Abstract This article addresses the problem of secure communication in an unmanned aerial vehicle (UAV)-aided wireless communication system in which a source sends confidential messages to a destination via an untrusted UAV mounted with a miniaturized energy-harvesting transceiver. The UAV can overhear the source’s confidential messages. To create a positive secrecy rate, the destination sends jamming signals during the communication. We aim to maximize the achievable secrecy rate for two scenarios: security-on-demand UAV (SoD-UAV), where the UAV can modify its trajectory and power-splitting (PS) ratio to serve the secure communication, and non-security-on-demand UAV (nSoD-UAV), where the UAV only forwards the source’s messages. For the nSoD-UAV, we optimize the transmit powers at the source and the destination for a given UAV’s trajectory and given PS ratios. For the SoD-UAV, the UAV’s trajectory, the PS ratios and the transmit powers are jointly optimized. Due to the non-convex optimization problems, we use successive convex optimization and block coordinate descent methods to find efficient approximate solutions. Numerical results verify that the achievable secrecy rate is significantly improved using the proposed algorithms.

Abstract Optical fiber access systems are one of the driving forces behind the success of the Internet. Time- and wavelength-division multiplexing passive optical network (TWDM-PON) is regarded as the key technology for future Internet access networks. In this paper, we propose an ITU TWDM-PON module for the ns-3 network simulator and describe its concept and design specifications. The proposed module is developed based on the XG-PON module for ns-3. It can simulate G.989 standard-compliant data packet transmission in the upstream and downstream directions using multiple wavelength channels. It enables us to evaluate the performance issues that arise with the TWDM-PON development, including various dynamic bandwidth allocation and dynamic wavelength allocation algorithms. The proposed module is expected to become a good platform for studying future access networks composed of TWDM-PON and mobile networks by enabling us to simulate dynamic wavelength and bandwidth allocation.

Wireless group communication has gained much popularity recently due to the increase in portable, lightweight devices. These devices are capable of performing group communication by enabling every participant to agree upon a group key for secure communication in an insecure network. Many authenticated group key agreement schemes have been proposed, but most of the schemes lack the dynamic property of mobile node joining/leaving the group such that the session key is updated without affecting the protocol. In this paper, we propose a pairing-free certificateless authenticated group key agreement protocol based on elliptic curve cryptosystem for resource-constrained mobile nodes. The objective of the proposed key-agreement protocol is to ensure the un-deniability of any message exchanged between the sender and receiver, contributory property for group key agreement, and allowing mobile users to join or leave the group dynamically by enabling forward and backward secrecy. Through the rigorous security analysis, we show that the proposed scheme achieves the well-known security functionalities against various types of attacks using informal security analysis and rigorous formal analysis using the random oracle model. Moreover, the protocol validation result using broadly-accepted automated validation of internet security protocols and applications shows that the protocol is safe under OFMC and CL-AtSe back-ends. Furthermore, the result of performance analysis shows that our proposed scheme achieves desirable security properties as compared to the existing related schemes by reducing the overall computation and communication cost despite a gradual increase in the number of participating mobile nodes.

Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.

Abstract Multi-Input Multi-Output (MIMO) is the need for recent communication system for the enhancement of channel capacity. While the number of antenna array elements increased, the spacing between the array elements and size of the antenna reduced. Hence high coupling between the elements occur, and thus the channel capacity reduced. There were several existing methods employs, however there were some limitations like reduced gain, dielectric constant, and bandwidth. So as to overcome this and to increase the performance characteristics such as bandwidth, gain of MIMO antennas and to lessen the dielectric constant of substrate material this proposed scheme is introduced. Using these considerations and requirements, an array of two-element MIMO system will be designed and developed with a less dielectric material (\( \varvec{\varepsilon}_{{\varvec{r }}} < 4 \)). So in this Taconic RF-35 (dielectric constant is 3.5), Two T-Shaped antennas are employed for 2.45 GHz (ISM band) with multiple modes of operation to achieve the improved bandwidth of about − 10 db. Simulation is done by HFSS 13.0.

