# Wireless Networks

Online ISSN: 1572-8196
Recent publications
Article
Orthogonal frequency division multiplexing (OFDM) has been investigated for the high-speed transmission of data in radio frequency and optical wireless communications. The OFDM systems usually experience high amplitude variations called peak-to-average power ratio (PAPR). The high PAPR makes non-linear distortion and performance degradation because of clipping the signal. To alleviate the high PAPR, we introduce a new technique based on the compressive sensing approach. In the offered method, the OFDM signal is compressed in the time domain and then transmitted. At the receiver, a G-LASSO (group least absolute shrinkage and selection operator) recovery algorithm is applied to reconstruct the original signal. The reconstruction accuracy of the suggested G-LASSO algorithm is compared with the original LASSO algorithm. Numerical results indicate the effectiveness of the offered approach in terms of PAPR reduction and bit error rate performance.

Article
• Vu-Duc Ngo
• Thien Van Luong
• Nguyen Cong Luong
• [...]
• Xuan-Nam Tran
This paper proposes a novel spread spectrum and sub-carrier index modulation (SS-SIM) scheme, which is integrated to orthogonal frequency division multiplexing (OFDM) framework to enhance the diversity over the conventional IM schemes. Particularly, the resulting scheme, called SS-SIM-OFDM, jointly employs both spread spectrum and sub-carrier index modulations to form a precoding vector which is then used to spread an M-ary complex symbol across all active sub-carriers. As a result, the proposed scheme enables a novel transmission of three signal domains: SS and sub-carrier indices, and a single M-ary symbol. For practical implementations, two reduced-complexity near-optimal detectors are proposed, which have complexities less depending on the M-ary modulation size. Then, the bit error probability and its upper bound are analyzed to gain an insight into the diversity gain, which is shown to be strongly affected by the order of sub-carrier indices. Based on this observation, we propose two novel sub-carrier index mapping methods, which significantly increase the diversity gain of SS-SIM-OFDM. Finally, simulation results show that our scheme achieves better error performance than the benchmarks at the cost of lower spectral efficiency compared to classical OFDM and OFDM-IM, which can carry multiple M-ary symbols.

Article
• Xiaowei Shao
• Yajun Guo
• Yimin Guo
Wireless medical sensor networks (WMSNs) play a major role in remote medical monitoring systems. Generally, in a WMSN, professionals need to obtain real-time physiological data of patients, and these data often encounter various security and privacy issues during the transmission process. Thus, the secure transmission of data is particularly critical. To ensure data security and patient privacy, many authentication schemes have been proposed. However, most of the existing schemes either cannot withstand known attacks (such as privileged-insider attack, desynchronization attack, etc.) or require more communication and computation costs, and are not suitable for resource-constrained WMSNs. Therefore, this paper proposes a new anonymous physically unclonable function (PUF)-based authentication protocol for WMSNs by using PUFs, fuzzy extractor, cryptographic one-way hash functions, and bitwise XOR operations. Formal security analysis under the real-or-random model shows that this scheme is provably secure. And informal security analysis shows that our scheme is secure against various known attacks. At the same time, compared with other existing related schemes, the proposed scheme not only provides more security and functionality features, but also requires less communication (5360 bits) and computation costs (57.047 ms).

Article
To prevent detection of communication between legitimate users by warden and guarantee a strong security in wireless networks, covert communication technique is recommended. The covert communication is based on hiding the main message in the background of noise which poses challenges such as low transmission rate. On the other hand, the transmit power in the conventional networks is supplied by separate built-in batteries, which are sometimes hard to be recharged or replaced especially in the military applications. In this work, to tackle the low transmission rate, we employ three dimensions beamforming in which we can focus the main beam on the legitimate user and minimize the leakage information at the warden. Moreover, in the proposed network, we intend to employ cooperative jamming (CJ) to increase covert rat in which the jammer is equipped to multiple antennas. Furthermore, to tackle the recharging or replacing batteries in the proposed network, we employ the energy harvesting (EH) technique. The simulation results reveal that the energy harvesting from the jammer’s signal reduce the total power consumption. Moreover, the simulation results show that the optimality gap is about 50% according to energy harvesting technique, and the 3D beamforming increases covert rate about 70% compared to two-dimensional beamforming method.

Article
In the early stage of large-scale disasters, the first batch of emergency supplies are often in short supply, and decision-makers responsible for material distributions need to send emergency materials to the recipients in the shortest possible time, while also taking into account the minimum transportation costs. In these scenarios, the traditional particle swarm algorithm has been frequently used, however it faces the challenge of “precocious puberty" and is unable to resolve the scheduling problem. To solve this issue, this paper proposes an optimization model for material dispatch in emergency events using a non-dominant sorting algorithm for vehicular communication. The model first satisfies the shortest delivery time and material demand, establishes the shortest route for vehicle travel, and then proposes a multi-objective uncontrolled solving ant colony algorithm to break through the bottleneck of the juvenile algorithm by solving the problems of convergence of NSGA-II algorithm and uneven distribution of Pareto front surface. Moreover, the objective function and constraints for vehicles at each emergency supply point are defined, which must not exceed the total number of available vehicles. The case study shows that the Pareto non-inferior solution searched by NSGA-II is ideal under the premise that multiple goals are optimal, and the Pareto non-inferior solution scheme available for researchers to choose is improved. The model and algorithm objectively optimize the overall layout of emergency material distribution.

Article
In this paper, we consider a multi-source decode and forward cooperative network coding (NC) relay system based on single-carrier zero-padded (SC-ZP) transmission scheme. All channels are L-tap frequency selective with Rayleigh fading. The cooperative maximum ratio combining (C-MRC) and the selection relaying combiners are applied and investigated at the destination, separately. We analyze the performance of proposed method in terms of symbol error probability at high signal-to-noise ratio regime, by adopting maximum likelihood (ML) and zero forcing (ZF) detectors. We prove that the considered system using the C-MRC and the ML detector, successfully exploits maximum achievable multhipath and cooperative diversity gain of 2L. To reduce the ML decoding complexity which increases with the number of sources, linear low complexity detection methods are used even though they usually do not succeed in collecting the receive diversity gain. We analytically demonstrate that proposed SC-ZP based NC system using the linear ZF detector achieves full diversity gain as like as the ML for arbitrary modulation schemes and number of sources. Moreover, an optimal NC mapping coefficients has been designed to maximize the system coding gain. Also, we extend proposed single relay NC system to multi-relay cooperative network. Single relay selection protocol is implemented based on a proposed selection criterion. In addition, simulation results illustrate that the proposed NC system employing SC-ZP technique and without need of any channel coding outperforms its competitors about 1 dB, also the processing time is reduced up to 80 percent.

