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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.

Welcome to the special issue of Wireless Personal Communications. This special issue constitutes a selection of best papers from 2014 ICUFN (International Conference on Ubiquitous and Future Networks). All the papers have been remarkably extended by the authors and formally reviewed by at least three independent reviewers. ICUFN is an annual international conference which is co-sponsored by the IEEE Communications Society and organized by the KICS (Korean Institute of Communications and Information Sciences). The first paper entitled ‘‘ID-Based Communication Framework in Future Networks’’ presents a generalized ID-based communication framework and then show how the recently proposed ID/locator split based networking and information-centric networking architectures fit into the framework. The second paper entitled ‘‘Continuous Reference Broadcast Synchronization with Packet Loss Tolerance’’ proposes a packet loss tolerance

International audience; IEEE 802.15.3 High data rate wireless personal area networks (HDR WPANs) have been developed to communicate with devices within 10 m at high speed. A mesh network made up of a parent piconet and several child piconets can support multi-hop communications. Wireless mesh networks (WMNs) have been expected to be the ultimate solution for the next decade wireless networking, showing rapid progress and many new inspiring applications. The international standardization organizations formed working groups to address the problem of standardization for WMNs. These groups were the IEEE 802.15.5 (mesh extensions for WPANs),the IEEE 802.11s (mesh extensions for WLANs), and the IEEE 802.16a (mesh extensions for WiMAX). The IEEE 802.15.5 standard is the standard which defines specifications for including multi-hop functionality in the legacy 802.15.3 and 802.15.4 low data rate (LDR) WPAN networks. The impetus for a WPAN to operate in a mesh topology is to increase the network coverage without increasing the transmit power, to increase the route reliability via route redundancy, self-configuration, and efficient use of device battery life. In the case of meshed WPANs, multiple WPAN clusters compete for channel time in a shared superframe. Therefore, it is essential to determine the channel time requirements of each cluster with a certain number of devices and to determine how these clusters can compete to the shared channel time. In this paper, we investigate the different resource allocation mechanisms related to the meshed HDR WPANs for the 802.15.3 and the 802.15.5 standards. We introduce the single hop and the multi-hop IEEE 802.15.3 WPAN architectures. This is followed by the introduction of the IEEE 802.15.5 standard that provides the mesh capabilities for extending the coverage area of HDR WPANs. The current on-going research issues for resource allocation, including beacon interference, reservation collision etc., in both meshed 802.15.3 and 802.15.5 are alluded to.

In this paper, the analytical expressions for the outage probability of different decode-and-forward (DF) relaying strategies for two-source two-destination networks are evaluated allowing an investigation of their effectiveness in energy saving to be determined. Each source node transmits data to an interested destination node with the help of the remaining source node in a cooperative DF manner. Specifically, we consider four DF protocols, including repetition DF (RDF), parallel DF (PDF), network coding-based RDF (NC-RDF), and dirty paper coding-like network coding-based PDF (DPC-NC-PDF). The closed-form expression of the outage probability for each protocol is derived at high signal-to-noise ratio. The results show that the DPC-NC-PDF protocol achieves the best performance while the NC-RDF protocol is better than both the RDF and PDF protocols with proper linear NC coefficients. All the DF protocols are shown to achieve diversity order one and the highest multiplexing gain is achieved with the NC-RDF and DPC-NC-PDF protocols. Finally, simulation results are presented to verify the analytical findings and show the system throughput comparison of various DF protocols.

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.

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.