In such mobile platforms as ships and aircraft, the detection and reconnaissance devices are near to the communication facilities. When working at the same time, they will create interference or jamming themselves. Especially, the high-power radar equipment tend to cause a high bit error rate (BER) in the data link system (DLS), with the result that the DLS cannot operate normally. For this reason, this paper makes a study of the BER performance of the DLS under the pulse linear frequency modulation (LFM) interference in the time-varying Rayleigh channel. In view of the the time-varying characteristics of channel in the practical conditions, the interference channel is modeled as a time-varying Rayleigh fading channel and the formula of BER is derived. Through simulation, the theoretical deduction is proved to be correct. This is of much importance to analyzing the variation of BER performance with the pulse width, power and doppler frequency shift under the pulse LFM interference.

Abstract The conducted researches have shown that the features of the housing and communal sector do not allow talking about the possibility of calculating the “optimal” tariff rate. The development of an effective method of tariff formation for rural areas is particularly acute. The use of traditional method to calculate the amount of tariffs for housing and communal services provided to the population and enterprises (called “cost plus” approach) consists in a simple summation of the cost price of a service with a premium that was set directly by a particular housing and communal enterprise within the maximum and minimum values. The authors found that none of the current pricing and tariffs’ setting methods fulfills the requirements for an effective and economically founded tariff policy in the housing and communal services sector. In this regard, the development of a new methodology that will ensure the receipt of compromise tariffs for housing and communal services is required. Compromise analysis, the main purpose of which is to obtain optimal prices, can be used as a basis of such methodology.

Since 1999 IEEE 802.11 has become a dominating wireless technology for providing WLAN in both public and private places. The protocol has evolved with time and the current version of protocol allows the maximum data rate up to 6.76 Gbps. Providing high data rate makes it capable of supporting applications demanding high bandwidth and less delay. The protocol is built on carrier sense multiple access with collision avoidance (CSMA/CA) principle to minimize the collisions. In real-time networks as the traffic increases, collisions take place between different flows leading to delay and wastage of bandwidth. In literature, various works are dedicated to predicting the collision probability to estimate available bandwidth of the network. One of the popular approaches “ABE” (Sarr et al. in IEEE Trans Mob Comput 7(10):1228–1241, 2008) focused on this issue and calculates collision probability of “Hello Packets” and interpolating it to get the collision probability of data packets to estimate the available bandwidth. The current work proposes to use an analytical approach to predict the collision probability without any intrusiveness and estimate the available bandwidth for accurate admission control. Most of the literature that uses mathematical approach assumed homogeneous conditions. The current paper takes into consideration heterogeneous conditions to resemble real networks. The paper proposes amalgamation of passive approach and analytical approach, yielding more accurate results. The work is verified through extensive simulations to validate our proposed model. Since the model is based on analytical approach, major benefits are: (a) there is no need to send packets for estimating the collisions, hence scaling down the overheads; (b) One can change the parameters and predict the result set for any given conditions; (c) the method is cost-effective and non-intrusive in nature.

Before a dense Wi-Fi network is deployed, Wi-Fi providers must be careful with the performance promises they made in their way to win a bidding process. After such deployment takes place, Wi-Fi-network owners—such as public institutions—must verify that the QoS agreements are being fulfilled. We have merged both needs into a low-cost measurement system, a report of measurements at diverse scenarios and a performance prediction tool. The measurement system allows measuring the actual goodput that a set of users are receiving, and it has been used in a number of schools on a national scale. From this experience, we report measurements for different scenarios and diverse factors—which may result of interest to practitioners by themselves. Finally, we translate all the learned lessons to a freely-available capacity-planning tool for forecasting performance given a set of input parameters such as frequency, signal strength and number of users—and so, useful for estimating the cost of future deployments.

Magnified resource consumption and depletion of natural resources calls for non-flexible or strict regulations and penalties on industrial operations, increased rate of processing and reuse of waste material as a substitute for raw material and political and legal interventions at global scale. Product recovery involves reuse, repair, refurbishing, remanufacturing and materials recycling, requires an efficient network design known as reverse logistic network and offers economical benefits in terms of fewer procurement of raw material, inventory management and less disposal. In current study, a mixed integer linear programming model designed on a multi-stage reverse logistics network for product recovery is proposed which considers different recovery options-product remanufacturing, component reprocessing and material recycling for sustainable outcomes. The model is designed to find optimal solutions for fulfilling demand and revenue needs by focusing on strategic locations for collection centers, reprocessing centers, remanufacturing plants and transportation options and simultaneously achieving sustainability goals. The model is applied on an Indian based manufacturing unit of a Saudi Arabian Industrial Air conditioner manufacturing organization and the case is presented here. The model is converted into a multi-objective programming model in accordance with the importance of each objective suiting the business needs. All relevant objective functions are evaluated using BWM, AHP and FAHP methods to obtain weights for integration into a fuzzy linear programming model which eventually provides three separate results. The model applied has originality and uniqueness for applications to solve multi-objective problems under uncertain environment and tends to strike a balance between economic and environmental objectives. The study provides for a base for further scope covering uncertainty about the amount and quality of returned products and even can be implemented by practitioners and academics for making a significant contribution in improving the efficiency of supply chains.