Article
In most applications of wireless sensor networks the specification of the corresponding topology can be useful for the optimization of some important features, such as: node energy consumption, connectivity and coverage area. This is known as the Sensor Allocation Problem (SAP). Our work proposes an approach based on memetic algorithm concepts to find high-quality solutions. In our approach, each node can be associated with one of four operation modes (classified according to its maximum range). The algorithm optimizes the position of each node and produces solution clusters. In order to evaluate the efficiency of the method, we analyze case studies with different coverage areas that are then compared against results previously found in the literature. Our experiments show that in order to achieve a smaller energy consumption and an increase in network coverage area, one needs to operate with a sizeable number of sensors, but with few nodes operating in larger transmission power modes (which require an increased energy expenditure).

Article

Article
As more vehicles are being connected to the Internet and equipped with autonomous driving features, more robust safety and security measures are required for connected and autonomous vehicles (CAVs). Therefore, threat analysis and risk assessment are essential to prepare against cybersecurity risks for CAVs. Although prior studies have measured the possibility of attack and damage from attack as risk assessment indices, they have not analyzed the expanding attack surface or risk assessment indices that rely upon real-time resilience. This study proposes the PIER method to evaluate the cybersecurity risks of CAVs. We implemented cyber resilience for CAVs by presenting new criteria, such as exposure and recovery, in addition to probability and impact, as indices for the threat analysis and risk assessment of vehicles. To verify its effectiveness, the PIER method was evaluated with respect to software update over-the-air and collision avoidance features. Furthermore, we found that implementing security requirements that mitigate serious risks successfully diminishes the risk indices. Using the risk assessment matrix, the PIER method can shorten the risk determination time through high-risk coverage and a simple process.

Article
As the processing power of mobile terminals increases, wireless network applications such as voice assistants can put more context-sensitive tasks on the mobile terminals, thus reducing the wireless network bandwidth needed and the cost of data storage in the cloud. Co-reference annotation, identifying the same semantics in context, is one of the critical techniques in these tasks. However, there are some problems with the existing co-reference annotation standards. First, the annotation is incomplete. Second, the types of annotated mentions are inconsistent. Third, there are currently no metrics for the above characteristics. Analyzing the above-mentioned issues, this paper proposes a new co-reference annotation standard. The new standard can annotate more semantics and co-reference relations and only adopts two types of mentions for annotation. Meanwhile, this paper presents a performance evaluation corpus and designs three performance metrics for evaluating the new standard according to the completeness of semantic annotation, the completeness of co-reference annotation, and the consistency of mention. The experiment shows that the new standard outperforms all the baseline methods and achieves 0.95 in the completeness of semantic annotation, 0.68 in the completeness of co-reference annotation, and 0.57 in the consistency of types of mentions.

Article
To make the network more reliable and to address energy imbalance issues the cooperative selection of dynamic relay beamforming and energy balanced operation is proposed. Statistic Autonomous Beamforming (SAB) selection of nodes includes picking dynamic relay’s that is used to transmit data. SAB requires very low feedback load, and this strategy is applied for multi user methods like Multiple Input Multiple Output (MIMO). SAB based cooperative scheme is applied by the base station where maximum number of dynamic beams is selected for the active transmission. With the help of transmission power base station can increase the energy harvest at the receiving hub by focussing the power utilized for data transmission on dynamic relay beams. A close-form statistical distribution is derived to calculate the amount of energy harvested in the selected dynamic beams with respect to MIMO users. The performance trade off of average harvested energy, secrecy rate and residual energy of the dynamic relays and the sum rate of multiple MIMO users are analysed. The energy consumption for the proposed scheme is 28% better compared to the existing methods.

Article
This paper proposes an overlay network wherein a pair of licensed source (LS) and licensed destination (LD) is adaptively assisted by a pair of unlicensed source (US) and unlicensed destination (UD). By processing adaptively at US and UD relied on the decoding statuses of LD and US, licensed communication is always supported with a high diversity gain from which unlicensed communication also benefits from removing licensed interference as well as transmitting with the highest available power, eventually improving reliability of both unlicensed and licensed communication. This paper also proposes closed-form formulas of outage probabilities at LD and UD for prompt reliability evaluation over κ-μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa -\mu$$\end{document} shadowed fading channels. Multiple results illustrate the superiority of the proposed adaptive processing scheme as compared to its counterpart. Further, the reliability of the suggested scheme can be flexibly controlled and optimized with setting specifications reasonably.

Article

Article
Non-orthogonal multiple access (NOMA) is very promising for the future wireless communication systems. The primary goal of this paper is to provide precise outage probability formulas for downlink-NOMA-based communication systems over non-homogeneous fading scenarios. The outage probability of NOMA is analyzed over κ-μ,α-κ-μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa -\mu , \alpha -\kappa -\mu$$\end{document} faded and shadowed faded channels. Other fading channels result as special cases of this analysis. The closed-form expressions of outage probability are derived in a more realistic scenario considering the imperfect-channel state information (CSI) and successive interference cancellation, intra and inter-cellular interference. The derived expressions are very beneficial to asses the performance of NOMA with different fading parameters. We calculate the outage probability for two users in a Voronoi cell: near user and edge user. The obtained results are very promising, when compared with the simulated NOMA based wireless communication system. It is observed that the impact of imperfect-CSI on both users is the same. The effect of inter-cell interference is not a serious concern for the MSs which lie at the proximity of the BS. However, the performance of the MS which lies at the edge of the cell is degraded with the number of interferers.

