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The medical field has witnessed an exponential growth of wearable devices mainly due to the advancement in wireless communication and antenna technology. There is a demand for wearable antennas, which are lightweight, flexible, and ease of integration into the fabric which suits on-body applications. Therefore, this work presents the design, fabrication, and analysis of two microstrip antennas using denim cloth and stacked photo paper as substrate material. The novelty of these antennas is the use of silver fabric as ground and radiating patch-conducting layer, which has resulted in significant improvement in overall antenna performance. It is observed that our fabricated antennas have exhibited a gain of 8.71 dB with VSWR of 1.32 for denim and gain of 2.45 dB with VSWR of 1.03 for photo paper substrates respectively.

Resource discovery is a key challenge in dynamic environment such as Peer-to-Peer (P2P) MANETs. To leverage the lookup costs and efficiently discover the resources, the peers in a P2P network commu ...

Over the last few years, the computer vision has a huge impact in the field of information technology. The foundation for computer vision is laid by image processing. Digital Image Processing is one of the vast area of research; with three indispensable aspects as; Image De-noising, Image Compression & Image Security; with multiple applications in the field of automotive, medical, space, security systems, military & many more. This survey paper includes the evolutionary analysis of various image de-nosing techniques such as Median filtering, Pixel similarity weighted frame averaging, Gaussian filtering, Bilateral filtering, Anisotropic diffusion; that are used for betterment of system performance in terms of noise suppression, edge preservation, texture detection, Operating frequency, PSNR & many more. Moreover, the paper also includes the evolutionary analysis of multiple image compression techniques based on DCT, DWT, fractal image compression & neural network. Finally, the various image security techniques are analyzed. From the study of various literatures on image processing, it is found that combine model of image processing with all the three stages including image de-noising followed by image compression & image security is not yet discussed.

The wireless sensor network (WSN) consist of battery-powered sensor nodes which are self-configured and are deployed for monitoring several physical or environmental conditions such as temperature, pressure, humidity, vibration, pollutants etc. The major constraint in most of the WSN applications is the replacement/recharging of the battery contained by the node once it gets exhausted. This limitation reduces the lifetime of WSN. The placement of energy harvesting device within the sensor node may be the best probable solution to recharge the exhausted battery. In this paper, the integration of low cost, light weight and foldable flexible solar cells with WSN has been focused. The aim of this paper is to fabricate the flexible solar cells and showing the potential use of them in WSN. Moreover, the use of flexible solar cell is the better selection for emerging wearable WSN. This paper also describes the various issues in the already developed energy harvesting models and suggested a self-powered model for energy management based on finite state machine (FSM). The proposed models completely avoid the overcharging and the frequent charging of the batteries. This optimal utilization of the battery maximizes the lifetime of WSN network. In the proposed model, the flexible p–i–n solar cells are used to convert solar energy into electrical energy that can charge the battery of the WSN node. Finally, it can be concluded that the node will continue to function actively till the battery lifetime i.e. approximately 25–30 years.

In the modern era, WSNs broadly used in many research areas. Mainly researchers are focusing on rising the network lifetime, throughput and decreasing the energy utilization to make the network more reliable, robust and more responsive for a longer period of time. In this paper, two key aspects are taken into account; (i) network lifetime (ii) throughput of the network. The proposed approach is based on multilevel heterogeneity inspired by SEP (Stable Election Protocol). First node dead in the network plays a vital role in network lifetime because if the first node dead after a long period then definitely network lifetime becomes better. To get better the network life time, the proposed approach is another effort to make the network more responsive. Proposed approach compared with NEECP (Novel Energy-Efficient clustering protocol), ICACO (Inter Cluster Ant Colony optimization) and DCHSM (Dynamic Cluster Head Selection Method) gives the improved outcome in conditions of Network lifetime and throughput. In addition to this, comparison with existing approaches is carried out by considering the research papers from the year 2000 to 2017for 16 approaches.

Numerous devices are expected to upsurge profoundly in the proximate future with an estimated figure exceeding the 50 billion benchmark of connected devices by 2020. There are some rudimentary demands of the subscribers like system capacity, improved data rates with reduction in the latency and secure transmission of data in an unsecured media and to meet these demands, the cellular network has to go under suitable progression. As a building prospective for fulfilling these demands, to overcome the threats and challenges in the transmission of the data and to provide security to the information from the possible attacks, 5G is emerging as an optimal solution. For the demands of the tremendous count of subscribers, Device-to-Device Wireless Communication Network is an operative technology. The escalating content sharing amongst the users has been resulting in an exponential intensification in the wireless data traffic, coercing networks of cellular users to experience a suitable cataclysm. For allocating the resources to the Cellular Users under an attack scenario, numerous advances have been made till now. In this paper, an adaptive resource block allocation algorithm using HMM with beamforming approach has been projected to achieve high values of secrecy rate and low secrecy outage probability in 5G Wireless Communication Networks with the consideration of different applications demanded by Cellular Users and the priority of the applications (video, voice and data) in accord with enhanced Quality of Service of the channel. As the study of inherent secrecy rate for secure transmission of data in Wireless Communication Networks, random techniques are currently in great interest over the communication field. In this paper, it is to explore a multi-user scenario demanding different applications from BS under threat scenario of multi- eavesdropper. In this paper, different types of Beamforming are studied and different techniques involved for secure communication. An architecture has been provided for the security aspect in accordance with beamforming approach. As for better channel capacity, beamforming approach with diversity is an optimal solution for the secure transmission of information for Next Generation Networks.

This paper proposes a new way of Quantum signature masked authentication, in which both user and certifying authority is needed for generating final signature. Our scheme uses decoy particle to protect key from all the attacks in the transmission process and does not require encryption of each key. The paper also shows that our scheme is protected against impersonation, forgery and outside attack and is more efficient than the other masked schemes existing in quantum cryptography.

