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For each vehicle in a trusted clustering model, its movement is analyzed by vehicle network mobility routing protocol with identified speed and position. The fifth paper entitled "Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles" by Gunasekaran Manogaran et al. proposed a fitness-ant colony optimization (FACO)-based route optimization for improving the driving range of electric vehicles (EVs). The second paper entitled "A SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic" by Liuwei Huo et al. proposed a novel accurate and software defined networking (SDN) based fine-grained traffic measurement approach to obtain comprehensive traffic in VCN. [Extracted from the article]

Summary: Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self‐adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self‐adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction. [ABSTRACT FROM AUTHOR]

Summary: In recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year‐wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy‐efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a system of physical objects embedded with various sensors to receive information, software, chips, and other technologies that allow connecting and transferring data to other devices through the Internet without human intervention. As the number of smart devices increase, IoT has started to be applied in many more fields. Therefore, there is a lot of information that should be processed. To manage this amount of data, some researchers proposed the usage of big data techniques. Big data are a collection of structured and unstructured data incoming with a high speed and large amounts. This paper investigates big data applications in IoT to comprehend the different published approaches using the systematic literature review (SLR) technique. This paper systematically studies the latest research methods on big data in IoT approaches published between 2016 and August 2021. A methodical taxonomy is shown for big data in IoT‐related fields consistent with the content of existing articles chosen with the SLR process in this research like healthcare, smart city, algorithms, industry, and general aspects in those environments. The advantages and drawbacks of each paper are presented, with specific proposals for stating their pros and cons open issues and advising possible research challenges in big data implementation in the IoT. The evaluation factors of big data applications in IoT are distributed as follows: security (18%), throughput (17%), cost (17%), energy consumption (15%), reliability (15%), response time (9%), and availability (9%). [ABSTRACT FROM AUTHOR]

Summary: Art painting is a very important major in China's education system. Under the background of the new era, the country vigorously promotes the modernization of education and the development of intelligence. Many subjects of teaching have been more mature and carried out intelligent teaching based on virtual reality technology. But throughout the art painting profession, the research on this aspect is almost blank. The main reason is that there are deep academic barriers between art painting and computer science. Therefore, this paper puts forward the application research of multi‐source information art painting fusion interactive 3D dynamic scene virtual reality technology. This paper deeply analyzes the needs of art painting and its teaching reform and believes that the existing virtual reality technology cannot adapt to the teaching environment of art painting. In view of the possible inaccurate calculation and other problems, this paper puts forward the optimization scheme. The core of the scheme is to use the information integration advantages of multi‐source information fusion to provide more comprehensive and complete object information for 3D modeling of virtual reality technology, so as to improve the imaging quality of virtual reality technology. In order to meet the requirement of high precision, the calculation method is optimized, the calculation steps are simplified, and the sensor selection is optimized again. This paper introduces the design principle and operation steps of the optimization method in detail. In order to further verify the actual effect of this method, comparative analysis method is used to test the comprehensive performance. In order to ensure the test quality, the median filtering algorithm and fuzzy algorithm are compared. Through the analysis and comparison of the data, the optimization method in this paper greatly improves the accuracy of calculation and the comprehensive performance is improved. [ABSTRACT FROM AUTHOR]

Summary: Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years. [ABSTRACT FROM AUTHOR]

Summary: Artificial intelligence, which simulates the behavior of the human brain with a computer, including learning, reasoning, decision‐making and prediction, association, and other aspects, with the help of artificial intelligence technology to help visual communication designers complete high‐quality, efficient, and high‐volume design output, has become a trend. However, there are many defects in the existing visual design and creation system under the intelligent human–computer cooperation. Therefore, based on 5G virtual reality, this paper proposes the framework of visual design and creation system under intelligent human–computer cooperation research that has been carried out. This paper uses the method of literature analysis and other methods to study. In the research process, based on 5G virtual reality, this paper builds the framework of visual design and creation system under the intelligent human–computer cooperation of virtual reality and tests the visual design creation system based on 5G virtual reality from the aspects of response time, visual design completion time, and visual presentation effect; the system is compared with other intelligent human–computer cooperation visual design creation systems. The results show that the clarity score of this system is 96.5, the visual presentation quality score is 95.8, the clarity score of other systems is 87.6, and the visual presentation quality score is 86.3. Therefore, this system has very high superiority compared with other intelligent human–computer cooperation visual design and creation systems. [ABSTRACT FROM AUTHOR]

Summary: 5G virtual reality technology is a technology that has attracted widespread attention in the computer field in recent years. Establishing a harmonious human–computer interaction environment in the multidimensional information space is one of the development goals of information technology. Face information is important information for identifying the identity of others. Three‐dimensional information has important applications in many fields and directions of machine vision. Therefore, the three‐dimensional common face image has a significant meaning. In this paper, binocular stereo vision system is used to collect multiple pairs of facial information images. The image correction method was used to correct the collected image pairs. According to the basic criteria of stereo matching, the advantages and disadvantages of various traditional image matching methods are analyzed. On the basis of completing the image correction, the graph cut matching method is selected as the matching method, and multiple pairs of collected image pairs are matched by this method to obtain a multi‐angle face disparity map. In this paper, after studying the basic dense matching algorithm and its improved algorithm, the SAD, SD, and NC methods of fixed window are used to compare the matching cost and the performance of the adaptive weight method in face matching. This paper studies the virtual roaming technology and the basis of the related Internet of Things (IoT) 3D face graphics theory and develops a building virtual roaming system based on MFC and OpenGL. The system draws on the basic I/0 device of the computer and provides users with a friendly MFC interface and multiple viewing angles and multiple roaming mode options for virtual faces. Experimental research shows that the reconstruction accuracy of the spatial points of the reconstructed face and the actual situation are both about 1%, which shows that the IoT 3D face modeling algorithm designed in this paper has high accuracy. [ABSTRACT FROM AUTHOR]