Article
Beyond five generation (B5G) systems will demand strict and heterogeneous service requirements for the emerging applications. One solution to meet these demands is the dense deployment of small base stations to provide more capacity and coverage. However, this will lead to high power consumption and greenhouse emissions. Therefore, the resource control policies need to adapt to these network fluctuations to balance the power consumption and meet these demanding requirements. One approach is to implement intelligent algorithms for resource management, such as deep reinforcement learning models. These models can adapt to network changes and unknown conditions. However, while these models adjust to the new requirements, the performance is degraded due to state-space exploration. Therefore, accelerating the learning process is needed to minimize this performance degradation in dynamic environments. One of the approaches to address the above is to transfer the knowledge of other models to improve the learning process. This paper implements a training strategy in an ultra-dense network for power control. The method consists of reusing the previous experiences of models to train new models in complex environments, such as environments with more agents. We evaluate our proposal via simulation. The numerical results demonstrate that adding experiences to the buffer can accelerate the decision on power allocation to increase the network’s performance.

Article
With the continuous maturity and adoption of mobile devices enabled by wireless communication technology, people are more apt to record their sport exercise data or healthcare data through various lightweight and smart devices, e.g., mobile phones and smart watches. Meanwhile, massive sport data or healthcare data keep being produced with time, which forms a main source of big healthcare data. Deep mining and analysis of such healthcare data are of positive significance for accurately recognizing the real-time health condition of mobile users and further recommend appropriate sport items to them. However, traditional centralized healthcare data mining and recommendation approaches require mobile users to transmit their health data collected by mobile devices to a remote cloud platform, which often involves heavy data transmissions from mobile devices to cloud platform. As a consequence, the transmission cost is high and the time delay is long. Moreover, long-distance data transmissions are prone to disclose user privacy. Considering these limitations, we bring forth a novel time-efficient and privacy-preserving healthcare data integration and mining approach for sport item recommendation, based on edge-cloud collaboration mechanism. At last, we design a group of simulation experiments to validate the effectiveness and efficiency of our approach. Experimental comparisons indicate a good balance between different evaluation metrics.

Article

Article
Automatically increases the danger of starting fires due to global warming, and the number of forest fires is growing. Natural disasters are a regular occurrence in which both living and nonliving things in the environment are affected. Humans, on the other hand, can anticipate disaster and take significant measures to avoid it. There are a variety of technologies that can predict abnormalities or changes in exposed open areas. As autonomous sensors, Wireless sensors and Actuator Networks are used to monitor physical and environmental conditions. Though WSANs applications are being implemented with various location prediction techniques, there are still issues predicting more accurate data. The work mainly aims at identifying the abnormalities in certain areas, given location and distance information to wireless sensor networks. The proposed system includes the phases such as network construction, cluster head (CH) node selection, forest fire prediction, and data transmission. In the network construction, sensor nodes and actuator nodes are associated with sending and receiving the packets.The energy model and mobility model are built to provide efficient packet transmission over WSAN. Then the improved firefly algorithm (IFFA) is designed to select the best CH node depending on better energy utilization, delay, and lifetime between sensor nodes. Enhanced nearest neighbor interpolation with semi-supervised algorithm (ENNISSA) ENNISSA is proposed for a better forest fire prediction model using nearest neighbor interpolation (NNI) method and support vector machine (SVM) & K-means clustering (KMC) algorithm. To predict the oddity, the temperature, positions of heat sources, and pressure around that location are considered for retrieving optimized data using interpolation techniques.The semi-supervised classifier [high active (HA), medium active (MA), and low active (LA)] is presented to resolve this issue by separating the forest area into various zones. The significance of the work is to predict more accurate information of anomalies in uncovered regions. As a result, the suggested ENNISSA method is superior to existing techniques for energy consumption, throughput, end-to-end delay, network lifespan.

Article
Energy efficiency and data gathering are the primary goals of wireless sensor networks (WSNs), challenging. Mobile sink and mobile chargers are two promising techniques for data collection by visiting (a set of) sensor nodes and energy replenishment, respectively. These two operations joined in a single mobile element for data gathering and energy replenishment simultaneously to prolong the energy efficiency in WSNs. However, it is challenging to identify the optimal set of anchor points and their visiting order. In this context, this paper proposed an efficient algorithm called Joint Mobile Wireless Energy Transmitter and Data Collector (J-METDC) to mitigate the above-discussed challenges. The J-METDC algorithm uses the Spectral clustering (SC) algorithm to partition the network where the centroid is in the communication range of the mobile element to recharge or data gathering purposes. Next, a cat swarm optimization (CSO) algorithm decides the order of visiting to recharge an SN by trade-offing the available number of packets and residual energy. The SC executes once, whereas the CSO is performed multiple times depending on the request received from any partition. The SC and CSO are lightweight algorithms over the traditional machine learning and swarm intelligence algorithms. So, these two algorithms address the challenge with quick decisions by providing the optimal solution.

Article
As one of the most popular IoT (Internet of Things) devices, smartphone stores sensitive personal information. As a result, authentication on smartphones attracts widespread attention in recent years. Sensor-based authentication methods have achieved excellent results due to their feasibility and high efficiency. However, the current work lacks comprehensive security verification, undetected potential vulnerabilities are likely to be leveraged to launch attacks on these authentication approaches. We propose a novel attack to evaluate the reliability and robustness of the existing authentication methods. The basic idea behind our strategy is that the system has its authentication error; we elaborately analyze the false-negative samples to summarize its vulnerable properties and leverage such vulnerabilities to design our attack. The experiment result proves the feasibility of our attack and also demonstrates the drawbacks of the existing approaches. In addition, we propose a corresponding protect approach to defend against this attack, of which the scheme has the self-learning ability to update according to the newly detected attacks. Compared with authentications using multiple sensors, we only adopt a single accelerometer to achieve better performance, showing the convenience and effectiveness of our system.

Article
Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet’s frame rate from 71.2 to 89.17 and VGG19’s frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.