Present-day wireless methods are necessary to support a variety of higher-speed data communication facilities for its subscribers such as cloud-based video streaming facilities. One method to attain this is by using efficient resource allocation systems for transmitters and receivers using wireless communication methods. Wireless strategies and technologies are ubiquitous and enhancing progressively in the present world. In wireless communication technology, power and resource allocation remain the main issue due to the lack of resources by optimizing the number of subscribers and services. Hence, in this paper, we discuss different algorithms and techniques such as power-efficient resource allocation algorithm, real-time scheduling algorithm, NOMA, SCMA, Device-to-Device (D2D) communication, OFDMA, power and resource distribution in 5G, wireless sensor networks (WSNs), smart grids, cloud computing, and fog computing which converges stably and quickly. The Harmony search algorithm is also another way used for resources in wireless communication networks and discussed in this paper.

Massive Multiple Input Multiple Output (m-MIMO) or Large Scale Antenna System (LSAS) is the latest version of cellular network technology with the purpose to send data from the base station to different users. The huge determined expansion in the mobile equipment and data rate requirement has created the rigid necessity for future wireless communication networks. The m-MIMO envisages a significant increase in the capacity but at the verge of excessive hardware complication. In this paper, we put forward a less complex precoding scheme based on the hybrid architecture to achieve a performance higher than the conventional baseband Minimum Mean Square Estimation (MMSE) precoding. In this paper, we propose a precoding schema namely amplitude and phase MMSE (APMMSE) to attain the performance of traditional MMSE precoding. The informed greedy best-first search has offered to estimate the baseband precoding matrix. It formulates the amplitude of the baseband precoder. Radio Frequency Precoder modifies the phase and provides an optimal signal-to-interference -noise ratio SINR and thus improves spectral efficiency of hybrid architecture.

In the past decade, the OFDM access method has been widely used in different types of networks. Indeed; OFDM is the technology of choice for all major wireless systems, including WIFI, WiMAX, 3G, 4G and 5G. In this paper, we are interested in its application within a cognitive radio networks. The main objective is to provide an acceptable quality of service for the secondary user while minimizing interference with the primary user. This problem has been formulated in the literature in the form of a multi-objective function with three modes of communication (multimedia, reliable and low battery). In this paper, we exploit the efficiency of the bounding operators of the branch and bound method in order to solve this problem. The simulation results showed the effectiveness of our proposal by comparing it with the cuckoo search algorithm which has already been validated in the literature for this type of problem. Our proposal surpasses the cuckoo search algorithm for two modes of communication in terms of fitness and execution time.

With the emergence of various types of wireless communication products and the explosive growth of wireless network services, spectrum resources have become increasingly scarce. As the basis of cognitive radio technology, cognitive devices need to have real-time spectrum sensing capabilities, and classify and predict the perceived radio signals. Introducing Bayesian network for model construction, and using genetic algorithm to optimize and improve the model structure can more accurately establish the optimal network directed graph in structural learning, so as to better explore the high-risk predictive factors of machine learning. This paper makes a comparative analysis of the basic methods of the Bayesian network model in structural learning and parameter learning, and discusses the algorithm models that may be used to improve the structural learning. The adaptive search scoring function genetic algorithm is introduced into the network topology map construction part of the Bayesian network model structure learning, and a Bayesian network machine learning prediction model based on genetic optimization is proposed. The prediction algorithm of the maximum posterior probability of the perceptual signal considers which system the signal is most likely to come from at the next moment, and uses the estimation of the signal characteristics of the system as the signal prediction result. This paper further derives the upper bound of signal feature estimation error. Based on the prediction algorithm, the cognitive device can avoid the communication channel that will be occupied by the main user system in advance, thereby saving channel switching overhead. Simulation analysis verifies that the prediction algorithm designed in this paper has better prediction accuracy, and shows that as the number of signal samples sensed increases, the accuracy of the prediction algorithm is also improved accordingly. The classification accuracy rate based on Bayesian network is above 83% and the fluctuation rate is reduced to 2.5%.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

Due to the limitations of English learning environment and teaching conditions in China, English learners in China generally have difficulties in oral English learning.With the development of computer science and technology and the progress of language teaching and learning methods, it is possible to solve this problem.Therefore, this paper applies the mature deep learning technology to English speech recognition and constructs a speech evaluation model.Through experimental research, it is found that the intonation, speech speed and intonation evaluation methods adopted in this paper are highly robust and the English pronunciation quality evaluation model is also feasible. It can effectively promote the development of foreign language teaching in China.

The emergence of Internet-of-Things (IoT) resulted in building an environment of intelligent applications and services activated by a multitude of sensors. These sensor nodes are installed in a transparent and remote environment which makes devices vulnerable to security threats, especially when there are malicious node attacks in those networks. Malicious nodes can initiate multiple attacks, based on methods of coercion, retransmission, and discard. This paper suggests a centralized model of trust based on clusteringto address the security risks and challenges associated withIoT. Trust evaluation is a significant factor in planning and assessing defence solutions. Many theoretical works have been carried out to suggest trust evaluation and prediction methods. The main objective of this paper is to develop a centralized trust model for IoT. Initially, a cluster is formed based on the node location. Then for each group, the master node is selected based on their direct and indirect trust value, which includes Quality of Service (QoS) and Social trust properties. The master node is periodically updated using node data trust value and each node cluster sensed information using a regression model-based clustering. The intra-cluster and inter-cluster analysis is utilized to find the data trust value of the node, as well as outlier data. An extensive simulation is performed to analyze the performance of the proposed work. The result indicates that the projected model achievesimproved in terms of various operational metrics.