The detection and transmission of a physical variable over time, by a node of a sensor network to its sink node, represents a significant communication overload and consequently one of the main energy consumption processes. In this article we present an algorithm for the prediction of time series, with which it is expected to reduce the energy consumption of a sensor network, by reducing the number of transmissions when reporting to the sink node only when the prediction of the sensed value differs in certain magnitude, to the actual sensed value. For this end, the proposed algorithm combines a wavelet multiresolution transform with robust prediction using Gaussian process. The data is processed in wavelet domain, taking advantage of the transform ability to capture geometric information and decomposition in more simple signals or subbands. Subsequently, the decomposed signal is approximated by Gaussian process one for each subband of the wavelet, in this manner the Gaussian process is given to learn a much simple signal. Once the process is trained, it is ready to make predictions. We compare our method with pure Gaussian process prediction showing that the proposed method reduces the prediction error and is improves large horizons predictions, thus reducing the energy consumption of the sensor network.

Abstract Energy conservation is the main issue in wireless sensor networks. Many existing clustering protocols have been proposed to balance the energy consumption and maximize the battery lifetime of sensor nodes. However, these protocols suffer from the excessive overhead due to repetitive clustering resulting in high-energy consumption. In this paper, we propose energy-aware cluster-based routing protocol (ECRP) in which not only the cluster head (CH) role rotates based on energy around all cluster members until the end of network functioning to avoid frequent re-clustering, but also it can adapt the network topology change. Further, ECRP introduces a multi-hop routing algorithm so that the energy consumption is minimized and balanced. As well, a fault-tolerant mechanism is proposed to cope up with the failure of CHs and relay nodes. We perform extensive simulations on the proposed protocol using different network scenarios. The simulation results demonstrate the superiority of ECRP compared with recent and relevant existing protocols in terms of main performance metrics.

Abstract Government now-a-days are looking forward to achieve “Energy for All” standards. Countries are committing themselves to reduce carbon emissions. Recently, every IT industry giants, who typically own and operate huge Data Centers (DC) are looking forward to achieve “100% usage of green energy to power their datacenters, contribute to the utility grid in equivalence to their electrical power consumptions”. Electrification and gradual decarbonisation still continue as significant, global concern. Hence it is pretty clear that need for MicroGrid (MG) based facilities is actually a mandatory. But intermittent-characteristic (unreliability/unavailability) of Renewable-Energy (RE) generating sources (like Solar Panels, Wind Turbines) regularly hinder both the stake holders-Government and IT industry from accomplishing such social and economic initiatives. Hence SmartMicroGrid (SMG) connected with utility grid are envisioned to support powering industries inorder to avoid future power outages. Interconnected industry4.0 factories where, actually the smart-management interms of two-way power supply and two-way communication network is foreseen. They are highly dependent on modern, integrated, Information and Communication technologies (IoT sensors, Fog and Cloud computing etc.). In this paper we have focussed on two objectives. Firstly our objective is to identify, optimum renewable-generation-capacities inorder to minimise investment cost of a microgrid. Secondly our objective is to maximize usage of more clean energy for applications running on FogMicroDataCenter (fogMDC), powered by microgrids and controlled by Sofware Defined Networking (SDN). We have used realistic meteorological data of Tamil nadu, India for our proposed work. To the best of our knowledge, this is the first work proposed for two different scenarios, performance modelling and economic modelling aiding MG powered FogMDCs. Proposed economic modelling work is related to finding, the optimum sizing of distributed energy resources and financial cost. Proposed performance modelling is about, the clean energy usages in micro grid powered fogMDC for operations, supported by SDN for efficient distributed Virtual Machine (VM) based resource utilization to guarantee QoS in time sensitive IoT applications.