Article
Mobile Ad-Hoc Networks (MANETs) are useful and appropriate, especially for complex scenarios, including law, military enforcement as well disaster recovery, and emergency rescue. Among different cryptographic algorithms, there is a chance of data hacking according to random key generation. The major purpose of this work is to plan and implement a new security protocol in MANET that is applicable for IoT platforms. The approach focused on this research is the modified chaotic map for encryption and decryption to deal with MANET and IoT data. The privacy preservation model is designed with a suggested cryptographic algorithm for enhancing the privacy and security of the MANET and IoT. An enhanced chaotic map is developed for processing the key generation that maintains the encryption and decryption process and prevents the loss of data while recovering the data. The adaptive key management strategy under the chaotic map is developed in this paper using a hybrid meta-heuristic algorithm named Crow Harris Hawks Search Optimization (CHHSO). Finally, the security analysis and performance evaluation in comparison is made in terms of “statistical analysis, convergence analysis, and communication overhead” that show the improved performance of the proposed model.

Article
Recently, academia and industry have shown keen interest in achieving ultra-reliable and low latency communication (URLLC) through short-packet communication to meet the strict demands concerning high reliability and latency for 5G and beyond applications. Un-manned aerial vehicle (UAV) has caught attention recently because of its cost, air time, and mobility, whereas, the non-orthogonal multiple access (NOMA) technique has proven effective in dense user network. Hence, a UAV based system with NOMA has been studied in this paper for remote coverage. In this system, devices communicate to the base station (BS) through a UAV relay where direct link from devices to BS is absent. As UAV has direct line of sight (LOS) to the remote devices, hence, all the analysis is done over a Rician fading channel. In this work, a closed-form expression of average block error rate (BLER) has been formulated for the given system model, which is used as performance metric to analyse the system performance. Moreover, exact BLER expression facilitates in optimization with partial channel state information (CSI) only, which reduces the latency and complexity. Furthermore, we derive an asymptotic expression for BLER in high signal to noise (SNR) regime. Also, a blocklength minimization problem is formulated and optimized with reliability constraints. Simulation results are presented to verify analytical work, as well as comparison of the results with orthogonal multiple access scheme are also shown.

Article
Heterogeneous cellular networks are a viable solution in response to the growing demand for broadband services in the new-generation wireless networks. The dense deployment of small cell networks is a key feature of next-generation heterogeneous networks aimed at providing the necessary capacity increase. However, the approach to apply green networks is very important especially in the downlink because uncontrolled deployment of too many small-cells may increase operational costs and emit more carbon dioxide. In addition, given the novel services and resource limitation of the user layer, energy efficiency and fairness assurance are critical issues in the uplink. Considering the uplink fairness criterion, this paper proposes a dynamic optimization model which maximizes the total uplink/downlink energy efficiency in addition to providing the essential coverage and capacity of heterogeneous cellular networks. Based on the non-convex characteristics of the energy efficiency maximization model, the mathematical model can be formulated to two subproblems, i.e., resource optimization and user association. So that, a subgradient method is applied for fair resource management and also successive convex approximation and dual decomposition methods are adopted to solve the proportional fairness problem. The simulation results exhibit considerable throughput increase by 30% and 22% on average for random and hotspot user distributions, respectively. It also proved that the proposed approach managed to significantly improve the total network energy efficiency by up to 35%.

Article
In Wireless Body Area Networks (WBANs), on the one hand, the energy of nodes is limited. On the other hand, the network topology often changes due to human movement or posture changes. Unstable network topology is easy to cause packet loss, and packet loss will cause inaccurate data collection. Therefore, how to effectively use energy to transmit data reliably becomes a key issue. For this problem, we propose an optimized routing protocol namely Energy Efficient and Reliable Routing based on Reinforcement Learning and Fuzzy Logic (EERR-RLFL). In EERR-RLFL, considering the heterogeneity of nodes in WBANs, we first establish a node rank division mechanism, by which sensor nodes are divided into different ranks from three aspects. Each rank is considered to be one of the factors that affect the link quality. Then, we propose the Fuzzy-Logic-based Link Quality Evaluation (FLLQE) algorithm. It makes use of the fuzzy evaluation method of fuzzy logic and considers the comprehensive influence of multiple factors to evaluate the link quality between two nodes, which will provide reference for routing path selection. In the process of data transmission, based on the FLLQE algorithm, we use a hybrid data transmission mode, in which the time when a forwarding node is needed is first determined, and then the Reinforcement Learning algorithm is used to select the global optimized routing path. Simulation results show that EERR-RLFL outperforms Single Hop Transmission and Optimized Cost Effective and Energy Efficient Routing in terms of network lifetime, packet loss ratio and energy efficiency.

Article
Orthogonal frequency division multiplexing (OFDM), one of the most dominant technology for fifth-generation (5G) wireless communication systems offers a high data rate with better spectrum efficiency. However, OFDM based systems suffer from various channel imperfections such as channel distortion, carrier frequency offset (CFO) due to transceiver local oscillator frequency mismatch, sampling frequency offset (SFO) between DAC/ADCs, nonlinear distortion (NLD) due to the nonlinear power amplifier, and fading caused by frequency selective channels. Recently OFDM based non-orthogonal multiple access (NOMA) scheme is attracting researchers’ attraction owing to its high spectral efficiency, massive connectivity, and resilience to frequency selectivity for the upcoming 5G wireless communication and beyond. However, NOMA-OFDM systems suffer the same demerits as OFDM-based systems to deploy such a multicarrier system for 5G and beyond applications. This paper studies the problem of various channel imperfections on OFDM and OFDM-NOMA systems and proposes machine learning (ML) based estimation and signal detection for OFDM and NOMA-OFDM based systems in presence of the aforementioned channel imperfections. Various ML models like neural network (NN), Recurrent-NN, long-short-term-memory are adopted in this research for channel estimation-equalization, CFO-SFO estimation and compensation and mitigation of NLD in OFDM and NOMA-OFDM receiver. Extensive simulation studies show that the ML-based techniques outperform traditional LS, MMSE-based methods for OFDM receiver, and LS-SIC, MMSE-SIC based methods for NOMA-OFDM receiver.