The estimation of power consumption for various wireless systems involves complex computational algorithms for digital signal processing, as accessing and implementing these algorithms in the form of hardware is critical. There is therefore a need to design energy-intensive wireless communication systems. This paper presents a novel method based on the evaluation of practical systems implemented by the Application Specific Integrated Circuit and Field Programmable Gate Array for the assessment of power consumption and computational complexity for the different mathematical operations used in the DSP system algorithms. This paper focuses on the development of a novel metric to map the power consumed to the complexity of computing using the mathematical operations of the wireless system transceivers. This makes it possible to combine complex computation metrics for every single operation of mathematical computing. Thus, the whole algorithm can be described by a single metric, making comparison with other algorithms easier, besides being informative. This approach is useful in assessing the computing power of various algorithms involved in DSP wireless communication systems, which, in turn, makes it possible to compare the complexity involved in computing the various systems, which in most cases is misleading. Based on the assessment of the power consumption of a few DSP algorithms, it is evident that higher power is required by some algorithms because they are not suitable for systems operating at constrained power in wireless communication. The proposed method can also be used to implement different hardware systems with the required calibration to be adapted to the platform.

Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized PCA-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized PCA for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies.

This paper deals with the coverage probability analysis of mmWave simultaneous wireless information and power transfer (SWIPT) system using stochastic geometry. mmWaves are higher frequency waves results in dense deployment of base stations (BS) leading to a higher data rate. Stochastic geometry provides a mathematical model to the randomly distributed nodes in the network. The Poisson point process (PPP) is the most analytically tractable and widely used model for the location of BSs, but it fails to capture the underlying separation between the BSs of cellular networks. Ginibre point process (GPP) model is used for the repulsion between the two nodes and provides a realistic model compared to PPP. In this paper, mmWave interfering base stations are considered to be GPP for deriving the coverage probability analysis. The proposed model considers the interfering base stations for analyzing the coverage probability. The closed form expression for the coverage probability is derived and is analyzed. The theoretical results are validated through simulation results.

Cooperative spectrum sensing (CSS) is an efficient method to detect the reliability of the free spectrum, however, with great overhead and energy consumption. In this paper, we study a much broader application of CSS, where the sensors are used to sense multiple channels. On the one hand, since the tiny and low-cost sensors do not have high-speed analog-to-digital-convertors, they cannot simultaneously sense more than one channel. Therefore, the simultaneous multi-channel CSS is an important issue in a cognitive sensor network (CSN). On the other hand, these tiny sensors do not have high-power batteries, which makes the network lifetime as an important metric. In this paper, node selection is proposed for the multi-channel CSS to maximize the lifetime of a CSN under some detection constraints with lower overhead than cooperative sensing by all the sensors simultaneously. We analyze the problem for the OR and the AND rules, which can be implemented at the fusion center. The problem is solved by using convex optimization methods where assignment indices for every sensor are assumed. We provide a performance analysis through simulations using MATLAB, which shows that the sensor selection scheme provides a significant long lifetime for a CSN compared to the case where sensors are selected randomly and where all sensors are just classified to sense the channels simultaneously.

Up to now, considerable results have been concluded for two-way relaying system with linear modulations. The performance of both transmit antenna selection (TAS) and receive antenna selection (RAS) needs to be investigated in some two-way relaying multiple-input multiple-output (TWRM) systems. In this paper, the goal is to investigate the nonlinear modulations, i.e., continuous phase modulation (CPM) in TWRM systems. Firstly, it is urgent to decrease the decoding complexity of the phase states subject to various modulation index of CPM. Then, the performances of both TAS and RAS with CPM modulations in TWRM systems are analyzed and evaluated. More exactly, the pair wise probability (PEP) is used to evaluate the performance based on analysis of CPM signal matrix, which is computed in a new form of the Laurent expression. Further, the tight upper-bounds for the frame error rate (FER) are derived for CPM in TWRM systems. Finally, simulations show in good agreement with the analysis in this paper.

The Internet of Things is an emerging area which deals with transfer of the data through the wired or wireless network. The prime thing that needs to be addressed in this is the security of the data that must be transferred within the optimized time limit. In this paper, throughput and time delay are need to be considered for the optimized data transfer and while concentrating on this, there is a possibility of allowing the data to be vulnerable to attacks. Security algorithms currently available may be adequate for the wired system and not as the same for wireless scenario. PRESENT cipher is a one of the popular cryptosystem used in wireless which falls under the light weight cryptography category. Gift cipher is an enhanced version of PRESENT cipher. Which aims that maximizing the throughput. In this, iteration structure used for encryption. This can still be improved and optimized in terms of increased data rate and reduced time delay. In this paper, implements the optimization technique of the existing GIFT cipher and throughput is considered as the performance metrics. Pipeline and sub-stage pipeline techniques are used for enhancing the architecture.

The medical enhancements have been improved to increase the life span of mankind. High penetration of Wireless Sensor Network in medical field helped the doctors to diagnose the patience in accurate manner and prescribe the medicines accordingly. In this modern Era, many people have permanent implant like face maker and it is life threatening to keep changing this body enhancement and it is need to have a system in place to increase the functionality of the Wireless Body Sensors. The important parameter which drags the battery energy and reduces its life span is path loss, and transmission loss. This paper proposes enhanced life time improvement for wireless body sensor networks using optimal clustering and advanced path selection protocol in short Energy Proficient Reliable Multi-hop Routing protocol. The EPMR designed in two stages, the first stage start with the collection of data from all body sensors is done through optimal clustering technique based Modified Conditional Spider Optimization (M-CSO) which avoids continuous individual transmission of data by the sensing nodes to base station in multi- hop distance (this stage the transmission loss is eliminated or reduced) hence the protocol concentrate to reduce transmission losses and the second stage implements the discovery of distance is done through by deploying Modified Flower Bee Algorithm (M-FBA), this stage the protocol finds best suitable and shortest next node to transmit the data. The finding of this paper is simulated using Math lab Simulink and verified through Network Simulator – 2 (NS-2) tools for validation and real time implementation of the results. The result obtained by this proposed method suggests that it has enhanced the sensor life term in many folds in comparisons with existing methodologies.