Summary: With the continuous proliferation of sensing technology, it has become possible to utilize energy harvesting (EH)‐enabled sensor nodes for a variety of applications. However, conventional wireless sensor networks (WSNs), that is, those without EH‐enabled nodes, still have limited applicability due to their limited battery resources. Further, the utilization of EH‐enabled nodes in the network not only imposes a financial burden on the user but also limits its performance due to its dependence on environmental conditions. To address this concern, in this paper, we propose the EH‐enabled energy‐efficient routing (EHEER) technique for green communication in WSN. The predominant concern being addressed in this paper is the selection of cluster head (CH), which helps in gathering, aggregating and forwarding the data from the cluster‐based routing paradigm. We use the spotted hyena optimizer (SHO) algorithm for optimizing the fitness parameters for CH selection, namely, energy ratio, distance considerations, node density, load balancing and the network's average energy. We use EH‐enabled nodes in the network strategically so as to keep control over the costs incurred in the network. The simulation outcomes empirically prove the efficacy of the proposed work, as it effectively increases the network stability and operational period by a huge margin as compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates optimizing the collaborative energy power consumption between nodes with energy harvesting (EH) capability at the fusion center (FC) and multi‐antenna sensors using game theory. Using multi‐antennas at FC on sensor networks (SNs), we can benefit from the spatial diversity in receiving the data and using multi‐antenna sensors and energy to transfer the data and optimize the SN, respectively. Also, in this paper, the data transmission in the network is formulated by considering the limitations of storage resources of each sensor, minimum data receiving, minimum EH, and maximum collaboration among sensors. In this paper, some modes are suggested to minimize the collaborative energy among sensors for transmitting the data and energy among sensors in the collaboration of sensors to optimize the power control and allocation power policy. Suggestions have been studied based on the number of antennae to transmit, using the cooperative games in sharing the data and the energy and auction game in transmitting to FC. The simulation results of the proposed problem using MATLAB software show optimization in participation function, EH, allocation in cooperation time, and transferring to FC to improve the detection of the transmitted data based on the increasing number of antennas in FC. [ABSTRACT FROM AUTHOR]

Summary: This paper evaluates the performance of a spectral‐amplitude‐coding optical code division multiple access (SAC‐OCDMA) system over a free space optical (FSO) communication link under various weather conditions such as fog, snow, and rain. The system uses different code families, including modified quadrature congruence (MQC), random diagonal (RD), and diagonal eigenvalue unity (DEU), to address user codes and reduce the effects of multiple access interference (MAI). The system's bit error rate (BER) performance has been analyzed taking into account receiver noises and MAI, and calculates the required optical power for a BER value of 10−9 under different weather conditions. The results show that fog has the most significant impact on the system's performance, while rainy conditions require the lowest optical power. The performance of the system also declines with increasing transmission length and concurrent user count. Finally, the paper compares the performance of different code families under unfavorable weather conditions and concludes that DEU performs better than MQC and RD in bad weather. [ABSTRACT FROM AUTHOR]

Summary: In the era of big data, massive amounts of data hold great value. However, much data exists as isolated islands, and the maximum value of the data cannot be fully utilized. Federated learning allows each client to train local data and then share the training model parameters securely, which can address the isolated data island problem and exploit data value while ensuring data privacy and security. Accordingly, in order to securely complete the electric power load forecasting using existing data, this paper constructs a federated learning‐based privacy‐preserving scheme to support electricity load forecasting in edge computing scenarios. To address the problems of the data‐isolated islands and data privacy in electric power systems, this paper proposes a decentralized distributed solution based on the federated learning technique. Our scheme achieves electricity load forecasting for power systems through the federated learning‐based framework and uses edge computing architecture to improve real‐time data capability and reduce network latency. For the hierarchical scheduling structure in power systems, we divide the system into a cloud‐side‐device three‐layer architecture, which achieves structural coordination and balance, and each layer collects information according to the scheduling control tasks, promoting scheduling effectiveness. Finally, different privacy protection methods are used on the cloud‐edge and edge‐device sides to significantly enhance data security. Moreover, We have conducted extensive experimental simulations for our proposed scheme. The experimental results show that the relative error of electricity load forecasting is around 1.580%. Meanwhile, our scheme achieves high accuracy and low memory usage. The security analysis proves the feasibility and security of our scheme. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a rapidly growing trend within many domains, such as automotive, avionics, automation, energy, and health. IoT architecture is the system of numerous elements, including sensors, protocols, actuators, cloud services, and layers. IoT architecture plays an important role to provide desired services. Nowadays, tens of IoT architectures are provided by the research community. Many challenges have been identified by the research community, including interoperability, security and privacy, reliability, energy constraints, scalability, and lack of common standards. However, to provide suitable IoT architecture, the importance and priority of requirements in different scenarios may vary, and requirement analysis should be regarded. To this end, this paper presents a systematic mapping survey to give a review of IoT architecture and provide a structured overview of research trends. Moreover, a technical taxonomy is presented for these challenges according to reviewed studies. This classification model can be used as a guideline for future works. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real‐time data‐centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data‐centric approaches. This paper systematically categorizes the 48 recent articles on data‐driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data‐centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data‐driven approaches in IoV. [ABSTRACT FROM AUTHOR]

