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Microwave imaging has become a popular research issue in recent decades as a result of its several benefits over traditional imaging technologies. To analyse biological tissues in depth, a microwave imaging instrument is employed.The test determines the presence and location of morphological alterations in specific biological tissues. Ultrawideband (UWB) microwave imaging is a new technique that produces improved results while avoiding the use of ionizing radiation. Antennas play a critical part in these systems, and as a result, antenna optimization has become a hot topic due to the device's proximity to the human body. Recent research has revealed a number of initiatives to improve the electromagnetic sensors employed in these systems, whether as single or array components. In this article, the development of a compact ultrawideband (UWB) antenna functioning in the frequency range of 3.25-14.63GHz is proposed. The reported antenna comprises of a rectangular patch and a modified ground plane “etched on a FR4” substrate excited by a 50Ω feed line. The overall dimension of antenna is \(0.44 {\lambda }_{0}\times 0.38 {\lambda }_{0}\times 0.02{\lambda }_{0}\). The proposed antenna has been fabricated on FR-4 substrate. Measused and simulated results are in good agreement. The proposed antenna is compact and displays good radiation characteristics along with good gain. 1. INTRODUCTION In recent years, UWB technology has gained significant interest from researchers throughout the globe. In February 2002, the Federal Communication Commission (FCC) released the frequencies of 3.1GHz to 10.6GHz for commercial applications [1]. The design of a UWB antenna requires the antenna to be small and compact, it should have wide bandwidth performance, the radiation pattern has to be Omni-directional and the antenna should be easy to fabricate [2–3]. The benefits of an ultra-wideband antenna are its low complexity and it is inexpensive to manufacture. UWB patch antenna can be designed with various geometries such as circular, triangular, square, or rectangular. UWB technology is applied in several fields such as microwave imaging and radar systems. Microwave imaging utilizes EM waves to see through the inner structure of a tissue. It recognizes tumors based on dielectric properties, which are different compared to normal tissue [4–5]. X-rays, ultrasound, and MRI scans are used in the early detection of cancerous cells [6]. However, these methods pose some disadvantages. X-ray mammography is agonizing for patients, and long exposure to X-ray may cause healthy cells to become cancerous [7]. Another technique used to detect cancerous cells is ultrasound, but this technology is unable to detect deeply buried tumors and it is comparatively expensive [8]. To overcome these limitations, microwave imaging employs low ionizing radiation and is cost-effective compared to other methods [9]. Microwave imaging incorporates the use of UWB pulses which range from “low to high” frequencies in order to generate images of human tissues [10]. The higher frequency band is used to create high-resolution images of the tissue, whereas the lower frequency band is used to detect deeply buried tumors [11]. A microwave imaging system has two parts, a “front end,” which requires the antenna to be inexpensive, compact and it should operate efficiently, whereas the second part requires analyzing the performance of the antenna to detect the tumor and generate the image using the various processing techniques. The need for the antenna to be is compact is because it is to be incorporated into portable microwave imaging systems. This paper overviews the design and optimization of a novel microwave antenna which must serve as an element in a sensor array for early detection. The proposed microstrip patch antenna in this work displays good UWB characteristics and also displays good gain characteristics. The antenna consists of a modified square patch and a ground plane built on a FR4 substrate. Parametric analysis is done on the proposed antenna to observe the effect of various design parameters on the antenna performance, further, the simulations of the current density, gain and radiation pattern are also reported in this paper.

The increasing demand for data and the rapid increase in the number of wireless connected devices make the shortage of energy and spectrum resources more serious. This paper considers a wireless powered communication network (WPCN) composed of N wireless devices (WDs) installed with single-antenna and a hybrid access point (HAP) equipped with multi-antenna, where HAP sends wireless energy to WDs in the downlink and receives information transmission from WDs in the uplink. To overcome “double near and far” problem, this paper adopts a clustering cooperative transmission method to enhance some WDs’ throughput performance far from the HAP, i.e., one of N WDs is selected as a cluster head (CH) and the remaining (N − 1) WDs as cluster members (CMs), and the CH helps relay CMs’ information to transmit. However, because the CH needs to transmit N WDs’ information, its energy consumed during information transmission will be the bottleneck of the system performance. To achieve a tradeoff between energy and data rate, this paper adopts multi-antenna energy beamforming technology to concentrate more energy to transmit the CH, so as to balance the energy consumption among all WDs. Considering the influence of in-phase/orthogonal imbalance,nonlinear amplification amplitude and phase noise on those physical transceivers of low-cost sensor nodes, nonlinear energy harvesting technology is employed to improve throughput performance of the WPCN system. Particularly, the proposed scheme’s throughput performance is derived, and simulation results demonstrate that this scheme can be effective to increase the WPCN system’s throughput fairness and spectral efficiency.

Different applications of Wireless Sensor Networks (WSNs) have different expectations from the working of Medium Access Control (MAC) protocols. Some value reliability more than delay incurred while some demand a fair trade-off for the factors like: Throughput, Bit Error Rate (BER) etc. This paper evaluates the performance of Wireless Personal Area Networks (WPANs) from 802.15.4 group for WSNs with modified algorithm which helps in reducing the Medium Access Delay and Delay in reaching of the packet from one end to another end. In this paper certain modifications to existing algorithm have been proposed for reducing the Medium Access Delay and to reduce the number of packets dropped. The result comparisons on the performance parameters like: network output load, generated acknowledged traffic, media access delay, battery consumed and delay in packet transmission from one end to another end that the back-off number & exponent values used for transmission play vital role for improving the performance of WSNs as they directly affect the number of packets dropped, successfully acknowledged and Medium Access Delay.