In general, WMSN follows many-to-one technique to transmit the data and to sense the information. There occurs a rapid increase in congestion or network traffic due to the generation of a large number of sensors. Moreover, the performances are jeopardized due to transmission and a high rate of packet losses. In order to address such shortcomings, this paper aims in developing a Hybrid Red Deer Salp Swarm (HRDSS) based routing approach. The HRDSS approach is the integration of a red deer and the salp swarm optimization algorithm. The work outlined in this paper is to minimize four different objectives namely packet loss, memory, delay and expected transmission cost. The main intention of the multi-objective function involves generating a diverse optimal solution set that is utilized to evaluate the trade-off among various objectives. We also presented the simulation results for two different scenarios comprising of the network grid and the optimization test functions that are carried out to determine the effectiveness of the system. In addition to this, the comparative analysis is done and the results reveal that the proposed HDRSS approach provides the best optimal routing path when compared with various approaches.

Recently, the Internet of Things (IoT) had emerged very rapidly and became the most important technology in today’s era. In an IoT-based environment, every physical object is connected to the internet. It has many applications in the fields of home automation, healthcare, military, weather forecasting, and industrial monitoring. Wireless Sensor Networks (WSNs) acting as a backbone of any IoT-based system where IoT-enabled sensor nodes are deployed to collect real-time data from the monitoring environment. IoT-enabled WSNs require well-designed network architecture so that the overall lifetime of the network increases since the nodes are battery-operated. The overall performance of the architecture depends on how the data is transmitted from the source nodes to the Base Station efficiently and effectively by minimizing the data losses. Static Sinks and Mobile Sinks are used for data gathering in IoT-enabled WSNs. But the performance of Mobile Sink (MS) based data gathering approaches are more efficient as compared to Static Sink based data gathering approaches. However, MS-based data gathering approaches have several drawbacks and limitations. Therefore, a detailed study of the existing state-of-the-art MS-based data gathering approaches can help further development in this direction. In this paper, the working of the sensor network is explained with its application in various fields. It also discusses the various path selection algorithm used for the MS to gather the information in the shortest possible time by minimizing the energy dissipation of each node to enhance the overall lifetime of the whole network. Some other factors also affect the path planning for a MS which is discussed and explained in the paper.

Acquisition schemes for hybrid spread spectrum (HSS) systems with a fixed threshold cannot provide satisfactory performance, due to the instantaneous changes of channel state information, environmental interferences, and mobility referred to as outliers. In these conditions, the received signal level varies rapidly. The fixed thresholding method (TM) can cause too many false alarms or the reduction of the detection probability for the selected threshold value. In this paper, we consider a robust acquisition method for the HSS systems using an adaptive constant false-alarm rate (CFAR) TM. In our proposed algorithm, the adaptive threshold is calculated based on the statistical analysis and average energy of the adjacent cells. We derive the exact closed-form solution of the acquisition time as a function of the detection probability, false-alarm probability, and the systemic parameters. Another issue is the extraction of the secure frequency-hopping pattern search period, which we examine in this paper. The theoretical analysis and simulation results show that the proposed algorithm can reduce the acquisition time by more than 45% and prevents the performance degradation in the outliers conditions without increasing hardware cost, compared to the conventional fixed TM.

Information retrieval (IR) defines the process of searching and attaining specific information resources which are related to the specific information requirements from the available resource pool. It finds useful in several real time application areas namely digital library, healthcare, education, internet browsing, etc. Recently, deep learning (DL) models become popular in different fields of image processing, object detection, and natural language processing. Therefore, in this paper, DL models are employed to retrieve the text and images proficiently. This paper presents an ensemble of DL based IR models for text and images. The proposed model intends to develop DL models individually for text and images. Initially, convolutional neural network based VGGNet-19 model is used as a feature extractor and Euclidian distance based similarity measurement for the retrieval of images. At the same time, bidirectional-long short-term memory (BiLSTM) technique is applied for retrieval of textual documents. The presented BiLSTM model sequentially considers every word in a sentence, extracts the details and embeds it to the semantic vector. In addition to the feature extraction using deep learning techniques, the similarity measurement emphasis the closeness of the document to the given query. The proposed retrieval system has tested on text and images for both general and specific domain (agriculture) with the datasets of Yahoo, Google and Corel10K. With the datasets the performance has been computed by the standard measures such as precision, recall and F-score where the proposed deep learning model produces better results when compared to existing techniques. The proposed model has been tested for the specific domain and achieves the performance of 93% precision and 85% recall and 90% F-score when compared to the existing model.

Digital watermarking has emerged as a potential solution to the copyright protection related issues of digital data. This paper presents key paradigms of research in robust watermarking techniques for copyright protection, copy protection, and authentication of digital multimedia. The main issues that derive research in robust watermarking schemes are imperceptibility, security, and robustness. The purpose of this paper is to provide a gist of robust watermarking schemes in the transform domain with the help of some brief theories proposed in the literature. Frequency transformation techniques such as DCT, DFT, and DWT, RDWT have been the most widely used methods to develop robust watermarking algorithms in the transform domain. The general framework of the watermarking system, recent application areas, characteristics, classification of information hiding methods, and various performance evaluation parameters considered by researchers have also been presented in this review. Broadly, this study reviews and compare performance summary of the several state-of-art robust watermarking methods available. This survey paper will be beneficial for the researchers keen to contribute to the field of robust digital watermarking particularly in the transform domain.