Summary: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

Summary: The growth of small communication devices has pushed designers to design compact‐size antennas. These antennas must be low cost, lightweight, needed for mechanically robust construction, and ease of installation. In this paper, we use a novel genetic algorithm (GA) to produce an ensemble of compact self‐avoiding (SA) antennas such that the voltage standing wave ratio (VSWR) is less than 2 at the desired frequencies (as a measure of efficiency); an optimization criterion to get a desirable multiband antenna in the ultra high frequency (UHF) band. First, the electromagnetic properties of these antennas are simulated using the method of moments in MATLAB. SA antennas form a particular set of dipoles introduced here by applying a novel backtracking algorithm. We also use a Lindenmayer system that generates the geometries by a Turtle graphics render to reach our goal. Next, we simulate the VSWR for 150,000 SA antennas with lengths L from 0.1 to 0.4 m; 10 different frequencies are chosen randomly within the UHF band to obtain our results. All the VSWR ≤ 2 are clustered using density‐based spatial clustering of applications with noise algorithm (DBSCAN), so our method provides the frequency ranges where this condition is fulfilled. From this, we obtain the VSWR minimums used for the antenna design. Finally, as an instance, we use our results to select the appropriate size of a multiband antenna and, through genetic algorithms, modify the geometry to satisfy the design requirements. [ABSTRACT FROM AUTHOR]

Summary Autonomy is becoming a prime requirement for satellite mission control operations. Data‐driven methods like Machine Learning models are playing a key role in bringing in autonomy. Health keeping data from satellite telemetry is a key ingredient in these data‐driven methods. In real‐world satellite operations, the health‐keeping telemetry data gradually drifts due to adverse space weather effects and wear and tear of electronic and mechanical components. The key question that arises is how to detect and quantify the data drift which is generally a gradual phenomenon. This paper discusses a novel statistical method for detecting data drift occurring in satellite telemetry. For the purpose of experimental work in this paper, an actual telemetry data set of the BUS CURRENT sensor which is part of the Electrical Power System of a Low Earth Orbit Satellite was considered. Data drift detection test was carried out using this sensor data using the developed novel statistical method and with Kolmogorov Smirnov test which is a probabilistic method. Both results are analysed and compared. Thereafter novel statical method was used to check its efficacy using a synthetic data set with induced drift. [ABSTRACT FROM AUTHOR]

Summary: In recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud‐edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud‐edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud‐edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud‐edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side‐by‐side taxonomy is proposed to categorize the relevant studies on cloud‐edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented. [ABSTRACT FROM AUTHOR]

Summary: A sensor network is a situation‐based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load‐balanced and fuzzy‐ACO (DLB‐fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree‐ and energy‐balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster‐based hierarchical routing. Communication is performed at two levels. In the first level, node‐to‐cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy‐integrated ACO method is applied for inter‐cluster route formation. The route optimization is here performed under fuzzy‐based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed load‐balanced protocol is analyzed against the LEACH, LEACH‐C, LEACH‐CC, M‐LEACH, Fuzzy‐PSO, Fuzzy‐ACO, Fuzzy‐Cuckoo, and FMCB‐ER protocols. The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols. [ABSTRACT FROM AUTHOR]

Summary: In the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub‐bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal‐to‐noise ratios (SNRs). [ABSTRACT FROM AUTHOR]

Summary: Internet of Mobile Things (IoMT) have become very popular recently. The routing protocol for low power and lossy networks (RPL) is standardized for static topologies. However, mobility is the nature of IoT. Mobility serves as a promising candidate to harness hand‐off time issues, delay in data transmission, overhead, and low packet delivery rate (PDR) effectively. This study presents a comprehensive account of the mobility‐aware RPL‐based routing protocols to validate and compare the experimental results. Remarkably, classification methods are used in many articles. The aim is to introduce significant research efforts to improve RPL objective functions (OF) performance in hand‐off time, PDR, delay, overhead, and so forth. In this regard, a complete analysis of the existing routing protocols in IoMT has been presented to compare the results. The main focus of this study is on approaches that proposed new OFs for supporting mobility in RPL. Two main categories are considered to study RPL‐based routing protocol mechanisms: The mobile and static sink. The related studies on the mobile sink are divided into three groups: Single metric‐based OF, composite metric OF, and hybrid routing protocols. Also, the related works based on the static sink are categorized into four groups: Fuzzy logic‐based OF, trickle timer‐based OF, composite metrics‐based OF, and modification control messages‐based OF approach. This paper presents a detailed comparison of mechanisms in each category. It also highlights the pros, cons, open issues, and evaluated metrics of each paper. Besides, challenges of mobility in the RPL‐based routing protocol mechanism in IoMT for future studies. [ABSTRACT FROM AUTHOR]