In order to improve the scientific quality of the public, the Chinese Association for Science and Technology has put forward a call to combine popular science education with leisure and entertainment. In view of the fact that online shopping involves a wide range of areas, and the people pay more attention to it, the paper completed the innovation of online shopping risk warning science knowledge, the design of popular science mechanism and the dissemination of popular science knowledge. The paper used complex network’s knowledge discovery methods and decision theory to design online shopping risk warning science knowledge; Using the complex network public opinion dissemination trust analysis realize the dissemination of popular science knowledge and promote the improvement of the public's quality of popular science. The spread of risk early warning science knowledge in the network shows that the risk early warning mechanism designed can achieve the purpose of improving public science knowledge when the reward and punishment measures are appropriate.

Thyroid disorder affects the regulation of various metabolic processes throughout the human body. The structural and functional disorders can affect the body as well as the brain. The computer-aided diagnosis system can identify the kind of Thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. In this paper, a fuzzy adaptive feature filtration, expansion, and again filtration based model is presented for generating the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multi-measure evaluation. At the first level, the composite-fuzzy measures are combined with expert’s recommendations for identifying the ranked and relevant features. At the second level, the statistical computation based distance measure is applied for expanding the featureset. The fuzzification is applied to expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the ELM classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the NaiveBayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and RBF Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy based parameters. The proposed framework clams the significant gain in accuracy, relevancy, and reduction in the error rate.

In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

Coordinated multipoint joint transmission (JT-CoMP) is a promising solution to address inter-cell interference in dense future wireless networks due its strength in converting interfering signals into useful signals, thereby enhancing capacity especially at the cell edge. However, allowing all user equipments (UEs) to operate using the JT-CoMP mode reduces the availability of radio resources. This paper develops an efficient algorithm that can identify which UEs will benefit from operating in a JT-CoMP mode and how to efficiently allocate radio resources from multiple base stations. Joint user-centric JT-CoMP clustering and multi-cell resource management is used in two steps where user-centric clusters are constructed as a first step and according to the clustering results obtained, resources are assigned. This paper also provides a new user-centric clustering approach that allows a user to utilize the JT-CoMP technique only if JT-CoMP boosts its rate above a certain threshold level. A multi-cell resource allocation scheme that can address the resource mismatching problem between cooperative BSs that happens due to load imbalance is proposed. Simulation results show that the proposed user-centric clustering algorithm outperforms the traditional power level difference scheme in terms of the system’s overall throughput as well as the throughput of cell-edge users. Also, results show that the performance of JT-CoMP is mainly affected by the user-centric approach and the amount of physical radio resources assigned to CoMP UEs.

Information and Communication Technologies, to a long extent, have a major influence on our social life, economy as well as on worldwide security. Holistically, computer networks embrace the Information Technology. Although the world is never free from people having malicious intents i.e. cyber criminals, network intruders etc. To counter this, Intrusion Detection System (IDS) plays a very significant role in identifying the network intrusions by performing various data analysis tasks. In order to develop robust IDS with accuracy in intrusion detection, various papers have been published over the years using different classification techniques of Data Mining (DM) and Machine Learning (ML) based hybrid approach. The present paper is an in-depth analysis of two focal aspects of Network Intrusion Detection System that includes various pre-processing methods in the form of dimensionality reduction and an assortment of classification techniques. This paper also includes comparative algorithmic analysis of DM and ML techniques, which applied to design an intelligent IDS. An experiment al comparative analysis has been carried out in support the verdicts of this work using ‘Python’ language on ‘kddcup99’ dataset as benchmark . Experimental analysis had been done in which we had found more impact on dimensionality reduction and MLP performed well in the true classification to establish secure network. The motive behind this effort is to detect different kinds of malware as early as possible with accuracy, to provide enhanced observant among various existing techniques that may help the fascinated researchers for future potential works.

The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method’s capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT’s procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.

In Wireless Sensor Network (WSN), the consumption of energy is high due to the data transmission in the sensing region. The Optimized Distributed Voronoi-based Collaboration (ODVOC) optimizes data delivery and it suffers from huge energy consumption. To overcome this drawback, a data aggregation scheme is introduced to expand the lifetime of the network and to minimize the energy utilization. Data aggregation is a significant approach that preserves the unwanted usage of energy during data transmission. In this paper, the Optimized Distributed Voronoi-based Cooperation scheme with Energy-aware Dual-path Geographic Routing (DAOHS-ODVOC-EDGR) is proposed which incorporates the polydisperse aggregation scheme and it aggregates the indispensable data in the intermediary nodes. The effective data aggregation is achieved by the estimation of the waiting time of the data at every intermediary node and cost function that is used for electing the next-hop node. In the proposed protocol, the buffer value of every node is divided to retain diversified kinds of flow for effective and fair delivery of data. The rate of transmission at the source and intermediary node is altered during the congestion. The proposed DAOHS- ODVOC-EDGR protocol is tested via simulation scheme. The result of the simulation exposes that DAOHS-ODVOC-EDGR outperforms the TESDA and DVOC protocol in terms of network lifetime, energy efficiency, and hop count delay.