In this paper, a massive MIMO enabled backhaul model for two-tier heterogeneous networks with in-band full-duplex transmission and imperfect CSI is provided. Because of a massive number of antennas at the macro base station and densification of small cells, circuit power consumption increases in the system. It is requisite to study energy efficiency of such backhaul networks. This paper aims at maximizing EE for optimal user association, spectrum allocation, and power allocation while accounting for QoS and backhaul constraints. A joint optimization problem is formulated as a non-convex mixed integer non-linear problem which provides an un-affordable computational complexity. To crack the problem efficiently, it is progressively divided into sub-problems until each one is convex and is solved separately to obtain the optimal solution. Furthermore, complexity of the proposed algorithm is evaluated and Jain’s fairness index is measured. Simulation results of the proposed distributive algorithm are shown and compared with that of the existing algorithm (Max SINR) to manifest the strength of the proposed algorithm. The impact of self-interference cancellation factor and channel estimation error on EE is clearly reflected by the numerical results. In addition, it is observed that using the proposed algorithm results in better EE than increasing the number of antennas.

Massive Multiple-Input Multiple-Output (MIMO) technology has the potential to deliver high spectral efficiencies by leveraging the ability to form narrow beams that can be resolve users based on location. Complexity is a significant barrier, particularly for massive MIMO systems employing orthogonal frequency division multiplexing (OFDM). This paper presents a simple technique for employing multiple-beamforming on the downlink of massive MIMO-OFDM systems. With a focus on practicability, the channel state information (CSI) is specified with only two scalar parameters, namely, beam angle and average channel signal to noise ratio. A novel channel estimation procedure is described, wherein, all mobile users will acquire CSI simultaneously using circularly-shifted pseudo-noise training sequences. The base station then performs an optimization task to identify suitable spectrum (or subcarriers) for each user. We have shown an iterative algorithm for orthogonal multiple access in this paper. Extension to non-orthogonal multiple access is straightforward. Simulation results are presented to show the behavior of our multiple-beam OFDM technique. We have considered only the cellular downlink in this paper.

Wireless sensor network contains a huge volume of sensors that are capable of observing environmental data and conveying that information to the base station. The zone routing is a way to group sensors into multiple clusters in sensing field. In general, each zone has a single co-ordinator that operates as a zone head and accountable to collect the data from all sensors inside the zone and then forward it to the base station. Clustering and energy efficient path construction to base station is a major issue in sensor networks. This paper concentrates on clustering the sensing field into zones and further splitting it into zone quadrants to maximize the energy efficiency of sensor nodes with minimum delay transmission time. An “Energy Minimized optimal zone selection of Sensor Networks to build the Data Path” protocol is proposed in this paper to minimize the load on zone heads by using the zone coordinators to gather the information from different quadrants of zone. Distance-based communication with the minimum hop is opted to minimize the energy cost. A critical evaluation is carried out on the proposed protocol in the context of total energy consumption of network and average energy consumed per node, lifetime of the Network, delay in reception of packet and packet delivery ratio. The results of our research work and analyzed and benchmarked against similar protocols using Network Simulator 2. The outcome of this research is a reduced delay in packet processing with low energy consumption which significantly increases lifetime of sensor network.

Wireless sensor networks, generally, are grouped into clusters to collect information effectively. Such grouping of nodes helps immensely to elongate the life of Wireless Sensor Networks. Message exchanges between nodes for consecutive and periodic clustering overload the sensor nodes and cause a shortfall of energy. Additional overhead during cluster formation, instability in energy use and the difficulty of information sharing during clustering, uncertain network structure, etc. are the current clustering problems. There is also a need to enhance intra-cluster transmission and to find effective methods to extend the network's lifespan. This paper aims to reduce the energy loss of nodes by reducing the message transmission overhead and simplifying the creation and upgrading of clusters to improve the lifespan of the network. A clustering strategy where the cluster is regularly restructured to decrease the overhead on cluster head nodes is also proposed in the paper. The suggested approach reduces data transmission using machine learning by the cluster member nodes and reduces the energy consumption of individual sensor nodes by implementing a suitable active/sleep schedule. To calculate the cluster update cycle and sleep cycle, it also makes use of the advantages of fuzzy logic by selecting appropriate fuzzy descriptors such as average data rate, distance from the head node to the sink and the remaining energy. The proposed approach optimizes the energy utilization of cluster heads and node members thereby enhancing the lifespan of the sensor network.

Web citation analysis is primarily used to examine the impact of an author, an article or a publication by counting the amount of intervals that has been cited by other authors. The significant goal of web citation analysis is helping the researchers to find their related papers for their further analysis. The references of the paper must be cited by the author, which is used to recognize the link among the previous relevant research. The citation provides the valuable information that directs the researchers to gain knowledge about the current trends and future developments and obtain new ideas in their respective fields. The citation’s information are incorporate in the database called web citation database such as Google Scholar, Web of Science, Scopus and so on. From the web citation database, extracting the user required information is very complex task. Most of the open source tools are available online but manual process is needed to select the user-required information from the web page. For instance if the user need author name and publisher from the web citation database, they required to choose the exact information tags manually in existing tools which consumes more time. To overcome this difficulty we proposed an algorithm Firefly Optimization Algorithm based Web Scraping for web content extraction from web citation database. The primary purpose of this research is to extract author information extraction process extracts citation information published by an author, journal name, publisher, year and citation using web citation analysis. The user’s query input will be the keyword for example big data, artificial intelligence, etc. The web citation information from multiple web pages using Web crawling and web scraping techniques are applied for web citation information based on the user query and Particle Swarm Optimization, Hidden Markov Model are applied for finding the best solution from all the feasible solutions. Experiments illustrate the proposed FOAWS algorithm outperforms well comparing to other two algorithms.

This paper summarizes the ongoing research initiatives based on the advanced multiple access techniques towards the fifth generation (5G) wireless communication and beyond. Prior cellular network generations have embraced various multiple access techniques with a common feature that is to have orthogonal signals at the side of the receiver for different users. The diverse requirements of huge number of connections about latency and throughput is the need of the hour. Therefore, 5G wireless systems and beyond, are preferring the design method to shift towards non-orthogonal from orthogonal in multiple access techniques. The paper will mainly focus on the non-orthogonal multiple access techniques considering the types of its candidates in multiple domains which can help us in identifying the impacts on the designs of multiple access for 5G network and beyond. This survey aims to discover the obstacles and possibilities of utmost importance for multiple access designs for 5G networks.