In this paper, we investigate the content deployment problem from precaching and device-to-device communication perspectives. In the precaching stage, contents are prefetched and stored in edge nodes to be quickly provided to end users. In the device-to-device communication process, intermediate nodes face a dilemma in deciding whether to cache contents coming from or going to neighboring nodes to accelerate the content delivery. We call the former proactive caching and the latter reactive caching. We then design ProRec, a unified caching framework, by jointly considering the two cases with the goal of maximizing the content hit ratio. ProRec first addresses the optimization problem using the method of Lagrangian multipliers and obtains a general solution to the optimal content copies. Second, a greedy solution, proven to achieve the optimum with a probability of at least \(1-1/e\), is used to cache and replace contents. Finally, an edge computing simulation platform that includes real and synthetic traces is built as a case study to verify the effectiveness of ProRec. The numerical results show that it simultaneously improves the cache hit ratio and content delivery delay.

In a vehicular ad hoc network (VANET), a road side unit (RSU) is a network traffic transmitter statically placed along the route to facilitate communication between vehicles and network infrastructure. The transmission and propagation delays, and the capacity of a VANET greatly depend upon the relative positions of RSUs along the road. In this paper, we investigate the problem of RSU placement on a highway-like roadway and propose a scheme that reduces network latency while ensuring good network capacity. In this regard, an integer linear programming model with the objective of minimizing network latency has been developed that depicts the network under consideration. Optimization techniques have then been applied to determine the RSU deployment that provides the minimum network latency. The proposed scheme was validated by generating traffic mobility patterns by using VanetMobiSim and performing simulations in NS2. In-depth comparative analyses of the proposed scheme with uniform distribution scheme and cost-effective strategy showed a reduction of 25% and 10% in network latency, respectively and thus established the superiority of the proposed solution.

Vehicular delay tolerant networks (VDTNs) enable communications in sparse vehicular ad-hoc networks and other challenged environments where traditional networking approaches fail. We propose a VDTN routing scheme that combines the message deliver strategy of PRoPHET protocol, the message copy control strategy of Spray-and-Wait protocol and an enhanced buffer management scheme. In our proposal, the buffer management scheme is designed to improve certain network performance goals, namely, maximizing the average delivery ratio and minimizing the average delivery delay. Furthermore, we use computer simulations to show that the proposed routing scheme achieves better system performance than the existing baseline routing protocols.

For different network traffic and network state’s time-varying characteristics, this paper studies the highly energy-efficient network selection algorithm under dynamic change. Network selection algorithm has an important impact on network performance and users’ experience, while current network selection schemes depend on a prior. They cannot effectively select the appropriate network. Targeting users’ quality of experience (QoE) and networks’ energy consumption, this paper uses online dynamic learning property of Q-learning method, consider users’ QoE, networks’ energy consumption, and switch times together, and proposes a QoE based dynamic network selection algorithm. This algorithm can dynamically select the network, obtain the maximum users’ QoE and optimize networks’ energy consumption and switch times. Simulation results show that the proposed algorithm exhibits better performance.

This paper presents an extensive path loss measurement campaign carried out in rural areas at 3.7 GHz, including line-of-sight (LOS) and non-LOS conditions. For this purpose, a commercially established fixed wireless access (FWA) network is exploited, operating with time-division long term evolution configuration. Furthermore, various models are examined and validated regarding their ability to predict accurately the path loss. The results reveal that the standard propagation model (SPM) achieves the best performance, thus being an attractive option for planning rural FWA links. The WINNER II and 3GPP/ITU-R models exhibit very good performance, as well. From the statistical assessment, the shadow fading follows the Lognormal distribution with a standard between 4.6 and 5.4 dB. An almost excellent fit is obtained regardless of the diverse propagation conditions in the specific area. Finally, from the model evaluation was concluded that SPM is highly recommended as the best option for a precise network dimensioning and planning.

Abstract Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network.