Mobile ad-hoc networks (MANETs) play an important role in the future of the industrial internet-of-things communication, where smart devices will be connected in a completely distributed manner. In the digital properties owing to the digital data properties, there exist difficulties in directly applying the encryption schemes to the one-dimension data. Thus, it is necessary to develop secure lightweight key frame extraction technique for improving privacy in the e-healthcare. This paper plans to develop the robust and reliable security protocol in MANET IoT application. A chaotic cryptography-based privacy preservation model is proposed in this paper for the purpose of improving the security in the MANET IoT. The key generation process in the chaotic map is optimized by generating optimal key pairs through the newly developed SA-SFO algorithm. The key selected from the chaotic map is influenced by selecting the optimal parameters through the proposed Self Adaptive Sail fish Optimization (SA-SFO). Finally, the experimental analysis is conducted, where for the case of character length as 100; the proposed SA-SFO eventually surpassed the existing ones with the cost function 22% as higher than PSO, 20% higher than GWO, 19% higher than WOA, and 21% higher than SFO respectively. The comparative analysis over the conventional models ensures the efficient performance of the proposed model in terms of diverse analysis in MANET and IoT platform.

An efficient sign language recognition system (SLRS) can recognize the gestures of sign language to ease the communication between the signer and non-signer community. In this paper, a computer-vision based SLRS using a deep learning technique has been proposed. This study has primary three contributions: first, a large dataset of Indian sign language (ISL) has been created using 65 different users in an uncontrolled environment. Second, the intra-class variance in dataset has been increased using augmentation to improve the generalization ability of the proposed work. Three additional copies for each training image are generated in this paper, by using three different affine transformations. Third, a novel and robust model using Convolutional Neural Network (CNN) have been proposed for the feature extraction and classification of ISL gestures. The performance of this method is evaluated on a self-collected ISL dataset and publicly available dataset of ASL. For this total of three datasets have been used and the achieved accuracy is 92.43, 88.01, and 99.52%. The efficiency of this method has been also evaluated in terms of precision, recall, f-score, and time consumed by the system. The results indicate that the proposed method shows encouraging performance compared with existing work.

In this paper, an enhanced independent component analysis (ICA) method is proposed for blind separation of noisy mixture signals. Considering the conventional ICA methods always have an inadequate capacity of resisting noisy effect. This phenomenon is usually encountered in wireless receiving processing. To obtain the improved separation quality, two mechanisms are conducted to formulate the modified cost function and the powerful optimization learning for investigating an enhanced ICA method.The fundamental of the proposed method is derived from the minimum bit error rate (BER) criterion and the Nesterov-accelerated adaptive moment estimation (Nadam) optimization approach. The main work of this paper includes the following facets. Firstly, a maximum likelihood (ML) principle cost function with minimum BER constraint is derived. Secondly, the modified cost function is by utilizing Nadam learning processing. Lastly, theoretical analysis and experiment results verify the improved performance quality of the proposed enhanced ICA method compared with popular representative ICA methods.

The deployment of blockchain technologies for multiple use cases has been widely investigated in the academic and business sectors over the last few years. The blockchain model has attained considerable attention due to its decentralized, persistent, anonymous, and auditable features. This review does a comprehensive literature analysis of broad blockchain implementations across several domains. The study’s key objective is to present a thorough overview of the widespread deployments of blockchain technology and demonstrate how particular aspects of this innovative technology can change the business community’s activities. Several papers are addressing the feasibility of using blockchain technologies in various fields. However, we include a description of blockchain concepts and comparative analysis of the application in six main fields: the Internet of Things, artificial intelligence, supply chain, cloud, healthcare, and multimedia networks. For each area, we analyze in-depth the approaches proposed by the research community and industry. This paper also discussed the different problems involved in each area. Finally, we explore the critical issues needed for the broader implementation of blockchain technologies in these sensitive areas.