Multimedia is the process of handling multiple medium of messages over network with high rate data services in wireless cellular area networks. Communication is the process of exchanging information form one service to another. In wireless networks are significantly growth of affecting network performance and energy consumption. The major problem is end to end delay in each node and meets the quality of services. The followings are considered for implementing wireless sensor network such as reduces the network delay, propagation delay and energy consumption. The senor node can sense the encoding value and reduce the network traffic delay using mitigation method. This paper propose a unique approach to provide simple routing services with reduced traffic delay, end to end delay network performance and to achieve better performance using Distributed Source Coding and Effective Energy Consumption methods. In this paper we use optimal early detection algorithm for improving network performance and energy consumption problem. An iterative Shannon fano and Tuker method is used for finding optimal solution of each node values. Network Simulator-3 is used for simulating network environments and setup the experiments. Our proposed method shows high data rate, good performance and low energy consumptions. The results compare with existing methodologies and performance is good.

Recently, Device-To-Device (D2D) communications as a novel technology for improving the capacity and reducing power consumption in wireless networks have been taken into account. In D2D communications, multimedia programs utilize a great amount of energy sources and bandwidth to meet the increasing needs of mobile users. To solve the above-mentioned problem, in this paper, a new approach was presented for the localization of D2D communications on smartphones, which was based on the Dynamic Packet Transport and Multimedia Presentation Tracking, protocols as well as the distortion rate. Due to this new approach, the energy-aware optimization space and service quality standards were improved with an evolutionary swarm intelligence algorithm, called the Scaling Optimization Algorithm. Furthermore, 5G technology was used in the current study to speed up internet connections. In this regard, infrastructure was required. Therefore, the Internet of Things as infrastructure was considered in 5G technology. The results of the simulation of the proposed algorithm and its comparison with other algorithms reviewed in this paper hinted at the superiority of this algorithm in reducing energy consumption and improving the service quality standards.

A MIMO antenna having circular patch with CPW feeding is proposed in this paper. Design, Simulation, Fabrication and testing of MIMO antenna with different band notching characteristics are proposed in this paper. The designed antennas are suitable for portable UWB applications. UWB is the most promising technology which provides high data rate with in short range. In 2002 Federal Communication Commission (FCC) has approved a frequency band of 3.1–10.6 GHz for unlicensed applications. Within the band of UWB some of the wireless Communication bands with different frequencies are also operating. So to avoid interference, band dispensation characteristics are proposed. In this paper single dual, triple and quad band notch MIMO antennas are designed, simulated and tested and their results are reported. Elimination of interference with existing wireless bands like WiMAX, WLAN, X—Band Satellite Communication System and ITU is the prime focus of the paper. Concentric rings with rectangular split bricks are placed on the patch to eliminate the interference with the existing bands. Different antennas are designed and simulated using CST microwave studio software and the antenna parameters like Return losses (S11), Gain, VSWR, Radiation patterns and Correlation Coefficient are measured. Quad band notch antenna is fabricated using RT/Duroid 5880 substrate material with height 1.57 mm. To get better Isolation the antennas are placed vertically and a stub is placed in between the antennas. The quad band antenna able to notch WiMAX, WLAN, X—band and ITU band and port Isolation (S12) is very good and envelope correlation is below 0.001 in UWB frequency spectrum.

Narrowband-IoT (NB-IoT) is a standard-based Low Power Wide Area Network technology developed to connect a wide range of new Internet of Things (IoT) devices and services. NB-IoT bandwidth is limited to a single narrow-band of 180 kHz. Although NB-IoT provides low-cost connectivity, it provides channel to large number of smart IoT installed in households, building etc. However, in NB-IoT systems, repeating same signal over additional period of time has been taken as a key technique to enhance radio coverage up to 20 dB compared to the conventional LTE. Performance of NB-IoT system optimization and modeling are still challenging particularly coverage improvement in the case of real applications. For example, the narrow bandwidth in IoT and low energy have led to problematic issues in communication between IoT devices and network station, which results in low transmitter channel quality. Repetition process is used in the paper to enhance coverage and throughput, however in mean time increase the number of repetitions demands high bandwidth. So, an enhanced cooperative relay is used with repetition to reduce the demanded bandwidth. In this paper, we proposed an enhanced repetitions cooperative process of narrowband physical uplink shared channel (NPUSCH). The NPUSCH is transmitted using one or more resource units (RUs) and each of these RUs are repeated up to 128 times to enhance coverage as well as to meet requirement of ultra-low end IoT. The optimum number of repetitions of identical slots for NPUSCH per RUs is calculated and then simulated. In addition, the paper describes the analytical simulation to evaluate the proposed repetition of cooperative process performance for LTE-NPUSCH channel. Results show dramatical enhancement of uplink NB-IoT channel quality where the performance evaluation metrics were BLER, data rate, system throughput, spectral efficiency and transmission delay. The enhanced cooperative communication scheme for NPUSCH transmission channel in NB-IoT is achieved an average 23% enhancement in overall network throughput.

A Delay Tolerant Network (DTN) is a network capable of handling long delays and data loss efficiently in various challenged scenarios and environments. Owing to the mentioned challenges, it becomes crucial to study how the routing protocols are designed to adapt in such networks. Currently, the research in this domain is focused on the improving the data delivery ratio and minimizing delays in the routing protocols. This paper reviews state-of-the-art routing protocols for Delay Tolerant Networks (DTNs) as well as performs their comparative analysis. DTN routing protocols are grouped into four categories viz encounter based, time based, infrastructure based and hybrid & others. This categorization depends on the information they use for relay selection routing. We have also discussed some of the inherent drawbacks such as energy consumption, delivery rates and buffer constraints of the existing routing algorithms. We have also conducted an empirical analysis and observed the performance of the most popular existing algorithms of encounter and time based category. This paper offers a widespread study of the routing protocols with their pros and cons.