Summary: Multi‐user multiple‐input multiple‐output (MU‐MIMO) system has the capability of delivering optimal system capacity that provides the simultaneous service to a large number of users using dirty paper coding (DPC) scheme. However, the DPC scheme is quite difficult to be implemented in the real‐time scenario as the computational complexity is very high and the process cannot be accomplished within the duration of few coherence periods. In this paper, we have adopted a newly developed binary spider monkey optimization (binary SMO) algorithm for the joint user and antenna scheduling (JUAS) problem to maximize the achievable system sum‐rate performance. It has been shown that JUAS with binary SMO achieves nearly 99% of system throughput achieved by the extensive search algorithm (ESA) using DPC. Also it is compared with binary flower pollination algorithm (binary FPA) to check which of the two gives better result. It is observed that the binary SMO in JUAS achieves a globally optimal solution quite rapidly, to remain well within the modern‐day packet data communication interval. The proposed binary SMO shows low computational complexity and computation time as compared to ESA (i.e., DPC). Also when it is compared with binary FPA, it shows quite a significant improvement in system throughput. Moreover, the effectiveness of binary SMO in the MU‐MIMO broadcasting scenario is verified through extensive simulation results. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a novel hybrid energy harvesting framework integrated with a graphene‐based supercapacitor to address the energy demands of 6G communication systems and smart grid networks. To ensure consistent power generation, the proposed framework combines diverse energy harvesting mechanisms, including acoustic, vibration, and radio frequency (RF) energy sources. These harvested energies are then efficiently managed and stored in a proposed graphene‐based supercapacitor compared with existing Li‐ion batteries, due to its high energy density and rapid charge‐discharge capabilities. Smart grids and 6G networks can utilize the energy that hybrid systems harvest. This paper examines the analysis of RF energy extracted from various antenna configurations. In the acoustic energy harvesting system, energy extraction has been achieved by subjecting it to acoustic pressure and impedance. Notably, the inclusion of acoustic impedance has resulted in a greater amount of harvested energy. The vibration energy harvesting analysis with angular frequencies under various force conditions is employed. The magnitude of force applied to the harvesting system significantly influences the efficiency of the harvested energy. The three‐dimensional structure and voltage levels of the graphene‐based supercapacitor have been examined for various electrodes. For maximizing the harvested hybrid energy, convex optimization is formulated due to its ability to efficiently navigate complex design spaces while ensuring reliable and globally optimal solutions. CVX tool in MATLAB is used for validation. The results improve the energy harvesting efficiency and enhance the power availability of all the harvesting sources. Furthermore, the compatibility of the proposed framework with existing network infrastructure highlights its potential for seamless integration into real‐world communication and energy distribution systems. [ABSTRACT FROM AUTHOR]

Summary: In the present scenario, wireless body area network (WBAN) use case has gone beyond medical monitoring (e.g., E‐Healthcare) and has become a vital part of our daily lives. WBANs, applied in E‐Healthcare systems, extract the physiological parameters through the implanted (in‐body) and/or wearable (on‐body) sensors applied to the human body and route them to the remote server in a systematized and well‐grounded manner. Thus, routing is the most primitive and non‐trivial task in WBANs for prompt and proper delivery of sensed data. The design of routing protocol is crucial for efficacious communication and high network performance. Therefore, the target of this paper is to present an all‐encompassing taxonomy of the state‐of‐the‐art routing protocols in WBANs. Firstly, this paper provides an insight into WBANs and their related projects. Next, WBAN routing challenges are explored, and after clustering these challenges based on their operating mechanism, the routing protocols are categorized into eight categories, which correspond to cluster‐ and tree‐based, cross‐layer‐based, fuzzy‐based, interference‐based, posture‐based, QoS‐based, SDN‐based, and temperature‐based routing protocols. Furthermore, comparison tables highlighting the goals, strengths, limitations, and novelty of each protocol, along with a qualitative comparison table of the routing protocols for each category based on their features are furnished. This exhaustive survey can serve as a starting point for WBAN researchers, as open research issues have been highlighted in the discussion section. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad‐ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three‐tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid‐based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering‐based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid‐based dynamic sink routing scenario. The significance of clustering‐based protocols for improving lifetime of the network is evaluated as case study. The clustering‐based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) has a significant impact on our daily lives as applications, services, devices, and industries become more intelligent. Artificial intelligence (AI) is expected to significantly influence machine learning training on IoT devices without data sharing. Federated learning (FL) is a distributed machine learning method used in many IoT smart devices; however, FL ensures IoT security and privacy. The systematic literature review (SLR) method is used in this paper to review recently published articles in the FL‐based IoT domain. We analyzed 39 papers that were published between 2018 and March 2022. According to the evaluation factors, the accuracy factor has a high percentage in the FL‐based IoT domain by 29%, and the epoch has 23%, the time has 18%, the energy consumption has 9%, the delay has 9%, the communication overhead has 6%, and the privacy has 6%. Finally, we discuss future research challenges and open issues in the context of FL‐based IoT. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) is a platform which connects the objects through the Internet. The IoT devices have limited processing capabilities. This makes the processing and analyzing huge amount of data as a challenging task in IoT environment. Due to this disadvantage of IoT devices, it cannot be used in many useful applications where they are mostly required. Hence, a technique which helps in overcoming this disadvantage is mostly desired. One of the desired technologies is the cloud computing. The cloud computing has rich set of resources, and it is efficient in processing and analyzing data irrespective of its type and quantity. The services of the cloud will be provided by a cloud service provider (CSP). Selecting an appropriate cloud service provider is a major issue in an integrated environment of IoT and Cloud. Therefore, the focus of this paper is on cloud collaboration with IoT environment with suitable CSPs. A multi‐objective genetic algorithm to find the optimized solution is proposed in this paper. The Non‐dominated Sorting Genetic Algorithm 2 (NSGAII) and Strength Pareto Evolutionary Algorithm 2 (SPEA2) are used to analyze the performance of the proposed algorithm. The Profit Information (PI) to the individual and Trust Relationship (TR) value are considered as the objectives for selecting the CSPs. The results demonstrated that the proposed Multi‐objective Genetic Algorithm (MGA) algorithm along with NSGAII performs more efficiently than MGA algorithm with SPEA2. [ABSTRACT FROM AUTHOR]

