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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 ...

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.

This comment points out some inaccurate formulae, and shows some irrelevant comparison of different estimators. Moreover, the correct formulae are provided and the modification to the fractional frequency offset (FFO) estimator is indicated. On the other hand, Our analysis have shown that the integer frequency offset (IFO) estimator of Ai is of serious theoretic problem.

In this paper we consider user scheduling, ordering and transmit covariance matrix optimization problems under successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output downlink. We propose a heuristic user scheduling metric and an intermediate user grouping technique to develop a low complexity greedy scheduling algorithm. A suboptimal user ordering technique is also proposed for transmit covariance matrix optimization under SZF. Proposed algorithm is of low complexity, but performs closely to the highly complex exhaustive search algorithm. For transmit covariance optimization under SZF, a dirty paper coding based algorithm has been previously proposed, which is computationally very complex. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to a known multiple access channel (MAC) covariance optimization algorithm, but does not involve multiple levels of covariance matrix transformations. With the proposed suboptimal user ordering the exhaustive search through all possible user orders is avoided during transmit covariance matrix optimization resulting in a significant complexity reduction, and without a significant performance penalty. Simulation results show that the proposed algorithm performs very close to the known algorithm in the low SNR region.

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.

This paper investigates the ergodic sum capacity for a MIMO group-broadcast channel with time-division scheduling (TDS). In this scheme, the overall user set is divided into subgroups, among which a single user subgroup which maximizes the instantaneous sum capacity will be scheduled at a time. In order to characterize the TDS performance, we first derived an asymptotic bound to the full capacity obtained by dirty paper coding (DPC). This bound is a closed-form expression and performs well for different system configurations. Further concerning practical precoding techniques, we studied its achievable sum capacity by using block-diagonalization (BD) and zero-forcing (ZF) precoding. Based on these results, the achieved scheduling gain by TDS over random scheduling is analyzed. We also compared the scheduling gains under different transmission strategies including DPC, BD, and ZF precoding. The results reveal that TDS provides the largest scheduling gain for the system with ZF precoding. Finally, we also discussed the effect of the group cardinality on the performance of TDS. Numerical results show the tightness of derived capacity bounds and verify the correctness of our analysis.

The cognitive radio multiple-input multiple-output Gaussian broadcast channels are studied where multiple antennas are available for both primary users and secondary users in a spectrum sharing environment, and the sum-rate capacity is also obtained under both the SUs' transmit power constraint and interference power constraint at the primary receivers. The paper principally consists of two steps. First, a duality technique and dirty paper coding are adopted to simplify the channels. Second, we propose an iterative power allocation algorithm to obtain the maximum sum-rate capacity and examine the effects of the constraint parameters on the concerned quantities. Finally, numerical simulation results are presented to validate the proposed theoretical analysis.

In a multi-user MIMO system using a successive precoding method such as dirty paper coding, it is combinatorially complex to determine the optimal set of users to schedule and the proper order to encode their signals in order to optimize a utility function in a scheduling algorithm. Genetic algorithms represent a fast suboptimal approach to reducing the complexity of the search. In this paper, we build upon prior work that implements scheduling via genetic algorithms. We examine the impact of parameter values within the adaptive mutation rate of the algorithm on its convergence time. We demonstrate that although there is a range of values for the parameters that yields similar near-minimum convergence times, it is nonetheless important to ensure that the parameters are tuned to be within that range. In one case, tuning the parameter values reduces the time of convergence to less than 30% compared to that achievable with the initial parameter values. We also demonstrate that the proper parameter values are dependent on both the number of transmit antennas and the number of users in the pool of users to be scheduled. A simple equation is proposed that is linear in the adaptive mutation parameters to tune the values for different numbers of transmit antennas and users.

Block diagonalization (BD) and successive optimization (SO) are two suboptimal but more practical (compared to dirty paper coding (DPC)) orthogonal linear precoding techniques for the downlink of multiuser MIMO systems. Since the numbers of users supported by BD or SO for a given number of transmit antennas are limited, BD or SO should be combined with scheduling so that a subset of users is selected at a given time slot while meeting the dimensionality requirements of these techniques. On the other hand, receive antenna selection (RAS) is a promising hardware complexity reduction technique. In this paper, we consider user scheduling in conjunction with receive antenna selection. Since exhaustive search is computationally prohibitive, we propose simplified and suboptimal user scheduling algorithms for both BD and SO. For BD, we propose capacity and Frobenius-norm based suboptimal algorithms with the objective of sum rate maximization. Starting from an empty set, each step of proposed algorithms adds the best user from the set of users not selected yet until the desired number of users have been selected. Proposed receive antenna selection works in conjunction with user scheduling to further enhance the sum rate of BD. For SO, a Frobenius-norm based low complexity algorithm is proposed, which maximizes the ratio of the squared Frobenius norm of the equivalent channel (projected to the joint null space of the previously selected users) to the sum of the squared Frobenius norms of the previously selected users' preprocessed channels. Simulation results demonstrate that the proposed algorithms achieve sum rates close to exhaustive search algorithms with much reduced complexity. We also show that in addition to reduced hardware complexity at the receiver, antenna selection enhances multiuser diversity gain that is achieved with user scheduling.