In this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures, such as reset gate and update gate, the GRU neural network avoids the problems of gradient disappearance and gradient explosion. It can effectively represent the characteristics of long correlation traffic, and can realize the expression of nonlinear, self-similar, long correlation and other characteristics of satellite network traffic. The paper combines the transfer learning method to solve the problem of insufficient online traffic data and uses the particle filter online training algorithm to reduce the training time complexity and achieve accurate prediction of satellite network traffic. The simulation results show that the average relative error of the proposed traffic prediction algorithm is 35.80% and 8.13% lower than FARIMA and SVR, and the particle filter algorithm is 40% faster than the gradient descent algorithm.

This paper proposed a new impulse radio ultra wideband (IR-UWB) signal to reduce interference on narrowband systems at high usage 5.2 GHz frequency. The combination of 4th and 5th order derivatives of Gaussian pulses were employed to generate the signal so that satisfy the US Federal Communications Commission (FCC) mask. The genetic algorithm technique was used to find the pulse optimized parameters. In this paper, spectrum was optimized by changing the shape factor (sigma) and amplitude of Gaussian pulses so that the transmitted power spectrum density was minimized at a frequency of 5.2 GHz and maximized at the other frequencies from 3.1 to 10.6 GHz according to the FCC mask. The signal was applied in an IR-UWB transceiver system with binary-phase shift keying modulation. The IR-UWB waveform is generated by a programmable digital to analog based circuit. The proposed system was compared with conventional IR-UWB systems at the same condition. The simulation results in multipath channel showed that the proposed system not only reduced interference with the narrowband system, but also this was robust against narrowband interference at 5.2 GHz frequency.

Authentication is a term very important for data communication security. We see many frauds due to authentication failure. The problem manifolds when communication is over insecure channel. Entity authentication is a term which involves proof of sender’s identity and very useful in various applications like in banking sector and various other client server mechanisms. Availability of quantum computers increases the vulnerability of breaking old protocols. Researchers are finding new platforms to overcome this problem and one such example is non commutative polynomial rings [NCPR]. In 2012, M.R.Vallauri [MRV], in his paper suggested an authentication protocol using NCPR. He has proved security analysis under the assumption that polynomial symmetrical decomposition problem (PSDP) is hard. In this paper we show that the protocol suggested by him is breakable without solving PSDP. We also provide corrected protocol to overcome this problem.

In today’s scenario, demand for error-correcting codes with minimal error constraints for wireless communications. Multilevel coding scheme with trellis codes as component codes provides flexible data transmission rates, coding gain, diversity gain with improved spectral efficiency and low decoding complexity. This paper investigates the potential improvements by using the Multilevel coding scheme with massive Multiple-Input Multiple-Output in Cognitive Radio Networks with trellis codes as component codes. This paper discussed space-time coding with beamforming and antenna grouping according to the channel state information. Multilevel Space-time coding is based on multi-level Quadrature Amplitude Modulation signaling and beamforming to mitigate the effect of primary users for the enactment of secondary users in Cognitive Radio. The primary users provide channels dynamically to the secondary user for an unknown duration. Our transmission use Quadrature Amplitude Modulation based signals, with an adaptive grouping of antenna which weight according to the optimization, which inherently depends upon the resource allocation of the secondary user. The results show that the proposed coded system achieves Bit error rate/Symbol error rate/Frame error rate and Signal to noise ratio varies according to sources sensing time.

In the existing environment, 30% of the base station has to handle the 70% of the network load. For a fact, the installation of additional base radio stations increases the deployment costs. The Computing Improved Aggregator Node B (CIANB) has been introduced for the 5G vehicular environment to fix this issue. The CIANB is the base radio station which consists of virtual cloud, storage space, and Aggregator Node B (ANB) pool. This paper discusses the forthcoming infrastructure, such as CIANB, which is progressing towards its innovation in the 5G vehicular environment. The CIANB and virtual ANB instance have been introduced in this paper to reduce congestion and to maximize the utilization of services without interruption. The evaluation metrics justify that the forthcoming infrastructure, CIANB, and virtual ANB greatly enhances the efficiency of the 5G vehicular environment.

In urban areas, the traffic congestion is the major prevalent transportation issue, and the vehicular traffic management is the critical concern for the town planners. As the road network is added day by day, the effort on traffic management is more crucial. So far, more contributions have been made in the traffic management problem, still there lags a gap on attaining accurate result. This paper introduces a new traffic management system with the use of smart barricade system. Basic concept of this smart system is the localization of barricade that is based on nature as well as the intensity of traffic with respect to three barricade modes: open, close, and partially open. To develop the smart barricade system for dynamic traffic management, initially, the historical data collection is done. The collection of historical data is enabled by adopting various traffic centres that facilitate the collection number of vehicles per instant and the vehicles speed. In this model, the placing of barricade is decided by the fuzzy logic system, and the system also concentrates on finding optimal membership limits to attain better results. For this, this paper proposes a new algorithm named Modified Grasshopper Optimization Algorithm with New Position, which is the modification of GOA. Finally, the proposed model compares its performance over other methods like conventional GOA, Particle Swarm Optimization, Firefly, Grey Wolf Optimization with respect to measures like number of vehicle transmitting and vehicle distribution, and proves the betterment of proposed model.