Summary: In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing. [ABSTRACT FROM AUTHOR]

Summary: Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two‐stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step‐by‐step calculation method that is not feasible under real‐time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system. [ABSTRACT FROM AUTHOR]

Summary: Mobile sink nodes play a very active role in wireless sensor network (WSN) routing. Because hiring these nodes can decrease the energy consumption of each node, end‐to‐end delay, and network latency significantly. Therefore, mobile sinks can soar the network lifetime dramatically. Generally, there are three movement paths for a mobile sink, which are as follows: (1) Random/stochastic, (2) controlled, and (3) fixed/ predictable/predefined paths. In this paper, a novel movement path is introduced as a fourth category of movement paths for mobile sinks. This path is based on deep learning, so a mobile sink node can go to the appropriate region that has more data at a suitable time. Thereupon, WSN routing can improve very much in terms of end‐to‐end delay, network latency, network lifetime, delivery ratio, and energy efficiency. The new proposed routing suggests a reinforcement learning movement path (RLMP) for multiple mobile sinks. The network in the proposed work consists of a couple of regions; each region can be employed for a special purpose, so this method is hired for any application and any size of the network. All simulations in this paper are done by network simulator 3 (NS‐3). The experimental results clearly show that the RLMP overcomes other approaches by at least 32.48% in the network lifetime benchmark. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) is one of the paradigms related to the evolution of telecommunication networks which is contributing to the evolution of numerous use cases, such as smart city and smart agriculture. However, the current communication infrastructure and wireless communication technologies are not always able to guarantee a proper service for these IoT scenarios. Smart solutions are needed to overcome current terrestrial network limitations offering a cost‐effective way to extend the current terrestrial network coverage. For example, temporary extensions "on‐request" of the terrestrial infrastructure may be a viable solution to allow collecting data generated by nodes outside the current network coverage. Flying objects can help achieve this goal. Various studies supported the use of unmanned aerial vehicles (UAVs) as intermediate nodes between IoT devices and the network. However, such solutions have not been exhaustively tested yet in real‐case scenarios. This paper proposes an efficient solution to collect data from multiple IoT sensors in rural and remote areas based on UAVs. It describes the implementation of the proposed UAV‐based Long RangeWide Area Network (LoRaWAN) flying gateway able to collect data directly from LoRaWAN sensors during its flight, keep them stored in an onboard memory, and forward them at the end of its flying path to a platform where the authorized users can access them. A prototype of the gateway has been developed to assess the proposed solution through both indoor and outdoor tests aiming to test its feasibility both in terms of communication performance and UAV‐required hardware resources. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Things (IoT) that is usually deployed with the assistance of cellular backhaul and energy harvesting (EH) is characterized by the status update freshness. However, the traditional nature EH results in the loss of information freshness due to the randomness of nature energy process. Although using wireless energy transferring (WET) to charge sensors can overcome this problem, this method has low‐energy efficiency. With these considerations, this paper proposes a novel IoT system under cellular communication scenario by simultaneously exploiting the nature EH and WET. The proposed IoT system is consisted of a cellular backhaul subsystem, a wireless EH and transferring (WEHT) subsystem, and a wireless sensor status update subsystem. The WEHT subsystem employs the dedicated power beacons harvesting energy from natural energy source and transferring the harvested energy to sensor. For the proposed IoT system, this paper first investigates the WEHT subsystem and constructs the Markov Chain (MC) of discrete energy states as well as the MC state transition matrix. Second, the average AoI and peak AoI (PAoI) are formulated by separately considering cellular backhaul and EHTBs. The AoI (PAoI) comparison shows that the proposed EHTB‐based IoT system can outperform the conventional non‐EHTB one where the sensor directly harvests energy from natural source. At the same time, the numerical results exploit the impact of system parameters on AoI and PAoI, respectively. It is found that there exists a trade‐off between the nature energy arrival and EHTB energy transfer so that the average minimum AoI and PAoI are achieved. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the channel estimation and signal‐to‐noise ratio (SNR) estimation technique of single‐carrier frequency domain equalization (SC‐FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE‐KF) are proposed. This paper first introduces the SC‐FDE system and introduces the principle of MEE‐KF, and then, the channel estimation flow based on MEE‐KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE‐KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC‐FDE system has also been greatly improved. [ABSTRACT FROM AUTHOR]