Despite its various benefits, the Mobile Ad-hoc Network (MANET) has a number of obstacles due to its mobility, unstable topology, energy efficiency, and other factors. In such an environment, routing protocols are required to transfer packets from source to destination. Due to the limited range of each mobile node's wireless broadcasts, some mobile nodes can operate as intermediary nodes to forward a packet to its destination. In MANET, the multipath routing technique is used to decrease the routing overhead. It can be accomplished by minimizing network traffic. This paper aims to reduce the routing overhead to improve the effectiveness of broadcasting messages successfully. The Position-based Opportunistic Routing protocol is used to identify the optimal routing path in the network. The opportunistic routing system is mostly employed in MANET data transmission to reduce overhead. This paper presented a hybrid routing algorithm with the combination of Emperor Penguin Optimization (EPO) and the Bald Eagle Search (BES) algorithm (EPO–BES) for finding the best-forwarded path to moderate the routing overhead problem in MANET. NS2 platform is utilized to perform the routing operation and compared with existing techniques such as NKR, PSO-GA, PSO-BLAP, QUACS, and SGC-MO. The proposed method has obtained higher PDR, path optimality, end-to-end delay, throughput, routing overhead, load balancing, and energy consumption than other approaches.

Energy harvesting (EH) from radio frequency realizes simultaneous wireless information delivery and energy transfer, which greatly reduces energy consumption in the energy-constrained Internet of things (IoT) networks. On the other hand, cognitive radio (CR) enables the unlicensed secondary user (SU) to access the spectrum resource authorized to the licensed primary user (PU) to improve spectrum efficiency. Thererfore, the integration between EH and CR can address two critical problems of energy limitation and spectrum scarcity in massive IoT wireless networks. In the state-of-art power splitting-based overlay spectrum sharing strategy, the secondary transmitter superimposes its own signals on the primary signals received and forwards the combined signals with the harvested power from the primary transmitter. However, due to low energy harvested only from one primary transmitter for the combined signal, PU system performance e.g., outage probability (OP) and system throughput, declines seriously. Moreover, a perfect direct link between the secondary transmitter and the secondary receiver must also be required in the scenario. An alternative spectrum sharing strategy based on time switching can increase harvested energy, but cooperative communication cannot be conducted at the EH period, which also reduces the overall system throughput. Based on the consideration, a Two-User Relay protocol in this paper is presented that an energy-constrained relay node, as a center hub, harvests energy from two transmitter, i.e., primary transmitter and secondary transmitter, and perform cooperative communication concurrently for PU and SU systems satisfying the PU’s performance requirements (e.g., required target rate and OP). The expressions of OP and system throughput are derived and numerical simulations are made under the different system parameters, e.g., power splitting factor, power assignment factor, transmitting signal-to-noise ratio, channel fading coefficient, and required target rate. The results prove that our proposed Two-User Relay protocol improve spectrum sharing performance. Besides, the relay node can coordinate the harvested energy distribution between PU’s signals and SU’s signals to achieve the maximum system performance. Generally, the paper provides a new relaying concept for two-user communications in the future IoT wireless networks.

With the rapid development of information technology, issues such as network security and privacy protection have attracted more and more attention. The traditional way to solve these problems is to design complex cryptographic protocols to strengthen security, but the traditional solutions are based on centralized systems. The stronger the control of the central organization of the system, the more vulnerable it is to attackers. However, the blockchain technology that has emerged in recent years has revolutionized the use of decentralized solutions. The privacy and security issues of smart cities have become particularly important. Safety technology research in cities has also become very important. In order to better study the security technology in smart cities, this paper uses asynchronous transmission mode to study the blockchain security technology in smart cities on the premise of the Internet of Things technology. This paper compares and analyzes the data collection, sorting and calculation of the cryptographic elliptic curve algorithm before and after the improvement in the blockchain technology, the throughput and delay of different consensus algorithms, and the percentage of the computing power of the consensus algorithm. Transaction security. The final result shows that, the improved elliptic curve algorithm has more obvious advantages than the previous elliptic curve when the value of k is larger, and the higher the value of k, the faster its single scalar multiplication speed; The improved elliptic curve algorithm has more obvious advantages than the previous elliptic curve when the value of k is larger, and the higher the value of k, the faster its single scalar multiplication speed; The consensus algorithm has the largest percentage of computing power, BTC.com, accounting for 20.4%, and the smallest percentage of BWPool's computing power is only 1%. Therefore, it can be concluded that the decentralization and consensus mechanism of blockchain security technology can well guarantee the communication security of the Internet of Things.