Wireless sensor network (WSN) is an integration of sensing, communicating, computing in a board range environment. Efficient energy consumption becomes the most challenging task for sensor nodes. The clustering and routing techniques are promising methods to resolve the issue and extend the network’s lifespan. The clustering technique is defined as grouping data into classes, every cluster sharing a high degree of similarity in between them, and each cluster being dissimilar with others. This technique is the best data processing model for WSN, and it controls the redundant data inside the network. The nomination of the appropriate cluster head is a major factor in the clustering technique. The object of this proposed paper is to equipoise the energy of the clustering nodes and route the data from cluster head to sink. We propose an improved particle swarm optimization gravitational search algorithm for clustering and routing in WSNs. Here clustering algorithm makes equal energy in the entire network by the uniform distribution of cluster head, routing algorithm decides the ideal routing path to convey data packet between cluster head and sink. The proposed paper integrates the exploration capacity of GSA and the exploitation capability of PSO. Detailed simulation performs using MATLAB based simulator in terms of residual energy, network lifespan, and convergence rate. In comparing our proposed algorithm to other existing algorithms, it outperforms significantly.

Distributed amplifiers (DAs) are always considered the best choice for wideband amplification as they offer appropriate gain, matching and noise figure over the large bandwidth. With the recent advancements in microwave integrated circuits, monolithic microwave integrated circuits and device processing technology, DAs have discovered new applications of the wideband amplifier. At mm-wave frequencies, the wideband amplifier is used for several applications such as radar imaging, space radiometry, optoelectronics, high-frequency transceivers, plasma diagnostics, ultra-fast measurement systems and defence systems. The main requirement of these applications is to amplify short pulses or to realize a high data rate. Studies show that DA may also be used for next-generation 100+ Giga-baud/s coherent linear fiber optic applications. This paper presents an overview of various types of DAs. In the paper, the different technologies and design methods used for designing various types of DA (from 2009 to 2019) are reported. Furthermore, a comparative analysis of the gain-bandwidth product based on different technologies is given.

Massive multiple-input multiple output (mMIMO) is considered as one of the most in demand and innovative technologies for the fifth-generation wireless communication systems. This paper attempts to frame a mMIMO system model, intends to improve the spectral efficiency, energy efficiency and performance gain. Here, the system performance achievements are premeditated in a multi-cell downlink mMIMO system under the core considerations such as “imperfect channel estimation, perfect channel estimation and the effect of interference among cells due to pilot sequences contamination”. The performance gains such as “spatial multiplexing gain, array gain and spatial diversity gain” are considered to maximize in this paper. For attaining this multi-objective function, an improved meta-heuristic algorithm called rider optimization algorithm (ROA) known as trial-based ROA adopted, and analyse the performance of the proposed model by comparing over existing models.

In present decade, cloud computing provides utility-based IT services to the global consumers. According to pay-by-use manner, it facilitates hosting of persistent services from the user, business and technical fields. But, it is to be mentioned that the data centers hosting the cloud-based services utilize large amount of energy, power and resources. Hence, there is a need of an efficient resource management model for cloud that involves in reducing the resource consumption and computational cost. And, for managing the virtual resources with respect to the varying demands in cloud environment, dynamic virtual resource management is required. With that concern, this paper presents a model called Energy and Power Aware Dynamic Migration (EPADM). Based on the model design, the main objectives such as, efficient resource mapping and provisioning algorithms are presented. The dynamic Virtual Migration (VM) operation comprises the VM relocation and consolidation parts for achieving desirable results. Moreover, the paper also concentrates on reducing the SLA (Service Level Agreement) based violation, which is a significant factor to be considered on cloud. The proposed EPADM model is evaluated using the CloudSim toolkit. The results illustrate that the proposed model has massive potential than others, as it provides energy-power efficiency, reduced SLA violations under distinctive workload cases.

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink.

Searchable encryption schemes allow documents’ owners to store their encrypted documents on servers, search for the desired keyword and then download only the desired encrypted file and then decrypt. Storing files on remote servers can be further developed, which, in addition to allow access to files at any location and at any time, it also gives the data owners the confidence that their files are stored without any change in the servers. This property can be interpreted as the integrity preservation property of encrypted documents in the searchable encryption schemes. Recently, in Yang et al. (Concurr Comput Pract Exp 29:e4211, 2017), Yang et al. proposed a semantic keyword searchable proxy re-encryption scheme and claimed their scheme resists against collusion and provides data privacy. However, in this paper, we show that unfortunately, their searchable encryption scheme does not provide the perfect security and their scheme is vulnerable against integrity contradiction attack. Our proposed attack is implemented in three different scenarios, its success probability of each scenario is one and its complexity is only one run of the scheme. We also improve Yang et al. scheme and show informally and formally that the improved scheme is secure against the attacks presented in this paper and also other known active and passive attacks. Comparisons also showed that the proposed scheme, in addition to the complete security it provides, is acceptable in terms of communication, storage and computational costs.

The fifth generation new radio (5G-NR) system design is aimed to have ultra-large transmission rate, massive capacity, massive device connectivity, enhanced mobile broadband (eMBB) services, and ultra-reliable low communication latency (URLLC) services. To support the various challenges and specifications, new wireless communication technologies including new notorious waveforms, forward error correction codes (FEC), non-orthogonal waveforms as well as massive multiple input multiple output (MIMO) antenna schemes have been introduced to aid the progress of pliable 5G air interface system design. Cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM) has been broadly selected in 4G-OFDM based long term evaluation(LTE), Wi-Fi, and other mobile communications due to the benefit of high spectral efficiency, ease of development and easy to integrate with MIMO systems, etc. However, the conventional CP-OFDM technology has some drawbacks, such as high out of band (OOB) emission leakage, an exact requirement of synchronization, and a limited number of CP lengths. Filtered OFDM (FOFDM) is a flexible foundational waveform by applying subband filtering on existing CP-OFDM which can support the needs of the waveform for 5G air interface. In this paper, a spectrally efficient waveform called polar coded filtered OFDM (PC-FOFDM) is proposed to fulfil these requirements for high data rate ultra-reliable low latency communications. This paper investigates bit error rate (BER) performance of PC-FOFDM scheme over additive white Gaussian noise (AWGN) channel and compares the results with PC-OFDM. The simulation results show that the PC-FOFDM scheme outperforms PC-OFDM in terms of spectrum efficiency, BER performance, and error vector magnitude (EVM).