This paper investigates the sum rate capacity of MIMO broadcast channels (MIMO-BCs) in cognitive radio networks. A suboptimal user-selection algorithm is proposed to achieve a large sum rate capacity with reduced complexity. This algorithm consists of two steps. First, zero-forcing beamforming is utilized as a downlink precoding technique that precancels inter-user interference. Second, singular value decomposition is applied to the channel matrices of all the secondary users and only consider the singular vectors corresponding to the maximum singular values. The proposed user-selection algorithm chooses singular vectors which are nearly orthogonal to each other and nearly orthogonal to the vector of primary users. With this algorithm, the sum rate capacity of MIMO-BCs in CR networks with interference power constraints and transmit power constraints is derived. We formulate the sum rate capacity as a multi-constraint optimization problem and develop an algorithm to solve the problem in its equivalent form. Finally, numerical simulations are conducted to corroborate our theoretical results in flat Rayleigh fading environments. It is shown that the proposed algorithms are capable of achieving a large sum rate capacity with a very low complexity.

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

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

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

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

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.

This paper presents the advantages of using a wideband spectrum adopting multi-carrier to improve targets localization within a simulated indoor environment using the Time of Arrival (TOA) technique. The study investigates the effect of using various spectrum bandwidths and a different number of carriers on localization accuracy. Also, the paper considers the influence of the transmitters’ positions in line-of-sight (LOS) and non-LOS propagation scenarios. It was found that the accuracy of the proposed method depends on the number of sub-carriers, the allocated bandwidth (BW), and the number of access points (AP). In the case of using large BW with a large number of subcarriers, the algorithm was effective to reduce localization errors compared to the conventional TOA technique. The performance degrades and becomes similar to the conventional TOA technique while using a small BW and a low number of subcarriers.

Wireless sensor network (WSN) is an emerging research field in recent years. The advancement in sensory device and communication technologies has enabled the deployment of diverse sensor networks such as random network consisting of thousand sensors or carefully deployed deterministic network. Despite the plethora of applicability of sensor networks, there are some limitations too such as energy efficiency, lifetime, coverage, localization etc. As the sensor nodes are battery driven so conservation of energy becomes crucial in the hazardous applications. Coverage is also considered as the major quality of service (QoS) metric which aim to maximize the observation quality of the target region. Several approaches have been proposed in the literature to address the coverage problem but most of the approaches have the same objective to achieve the maximum lifetime while ignoring the QoS parameters. The real world applications of WSN require addressing of several QoS parameters too such as reliability, throughput, delay in packet transmission etc. This review paper provides the exhaustive study of the coverage problem concepts, issues and challenges. The paper provides the classification of coverage approaches especially related to that of target coverage. The paper provides a comprehensive comparison of different categories of target coverage approaches. The paper also discusses the future research direction in the field of target coverage which also considers the QoS considerations.

A standout amongst the most dangers to the cyber security is known as Botnet since it offers a conveyed stage for many undesirable activities. From the network traffic flow, the identification of Botnet is a fundamental test. Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) based botnet discovery is proposed in this paper. In this paper, ISCX dataset is utilized for botnet location. The features are classified as botnet flow and normal flow by giving the features separated from the dataset as a contribution to the grouping. For grouping, we have displayed ANN–PSO which lessens the false classification ratio and time multifaceted nature to 3.3% and 14 s. We contrast our proposed work with other existing work and demonstrate that our work is superior to anything that of alternate works in the simulation results.