With the recent developments in Wireless Mesh Networks (WMN), provisioning of Quality of Service (QoS) for real time applications is considered as an important but challenging area of research. QoS support for various real time applications are implemented in different layers of the protocol stack. The diversity of such research efforts has contributed to many protocols/schemes. This paper presents a comprehensive survey on various QoS enhancement schemes reported in the literature covering various angles of research domains. Diversified QoS challenges in WMNs and their reported solutions proposed in the literature are discussed using a layered approach. While presenting the state of the art research findings in MAC and routing, a classification framework for each of the layers is proposed first. The classification frameworks provide unified approaches for categorizing different protocols based on their distinctive features and sketch their correlations. However, the proposals for leveraging TCP performance in WMN have been discussed straightaway. Further, this paper provides an insight into the pros and cons of the surveyed protocols and points out the open research challenges for the future generation networking.

Benjamin Bloom had proposed three hierarchical models used to classify educational learning objectives (of the learner) into levels of complexity and specificity. These three models are cognitive (knowledge-based), affective (emotion-based), and psychomotor (action-based). The objective of this paper is to discover the outcome-based education mechanism to observe the transformation from LOTs to HOTs through unconventional teaching and learning practices in context with IT education that can be beneficial for teachers, students, and higher education institutions. Authors have explored the Cognitive model (knowledge-based), which is used to decide learners' competency with six different levels. These levels are: Remember, understand, apply, analyze, evaluate, and create. The former three levels are categorized in Lower order thinking skills (LOT), and the latter three are in higher-order thinking skills (HOT). A learner can upgrade competency by navigating various levels from LOT to HOT in particular. Bloom's Taxonomy (BT) competencies also indicate the probability of securing a leaner's employment. In this paper, the authors propose an outcome-based education mechanism to observe the transformation from LOTs to HOTs through unconventional teaching and learning practices in context with IT education. The results suggest a significant improvement in the educational practices and insights for educational stakeholders and policymakers. This transformation aims to create subject interest among learners and ensure a minimum gap between teaching and learning (short-term goal). The long-term goal of this practice is to provide the readiness of learners towards employment or entrepreneurship.

Trade-off between energy conservation and efficiency is one of the most important issues in designing Wireless Sensor Network (WSN) based applications. Network life time is primarily determined by the life time of battery. Recently, energy harvesting techniques that will recharge the battery in different non-conventional ways are being investigated by researchers. In this paper, an adaptive cross layer protocol is proposed which will provide trade off between energy harvesting time and active time for message transmission with the aim of increasing network lifetime. Depending on the value of various network parameters like, remaining energy of node, node density, message density in a particular region of the network, the cross-layer protocol will change its policy. The paper also proposes a cluster head selection method that ensures maximum network life time and higher quality of service. The result shows an overall increase in network lifetime as compared to other protocols.

Today, the use of large networks under the cloud and big data has become very common, as well as learning methods and artificial intelligence have been designed for them. One of the main issues in the banking and Big Data industry is granting bank facilities to people. The main challenge in evaluating the customers’ credit in the banking industry is that, due to the large volume of the data, it is impossible to review them manually, as we have to use computer algorithms for this purpose. Hence, the accuracy of the algorithm used for this purpose is essential and influential. In this paper, several predictive algorithms are employed on the problem, so that the result would be a prediction based on the combination of the results of each of them. To integrate the results of the algorithms, the weight to each of the algorithms is allocated, which presents the amount of importance of the algorithm in the result. The Ordered weighting averaging is used in this research. By applying these techniques, the proposed algorithms are described in this paper to achieve better accuracy than the existing ones.