Wireless sensor network (WSN) plays a crucial role in the Internet of Things (IoT), which assist to produce seamless information that have a great impact on the network lifetime. Despite the substantial application of the WSN numerous challenges like energy, load balancing, security, and storage exist. Energy efficacy is regarded as an integral part of the design of WSN; this can be achieved by clustering and multi-hop routing technique using metaheuristic optimization algorithm. This paper concentrates on design of metaheuristics cluster-based routing technique for energy-efficient WSN (MHCRT-EEWSN). The presented MHCRT-EEWSN technique mainly concentrates on the improvements of energy efficiency and lifespan of the WSN via clustering and routing process. For effectual clustering process, the MHCRT-EEWSN model utilizes whale moth flame optimization (WMFO) technique can be utilized by the use of fitness function involving intra-cluster distance, inter-cluster distance, energy, and balancing factor. Besides, the MHCRT-EEWSN model employs improved African buffalo optimization (IABO) based routing technique. To select optimal routes in WSN, the IABO algorithm designs a fitness function comprising multiple parameters like residual energy and distance factor. The experimental validation of the MHCRT-EEWSN model can be tested by making use of a series of simulations. A wide-ranging comparative study shows the promising performances of the MHCRT-EEWSN model than other recent methods.

Wireless Mesh Network (WMN) is surely one of the prominent networks in the modern era which is widely used in numerous evolving applications, viz. broadband home networking (BHN), community and neighbourhood networks (CNN), coordinated network management (CNM), and intelligent transportation systems (ITS), etc. It is a wireless network (WN) with multi-hop formed by many fixed wireless mesh routers (WMR) that are connected wirelessly with a mesh-alike backbone arrangement. In the IEEE 802.11s network, the node selection, scalability, stability, density of the nodes, mobility of the nodes, transmission power, and routing are major issues that WMN suffers. In this paper, a critical review of MAC protocols and their Quality of Service (QoS) parameters for WMN is presented to attain a better understanding of MAC protocols. Furthermore, the critical comparative analysis and recommendation of MAC procedures for WMN using Multi-objective optimization and statistical testing framework are performed. This framework is used for the analysis and recommendation of different protocols available for QoS parameters., Comment: 20 pages, 4 figures, Wireless Personal Communication

Smart farming is becoming the need of the hour nowadays in an effort to boost productivity and protect the crops. This can be done using sensors and internet enabled devices in the farm. The underlying wireless sensor network can sense various environmental parameters and can pass data to the internet enabled devices to support smart farming. The sensor nodes are, however, powered by smaller batteries and have limited lifetime. Therefore, this paper presents a clustering protocol which aims at increasing the lifetime of the sensor nodes. These sensors are considered to be pressure sensors which are deployed in the network and whenever any animal encroaches the farm, a signal can be passed to the internet enabled alarm system which can help the farmer to fend off the animals and protect his crops. The sensor network is considered to have three level of energy heterogeneity among the nodes and the cluster head is selected in such a way that the cluster formed by the head consists of more number of high energy nodes. The proposed protocol has been simulated in MATLAB environment and compared with SEP, DEEC and ISEP based on network lifetime and throughput. The protocol has shown better performance against these existing protocols.

Node failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. It is found that reliability and security are the two most essential concerns in IWSN. Reliability can be achieved by adequately managing network topology using the structural approaches, where the critical wireless sensor nodes are precisely detected and protected. In this paper, we address the problem of critical node detection and present two-phase algorithms. In Phase-I, a 2D critical node (C-N) detection algorithm is proposed, which uses only RSSI information of the neighbor. This algorithm is also extended to work in 3D topology. In Phase II, a correlation-based reliable approach is proposed to increase the node resilience against node failure to achieve low overhead. The proposed algorithms (ABCND) requires mathcalO (log (N)) time for convergence and mathcalO (delta (log N)) for critical node detection, N represents the number of IoT devices, and delta is the cost required to forward and backward message. We compare our algorithm with the current state-of-the-art on C-N detection algorithms. The simulation results show that the proposed ABCND consumes 50% less energy to detect C-N and 90% to 95% reliable C-N.

This paper presents the design of a flexible monopole antenna for wideband applications. The proposed antenna is printed on flexible substrate material having a dielectric constant of 3 for the design and fabrication of the proposed antenna. The antenna dimensions are 3.8 cm × 4.1 cm × 0.025 cm. The antenna characteristics are investigated not only in flat conditions but also under bending configuration. Both flat and bending configurations are operated at frequency bands from 2.4 GHz up to 10 GHz with S- parameters lower than − 10 dB. The antenna parameters such as gain, efficiency, and radiation patterns are studied for both configurations. A deep study for time-domain response is discussed to validate the performance of the proposed antenna to operate at a wide band with a highly correlated response.

A dual-band 28/38 GHz multiple-input multiple-output (MIMO) two elements antenna for the next 5G network is proposed in this paper. The single element is composed of a slot monopole antenna loaded with stub and a small crescent with an entire size of 15 × 15 mm2. The two elements are added to compose the suggested configuration. One of the antennas is printed on the upper layer while the other is located on it's back of the substrate to improve the coupling between elements without using any decoupling structures. The innovative antenna of dual-band works at 28/38 GHz and is achieved by adjusting the stub length and the crescent radius. The introduced MIMO antenna system has been experimented and the results have the same compatible trace with the simulated outcomes. The proposed two ports MIMO achieved a reasonable gain that reaches 5.7 and 6.9 dBi in the 28/38 GHz bands, respectively. The proposed MIMO antenna performance is also analyzed and achieved the envelope correlation coefficient, a diversity gain, and channel capacity Loss of about 10–4, 10 dB, and less than 0.3 bit/s/Hz, respectively over the 28/38 GHz bands. Finally, from the simulated and measured outcomes, it can be confirmed that the suggested MIMO antenna system can be applied to 5G wireless telecommunication applications.