Article
In this article, a novel ultra-high frequency radio frequency identification (UHF RFID) reader antenna is proposed and experimentally investigated. The proposed RFID antenna design consists of three layers; the first layer has a ring shape with a feeding line at the center, the second layer has a small periodic structure that affects the radiation performance of the antenna, and the third layer has a bent metal structure. The final design, with the dimensions of 129.2 mm × 129.2 mm × 26 mm, yields a 10-dB impedance bandwidth of 34 MHz (854–889 MHz) and a 3-dB axial-ratio bandwidth of 118 MHz (857–975 MHz). The antenna is fabricated and tested for its RF parameters and RFID metrics. In performance tests, ISO 18000–63 and Gen2v2 compatible RFID reader and tags are used. RFID tag reading tests have been successfully performed under specified communication protocols without any operational failure while providing RF communication. The measured results prove the technical potential of the proposed antenna to possess good RF performance in RFID based wireless networks.

Article
Wireless Networks (WNs) is a widely used technology that has found application in many fields due to their mobile and flexible nature. Many attempts have been made to secure the standard of WNs by utilizing useful security features. But due to the absence of an external robust defense mechanism such as Intrusion Detection Scheme (IDS), most of the time, the network fails to provide proper security to the application. To design an effective defense mechanism, the use of appropriate features is a must for any network. This article proposes an Optimized Maximum Correlation based Feature Reduction (OMCFR) technique for data networks. The proposed scheme utilizes maximum correlation as a major factor depending upon which individual rank is allocated to the features. The useful features are extracted using OMCFR for efficient detection. The selected features are utilized with multiclass classifier to classify the data into normal against intrusive activities. A Random Forest based multiclass classifier technique is utilized in the study. The standard dataset of Wireless Networks from the AWID family (2015), CICIDS2017 and NSL-KDD family is utilized to evaluate the proposed IDS. The results show promising performance with reduced False Positive Rate (FPR) (for NSL-KDD: 0.10, for AWID: 0.27), achieves high detection accuracy (for NSL-KDD: 99.95%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, for AWID: 99.2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}) and overall time complexity (for NSL-KDD: 182.5 s, for AWID: 812.45 s).

Article
Smart factories in harsh large-scale environments are achieved by installation of group-based industrial wireless sensor networks (GIWSNs), in which a group of sensors are deployed on each machine target, for security and saving deployment time. In GIWSNs, enormous data for production (e.g., 3D digital twin and automated optical inspection images) and surveillance is transmitted frequently among multiple machines, and consumes huge energy. Furthermore, a real-world factory whose radio environment is interfered by mobile devices is dynamic and uncertain. Therefore, this paper investigates reliable energy-efficient routing for surveillance in dynamic uncertain GIWSNs, and further proposes a fuzzy improved global-best harmony search approach, where improved operators are integrated to efficiently explore the search space locally and globally; and a fuzzy evaluation scheme is employed to address uncertain factors. Through simulation under various parameter settings, this approach can find reliable routing in dynamic environments, and shows high performance as compared with other approaches. In addition, this approach can always find the reliable surveillance routing under various data amounts, while activating fewer crucial sensors to effectively reduce energy consumption.

Article
The implementation of IoT in industrial management is referred as Industrial IoT (IIoT). It is used to increase the overall operational efficiency. IIoT is considered as the backbone of the contemporary industries. For this purpose, strong security foundations have to be deployed with IIoT. This accelerates the industrial automation process by enrolling thousands of IoT devices and thus befitting the scattered connection and constrained functionalities of the IoT devices. In the Industrial Internet of Things, abnormal traffic detection is an emerging dilemma in network security. It can be ensured by monitoring the traffic flow in large-scale networks. In this connection, the deep learning paradigm has been accepted as the mainstream phenomenon in the field of abnormal traffic detection. It has achieved optimal results due to the constant improvement of computer performance with the help of Artificial Intelligence. Majority of the existing methods are based on supervised learning. As a result, due to numerous constraints, obtaining and marking abnormal traffic data samples in real life is extremely difficult. In addition, due to the diversity and complexity of the abnormal network data, the adaptability of various detection methods is low as well as difficult to judge the new abnormal traffic. Based on the above problems, this paper has proposed a semi-supervised abnormal flow detection framework in the IIoT based network scenario. The proposed model is named Memory Augment Based on Generative Adversarial Network (MeAEG-Net). It detects anomalies by training only normal flow of sample data and comparing the reconstruction errors of the underlying characteristics of input flow of generator module. To improve the generator's training, generative adversarial network has been deployed to tackle the problem of the autoencoder which is susceptible to noise. The generator uses an autoencoder and decoder structure. In addition, the memory augments module is introduced to the autoencoder's sub-network to reduce the generator module's generalization ability and raise the abnormal traffic reconstruction error. Our experimental results show that the method proposed in this paper can achieve a good effect of abnormal traffic detection in IIoT based networks under the premise of learning normal traffic data samples and improving the overall performance in all sectors of the industry.

Article
Massive machine-type communications (mMTC) face spectrum scarcity problems at 5G mid-bands (i.e., sub 6 GHz) due to tens of billions of machine-type devices connected in a smaller region. The mMTC facing significant challenges are scalability and efficient connectivity. To enable cognitive radio (CR) in 5G mMTC technology to resolve issues and gives promising solutions. Dynamic spectrum assignment (DSA) is mainly used to solve spectrum scarcity issues that provide efficient connectivity to massive devices. Spectrum sensing (SS) is an integral part of the CR used to detect spectrum opportunities in the spectrum. Energy detection (ED) is a prominent sensing mechanism, and a threshold computation is essential to find accurate spectrum opportunities in the spectrum. The massive devices in mMTC communication send small messages and face short-term fluctuations during sensing at the sub 6 GHz bands. Hence, threshold calculation is a critical challenge in SS at sub 6 GHz bands that depend on two key factors: signal and noise variance. At 5G mid bands, the high random nature of the signal input gives poor accuracy to find spectrum holes under the assumption that the channel is either static or quasi-static in a non-line-of-sight (NLOS) scenario. Thus, a novel spectrum sensing with an efficient threshold over η-μ fading channel is proposed. The key implementation is the moving average energy (MAE) mechanism to minimize short-term fluctuations in signal energy computation and delay variance that reduces sensing delay under fading conditions. The noise variance is measured from the upper bound limit of the noise interval. The SS mechanism implemented on the real-time testbed and GNU radio processing blocks on 3.3–3.8 GHz frequency bands using universal software radio peripheral (USRP)-2953 RIO. The experimental outcomes show a high probability detection rate and low sensing delay at 5G mid-bands.