One of the main issues experienced in wireless body sensor networks (WBSNs) is the destructive impacts of “mutual interference” caused by neighboring WBSNs on each other’s performance. Research communities have proposed several approaches to mitigate the impacts of mutual interference on the reliability of data transmission and sensor’s energy consumption. However, the proposed approaches came with a number of limitations, such as significant modification of the standard protocol or imposing a high level of complexity. In this paper, a range of schemes are proposed, and their performances are evaluated in the presence of mutual interference experienced in a dynamic environment. More specifically, we consider a situation where a large number of people (each individual covered with a number of sensors to fetch the human vital sign) are gathered at a sport centre to enjoy an event. In such a dynamic environment, people would highly likely experience mutual interference which would destructively impact on WBSN’s performances and eventually would result in an unreliable medical outcome. A simulation study is conducted in which a set of schemes proposed that indicates a gradual improvement of WBSN’s performances in terms of reliability of data transmission and sensor’s energy consumption. Our obtained results show that the frequency-adaptation strategy combined with phase-adaptation approach significantly improves the performance of WBSNs in the presence of mutual interference in a dynamic environment. Moreover, an experimental study is carried out to examine the feasibility of implementing the predominant scheme on real-world sensor devices and to further support the outcome of the simulation study.

In wireless sensor networks, a significant amount of energy is consumed by the sensor nodes during data packet transmission and reception. An IEEE 802.15.4 MAC protocol is not able to completely satisfy all the requirements of wireless body sensor networks (BSNs) in a healthcare environment. Hence there is a demand for the design of a new scalable and energy saving MAC protocols. In this paper the challenging healthcare requirements are considered and based upon these requirements, an energy efficient distributed queuing random access (EE-DQRA) MAC protocol is proposed for BSN scenarios which utilizes the concept of distributed queuing for enhanced radio channel utilization. The theoretical analysis of energy efficient EE-DQRA MAC protocol is being carried out systematically considering the limitations of IEEE 802.15.4 and DQ-MAC protocol. Further, EE-DQRA performance is validated with IEEE 802.15.4 system parameters using computer simulations. The effect of relative traffic load and payload length on the energy consumption, delay and throughput are also analyzed. The simulation results shows that the proposed EE-DQRA MAC with M/M/K queuing has better energy performance than the existing DQ-MAC and IEEE 802.15.4 MAC in BSN scenarios due to collisionless transmission in the data transmission queuing (DTQ) module while keeping the control packet overhead smaller in collision resolution queuing (CRQ) module. It is also evident that EE-DQRA requires a minimum delay in comparison to IEEE802.15.4 and DQ-MAC, due to overhead minimization and M/M/K queuing utilization in DTQ system.

Abstract Based on computer generated hologram (CGH) and multiple chaotic systems, a novel image encryption scheme is presented, in which shuffling the positions and changing the values of image pixels are combined to confuse the relationship between the ciphertext and the original image. In the encryption process, the complex distribution is permuted by use of the designed scrambling algorithm which is based on Chen’s chaotic system and logistic maps firstly. Subsequently, the Burch’s coding method is used to fabricate the CGH as the encrypted image. Finally, the pixel values of the encrypted CGH are changed by sine map to withstand statistical analysis attacks. Simulation results demonstrate that the proposed method has high security level and certain robustness against statistical analysis attacks, data loss, noise disturbance and differential attack.

This paper presents an effective text-line segmentation algorithm and evaluates its performance on Uyghur handwritten text document images. Projection based adaptive threshold selection mechanism is implemented to detect and segment the text lines with different valued thresholds. The robustness of the proposed algorithm is admirable that experiments on 210 Uyghur handwritten document image including 2570 text lines got correct segmentation by 97.70% precision and 99.01% recall rate and outperformed the compared classic text-line segmentation algorithm on same evaluation set. Additionally, the proposed algorithm is tested on the public handwriting dataset and get 98.05% correct segmentation rate which is robust and promising.

Abstract The topology management classifiers consist of several methods, such as the typical clustering-based method excelled in wireless network partitioning. However, most algorithms appear load unbalanced in the application of irregular network, resulting “energy hot zone” phenomenon. This paper proposes an improved intelligent clustering algorithm and applies it to the complex water system environment. Firstly, we build a new energy consumption model for wireless transmission network, and design a genetic clustering strategy via the minimum energy consumption principle. Secondly, we introduce the P matrix coding approach considering the search scale, so as to avoid the squared increasing relationship between the searching space and the data calculation. Thirdly, we employ adaptive genetic operator to enhance the directivity of the searching space, and utilize a fuzzy modified operator to enhance the accuracy of the cluster head selection, which may ensure the iterative efficiency. Through numerical simulations, empirical results show better performance than traditional methods in load balancing and clustering efficiency, which can effectively improve the network convergence speed and extend the network lifetime.