The complementary M-ary orthogonal spreading with orthogonal frequency division multiplexing (CMOS-OFDM) is an efficient scheme for providing high bit error rate (BER) performance and low peak to mean envelop power ratio (PMEPR) in high frequency (HF) communications system. In the CMOS-OFDM, orthogonal spreading OFDM is implemented based on orthogonal complementary sequence pairs where there is an issue that one data symbol is transmitted through two OFDM symbols. In this paper, we present the M-ary orthogonal spreading OFDM scheme using complex complementary sequences which satisfy real domain orthogonality. The complementary real orthogonal spreading OFDM (CROS-OFDM) is restrict the PMEPR to 3dB and provides two times the bit rate of CMOS-OFDM. The CROS-OFDM scheme uses zero-center complementary sequences like the CMOS-OFDM and hence it is suitable to the HF OFDM and Wi-Fi system where the direct current subcarrier is not used. The simulation results show that the CROS-OFDM provides higher bit rate and BER performance.

In this paper, we propose a non-orthogonal multiple access (NOMA) based grouping method for IEEE 802.11ah, a promising platform for the internet of things (IoT). The grouping method improves the scalability of IoT networks, by reducing collisions in the access points (APs). The proposed method puts those IoT devices (IoT-Ds) whose channel gains are far enough from each other, i.e., who satisfy NOMA constraints, in the same group. Therefore, using successive interference cancellation (SIC), the AP is able to decode the simultaneous signal transmissions from IoT-Ds in a group. To assign IoT-Ds into groups and determine their transmission power, we formulate a total throughput maximization problem as a joint optimal grouping and power allocation problem, which is a non-convex mixed-integer programming problem. We convert it to a convex problem using quadratic fractional programming (QFP), and then we solve it using augmented Lagrange multiplier (ALM) method. Moreover, to reduce the complexity of the solution, we propose a fast grouping method to allocate power to each group in parallel. Simulation results show that the proposed methods have outstanding performance compared to conventional association identifier (AID)-based grouping method; besides, scalability of the network in terms of throughput, power consumption and channel utilization improves dramatically because of the collision reduction of IoT-Ds, which is achieved by deploying NOMA and SIC. Furthermore, the fast grouping method decreases the computational complexity greatly at the expense of a small reduction in network performance.

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

This paper proposes a hybrid Dragonfly Algorithm (DA) for training Multi-Layer Perceptron Neural Network (MLP NN) to design the classifier for solving complicated problems and distinguishing the real target from liars’ targets in sonar applications. Due to improving the cost computation and reducing the waste of time, a modified low-cost DA is designed for evaluation. To assess the accuracy of the technique, some well-known meta-heuristic trainers include Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), DA, and Chimp Optimization Algorithm (ChoA) compared to show the accuracy of similar algorithms. DA and ChoA algorithms have remarkable features and a hybrid algorithm of them is proposed. The performance of the proposed classifier will be evaluated by two standard benchmark datasets. The results show that the modified hybrid DA-ChoA has 15% less time consuming and 4% better performance rather than the original dragonfly method.

This paper presents low mutual coupling multiple input-multiple output (MIMO) array antenna for millimetre-wave (mmw) application. The MIMO-array beamforming antenna with 2:1 VSWR band is proposed for 28.0 GHz and covers 27.04 - 28.35 GHz frequency band, which is suitable for mm-wave n261 5G-band which cover the frequency range from 27.5 - 28.35 GHz. It consists of 2×12 antenna array elements and the prototype is designed on low loss Rogers Duroid 5880 substrate of size 51.45×36.87 mm2. The beamforming MIMO antenna covers ±200 main lobe directions. The mutual coupling at the MIMO-array ports is less than 28.0 dB. The radiation efficiency and the gain in the presented band are more than 93.0% and more than 13.99 dBi. The ECC in the presented frequency band is ≤ 10−4, which is one of the advantages of the proposed design. The design covers indoor and outdoor Gaussian applications, and has 1.31 GHz TARC active bandwidth. It has 4.65% simulated and 4.73% measured fractional bandwidths.

Today’s internet world is moves to cloud computing to maintain their public data privately in a secure way. In cloud scenario, many security principles are implemented to maintain the secure transmission of data over the internet. And still, the main concern is about maintaining the integrity of our own data in public cloud. Mostly, research works concentrates on cryptographic techniques for secure sharing of data but there is no such mentioned works are available for data integrity. In this paper, a data masking technique called obfuscation is implemented which is used to protect the data from unwanted modification by data breaching attacks. In this work, enhanced Vigenere encryption is used to perform obfuscation that maintains the privacy of the user’s data. Enhanced Vigenere encryption algorithm combined with intelligent rules to maintain the dissimilarity between the data masking for perform encryption with different set of rules. This work mainly concentrates on data privacy with reduced time complexity for encryption and decryption.

At cellular wireless communication systems, channel estimation (CE) is one of the key techniques that are used in Orthogonal Frequency Division Multiplexing modulation (OFDM). The most common methods are Decision‐Directed Channel Estimation, Pilot-Assisted Channel Estimation (PACE) and blind channel estimation. Among them, PACE is commonly used and has a steadier performance. Applying deep learning (DL) methods in CE is getting increasing interest of researchers during the past 3 years. The main objective of this paper is to assess the efficiency of DL-based CE compared to the conventional PACE techniques, such as least-square (LS) and minimum mean-square error (MMSE) estimators. A simulation environment to evaluate OFDM performance at different channel models has been used. A DL process that estimates the channel from training data is also employed to get the estimated impulse response of the channel. Two channel models have been used in the comparison: Tapped Delay Line and Clustered Delay Line channel models. The performance is evaluated under different parameters including number of pilots (64 pilots or 8 pilots), number of subcarriers (64), the length of cyclic prefix (16 or 0 samples) and carrier frequency (4 GHz) through computer simulation using MATLAB. From the simulation results, the trained DL estimator provides better results in estimating the channel and detecting the transmitted symbols compared to LS and MMSE estimators although, the complexity of the proposed LSTM estimator exceeds the equivalent LS estimator. Furthermore, the DL estimator also demonstrates its effectiveness with various pilot densities and with different cyclic prefix periods.