Article
Due to intelligent communication, smart grids have been further developed compared to traditional power grids. In order to compensate for the growing demand for quality of service (QoS) requirements, the cognitive radio sensor network (CRSN) is being adopted. Most of the prevailing methods fail to meet the user’s spectrum demand, which causes the delay in data transmission. Hence to improve the spectrum allocation in CRSN, the deep Q-probabilistic algorithm based rock hyraxes swarm optimization (RHSO) is proposed in this work. The channel requested user’s id is stored in Q-table that is placed in the sink node. RHSO selects the high priority user from Q-table that search for the early requested user in the Q-table. The vacant channel is detected by a deep probabilistic neural network (DPNN) based on the user request. The DPNN search for vacant channel based on sleeping and active time. The proposed method improves channel allocation with reduced time by using DPNN. The proposed method is implemented in the Matlab platform. The proposed method offers higher throughput of 280 kbps for 25 idle channels by reducing the latency to 2 ms and retransmission probability to 0.01. These performance measures show the efficacy of the proposed method in channel allocation.

Article
One of the biggest challenges of distributed software defined networks (SDNs) is to create load balancing on controllers to reduce response time. Although recent studies have shown that switch migration is an efficient method for solving this problem, inappropriate decision making in selecting the target controller and the high number of switch migrations among controllers caused a decrease of throughput with an average increase in response time of the network. In the proposed method, named GOP-SDN, in first place, using a variable threshold based on controllers, the congestion or imbalance of the load is detected. Subsequently, regarding the capacity of controllers and switches connected to them and using the intelligent combination of genetic algorithm and OPSO, GOPS-SDN tried to choose the best controller with appropriate capacity to migrate. In other words, using genetic algorithm with the highest fitness and then the OPSO algorithm and using the speed of each particle to move to the best overall and best locations, the best solution is calculated from the particle imported into PSO. In parallel with the implementation of the PSO algorithm, GOSP-SDN used the same algorithm to compute the best weights for each particle in the algorithm (OPSO). Therefore, the best and optimal solution among the particles to migrate to the controller is found. The results of the implementation and evaluation of GOP-SDN in the Cbench simulator and Floodlight controller showed improvement of 24.72% in throughput and the number of migration has been reduced by 13.96%.

Article
In the unmanned ship networking scenario, the position of the unmanned ship changes continuously, leading to the result that the desired transmitters and the interference transmitters may exchange identities at any time. The traditional interference alignment technique cannot solve the interference. To solve this problem, this paper proposes an adaptive interference alignment scheme based on the dynamic selection of the desired transmitters for the unmanned ship network. Firstly, according to the characteristics of the unmanned ship networking scene, a new measurement value the non-directional user signal to interference ratio W is proposed, and the desired user set and connection mode are selected through the calculation of W. Secondly, the corresponding interference alignment scheme is designed. The iterative interference alignment algorithm is used for non-directional users, while partial interference alignment algorithm is used for directional users, and the proof of algorithm convergence is given. Lastly, the scheme can dynamically select and update the desired user set and connection mode when the position of the unmanned ship changes, and the corresponding interference alignment scheme will also perform adaptive updates. Experimental results indicate that the scheme can select the optimal connection mode according to the current position of the unmanned ship and eliminate the interference caused by the undesired transmitter.

Article
One of the most important requirements for effective UAV–WSN operations is to perform data collection in timely and safe manner. Identifying an effective path in an environment with various obstacles and ensuring that the path may efficiently cover the selected stop points for effective data collection are both necessary and difficult. We propose a UAV-based effective data gathering scheme for wireless sensor networks with obstacles, where a UAV is employed as a mobile sink to collect data from the ground sensor nodes (EDGO). The main novelty includes a UAV–WSN collaborative approach for data gathering, which incorporates a convenient method for UAV trajectory design in a three-dimensional environment with obstacles. We propose an improved heuristic evolutionary approach based on genetic algorithm to determine the optimized trajectory for the UAV to gather data. In contrast to existing methods, the proposed method focus to reduce the length and angle cost of the path, minimize the energy consumption, and delay, and includes different evolutionary operations to generate a collision free path for UAV. Our approach retains the infeasible path through sufficient modifications, which improves the diversity of paths, so that it is possible to jump out of the local optima. The results reveal the effectiveness of the EDGO scheme against the other related approaches in terms of path cost and data collection efficiency. The network lifetime is extended by approximately 11% and offers a reduction of 42% and 35% in the UAV path length and travel time, respectively, when compared to the existing schemes.

Article
The proposed manuscript represents the novel approach to achieve the frequency reconfigurability of the microstrip patch antenna. The tunability is achieved with the FR-4 based low profile material using the PIN diodes. Performance is compared for the simple patch antenna, radome based patch antenna without metamaterial elements and differently shaped metamaterial elements. The presented design provides a minimum reflectance response of −52.20 dB for the reconfigurable circular-shaped metamaterial ring loaded patch antenna. The maximum bandwidth of 5160 MHz is achieved for the circular-shaped metamaterial ring loaded patch antenna. The maximum gain of 2.93 dB is achieved in the radome based patch antenna without metamaterial elements. Maximum tunability of 80 MHz is achieved. The maximum normalized directivity of 110° is achieved with the Square shaped metamaterial ring loaded patch antenna. Simulated results compared with the measured results for the confirmation.The presented design opens new applications in satellite communication, surveillance, wifi devices and manymore. The performance of the presented design also compared with the earlier published work.

