benchmark for future researchers in this field to tackle the challenges related to the protection of active distribution networks.

Joint resource allocation scheme for target tracking in distributed MIMO radar systems

A joint resource allocation scheme concerned with the sensor subset,power and bandwidth for range-only target tracking in multiple-input multiple-output(MIMO)radar systems is proposed.By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them,this algorithm can help achieving a better performance within the same resource constraints.Firstly,the Bayesian Cramer-Rao bound(BCRB)is derived from it.Secondly,a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived.Thirdly,the optimization problem involved with three variable vectors is formulated,which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector.Then,the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation(SPCA)algorithm.Finally,the validity of the proposed method is demonstrated with simulation results.