Heterogeneous sensors are equipped with a limited battery source that is concerned with network lifetime problems. However, this problem can be tackled with the effective design of WSN-IoT by clustering and sleep scheduling mechanisms. This paper addresses this issue by presenting novel ideas involved in the WSN operations such as grid construction, cluster head selection, sleep scheduling, and data gathering by intelligent Agents (iAgents). An energy-efficient dual iAgents based Heterogeneous WSN (E2IA-HWSN) is proposed. iAgents are used in this paper to automatically collect the sensed data from IoT sensors. In this E2IA-HWSN, a 3 × 3 grid is built and each cell is sub-divided into four in which cluster heads (CH) are selected in each sub-division, followed by ring partitioning for selecting a CH present at the center. Multi-Objective Harris Hawks optimization (MO-HHO) algorithm is used to select CH and supernode, here to minimize the energy consumption of CH, the supernode takes responsibility to assign sleep schedules to devices. The scheduling slots are assigned only after a sensor reaches below the energy threshold. For scheduling, the Bayes rule-based Markov model (BR-MM) is applied with the determination of residual energy and sensed packet counts. Generator de Bits Pseudo Aleatorios (GBPA) eliminates redundant data in CH and then inter-cluster routing is performed in case of emergency events. If not, then the CH waits for the arrival of iagents, the trajectory of iAgents is dynamically predicted with Deep Policy Gradient (DDPG). The implementation is carried out in NS3.26 and the results show betterment to the well-known methods.

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.

In this paper, we derive the throughput of non orthogonal multiple access (NOMA) through reconfigurable intelligent surfaces (RIS) with energy harvesting. The transmitter harvests energy using the radio frequency (RF) signal received from another node N. Node N can be any node transmitting RF signals. The harvested energy is used to transmit data to N NOMA users using RIS. RIS is placed between the transmitter and users. Different sets of reflectors are dedicated to different users. Each user receives all reflections over RIS with the same phase. We also optimize harvesting duration and power allocation coefficients to maximize the total throughput. We suggest the use of two RIS: the first RIS improves the energy harvesting process and is placed between node N and transmitter T. The second RIS is placed between T and users so that reflections have a null phase at all NOMA users. The derived results are valid for Rayleigh fading channels where the transmitter harvests energy using the received RF signals. We derive the throughput at weak and strong users as well as the total throughput of NOMA systems using RIS with energy harvesting. In this paper, we derive the packet error probability and total throughput for NOMA using RIS with energy harvesting using RF signals. When there are two users and 16QAM modulation is used and for a throughput of 3.5 bit/s/Hz, the use of $$R=512,256,128,64,32,16,8$$ reflectors offers 69 dB, 62.8 dB, 57 dB, 50.9 dB, 45 dB, 38.5 dB, 31.8 dB gain with respect to the absence of RIS (Mondal et al. in Wirel Pers Commun, 2021; Le and Kong in Wirel Pers Commun 116:3577–3597, 2021).

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.

The problem of assignment and sequencing of tasks is a very complex problem, which is related to whether the computer system can effectively exert the overall efficiency. Solving this problem can make the lowest cost and obtain the greatest benefit. However, the current algorithms for coordinating job assignment and sorting are not completely suitable for heterogeneous computing systems. In order to rationally arrange the problem of computer assignment and sorting, this paper proposes a mathematical programming algorithm to effectively solve the inadaptability of assignment and sorting to heterogeneous computing systems. This paper adopts the control variable method and the comparative analysis method, selects the mathematical programming algorithm and the genetic algorithm, the simulated annealing algorithm these two algorithms, selects the relevant performance indicators, designs the experiments to perform calculations and collects the data. Through the comparison of different algorithms in heterogeneous computing systems, it can be seen that in terms of performance, the average response time and node utilization of the three algorithms are not much different, but the availability of the mathematical programming algorithm is significantly higher than that of the other two. When the rate is 1.0, it still has an availability of 0.59. With the increase in the number of tasks and CPU utilization, the advantages of the mathematical programming algorithm are gradually becoming obvious. Although the receiving capabilities of the three algorithms are decreasing with the increase of these two indicators, when the number of tasks reaches 140, the mathematical programming algorithm can receive tasks remains at 78%, indicating that the algorithm is stable. By applying heterogeneous computing systems on different platforms, GPU and FPGA each have their own advantages. The purpose of coordinating assignments and sequencing is to better allocate resources in the future and maximize benefits. Through the study of mathematical programming algorithms, the time required to execute programs in heterogeneous computing systems can be better reduced, thereby improving the overall system effectiveness.