The 5th Generation Networks (5g), the Internet of Things (IoT), and Edge Computing are essential infrastructure enablers for a range of new business and technology developments, often termed Industry 4.0, that cover areas like autonomous vehicles, smart city grids, e-health, automated factories, mobile content streaming, and data analytics. SOCIAL NETWORKING, MOBILE EDGE COMPUTING, AND AI APPLICATIONS Smart and connected communities is an emerging field of IoT, and it has potential applications to improve human life. In mobile edge computing, service migration can reduce not only the access latency but also the network costs for users. [Extracted from the article]

Summary: In this paper, gain improvement and impedance matching of THz patch antenna by using semicircular slot and modified ground plane are discussed. The proposed antenna consists of a simple rectangular patch configuration. The novelty of the proposed antenna is its semicircular slot, and modified ground structures are used to enhance the gain and impedance matching of the proposed antenna. Design evolution along with parametric studies is also offered in this paper along with the return loss comparisons. Surface current distributions that are presented for the validation of proposed antenna's operation are controlled by the slots. The effect of slots on performance of the proposed antenna is also offered in this paper. The proposed antenna is designed and simulated on CST software. The proposed antenna occupies a compact area of 100 × 150 × 10 μm3, which in terms of wavelength can be given by 0.33 λ0× 0.5 λ0× 0.03 λ0, where λ0 is the free space wavelength at 1 THz. The proposed antenna has a resonant frequency of 1 THz with a realized peak gain of 8.87 dBi and efficiency of 87%, respectively. The proposed antenna can be a suitable candidate for 1‐THz applications. [ABSTRACT FROM AUTHOR]

Summary: Based on the digital three‐dimensional (3D) panoramic technology of sequence pictures, the static pictures are stitched together to form a 360‐degree panoramic image, which can truly reproduce the real scene. Panoramic images are widely used, making this technology a hot spot in the research of virtual reality technology. This paper completes the research content of the application value of the digital 3D panorama, the key technology of digital 3D panorama technology, and the design and display of 3D panorama campus through practical application. This article comprehensively introduces the hardware equipment of panoramic technology and the technical requirements of panoramic image acquisition in the field of practical application, and combines the actual cases to systematically produce panoramic image stitching, panoramic image generation, and virtual roaming campus construction; 3D panoramic technology is a brand‐new form of viewing images, discussing and studying the artistic expression of 3D panoramic images, and researching its innovative application in visual communication art. In this paper, the chromaticity calibration of digital camera and projection equipment is used to eliminate the coupling difference between the two; then estimate the spectral reflectance of the three‐color channel of the object and separate the specular reflection component and the diffuse reflection component, at the same time correct the color and specular reflectance of the object itself, and analyze the influence of reflection on the color of the projection light source. This article describes the design ideas and key technologies of the school's 3D panoramic walkthrough system, describes in detail the process of the system from demand analysis to system design and finally realization, and focuses on the production method of panoramic images and the main functional modules in the walkthrough system. At the end, the system was tested, and the existing problems were analyzed, and solutions were provided. Research shows that when the roaming system has 3,000 concurrent users, it can read a single scene for about 3 s on average. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the design of reliable communication network is solved by genetic algorithm. In order to solve this problem, we must first provide the lowest acceptable network reliability. There are many methods to satisfy the network reliability constraints. The penalty function may produce an invalid solution, so it is not suitable to solve the problem. In order to overcome these difficulties, this paper proposes a heuristic repair method, which is based on the number of spanning trees in the graph. Compared with the heuristic maintenance method with lower cost, this method can generate a better solution with less computation. When the population size is 25, the hybridization rate is 0.96, and the mutation rate is 0.07, the best results can be obtained. This paper analyzes the node fault, grasps the influence rule of the fault on the network, and improves the network reliability. This paper summarizes and classifies the failure modes of communication network nodes. Combined with the classic BA network model in the complex network theory, through the simulation design of MATLAB and OPNET, the influence of node function failure and performance failure on network reliability is studied. [ABSTRACT FROM AUTHOR]