Abstract Matrix-variate Restricted Boltzmann Machine (MVRBM), a variant of Restricted Boltzmann Machine, has demonstrated excellent capacity of modelling matrix variable. However, MVRBM is still an unsupervised generative model, and is usually used to feature extraction or initialization of deep neural network. When MVRBM is used to classify, additional classifiers must be added. In order to make the MVRBM itself be supervised, in this paper, we propose improved MVRBMs for classification, which can be used to classify 2D data directly and accurately. To this end, on one hand, classification constraint is added to MVRBM to get Matrix-variate Restricted Boltzmann Machine Classification Model (ClassMVRBM). On the other hand, fisher discriminant analysis criterion for matrix-style variable is proposed and applied to the hidden variable, therefore, the extracted feature is more discriminative so as to enhance the classification performance of ClassMVRBM. We call the novel model Matrix-variate Restricted Boltzmann Machine Classification Model with Fisher discriminant analysis (ClassMVRBM-MVFDA). Experimental results on some publicly available databases demonstrate the superiority of the proposed models. Of which, the image classification accuracy of ClassMVRBM is higher than conventional unsupervised RBM, its variants and supervised Restricted Boltzmann Machine Classification Model (ClassRBM) for vector variable. Especially, the image classification accuracy of the proposed ClassMVRBM-MVFDA performs better than supervised ClassMVRBM and vectorial RBM-FDA.

Abstract In this manuscript, a compact superwideband monopole antenna with triple notched band characteristics is presented and is experimentally investigated. Proposed antenna is also suitable for lower band of applications including Bluetooth and LTE2600 bands. Superwideband bandwidth with bandwidth ratio ≥ 10:1 is obtained by using two identical ellipse which are placed at 70° with reference to major axis. A fractal stub is used to notch WiMAX interfering band while WLAN and DSS bands are notched by etching modified rectangular slots on the radiating patch. Antenna also constitutes slotted ground with chamfered corners for better matching of impedance. Proposed antenna is also investigated in terms of frequency, time and space domain. Antenna offers a wider superwideband bandwidth with VSWR ≤ 2 for 2.34 GHz to 20.00 GHz. In time domain, antenna offers constant group delay in entire operating bandwidth and acceptable impulse response for input signal. Also, antenna offers maximum gain of 4.98 dBi and radiation efficiency of 89%. Stable radiation pattern and above features of proposed antenna suggest antenna to be a good candidate for numerous applications in wireless system.

Abstract Medium Access Control (MAC) protocol plays an important role in full-duplex wireless networks. Theoretically, full-duplex communications have this ability to increase the capacity of traditional half-duplex wireless systems by up to twice. However, designing and implementing an efficient MAC protocol is a central issue in this way. Increasing throughput, collision avoidance, and fairness are common challenges in designing an efficient full-duplex MAC protocol. After giving a short introduction to the general classification of traditional MAC protocols, IEEE 802.11 MAC protocol, and the identified challenges of full-duplex communications in wireless networks, this review paper provides a comprehensive survey of current major MAC protocols for full-duplex wireless communications and classifies them in to three categories i.e. distributed wireless local area networks, centralized wireless local area networks, and other types of networks. This paper also explores and gives a comparative analysis of current full-duplex MAC protocols with stating their advantages and disadvantages as well as comparing them one with another.

A recent approach to tackle the ever increasing complexity of simulation system is model-driven engineering (MDE). However, it is mostly used to produce simulation tools, and seldom can perform formal analysis. Consequently, this raises issues like poor qualities of product, and falls short of non-functional requirements such as extensibility, maintainability, and reuse. In general, many of the success of MDE projects depend on the descriptive power of modeling languages and how conceptual models are implemented. Hence, this paper presents contributions in two main aspects: customizing domain specific language by metamodeling and enhancing model continuity by formalizing model transformations. A military application is used as a motivating example to illustrate the whole process by transforming the conceptual models into other more precise formalisms until they reach final executable models.

The user behavior characteristics of mobile social network services are of guiding significance to the evaluation of user experience, and test cases and test scenarios should be designed according to user behavior characteristics. Current studies have heavily addressed the action sequence and the frequency distribution of user behavior. However, there is little research on the amount of user action triggered by the communication angle and the fluctuation of the user’s action communication performance under different scenarios. This paper analyzes the distribution of data concerning different user actions and tests the waiting time and success rate of different user actions in different scenes. The results suggest that the complex scenarios can consist of some typical user behaviors.