In this paper, a hybrid technique using a low complexity selective mapping (LCSLM) cascading an improved μ law companding scheme (LCSLM-μ) is proposed to reduce the peak-to-average power ratio (PAPR) and complexity in coherent optical orthogonal frequency division multiplexing system. The simulation results show that at the complementary cumulative distribution function of $${10}^{{ - 4}}$$ , the PAPRs of the scheme LCSLM-μ(D = 4,m = 3, $$\mu { = 1}$$ ) are optimized by 1.488 dB and 1.166 dB compared those of LCSLM (D = 4) and companding( $$\mu { = 1}$$ ) schemes, respectively. Meanwhile, when the bit error rate (BER) is 10–3, the optical signal to noise ratios (OSNRs) of the LCSLM-μ(D = 4,m = 3, $$\mu { = 1}$$ ) scheme are optimized by 6.61 dB and 7.31 dB compared with those of companding ( $$\mu { = 1}$$ ) and improved $$\mu$$ law companding (m = 3, μ = 1) schemes, respectively. The computational complexity and PAPR reduction performance of various schemes, such as traditional SLM, LCSLM, improved law companding and LCSLM-μ schemes are analyzed respectively. The simulation results show that the LCSLM- $$\mu$$ (D = 4, m = 3, μ = 1)provides better PAPR reduction performance. When the alternative signal is 16, compared with the traditional SLM algorithm, the LCSLM- $$\mu$$ (D = 4, m = 3, $$\mu { = 1}$$ ) scheme reduces the computational complexity of complex addition and complex multiplication by 53.1% and 45%, respectively. This paper provides a choice for a trade-off among PAPR reduction, BER and reduced complexity.

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.

In present-day power generation systems, the integration of many renewable energy sources creating more sustainable hybrid energy network can be witnessed. The hybrid energy system regulates the escalation in the cost of the energy that are linked with currently established energy resources. One such predominantly used hybrid energy system happens to be wind/photovoltaic integrated system which is a viable alternative to meet the rising energy demands. Fault ride through (FRT) is one of the cardinal essentialities that is perceived as an indispensable grid code because the effect of faults may propagate to a larger geographical area if FRT is not achieved. In a Hybrid system the ability of the generator to remain connected at the time of short electric fault like voltage dip is identified as low voltage ride through (LVRT) and voltage swell is known as high voltage ride through (HVRT). When fault occurs at the point of the grid, LVRT or HVRT depending on the fault has to be determined and a pertinent compensation should be carried out. This paper proposes a compensation technique based on dynamic voltage restorer (DVR) employing fuzzy controller for LVRT and HVRT in hybrid systems. The disparityin the voltage at PCC is traced and is used to inject the necessary deficit voltage in case of sag or suppress the excessive voltage in case of swell in the hybrid system by means of DVR. Conventionally voltage tracking is done using PI controllers, hence the error obtained due to continuous tracking is sometimes not precise in noisy environment. A fuzzy controller is employed in this paper which provides compensation with much better accuracy than PI controller. This compensation in turn enables the parameters like DC link and stator voltage to stay within the admissible limits in the event of fault at the grid.

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.

Energy consumption of Wireless Sensor Networks including On/Off keying (OOK) transmitter is important for short range transmission and long battery life time requirements. In order to improve the energy efficiency of an OOK transmitter, the Minimum Energy (ME) coding strategy is adopted in this paper. We first give the energy consumption model based on a real OOK transmitter, which has an energy efficiency of 52 pJ/bit by completely switching off the transmitter during the transmission of low bit ’0’. Based on this energy consumption model, ME-Coding provides an energy efficiency of 30 pJ/bit for coding size $$k=3$$ . Moreover, larger coding size offers more significant improvement, at the sacrifice of spectral efficiency and transmission range. In this paper, we have also determined a closed-form solution for the optimal coding size for a given transmission range constraint.

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.

The most significant issue in the government registry department is the duplicate registry of land in the current scenario. These kinds of problems are increasing day by day by day, which necessitates the advancement of the existing registry process system. It is a manual process that a typical stamp-based or newly not secure online process (only a few places). Use of a manual process, there are many issues in the duplicate registry of a particular land. The final registry, where the landlord has a stamp paper copy and sale deed or a specific number of the property. One receipt copy at the land registry department for their record. Blockchain has emerged as a new technology to resolve or handle the above-said issue of the current system, a distributed ledger that is a timestamp and immutable. Being immutable, forgeries related to the particular land during the registry are not allowed. This concept provides trust and consensus among all entities in the network or system. In this paper, we propose a framework for automated maintains a record of registry papers. This framework will also resolve the most effort to loan clearance operations and provide transparency in government e-governance. The system handled by the blockchain network, instead of existing typical manual processor online registry record maintain the process, it includes: An authentication scheme at multiple levels to make the blockchain-based framework secure, a quick verification for all the stack holders (bank, registry department, buyer, and seller), and apply Smart Contract to automate check the loan process as government norms and facilitate buyer and seller selling and purchase. The obtained consequences are examined against the latest approaches to indicate the supremacy of the proposed framework and provide transparent, accountable, incoherent data with the different government departments.

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.