Mobile ad hoc networks are attaining popularity to its highest currently, as the users need wireless connectivity regardless of their geographical location. Threats of security attacks are growing on the Mobile Ad-hoc Networks (MANETs). MANETs must require a secure mode for communication and transmission which is rather challenging and vigorous issue. With the aim of providing secure transmission and communication, researcher worked explicitly on the security concerns in MANETs. Several secure protocols and security methods within the networks were projected but utmost of the security measures in their designs are not ruminated. Hence, a novel scheme is proposed in this paper for the secure and reliable data transmission in MANETs under black hole attack constructed on amended Ad hoc On-demand Multipath Distance Vector (AOMDV) protocol of our base scheme. This paper comprises AOMDV protocol for the multiple route discoveries along with K-Nearest Neighbor (KNN) for nearest neighbor node selection and use False key-build Advanced Encryption Standard (FAES) encryption scheme for cryptography method. FAES algorithm is used with the aim of securing the IoT devices and data from the hardware and network attacks. Also, the interaction of the scheme with the IoT based concepts making our work even smarter to the users. The proposed scheme performance is stable with higher throughput while that of base scheme. The quality of the proposed scheme is measured in terms of energy consumption, EE-delay and throughput. The results of simulation show that the Variance, EE-delay, energy consumption and throughput of proposed FAES-AOMDV protocol is lower than the original AOMDV protocol. FAES-AOMDV protocol ensures the secure transmission of data with least energy consumption in the presence of malicious nodes.

With the vigorous development of Internet of Things technology, the current distribution network is developing towards the information-based and intelligent distribution Internet of Things (D-IoT). D-IoT adopts the mode of the cloud computing center and the edge cloud network working together. The edge cloud network has a large number of intelligent terminals, which can well adapt to the current sharply expanding power data scale. In order to further improve the ability of the edge network in D-IoT to process data in real time, and to maximize the quality of user experience (QoE) while minimizing energy consumption when performing computing offload, this paper proposes a dynamic non-cooperative game based edge Computing task offloading strategy, considering the dynamic nature of task generation, designed a distributed iterative optimization algorithm, which decomposes computing offloading into a series of sub-problems to solve. The results of simulation experiments prove that the calculation offloading mechanism proposed in this paper can greatly improve D -Compute efficiency of IoT system.

For a wireless cooperative system in which feedback delay exists, the channel state information of the link at the time of relay selection may be outdated with respect to the time of data transmission. The use of outdated channel state information for the selection of relay node will inevitably affect the reliability of selection. Considering the existence of feedback delay, unlike other methods of estimating the true received signal-to-noise ratio during the data transmission to optimize relay selection, the security outage performance of the cooperative communication system is taken as the starting point in this paper, the minimum security outage probability is proposed as the selection basis to optimize the opportunistic relay selection strategy, and the theoretical derivation under the condition of high input signal-to-noise ratio is provided. The simulation results show that the relay selection strategy proposed in this paper can achieve higher security performance under outdated scenarios.

In this paper, the effect of perfect electric conductor (PEC) as a ground plane on antenna array is investigated. Vertical electric dipole which is of infinitesimal small length is considered as an array element. The effect of mutual coupling is neglected in this work. The effect of PEC as a ground plane is analyzed by considering two cases in this paper. Firstly, the array elements are kept at constant height above PEC and in second case, the array elements are kept with gradually increasing height above the ground plane. Performance of antenna array in presence of PEC for both cases is investigated in terms of main lobe beam width and side lobe level using array factor pattern. Side lobe level and beam width results are compared both in presence of PEC and without PEC i.e. array in free space. It has been observed that there is improvement in the main lobe beam width and reduction in side lobe level when the array is used with PEC as a ground plane with elements kept at gradually increasing height from the ground plane. In addition to this, overall directivity of the array is also found to be increasing exponentially with height on application of ground plane in comparison with that of antenna arrays without ground plane.

