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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: 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: 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: Underwater acoustic channel is a challenging medium for communication due to the presence of significant multipath, high noise, frequency‐dependent propagation loss, and high and non‐uniform Doppler spread. Doppler shift is non‐negligible in underwater communication due to the low velocity of underwater signals. Synchronization and Doppler estimation are important requirements for achieving good performance in this channel. Synchronization algorithms that give good performance in radio communication do not work well in underwater communication. Hence, this area has received a lot of attention from researchers. This paper surveys important works in the area. The techniques proposed in the literature for frame synchronization, frequency and phase synchronization, and timing synchronization in single carrier communications are reviewed here. The synchronization techniques proposed for OFDM, MIMO OFDM, and spread spectrum communication are also surveyed. Doppler estimation methods proposed in the literature are also reviewed. It is found that most of the recent works in underwater acoustic communication focus on OFDM synchronization. Deep learning‐based methods proposed in the literature are also reviewed. Key open problems and areas that require future research attention in the field of synchronization and Doppler estimation in underwater communications are highlighted in this paper. The area needing most attention of underwater communication researchers was found to be MIMO OFDM due to the difficulty in synchronization in such systems while used in underwater communication. Reducing the computational complexity of the algorithms used is also important for future work. Schemes that work with Doppler due to relative velocity over 10 m/s also need to be developed. [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: Location‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes. [ABSTRACT FROM AUTHOR]

Summary: The increasing prevalence of MIMO technology in wireless communication networks is attributed to its ability to augment system capacity and dependability. Consequently, there is a notable enthusiasm for the advancement and implementation of S‐band applications. Nevertheless, the mutual coupling between numerous antennas presents a crucial obstacle to MIMO system performance. This paper presents a novel technique that utilizes a defective ground structure with parasitic components to increase antenna isolation and gain. Additionally, an L‐shaped slot is incorporated into the patch element to enhance the antennas impedance bandwidth. Antennas are intentionally placed in a perpendicular arrangement to minimize the impact of the coupling effects. The antenna exhibits an impedance bandwidth in the frequency range of 2.62–2.79 GHz. Furthermore, the utilization of a four‐element MIMO antenna setup with a defective ground structure yields outstanding isolation properties, with values notably lower than −25 dB. To achieve an optimal MIMO performance, we assessed various diversity parameters, including diversity gain, channel capacity loss, total active reflection coefficient, and envelope correlation coefficient. The parameter values were within acceptable limits (ECC < 0.04, DG = 10 dB, TARC < 0 dB, and CCL < 0.1 bits/s/Hz). This research paper therefore offers essential insights into the design and optimization of MIMO antennas specifically for 2.7 GHz applications. [ABSTRACT FROM AUTHOR]

ABSTRACT It has been observed that the agriculture sector has picked exponential growth with the help of the integration of advanced technology like wireless sensor networks (WSNs), the Internet of Things (IoT), and machine learning (ML). They have not only created new opportunities but also boosted the efficiency and productivity in the sector. Out of so many significant hurdles, accurately pinpointing the positions of the sensor nodes and agricultural assets like crops, livestock, and machinery within the field is one major challenge. Merging ML with a WSN‐assisted IoT (WIoT) network has offered a promising solution to overcome the localization challenge for smart agriculture. This integration will greatly benefit agricultural practices, such as precision agriculture, fast decision‐making, accurate positioning of sensor nodes, monitoring, and managing real‐time data effectively. This paper comprehensively discusses state‐of‐the‐art techniques and methodologies employed in localization within WIoT networks for smart agriculture. It explores various ML algorithms, which include reinforcement learning, supervised learning, and unsupervised learning, to perform the localization activity precisely. Moreover, it explores how fusion technologies like WSN, IoT, ML, and sensors will enhance localization accuracy and reliability in various agriculture activities. Additionally, the paper discusses the application of localization in the agriculture sector, such as crop monitoring, livestock management, precision agriculture, environmental conditions, and crop monitoring. It also discusses the challenges and obstacles in the path domain of various evaluation parameters like energy efficiency, scalability, and robustness, along with research and societal implications. Overall, the paper provides valuable guidance and outlines potential directions for future research in ML‐driven localization in WIoT for smart agriculture, offering a clear roadmap for researchers. [ABSTRACT FROM AUTHOR]

Summary Software‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity. [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: 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: 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: Intelligent fire safety systems place higher demands on the speed and reliability of data communication between sensors, controllers, and other devices. The two‐wire bus has become a significant trend in developing fire safety communication systems because of its low cost and simple maintenance. This paper proposes a two‐wire bus communication system for fire safety based on orthogonal frequency division multiplexing (OFDM) technology. First, we introduce the low‐voltage power line channel model and conduct relevant simulations, providing the conditions for subsequent simulation work. Then, we design the system's transmission and reception schemes, provide the parameters, and introduce each technology used. Finally, we conducted system simulations based on three different channel models, and the system performance is compared among different encoding, modulation, and channel estimation methods. The simulation results show that the system designed in this paper has superior performance compared with other systems and can meet the requirements of intelligent fire safety systems for communication speed and reliability. [ABSTRACT FROM AUTHOR]