Abstract In order to solve the problem of spectrum resource shortage and energy consumption, we put forward a new model that combines with D2D communication and energy harvesting technology: energy harvesting-aided D2D communication network under the cognitive radio (EHA-CRD), where the D2D users harvest energy from the base station and the D2D source communicate with D2D destination by D2D relays. Our goals are to investigate the maximization energy efficiency (EE) of the network by joint time allocation and relay selection while taking into the constraints of the signal-to-noise ratio of D2D and the rates of the Cellular users. During this process, the energy collection time and communication time are randomly allocated. The maximization problem of EE can be divided into two sub-problems: (1) relay selection problem; (2) time optimization problem. For the first sub-problem, we propose a weighted sum maximum algorithm to select the best relay. For the last sub-problem, the EE maximization problem is non-convex problem with time. Thus, by using fractional programming theory, we transform it into a standard convex optimization problem, and we propose the optimization iterative algorithm to solve the convex optimization problem for obtaining the optimal solution. And, the simulation results show that the proposed relay selection algorithm and time optimization algorithm are significantly improved compared with the existing algorithms.

Abstract Both non-orthogonal multiple access (NOMA) and ultra-dense network (UDN) are promising technologies in future wireless networks. However, considering the overlapped coverage of small base stations (SBSs) and the spectrum sharing with NOMA, interference management (IM) becomes a more complex and fundamental problem. Moreover, considering the massive SBSs and dynamic network conditions in UDN, more efficient mechanisms need to be designed to deal with the IM issue. Thus, we propose a distributed self-optimizing interference management approach to address both the intra-cell interference caused by NOMA and the inter-cell interference among dense deployed SBSs. Aiming to minimize the interference and guarantee the users’ requirements, we mathematically formulate the joint resource allocation and user selection problem with consideration of the diverse user requirements, complicated interference topology, and limited resources. Furthermore, we consider the imperfections of successive interference cancellation at receivers for separating and decoding superimposed signals and analyze the impacts of residual interference and outage probability in NOMA-based UDNs. For tractability purpose, we introduce interference graph and satisfaction game theory and propose distributed algorithms to solve the problem. Simulation results show that interference can be reduced significantly in UDNs with NOMA compared with the traditional IM approaches.

This paper is based on the exploration of the effective method of erroneous phoneme pronunciation of Chinese mandarin learners whose mother tongue is Uyghur and the solution of major problems of language education, concerning the learner’s pronunciation, it uses a different method, namely data-driven approach, and the automatic speech recognition is also used to recognize phonemes of the pronunciation of Chinese mandarin learners. The phoneme sequence is identified and then the standard pronunciation phonemes corresponding to the recognized phonemes are used as the target phonemes to obtain the mapping relation of each target phoneme and recognition phoneme, thus the possible phoneme error categories and possible erroneous rules in pronunciation can be obtained, which may give some help to the learners to learn the Chinese auxiliary language system and the corresponding pronunciation evaluation model.

To address the problems of the slow convergence and inefficiency in the existing adaptive PID controllers, we propose a new adaptive PID controller using the asynchronous advantage actor–critic (A3C) algorithm. Firstly, the controller can train the multiple agents of the actor–critic structures in parallel exploiting the multi-thread asynchronous learning characteristics of the A3C structure. Secondly, in order to achieve the best control effect, each agent uses a multilayer neural network to approach the strategy function and value function to search the best parameter-tuning strategy in continuous action space. The simulation results indicate that our proposed controller can achieve the fast convergence and strong adaptability compared with conventional controllers.

Process optimization based on high-fidelity computer simulations or real experimentation is commonly expensive. Therefore, surrogate models are frequently used to reduce the computational or experimental cost. However, surrogate models need to achieve a maximum accuracy with a limited number of sampled points. Sequential sampling is a procedure in which sequentially surrogates are fitted and each surrogate defines the points that need to be sampled and used to fit the next model. For optimization purposes, points are sampled on regions of high potential for the optimal solutions. In this work, we first compared the effect of using different initial sets of points (experimental designs) in a sequential surrogate-based multiobjective optimization method. The optimization method is tested on five benchmark problems and the performance is quantified based on the total number of function evaluations and the quality of the final Pareto Front. Then an industrial applications on titanium welding is presented to show the use of the method. The case study is based on real experimental data.