For two-dimensional (2-D) incoherently distributed sources, this paper presents an effective angular parameter estimation method based on shift invariant structure (SIS) of the beamspace array manifold (BAM), named as SIS-BAM algorithm. In the proposed method, a shift invariance structure (SIS) of the observed vectors is firstly established utilizing a generalized array manifold of an uniform linear orthogonal array (ULOA). Secondly, based on Fourier basis vectors and the SIS, a beamspace transformation matrix can be performed. It projects received signals into the corresponding beamspace, so as to carry out dimension reduction of observed signals in beamspace domain. Finally, according to the SIS of beamspace observed vectors, the closed form solutions of the nominal azimuth and elevation are derived. Compared with the previous works, the presented SIS-BAM method provides better estimation performance, for example: 1) the computational complexity is reduced due to dealing with low-dimension beamspace signals and avoiding spectral search; 2) it can not only improve the angular parameter estimation accuracy but also have excellent robustness to the change of signal-to-noise ratio (SNR) and snapshot number. The theoretical analysis and simulation results confirm the effectiveness of the proposed method.

As of late, Convolutional Neural Networks have been very successful in segmentation and classification tasks. Magnetic resonance imaging (MRI) is a favored medical imaging method that comes up with interesting information for the diagnosis of different diseases.MR method is getting to be exceptionally well-known due to its non-invasive rule and for this reason, automated processing of this sort of image is getting noticed. MRI is effectively and widely used for tumor detection. Brain tumor detection is a popular medical application of MRI. Automating segmentation using CNN assists radiologists to lessen the high manual workload of tumor evaluation. CNN classification accuracy depends on network parameters and training data. CNN has the benefit of learning image features automatically directly out of multi-modal MRI images. In this survey paper, we have presented a summary of CNN's recent advancement in its technique applied on MRI images. The aim of this survey is to discuss various architectures and factors affecting the performance of CNN for learning features from different available MRI datasets. Based on the survey, section III (CNN for MRI analysis) comprises three subsections: A) MRI data and processing, B) CNN dimensionality, C) CNN architectures.

In order to solve the problem of hot spot caused by uneven energy consumption of nodes in Wireless Sensor Networks (WSNs) and reduce the network energy consumption, a novel cluster routing algorithm called CRPL for ring based wireless sensor networks using Particle Swarm Optimization (PSO) and Lion Swarm Optimization (LSO) is proposed in this paper. In CRPL, the optimal cluster head (CH) of each ring are selected by using LSO whose fitness function is composed of energy,number of neighbor nodes, number of cluster heads and distance. Moreover, PSO with a multi-objective fitness function considering distance, energy and cluster size is used to find the next hop relay node in the process of data transmission, and the optimal routing paths are obtained, so as to alleviate the hot spot problem as well as decrease the energy consumption in the routing process. The simulation results show that, compared with some existing optimization algorithms, CRPL has better effects in balancing the energy consumption of the network and prolonging the life cycle of the network.

Intrusion Detection Systems and Intrusion Prevention Systems are used to detect and prevent attacks/malware from entering the network/system. Honeypot is a type of Intrusion Detection System which is used to find the intruder, study the intruder and prevent the intruder to access the original system. It is necessary to build a strong honeypot because if it is compromised, the original system can be easily targeted by the attacker. To overcome such challenges an efficient honeypot is needed that can shut the attacker after extracting his attack technique and tools. In this paper, a Venus fly-trap optimization algorithm has been used for implementing the honeypot system along with Intrusion Detection System. Venus plants are a type of carnivorous plants that catch their prey intelligently. By adopting this feature we make an effective honeypot system that will intelligently interact with the attacker. A new fitness function has been proposed to identify size of the attacker. The effectiveness of the proposed fitness function has been evaluated by comparing it with state of the art. For comparison, remote-to-local attacks, probing attacks and DOS attacks are performed on both proposed and existing models. The proposed model is significant to catch/block all the intruders which were caught by the art and also the proposed model reduces the time of interaction between the attacker and honeypot system thereby giving minimum information to the attacker.

The current Sub 6 GHz band is highly occupied and unable to meet the demands of future wireless data traffic. As the millimeter wave technology is a promising way of enhancing the performance of next-generation wireless networks, it is essential to explore FR2 frequency bands. Massive Multiple-Input Multiple Output (mMIMO) technology has been introduced to improve the capacity and coverage of 5G wireless networks. Hybrid beamforming (HB) is an efficient technique to improve the channel gain and minimize the interference at reasonable complexity. The aim of HB in mMIMO systems is to maximize the sum rate that can approach the fully-digital beamforming system performance. Combination of mMIMO systems, mmWave, and HB techniques achieve higher data rates and cell coverage in 5G wireless networks. It mitigates the hardware complexity, energy consumption, and cost as number of required radio-frequency chains at base station gets minimized. In this paper, we have investigated the performance of a multi user-mMIMO HB system at 28, 39 GHz of FR2 frequency bands as well as at 66 GHz by analyzing the effect of changing variables that include the number of users, transmitting and receiving antennas, and the type of modulation. Simulation results include RMS values of error vector magnitude (EVM) that gives quantitative analysis and receive constellation of the equalized symbols that shows qualitative analysis. EVM is computed for different scenarios and it is used as an indicator of performance to assess the suitable design configuration of mMIMO system.