Article
The Internet of things (IoT) provides an infrastructure to constructing smart cities. Through the installed IoT sensors in the city, a large amount of information of the city is detected and collected in a cloud center, which provides residents to have immediate and convenient services. To achieve the goal of smart cities, the fundamental challenge has been to minimize the energy consumption of the IoT sensor network, which is involved with the deployment and sleep scheduling of IoT sensors. Most of the previous related works proposed two-stage approaches to address deployment and sleep scheduling problems of IoT sensors separately. However, the deployment at the first stage influences the optimality of the sleep scheduling at the second stage significantly. Consequently, this work jointly considers deployment and sleep scheduling of IoT sensors in a smart city. To improve the performance of sleep scheduling of IoT sensors, the deployment at the first stage concurrently considers a part of the optimality of later sleep scheduling, and it is optimized by the proposed improved geometric selective harmony search algorithm that incorporates crossover and dynamic schemes. The crossover scheme can effectively solve the problem of different decision variables in the solution representation; and the dynamic scheme can increase stability and diversity of searching for solutions. The performance of the proposed algorithm is evaluated by the simulation under various combinations of covering requirements, the number of sensors, and practical parameter settings.

Article
LoRaWAN is a promising LPWAN technology for IoT connectivity. It offers long-range and wide-area communication at low-power, low cost and low data rate. LoRaWAN performance has been evaluated according to many features such as coverage, scalability, physical layer parameters, communication reliability and latency. Existing studies assume that the LoRaWAN end-devices are already connected to the LoRa NetServer. Therefore, the performance of LoRaWAN activation procedure has not been well investigated. In this work, we study the performance of LoRaWAN during the Over-The-Air activation procedure. This process enables a large number of end-devices to join the network before being able to exchange any kind of packets. Thus, we analyze the average activation delay and the average energy consumption for an end-device in a large scale LoRaWAN. To achieve this goal, we first implement the Over-The-Air activation procedure in ns-3, especially in the ’lorawan’ module and conduct extensive simulations. Then, we elaborate a mathematical model using Markov-chain to evaluate both the delay and the energy consumption analytically. Our study shows that in a LoRaWAN cell composed of 1000 end-devices, the average activation delay for an end-device is about 35 minutes and this activation requires an average of three join-packet transmissions and an average energy consumption of 0.0887J.

Article
Friendly spectrum jamming is a flexible scheme to establish secure communications among heterogeneous wireless devices without the need of encryption. Previous works have indicated that this scheme however has weak security strength against multiple antenna eavesdropper in today’s wireless communication systems, which limits its wide applicability. To tackle this challenge, we propose a novel modulation method, called energy modulation. The basic idea of our method is to keep the secrecy of the channel state information in modulation, so as to bring high uncertainty to the MIMO’s separation and the eavesdropper’s decoding. As a result, the security strength of friendly jamming notably increases facing multiple antenna eavesdropper. To demonstrate the effectiveness of our method, we perform independent component analysis to decouple the components of the measured signals with maximum likelihood separation. We find that our solution dramatically decreases the eavesdropper’s partial information and has much less bits being compromised comparing with common amplitude and phase modulation.

Article
This paper investigates the application of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) transmission in the fifth-generation (5G) of heterogeneous cellular networks (HetNets). Due to the high penetration loss of mmWave, we propose that a small cell base station (SBS) serves small cell user (SCU) equipment in the mmWave band, and a macrocell base station (MBS) serves macrocell users (MCUs) in the microwave band. Cell association, user grouping, and power allocation are fundamental challenges in NOMA-based transmission. We formulate optimization problems for SCU and MCU to maximize the energy efficiency (EE) subject to the required minimum data rates and maximum transmission powers. User grouping algorithms are introduced to demonstrate the significance of selecting the best users. To allocate transmission powers, we formulate an EE maximization problem that is non-convex and NP-hard. We propose to use the Bat algorithm, which is one of the popular and efficient metaheuristic algorithms in solving non-convex problems. Analytical expressions for cell association and outage probabilities are derived. We present Monte Carlo simulation results to validate the analytical expressions and show that: (1) choosing the best user among far users influences the overall EE of system impressively; (2) the optimum values of transmission powers can be obtained by Bat algorithm; (3) the proposed grouping algorithms with power allocation methods outperform the other scenarios in terms of EE and spectral efficiency (SE).

Article
In this paper, spectrum sensing for primary user (PU) is considered in additive Laplacian noise. Further, we consider dynamic behaviour of PU, where the transitions of PU in both the null and the alternate hypotheses have been modelled by two state discrete time Markov chain (DTMC). We assume PU signal to be quadrature amplitude modulated (M-QAM) with ‘M’ modulation order. The considered Markov parameters are τo\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau _o$$\end{document} and μo\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu _o$$\end{document}, which represent the average number of samples present during active (ON) state and idle (OFF) state of the PU, respectively. Furthermore, the τo\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau _o$$\end{document} and μo\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu _o$$\end{document} are functions of transition probability matrix (TPM) in DTMC. At the cognitive terminal for spectrum sensing, we assume perfect information of the TPM or in other words τo\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau _o$$\end{document} and μo\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu _o$$\end{document}. Then, we use prevailing detection schemes such as improved absolute value cumulation detection (i-AVCD) with the TPM. We refer to this scheme as modified i-AVCD and derive the resulting detection variable along with threshold. In the detection variable, the received samples at the cognitive terminal are raised to a positive exponent P with range of P defined as 0<P≤2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0<P\le 2$$\end{document}. We also derive analytical expressions of the detection probability (PD)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P_D)$$\end{document} and false alarm probability (PF)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P_F)$$\end{document}. We present our results with receiver operating characteristics (ROC) for the considered scheme. We also present simulation results and find close matching with the analytical counterparts. We discuss the effect of increasing modulation order M on the detection performance. We also discuss a special case of M-QAM at M=2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M=2$$\end{document} which refers to binary phase shift keying (BPSK) modulation scheme for PU. Further, we discuss special cases of the considered scheme at P=1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=1$$\end{document} and P=2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=2$$\end{document}, i.e., modified AVCD and modified energy detection, respectively. Finally, we compare the performance of the considered scheme with the conventional i-AVCD detection scheme, where information of DTMC or in other words TPM, is not available at the cognitive terminal. We find that the considered scheme outperforms the conventional scheme.