Wind Farms (WF) can be located in isolated areas, or they may comprise Wind Turbines (WTs) which are geographically distributed. Therefore, the neccesary communication infrastructure, required for the control and monitoring system, can be expensive to install and to maintain. In this paper, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are presented as solutions for these problems. WSNs are quick to install, easy to maintain and they scale up easily. The requirements for a potential WSN, for both a WT and a WF, are studied in this paper. Different wireless communication technologies are thoroughly compared. Both long-range low-power protocols and short-range high-speed ones are considered. A possible LoRa-based architecture of a WF communication system is presented.

Today’s world has a keen interest in systems which are battery-less and can harvest energy from their surroundings. There is an increased demand for disposable sensors which can be used remotely in different areas. Energy harvesters are the key devices that help to capture the energy from the environment and convert it into exploitable electric power.Places, where there is no conventional power source available, is the best place where energy harvesters can play an important role allowing the electronic gadgets to operate thus eliminating the wiring and frequent visits to replace the batteries to a large extent. This paper thus proposes an energy harvesting system designed and optimized using high bandwidth rectenna for Wireless Sensor Networks (WSNs). The proposed system helps to effectively analyse the RF Energy Harvesting for WSN using rectenna.The Rectenna designed and proposed in this paper is used to harvest electric energy from the RF signals radiated from Mobile phones, GSM, PCS, WLAN, AM, FM, Wi-Fi and television broadcasting systems.This work contains rectennas with HFSS, the MATLAB software. The rectenna consist of high bandwidth system which works for both the narrowband as well as for the broadband in which wideband and frequency independent antenna is designed. The earlier researcher used different rectenna system for narrowband as well as for broadband. The frequency range of the proposed Rectenna covers both narrowband and broadband (800 MHz to 7 GHz). This makes this research beneficial for modern wireless communication systems.

This paper presents the synthesis results of a Hardware accelerator built for Wireless Body Sensor Network (WBSN). The WBSN is getting wide popularity because of the living environment of a human being. Nowadays the chronic diseases are increasing due to unhealthy living styles. The introduction of WBSN was in military application, where the health condition of the army personnel was known by themselves. WBSN will help in finding any discrepancy in health conditions during the war so that precautionary measures can be taken and hence started to grow this area of research in diversified applications. This led to the huge development in the field of Biomedical Applications. The paper concentrates on building a hardware accelerator for the WBSN. The Hardware accelerator is designed to process four health parameters that are Blood Pressure (BP), Electrocardiogram (ECG), Temperature, and Electroencephalogram (EEG). The implementation of the accelerator is simulated and synthesized on an FPGA Vertex-7 family kit. The proposed design consumes less power and also increases the speed of processing.

The elaborate architectures used in millimeter wave (mmWave) MIMO communication system make its channel characteristics prediction difficult. In this regard, we consider the problem of predicting the path power loss for both line of sight and non line of sight mmWave channels by employing deep learning (DL) based data driven model. The success of a DL technique depends on how well we train the model, which again relies on the training data. It is often hard to get the amount of data needed to build and train a model. The paper proposes a methodology to predict the channel characteristics using the available limited data. For this, the work effectively develops two deep learning models: Model 1: Autoencoder for creation of new data from the limited data and Model 2: Deep Convolutional Neural Network (DCNN) for channel characteristic prediction. Simulation results show that the proposed model 1 is able to generate more data in the desired direction and model 2 can accurately determine the path power loss associated with a mmWave channel, with very minimum error. The paper also demonstrates a method of extracting useful information using the constructed data driven model.