Summary: With the changes of the times and the rapid development of the mobile Internet and the Internet of Things, network communications are facing increasingly severe challenges. Due to the user exclusivity of the orthogonal resources of the existing orthogonal multiple access technology, it is difficult for the number of accessible users and the spectral efficiency to meet the application requirements of network communications. 5G technology will meet the diverse business needs of people in various scenarios such as life, work, leisure, and transportation. This paper analyzes the 5G‐oriented non‐orthogonal multiple access (NOMA) technology and designs and implements a NOMA technology simulation platform laboratory based on 5G technology. The entire simulation experiment adopts a modular design concept so that it can be modified and extended in the future network communication. In this paper, the user pairing scheme and power allocation algorithm of NOMA technology are studied in depth. For the user pairing scheme, this paper adopts two schemes: traversal search pairing scheme and grouping pairing scheme. For the same algorithm for power allocation, this paper studies two suboptimal power allocation algorithms including fixed power allocation and partial transmission power allocation. Simulation experiments show that the average cell throughput and cell edge user throughput of NOMA are slightly lower than when the traversal search pairing scheme is adopted: the average cell throughput has decreased by approximately 4%, and the cell edge user throughput has decreased by approximately 1.6%. The packet pairing scheme is a compromise between complexity and throughput performance. For different power allocation factor values, the overall throughput performance of some transmission power allocation algorithms is better than the flower pollination algorithm (FPA). [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of artificial intelligence, the intelligent distribution network can effectively improve the overall power supply quality of the distribution system. The designed distributed power has been able to achieve an optimal operation of grid connection, while the high‐speed operation of distribution equipment can achieve effective interaction with users. At present, intelligent distribution network is widely studied and applied in power industry because of its advantages of high efficiency energy and environmental protection. Because of its unique characteristics, such as low cost and fast speed, wireless sensor network is cleverly applied in intelligent distribution network. However, wireless sensor network has some defects in energy utilization due to its large range and too many nodes. This paper presents a cross‐layer cooperative control model, which can control the real time and reliability of network power data communication through stack protocol layer. This method can learn the weight value adaptively by training the influence relationship between the parameters of each protocol layer and achieve real‐time and reliable data communication by adjusting the policy and parameters of each layer in real time. This method takes the parameters of each wireless sensor node as the unit to establish the influence relationship between each other so as to realize the connection between each node of wireless sensor network and then to control the wireless sensor network as a whole to ensure the real‐time reliability of data communication in intelligent distribution network. In this paper, a lot of simulation experiments have been done, and the results show that the proposed cross‐layer cooperative control model can control the whole wireless sensor network to achieve efficient data transmission capacity of intelligent distribution wireless sensor network. [ABSTRACT FROM AUTHOR]

Summary: As the core technology of mobile communication system, multiple access technology directly determines the performance and capacity of the system. However, with the rapid growth of the number of mobile communication system users and the high rate service demand, these orthogonal multiple access technologies are limited by the spectrum efficiency and access capacity, so it is difficult to meet the future mobile communication demand. Therefore, for the fifth generation mobile communication system (5G), the research of non‐orthogonal multiple access (NOMA) technology to effectively improve the system capacity has been widely concerned. The purpose of this paper is to analyze the influence of interference and power distribution on system performance simulation based on the simulation results of two most representative NOMA technologies, sparse code division multiple access (SCMA) and NOMA. This paper firstly briefly analyzes the SCMA system model and key technologies and, on this basis, introduces the SCMA system simulation platform, including basic platform parameters and platform simulation flow chart. Furthermore, the influence of adaptive modulation coding on the performance improvement of SCMA system is analyzed by simulation. Then, based on the NOMA system model, this paper carried out the research on fairness among NOMA users. Based on Lagrange multiplier method, this paper simulates fairness under the restriction of power allocation optimization algorithm, the number of users in different area, different radius and different transmission power, the village of village fair index, the change of the frequency spectrum efficiency and energy efficiency curve, the throughput rate between 0.8 and 1.4 changes, and the conclusion that has a guiding significance for NOMA system design. [ABSTRACT FROM AUTHOR]

Summary: This paper considers an underlay cognitive radio (CR) wireless network with the assistance of a two‐hop decode‐and‐forward (DF) relay node. Different from the conventional CR relay networks, in this paper, the relay node using power‐splitting (PS) techniques harvests wireless energy transmitted from the secondary base station (SBS) and, then, uses its harvested energy to forward the received signals to multiple secondary receive users (SRUs). Due to the highly mutual dependence of the transmission strategies at nodes on the system performance, the optimal design of signal processing at transceivers to efficiently handle the interference and energy constraints is crucial. This paper studies the jointly optimal design of the precoders, PS factors to maximize the minimum user rate under the transmit power, harvested energy, and interference power constraints. Owing to the nonlinearly and mutually coupled design matrix variables in both objective function and constraints, the joint design of the precoders and PS factors for the concerned problem is mathematically challenging. To tackle these difficulties, we devise the appropriate surrogate functions to recast the nonconvex optimization problem into the convex one. By using the minorization–maximization framework, an efficient iterative algorithm with provable convergence is developed by solving the convex optimization problem in each iteration. Numerical results are provided to evaluate the achievable user rate performance of the considered system and investigate the effects of the interference power thresholds at the primary receive users and the number of antennas at the SBS and the relay on the minimum achievable user rate. [ABSTRACT FROM AUTHOR]

On the other hand, green energy-efficient computing has become a potential research focus in the IoT environment.2 Finally, energy consumption techniques are incoming a more advanced stage in the IoT communications. Over the last decade, the Internet of Things (IoT) had impressive growth and became the new direction of information technology. Green energy-efficient computing solutions in Internet of Things communications. [Extracted from the article]