Article
The internet of vehicles (IoV) paradigm remains the future of vehicular communication that supports unparalleled ubiquitous internet access during vehicular mobility. For reliable end-to-end data transfer, the cumbersome volume of global web traffic relies on transmission control protocol and its retransmission timeout (RTO) timer prediction algorithm. In the IoV network, the RTO estimation fails to withstand a sudden increase in roundtrip time (RTT) delays that lead to a spurious timeout condition and needless deflation in transmission rate. The enhanced learning RTO (EL-RTO) algorithm proposed in this article implements a spike suppression variable that minimize RTO prediction deficiency during sudden RTT delays in the vehicular network. The experimental results manifest that EL-RTO attains a considerable improvement in end-to-end data delivery performance, goodput, and message latency performances with minimum estimation error against the existing RTO approaches under the simulated IoV environment.

Article
The problem of Data acquisition in large distributed Wireless Sensor Networks (WSNs) scale is a hindrance in the growth of the Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted great interest from researchers. An open question of this approach is how to effectively integrate these technologies for specific tasks. The objective of this paper is two parts. First, we propose an effective deterministic clustering scheme based CS technique (EDCCS) for data collection in IoT based homogeneous and heterogeneous WSN to deal with the data acquisition problem, reduce the consumption of energy and increase the lifetime of network. Second, we propose random matching pursuit (RMP) as an effective CS reconstruction algorithm to improve the recovery process by reducing the error average at the base station (BS). The simulation results show that our proposed novel EDCCS scheme reduces at least 60% of the average power consumption and increases the network lifetime at least 1.3 times of the other schemes in homogeneous network while, it increases the network lifetime and residual energy by 1.9 times and 1.3 times respectively, compared to the other schemes in heterogeneous network. Also, our proposed RMP algorithm reduces the error average of reconstruction at least 35% compared to other reconstruction algorithms.

Article
In this paper, we propose the use of Intelligent Reflecting Surfaces (IRS) between the secondary source and K secondary users. The secondary source transmits the combination of K symbols dedicated to K secondary Non Orthogonal Multiple Access (NOMA) users. A set Ii\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I_i$$\end{document} of IRS reflectors are dedicated to user Ui\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U_i$$\end{document} so that all reflections are in phase at Ui\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U_i$$\end{document}. We derive the throughput when the secondary source harvests energy using nr\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n_r$$\end{document} antennas and transmits with an adaptive transmit power to generate low interference at primary destination. We show that the use of IRS with 32 reflectors per user offers up to 45 and 50 dB gain with respect to NOMA and Orthogonal Multiple Access (OMA) without IRS.

Article

Article
Traffic congestion is easy to occur in multimedia networks, so fuzzy adaptive prediction of data transmission congestion is conducted to improve the stability of multimedia networks. A fuzzy adaptive prediction algorithm for data transmission congestion in multimedia networks is proposed. The data transmission structure model of multimedia networks is established, and the data transmission congestion status feature extraction and time series analysis are conducted. The self-coherent matched filter detection algorithm is adopted to analyze the congestion state of data transmission, and the high-order cumulant feature extraction and post-focus search are carried out on the output filtered data, and the accurate detection and extraction of abnormal features in traffic sequence is realized. It can be concluded that the fuzzy adaptive prediction of data transmission congestion in multimedia network is accurate and has strong resistance to interference. After 10 rounds of iteration, the detection probability always keeps at a high level and rises steadily, and the minimum detection probability is 78%, which ensures the stability and security of multimedia network.

Article
This study proposes an algorithm for predicting the running data of information systems based on discrete second-order difference clustering. The wide stationary time series model of information system operation data is established, and the association rules mining and feature distributed transmission sequence fitting of information system operation data are conducted by binary semantic information representation method. The principal component feature detection and matching of information system operation data are carried out. High-order spectral feature analysis and extraction of information system operation data is realized based on big data analysis, and the prediction algorithm is improved. The proposed method has high accuracy, good convergence and high real-time performance, which can improve the scheduling ability of information system operation data.

Article
The method for optimal allocation of network resources based on discrete probability model is proposed. In order to take into account multiple coverage of the monitored points, the method constructs the discrete probability perception model of the network nodes. The model is introduced into the solution of the node coverage area, and the optimized parameters of the sensor optimization arrangement are used to optimize the layout of the multimedia sensor nodes. After setting the node scheduling standard, the interaction force between the sensor nodes and the points on the curve path is analyzed by the virtual force analysis method based on the discrete probability model At the same time On this basis, the path coverage algorithm based on the moving target is used to optimize the coverage of the wireless sensor network node in order to achieve optimal configuration of network resources. The experimental results show that the proposed method has good convergence and can complete the node coverage process in a short time. The introduction of the node selection criteria and the adoption of the dormant scheduling mechanism greatly improve the energy saving effect and enhance the network resource optimization effect.

Article
In order to solve the problem of high energy consumption caused by node overload in complex network flow, a simulation load separation control algorithm based on complex network flow is proposed. According to the characteristics of complex network flow, combined with the characteristics of traffic in the network, a tree combined classifier is designed to discretize the complex network, analyze the micro dynamics of nodes in the network, and simulate the load division of nodes in complex network flow by dividing simulation load, evaluating node bandwidth, transferring overload nodes, and controlling the tree combined classifier. The experimental results show that the designed discrete control algorithm has the advantages of low cost, good load balancing, low energy consumption, and good simulation load discrete control performance.

Article
In order to improve the transmission stability of sensor networks, a sensitive data mining method based on Pan Boolean algebra is proposed. According to the output correctness, reliability and operation efficiency of wireless sensor network, this paper analyzes the characteristics of sensitive data, extracts and clusters the associated features of sensitive data, establishes the information clustering model of sensitive data in sensor network, and detects the fuzzy factor of sensitive data in sensor network with grid block clustering method, The Pan Boolean algebra analysis model is used to realize the hybrid deep learning of sensor network sensitive data detection and realize the optimization of sensor network sensitive data mining. The simulation results show that this method has high precision in mining sensitive data of WSN, and improves the reliability of WSN.

Top-cited authors
• South China University of Technology
• Institute of Electrical and Electronics Engineers
• South China University of Technology
• Indian Institute of Technology (ISM) Dhanbad