Real-time data transmission is one of the objectives of MANET (mobile ad-hoc network) to handle emergencies like a forest fire, flood, and earthquake. In this scenario, quick delivery of data is itself a challenging task for MANET and there is a possibility of load imbalance due to packet transmission in between source-destination pairs via the shortest path due to congestion or control overhead. In this paper, a novel routing protocol, called ACOLBR (ACO based load balancing routing), is design to control the congestion and balancing the load among the multiple paths in between source to destination. The similarity between the environment of Ant Colony and the MANET inspires the authors to apply ACO (Ant colony optimization) technique during routing in MANET to control congestion and balance the load in the network. In our proposal, two colonies of ants (red/blue) carry their packets based on the network condition. A decision variable is designed to select red/blue ant for transferring packets based on different network parameters such as bandwidth, energy, mobility, and distance. The selection of red ant means the route where the concentration of red pheromone is maximum and reverse is true for blue ant. This protocol is also concerns about the link failure during packet transmission in a route. Simulation results using OMNET++ show that ACOLBR outperforms ARA, ANTHOCNET, FACO, AODV, DSDV, DSR, CA-ARTT, and MOAODV in terms of load balancing efficiently in the route for data transmission in between source to destination.

This paper proposes a novel design of a software-defined matched filter (MF) for digital receivers of synthetic aperture radar (SAR). The block diagram of the proposed receiver is described in detail. The purpose of this filter is to produce a SAR pulse with higher compression ratio (CR) and lower side lobe level (SLL) than that produced by the conventional MF. The proposed design is based on the idea of time windowing of the SAR pulse to construct the transfer function of the receiver filter. The shape of the proposed time-domain window is optimized to achieve the filter design goals including the minimization of the SLL and the realization of the target value of the CR. The transmitted SAR pulse is, first, subjected to linear frequency modulation and then subjected to the optimized window. The width (time duration) of the proposed window is divided into equal time intervals. The proposed time-domain window is constructed as a sequential continuous piecewise linear segments. The instantaneous value of the time-domain window at the start of each time interval is optimized so as to achieve the optimization goals. The width of the time-domain window is shown to be proportional to the width of the compressed pulse after optimization. The number of the time intervals into which the time duration of the window is divided is shown to have a significant effect on the optimization results. The particle swarm optimization (PSO) technique is then applied to get the window shape that minimizes the SLL for a specific predetermined value of the pulse CR. It is shown that the iterations of the PSO are fastly convergent and that the applied algorithm is computationally efficient. Also, it is shown that the desired value of the pulse CR is achieved with accuracy of 100%. Moreover, the achieved SLLs are about − $$65\,\mathrm{dB}$$ 65 dB , $$- 90\,\mathrm{dB}$$ - 90 dB , $$- 114\,\mathrm{dB}$$ - 114 dB , and $$- 133\,\mathrm{dB}$$ - 133 dB for pulse CR of 5, 3, 2, and 1.5, respectively. Finally, for practical implementation of the introduced SAR pulse processing technique, the proposed optimized window is placed as a building block in a software-defined receiver of the SAR system.

This paper presents the design of flexible trapezoidal radiating patch antenna array with FR4 substrate for onbody low frequency medical applications. The array resonates at 1.89 GHz with impedance bandwidth of 80 MHz and low return loss of -26.19 dB. The VSWR of 1.103 validate the activeness of the proposed antenna array having maximum surface current 133.1 (A/m) and directivity of 4.48 dBi. The antenna array exhibit the H-Field strength of 160.52 (A/m) and E-Field of 36093.4 (V/m) prove the radiation capability at low frequency on body application. These properties demonstrate the suitability of proposed array antenna for on body medical wireless applications.

Remote user authentication schemes provide a system to verify the legitimacy of remote users’ authentication request over insecure communication channel. In last years, many authentication schemes using password and smart card have been proposed. However, password might be revealed or forgotten and smart card might be shared, lost or stolen. In contrast, the biometrics, such as face, fingerprint or iris, have no such weakness. With the trend of mobile payment, more and more applications of mobile payment use biometrics to replace password and smart card. In this paper, we propose a biometric-based remote authentication scheme substituting biometric and mobile device bounded by user for password and smart card. This scheme is more convenient, suitable and securer than the schemes using smart cards on mobile payment environment.

The wireless sensor networks (WSNs) are special network which has purpose of gathering information in certain area. Multipath routing is the paramount path of addressing QoS and energy balancing concerns in low power and lossy networks (LLNs) especially in IoT technology. Most of the extant efforts bring the limited number of disjoint paths into play, and intersect the traffic among them pursuant to a compound metric or centralized optimization problem. This paper proposes a multi-class multipath routing protocol for LLNs (called M2RPL), that construct a braided multipath routing graph based on the standard RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) protocol, and an energy balanced optimal rate assignment mechanism (EBORA) that solves a local multi class optimization problem for minimum cost traffic rate assignment. Moreover the algorithm tries to maximize network lifetime by equalizing the energy dissipation rates of equi-level nodes. The simulation results expose the efficiency of the proposed framework, leading to an average 15% increase in lifetime, and improved QoS performance in terms of delay and reliability, compared to some well-known algorithms.