"Improvising Packet Delivery and Reducing Delay Ratio in Mobile Ad-hoc Network Using Neighbor Coverage Based Topology Control Algorithm" - Authored by P, Punitha; J, Shanthini; S, Karthik: This research work [ 16 ] is focused on delay and neighbor connectivity-based constrain in MANET architectures. 2019; e4263. https://doi.org/10.1002/dac.4263 16 Rajakumari K, Punitha P, Lakshmana Kumar R, Suresh C. Improvising packet delivery and reducing delay ratio in mobile ad hoc network using neighbor coverage-based topology control algorithm. The papers in this issue introduce new challenges, solutions, and networking algorithms in cognitive communication systems, advancing capsule network research towards deeper communication pattern understanding and the ability to communicate about that understanding. We are delighted to present this special issue of IJCS on CapsNet-based computing in cognitive communications. [Extracted from the article]

Summary: Uncoded space‐time labelling diversity (USTLD) is a recent scheme that improved the error performance compared to conventional multiple‐input, multiple‐output systems. Thus far, USTLD has suffered from limited achievable data rates, as the original model uses only two transmit antennas. This motivates for the work in this paper, where the USTLD model is extended to allow for any desired number of transmit antennas. An analytical bound for the average bit error probability of this high‐rate USTLD (HR‐USTLD) system is derived. This expression is verified using the results of Monte Carlo simulations, which show a tight fit in the high signal‐to‐noise ratio region. The increased data rates associated with larger transmit antenna arrays in HR‐USTLD systems come at the cost of increased detection complexity. Therefore, this paper studies the application of low‐complexity detection algorithms based on the popular QR decomposition technique and proposes a new algorithm specifically designed for HR‐USTLD systems. Analysis of this algorithm in terms of accuracy and computational complexity is also provided and benchmarked against maximum‐likelihood detection (MLD). It is shown that the proposed algorithm achieves near‐MLD accuracy, while reducing complexity by 79.75% and 92.53% for the respective 4 × 4 16QAM and 4 × 5 16PSK HR‐USTLD systems investigated. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a slotted printed rectangular monopole antenna (SPRMA) is proposed that covers the entire UWB (ultra‐wideband) except for WiMAX and WLAN (802.11/a‐n). In the first stage, a rectangular monopole antenna is designed, which covers the entire UWB frequency. Then, a T‐slot is inserted in the radiating patch to have the rejection of the band containing WiMAX and WLAN (802.11/a‐n). The notch characteristics of the proposed antenna are observed from 3.0 to 3.65 GHz and 4.5 to 6.65 GHz respectively. Measured peak gain of 3.28 dBi has been observed at 4.25 GHz frequency. To show the validation of antenna, mathematical analysis using circuit theory has been also given in the paper. The prototype has been also tested inside the anechoic chamber to give the validation in real‐time environment. Several other reported antenna structures have also the same notch band in UWB with different notching methods. The proposed structure's radiation properties are compared with other reported structures proving the compactness of the proposed structure. [ABSTRACT FROM AUTHOR]

Summary: In millimeter wave (mmWave) massive multiple‐input‐multiple‐output (MIMO) systems, hybrid precoding plays a pivotal role in reducing complexity and cost while providing a good spectral efficiency. However, implementation of digital precoders with large number of antennas is difficult due to hardware constraints, while analog precoders offer confined performance. This leads to high computational complexity and cannot fully exploit the spatial information. Previous studies on hybrid precoding were based on exhaustive search solutions or greedy schemes, which result in higher complexity system performance. To face these challenges, this paper proposes deep hybrid precoding framework with phase quantization and residual dense network to design the matrix of analog and digital precoders. The proposed deep hybrid precoding technique consists of offline training stage and online deployment stage. In offline training stage, hybrid precoding is obtained assuming the approximate phase quantization. While in the online deployment stage, the matrix of analog precoding is calculated by exchanging approximate phase quantization with ideal phase and grouping the analog precoding vectors. In this paper, we also propose a deep reinforcement learning‐based hybrid precoding. It consists of a deep reinforcement learning with employing convolutional neural network and long short‐term memory (LSTM) methods. In our proposed frameworks, structures of proposed techniques are trained for maximum spectral efficiency. Our proposed techniques are compared with other precoding techniques. Results illustrate that the proposed techniques outperforms the other precoding techniques in terms of the spectral efficiency. [ABSTRACT FROM AUTHOR]

Summary: Network virtualization (NV) has ubiquitously emerged as an indispensable attribute to enable the success of the forthcoming virtualized networks (eg, 5G network and smart Internet of Things [IoT]). Virtual network embedding (VNE) is the major challenge in NV that allows multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. A great number of VNE algorithms have been proposed, but over the past decades, most of them are only targeting for VNE node mapping. In this paper, we propose two distributed parallel genetic algorithms, which are based on two versions of crossover and mutation schemes, for online VN link embedding problems with low latency and high efficiency. Furthermore, we conduct a time analysis on the executing time of independently distributed parallel computing machines in details. This comprehensive analysis validates the parallel computing scalability on an identical number of predefined parallel machines. Extensive simulations have shown that our proposed algorithms can achieve better performance than integer linear programming (ILP)–based solutions while meeting the stringent time requirements for online VN embedding applications. Our proposed algorithms yield superior performance in running time with 32.78% up to 1727.8% faster than existing popular VNE algorithms. Additionally, the theoretical analysis indicates that the execution time can be reduced to logarithmic times by applying proposed distributed parallel algorithms. [ABSTRACT FROM AUTHOR]