Nowadays, the application of data caching in mobile edge computing networks is exponentially increasing as a high-speed data storage layer using deep learning (DL) approaches. This paper presents an DL-based resource allocation approach to find the optimal topology for cache-enabled backhaul networks. In the practical scenarios, a numerous of radial configurations of test systems have been applied for training stages. This paper also applied the continuation power flow analysis to achieve the maximum load limit in which the power of macro base stations with the caches of different sizes is provided through either smart grid network or renewable power systems. To increase the power efficiency index of this approach the power sharing capability was enabled among different layer of network components through smart grids. In order to obtain the optimal solution, the DL-based mathematical problem is reformulated into a neural weighting model considering convergence conditions and Lyapunov stability of the mobile-edge-computing under Karush–Kuhn–Tucker optimality constraints. The mathematical analysis and simulation results demonstrate that the performance of the proposed algorithm is better than other energy efficiency algorithms. The proposed approach can effectively increase the total system throughput and network’s utility in addition to guarantee user fairness index.

The rapid development and adoption of Internet-of-Things (IoT) and sensors such as Global Positioning System (GPS) in our daily life allows gathering of wealth of information. This tremendous amount of sensor data can be clustered to infer information about the whereabouts of users. Researchers have proposed various algorithms for analyzing spatio-temporal data and clustering such datasets. However, a big challenge is to discover clusters with large density variation and to solve this problem most of the existing clustering algorithms manually set the input parameters. In the Density-based spatial clustering (DBSCAN) clustering algorithm, the epsilon and minimum point parameters have to be set manually for further computation. In this paper, we propose an improved DBSCAN algorithm to adaptively set minimum point parameter for dynamic length of data, based on merge sort and silhouette analysis. Validation of the proposed algorithm has been done on GeoLife GPS dataset. Experimental results show that the proposed algorithm is competitive with state-of-the-art methods for identifying users’ significant locations and whereabouts with the density variation. The paper also aims to highlight the issues pertaining to the invasion of user privacy by the potential application of the clustering algorithm on the aforementioned data.

The massive multiple-input multiple-output (massive MIMO) system is the major section of the fifth generation (5G) future wireless cellular systems. It consists of hundreds of antennas in the base station that serves more number of users, concurrently. Thus, this system will get optimized energy usage, high data rate, and more precision because of their larger degrees of freedom. The computation power to the total power consumption ratio is considered for rapid increment owing to the more data traffic at the baseband unit that seeks more attention in the exploitation of massive MIMO systems for 5G wireless systems. The main intent of this paper is to develop the multi-user massive MIMO systems by deriving the joint optimization problem of computation and communication power. In the existing energy efficiency analysis, there is a negative effect on energy efficiency when increasing the count of RF chains and antennas by considering only computation power or communication power in massive MIMO. In order to overwhelm this problem, this paper focuses on two optimization problems. The first problem is focusing on the improvement of upper bound on energy efficiency with the optimal baseband and RF precoding matrices based on a new hybrid meta-heuristic algorithm. The combination of two well-performing meta-heuristic algorithms like electric fish optimization and dragonfly algorithm is used as the new algorithm, which is named as hybrid dragonfly with electric fish optimization (HD-EFO) for enhancing the efficiency of massive MIMO system. In the second phase, the joint optimization of both computation and communication power is performed by the same HD-EFO for developing the optimized hybrid precoding matrix. The extensive results have shown that the implemented multi-user massive MIMO systems with partially-connected structures using HD-EFO increase the cost and energy efficiencies, and save the maximum power.

The existing approaches of block based recovery of progressive secret sharing paradigm have support for limited $${k}$$ value in (k, n). Sian et al.’s as well Quan et al.’s block based visual secret sharing scheme has limited bound; works for a limited $${k}$$ value of $$\left( {{k},{~n}} \right)$$ threshold scheme. A novel threshold based (any value of $${k~and~~n}$$ ) block recovery in progressive secret sharing has been introduced and analyzed in this paper. Scheme achieves higher value of threshold than the schemes proposed by Sian et al.’s as well Quan et al.’s. The scheme discusses the sharing ability in progressive block based secret sharing up to any general number of thresholds. The contrast of the recovered block wise secret is reasonably good and the scheme allows for perfect security using at least $${k}$$ numbers of participants before revealing any of the blocks of secret image. The work in this paper offering a reference point for developing solutions needed in the area of secure image sharing techniques.