In this paper, we build a D2D-relay communications model where the D2D user is selected as a relay to forward data for the users at the edge of the networks. We aim at maximizing the achievable data rates of the cell-edge users, a resource allocation problem is formulated and an iterative power allocation algorithm is proposed. We derive the optimal closed-form power allocation expressions by the Lagrange dual method. Simulation results show that our communication scheme can achieve greater spectral efficiency than the traditional cellular-relay communication scheme.

In this paper a novel ladder resonator is introduced to decrease mutual coupling effect between two microstrip patch antennas. Applied patch antennas are operating at 2.45 GHz frequency (ISM band), which specially used for multiple-input–multiple-output (MIMO) systems. The edge-to-edge distance between two microstrip patch antennas is 0.05 λ. The proposed ladder resonator impressively blocks the surface current between two patch antennas at the operating frequency, which results in mutual effect reduction. The proposed configuration has been fabricated and tested. Scattering parameters with and without of proposed resonator has been investigated. The results show that, the proposed configuration increases isolation between two microstrip patch antennas about 42dB.

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN.

Wireless Sensor Networks (WSNs) are extensively used in diferent areas and especially in severe and harsh environments such as battlegrounds, volcanic areas, healthcare, and so on. The major constraint in WSNs is the limited power supply, which impacts the lifeycle of the entire network. Clustering is a performing method used in WSN to optimize power consumption and extend the lifetime of WSNs. LEACH is considered as the first classical clustering protocol used in WSN to optimize energy consumption. Many protocols had been developed to enhance the conventional LEACH protocol. BRE-LEACH (Balanced Residual Energy LEACH) is one of them. It serves to enhance the performance of the LEACH protocol. In this paper, we suggest a new improved BRE-LEACH protocol called IBRE-LEACH (Improved Balanced Residual Energy LEACH), which ameliorates the performance of the BRE-LEACH approach. The IBRE-LEACH combines clustering and multi-hop techniques. It elects CHs according to the residual energy, limits the maximum number of nodes in each cluster to balance energy consumption. Thus, IBRE-LEACH gives the Abandoned Nodes (ANs) the chance to send their data to the BS. Furthermore, it routes the gathered data using the CHs, ANs, and a root node, which is a node with maximum residual energy and minimum distance to the BS. The simulation results in MATLAB exhibit that the IBRE-LEACH increases the throughput, optimizes energy consumption, extends the network lifetime, and ameliorates the stability up to 81.99%, 94.33%, and up to 91.47% compared with BRE-LEACH, LEACH-C, and LEACH, respectively.

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and Internet of Things (IoT) is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, Adaptive Fuzzy C means clustering and Seagull Optimization Algorithm (AFCM-SOA) is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of Fuzzy C Means Clustering and Seagull Optimization Algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The Fuzzy C-Means Clustering is utilized to empower the energy efficient operation and load balancing. In the Fuzzy C-Means Clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as Atom Search Optimization (ASO) and Emperor Penguin Optimization (EPO) respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

The implementation of the optimal Huffman coding technique for achieving proficient data compression, lower information redundancy and minimal utilization of the scarce bandwidth is essential to handle the effective processing of massive network data. In this paper, we employ cross-layer design technique among the data link, network and presentation layers of the traditional protocol stack to develop the improved DSDV routing protocol based on n-ary Huffman coding procedure. The entropy-based adaptive prefix codewords with variable length are assigned to the probability of packet successful delivery across the ad-hoc wireless network. Several coding and network efficiency parameters are deployed to assess the performance of the proposed routing scheme under three distinctive IPv4 network scenarios. These investigated network protocols include the default IPv4, multi IPv4 and dynamic IPv4 routing methodologies. Diversified simulation settings are employed with deviating network size to measure the multitude of essential wireless network characteristics incorporating the average delay, packet error rate, packet delivery fraction, data rate, ping loss rate, entropy rate, and reception cache hit. In addition, the set of key data compression/coding indicators are examined through comprehensive numerical analysis such as codeword length, source information rate, mean redundancy, and coding efficiency. Beyond, we significantly compare the performance of our developed cross-layer coded mobile routing model with several previous algorithms to validate its enhanced feasibility and superiority in terms of crucial network operation metrics such as throughput, packet drop rate, mean delay and packet delivery ratio.

Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.

Wireless Sensor Networks are a collection of numerous nodes which contain tiny devices having low power and work with minimal cost. In such a network, the functioning of these nodes plays a very important role to sense data and also forward the sensed data to the target observer. For segregation of nodes in effective locations, there is a need to localize these nodes by combining location information with the sensed data for tracking and monitoring of malicious nodes, goods tracking etc. The approaches towards localization may be classified as range-free or rangebased, both having their own pros and cons. This paper presents a discussion on different localization approaches, followed by simulation results of localization using lognormal shadowing model for distance estimation and trilateration for location computation. The results depict the individual impacts of node density and area of sensed region on the localization error. The approach used is cost effective, since it uses loss in received power for distance computation and hence there is no cost overhead for additional hardware.

Purpose: Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose the existence of leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly.Methods: To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses marker-based watershed algorithm and peak local maxima.Results: The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was same i.e. 94% but Structural Similarity Index Metric (SSIM) and recall of HSV was better than other two.Conclusion: The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

The emergence of embedded technologies and Internet of Things (IoT), have perceived the proliferation of devices starting from tiny monitoring sensors, mobile devices, wearable devices, surveillance sensors etc. For the past few decades technological advancements in these devices leverages applications from home automation to health care industries. Due to this drastic growth in technology and newly emerging applications the number of IoT connected devices are expected to reach 42.62 billion with global mobile data traffic of 77.5 exabytes/month by 2022. So, offering required services with sufficient QoS parameters as per SLA is a challenging task. Also, the growing rate of wireless traffic exerts load to the core network and backhaul connections. Even the situations may get still worse with multimedia streaming applications. To mitigate the wireless traffic, Fog Caching (FC) is one of the promising solutions. In FC the popular contents in the mobile core network are cached in suitable places. However, identifying popular contents, locating cache, and replacing contents of cache are noticeable issues in FC. In this paper we propose an Intelligent framework(I-CADET) for efficient CAche management for Delay sensitive IoT applications in the Edge CompuTing era. To locate the cache in mobile core network Connected Dominating Set (CDS) construction of graph theory is used. The content popularity is predicted by efficient ML technique. Then the identified popular contents are distributed over a constructed semigraph based connected edge dominating virtual backbone. The efficient distribution of popular content on the constructed semigraph based virtual backbone (hot spot places) increases the Quality of Experience (QOE). The numerical results reveal that the proposed framework improves the QoE in terms of content delivery and cache hit rate, minimized average downloading latency and backhaul load. The efficient usage of cache and bandwidth has been ensured while meeting high QoS.

Wireless Sensor Network (WSN) comprises thousands of tiny sensor nodes used in various monitoring applications to sense environmental conditions. WSN collects and organizes the sensed data and transmits to the Base Station (BS). Due to the limited battery energy of sensor nodes, optimal data collection and transmission techniques are necessary for the prolonged working of the sensor network. The Particle Swarm Optimization (PSO) method is used in this paper to form the cluster in WSN and a Fuzzy based Energy Efficient Routing Protocol (E-FEERP) is proposed using energy of battery, average distance SN from BS, node density and communication quality to transmit data from Cluster Head (CH) to the BS optimally. The simulation results of the proposed E-FEERP is compared with the existing approaches, which show an improvement in the network performance-based parameters such as throughput, residual energy (RE), load balancing ratio and packet delivery ratio, energy consumption and network lifetime.

The on-going digitalization of our critical infrastructures is progressing fast. There is also a growing trend of serious and disrupting cyber-attacks. The digital services are often fragile, and with many weaknesses and vulnerabilities. This makes exploiting and attacking the services a little too easy. If the services verifies all inputs, many security threats will be avoided. Similarly, if one diligently tests the services with malformed inputs, one will uncover many security and software quality problems. In this paper we investigate “input validation” and “fuzz testing” as a means to improve security. The aim is not exhaustive coverage, but to provide indications of usefulness and to serve as a call for action.

Generalized frequency division multiplexing (GFDM) is non orthogonal multicarrier modulation scheme which is suitable for the fifth generation (5G) of wireless network. Pulse shaping filter design in GFDM system have effects on symbol error rate performance due to inter symbol interference (ISI). In this paper Contribute to symbol error rate (SER) performance in GFDM system with additive white Gaussian noise channel (AWGN), zero forcing channel (ZF) rayleigh fading has been analyzed for pulse shaping filter namely raised cosine (RC) and root raised cosine filter (RRC) and also Simulation is done and results are reported in terms of symbol error rate (SER), signal to noise ratio (SNR), different value of roll off factor and different modulation technique. Comparison of simulation results of this method with existing methods is done and improvements in result are obtained as compared to existing.

Wireless Sensor Networks (WSNs) play a crucial role in developing the Internet of Things (IoT) by collecting data from hostile environments like military and civil domains with limited resources. IoT devices need edge devices to perform real-time processing without compromising the security with the help of key management and authentication schemes. The above applications are prone to eavesdropper due to cryptographic algorithms' weaknesses for providing security in WSNs. The security protocols for WSNs are different from the traditional networks because of the limited resource of sensor nodes. Existing key management schemes require large key sizes to provide high-security levels, increasing the computational and communication cost for key establishment. This paper proposes a Hybrid Key Management Scheme for WSNs linking edge devices which use Elliptic Curve Cryptography (ECC) and a hash function to generate key pre-distribution keys. The Key establishment is carried out by merely broadcasting the node identity. The main reason for incorporating a hybrid approach in the key pre-distribution method is to achieve mutual authentication between the sensor nodes during the establishment phase. The proposed method reduces computational complexity with greater security and the proposed scheme can be competently applied into resource constraint sensor nodes.

This paper presents a multiband coplanar waveguide (CPW)-fed concentric circle shaped patch antenna for WLAN, LTE-A, 5G Wi-Fi, and X-band applications, which is loaded with metamaterial unit cell. The proposed patch antenna employs arc shaped patch and concentric circle shaped patch achieves ultra-wideband operation. The proposed two layer antenna configuration consists of metamaterial super-substrate. The measured reflection co-efficient (S11) demonstrated three wideband operation ranging from 3.06GHz − 4.72GHz (B.W of 42%), 4.94GHz-6.19GHz (B.W of 22%) and 6.50 GHz − 10.56 GHz (B.W of 47.5%). The metamaterial structure is obtained through the arrangement of the proposed unit cell in 5 × 5 order. The proposed metamaterial structure properties were examined for wave propagation through the material in two major axes directions (x and y-axis). For both axis, the metamaterial exhibits near Zero refractive index for wide range of frequency. The addition of this super-substrate provides significant gain enhancement by 181% when the maximum gain reaches 6.2 dBi at X-band frequency. Since the proposed antenna is more efficient, it might be used for X-band operations such as satellite communication, military, and medical monitoring.