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.

Wireless sensor networks (WSNs) consist of a large number of sensor nodes, which are primarily employed for collecting data from an environment of interest. Energy resources of WSN nodes are generally restricted, irreplaceable and non-rechargeable. Hence, lowering the level of energy consumption in such networks to save more energy is the key issue in the literature. Clustering, selecting the best Cluster Head (CH) among candidates, and performing the routing only among cluster heads would be an effective approach to reduce the WSN nodes energy consumption. Therefore, cluster-based routing leads to extending the network’s lifetime through aggregating data in CHs, uniformly distributing the energy among nodes, and, consequently, reducing the number of contributing nodes in the routing procedure. In this paper, an energy-aware cluster-based multi-hop routing algorithm is presented, in which the clusters would, if required, re-formed during the routing procedure. Furthermore, like other multi-hop routing algorithms, it guarantees minimizing the energy consumption through balancing energy within the network. In this paper, we have presented a cluster-based multi-hop routing algorithm. In our proposed approach, a combination of two algorithms, namely K-means and Open Source Development Model Algorithm (ODMA), are employed for clustering, and Genetic Algorithm, is applied for multi-hop routing. The simulation results confirm superiority of our proposed method in comparison with MH-FCM, EEWC, and GAFOR algorithms in terms of several metrics such as average residual energy, residual energy variance, number of packets received, number of dead nodes, and network lifetime.

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.

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.

With the significant increase in energy demands in the last decade, the issues of unnecessary energy usage have increased rapidly. Therefore, there is an immediate need to provide a cheap and easily accessible monitoring tool for the energy consumed by an appliance used in homes and industries. Instead of monitoring the total power consumption of the houses and/or industries, it is useful to monitor the power consumption of the individual appliance, which in turn, helps in saving the overall energy usage and thereby makes it cost-effective. This paper presents a cost-efficient design and implementation of a monitoring system that can precisely measure the current and voltage of each appliance. The design provides tracking of device activity in a real-time environment for the industries and helps in adopting to the green initiative. The design comprises of Arduino based micro-controller and Raspberry Pi, that performs precise measurements of current and voltage of the device, followed by measuring the power consumed by the device. This paper presents two different system designs, one for the single-phase measurements and the other for the DC measurements. The single-phase measurement device comprises of 10-bit ADC whereas, the 24 V DC measurement device comprises of a 12-bit ADC, which provides higher measurement accuracy compared to other systems available in the market. The implemented design uses the EmonCMS web application to accumulate and envision the monitored data. It provides a flexible and user-friendly solution to monitor the measured data easily on any android or iOS devices.

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.

Utilization of unlicensed spectrum under licensed assisted access ensuring fair co-existence with Wi-Fi networks is a good solution to address immense usage of mobile data. Radio communication operation of LTE in unlicensed frequency band is referred as LTE-unlicensed (LTE-U) or LTE-licensed assisted access. In this paper, we consider a HGNW in which coverage area of Wireless-Fidelity (Wi-Fi)’s Access Point is integrated within the LTE-U small base station’s cellular network coverage area. To overcome the disadvantages of existing LTE-U technics like carrier sense adaptive transmission and listen before talk, we proposed a new methodology i.e., sense before transmit in this paper by adopting a transmit power control mechanisms using reciprocity theorem based on the channel state information to assign the secondary carriers in the uplink as well as in the downlink directions in the unlicensed spectrum to carry the traffic. In our proposal, LTE-U users are allowed to use the unlicensed spectrum provided that the interference produced at Wi-Fi users due to LTE-U activities is remained below a certain threshold. We evaluated the performance of proposed network model in terms of outage probability and achievable throughputs.