Summary: 5G and beyond (5GB) systems show a flexible characteristic to meet different user communications requirements in parallel. Multi‐numerology waveform design is an important part of this human centric characteristic, however, it is necessary to make multi‐numerology planning. In this paper, the integrated sensing and communications (ISAC) concept is used with machine learning (ML) techniques to develop an intelligent numerology control mechanism for 5GB. Thanks to the ISAC capabilities, an environment‐aware system is designed, and several useful sensing information (USI) that affect the wireless channel characteristic is utilized. It is assumed that the USI is obtained through 5GB smart city networks. At that point, synthetic data generation is performed to form a new dataset with USI‐based features. Additionally, a novel feature control algorithm is proposed in the paper. Lastly, simulation results are presented for different type of ML models including several ensemble methods. [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 this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS. [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: 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: This paper investigates the performance of novel multicarrier index keying‐aided non‐orthogonal multiple access (MCIK‐NOMA) system. Unlike the classical scheme, the proposed system sends the information through both index domain and constellation domain symbols thereby increases the spectral efficiency. The impact of channel state information (CSI) uncertainty on the system's performance is investigated. More specifically, the performance of MCIK‐NOMA under three different CSI conditions is analyzed: perfect CSI, MMSE‐based variable CSI, and fixed CSI uncertainty. This paper also discusses the optimum power difference needed for the successful separation of symbols at the SIC receiver by alternating the power ratio in the proposed system. The influence of selecting different active subcarriers and modulation techniques on the system's performance is studied in detail. The simulation results show that the proposed system achieves better BER and spectral efficiency and outperforms the existing systems. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient R2. The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient R2 of the proposed EWC model is 0.9993, whereas the existing PE‐PSO, LNSM, and PSO‐Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively. [ABSTRACT FROM AUTHOR]

Summary: With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat‐reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel selective mapping (SLM) method is proposed for reducing the peak‐to‐average power ratio (PAPR) of orthogonal frequency division multiplexing with index modulation (OFDM‐IM) systems. In the proposed method, polar codes are used to construct the SLM phase generation mechanism. This systematic structural design is superior to conventional methods in which phases are randomly generated. Moreover, polar codes and Reed–Muller codes are selected to protect the input data of the OFDM‐IM system. The weight distribution of the Reed–Muller codes is then used to determine the location data of the active subcarriers in the OFDM‐IM system, and these data are used to determine the error correction capacity. Simulation results indicated that the PAPR improvement achieved using the proposed method is similar to that achieved with conventional SLM methods. Moreover, linear block codes are used to construct the active subcarrier selection and SLM phase generation mechanisms to ensure that both mechanisms have systematic structural designs. [ABSTRACT FROM AUTHOR]

Summary: Over the years, optical communication systems have been a significant source of fast and secure communication. However, factors like noise and mitigation error can degrade the bit error rate (BER) and quality factor (Q factor) of optical communication systems. Predicting the optimal threshold, Q factor, and BER is usually a difficult task. Therefore, in this paper, machine learning‐based linear regression, least absolute shrinkage and selection operator (LASSO) regression, and Ridge regression have been used for a dense wavelength division multiplexing (DWDM)‐based optical communication network to predict the signal quality. These techniques have been used to predict the desired BER, Q factor, threshold, and eye height of the system. To demonstrate this research concept, a DWDM‐based optical communication network of 50 km length is designed and simulated using Optisystem‐14.0. After data preparation, regression models have been developed and validated through diagnostic plots. Results show that mean square error (MSE) has a significant decline with an increase in the number of epochs for all four models. LASSO and Ridge regression have effectively resolved the issue of overfitting, which occurred in the linear regression case. Furthermore, the mean MSE plot proved the significant reduction of mean MSE in the case of LASSO regression. Results show that min BER for LASSO regression came out to be −173,627.14, providing a robust and cost‐efficient process. [ABSTRACT FROM AUTHOR]

Summary: With the popularization of 6G‐driven wireless access networks and the development of edge computing, the connection between piano performance and artificial intelligence algorithms is getting closer and closer. 6G was first used to solve large‐capacity, short‐latency communication problems, and research results in this field abound. However, in practical applications, the deployment of 6G high‐frequency network is a major problem faced by the industry. This paper mainly studies the artificial robust cluster piano demonstration platform from the perspective of the fusion of 6G and edge computing. We firstly consider that the edge computing is a technology in which artificial intelligence algorithms are transplanted to edge devices and chips. The vision of the 6G network is to realize ubiquitous wireless intelligence, which includes three dimensions: mobile ultra‐broadband, super Internet of Things, and artificial intelligence, and realizes the conversion of piano scores into digital signals. Artificial Robust Clustering is a comprehensive technique that incorporates many disciplines, including phonetics, artificial intelligence, computer science, and more. This paper analyzes the fusion technology of 6G and edge computing and studies the robust cluster intelligent piano demonstration platform. The mentioned technologies are combined together to make the designed efficient. [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: 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: 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: 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: 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, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology‐building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user‐defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT. [ABSTRACT FROM AUTHOR]

Summary: In recent years, neighbor discovery techniques using directional antennas have attracted widespread attention. Most currently available directional neighbor discovery techniques are designed based on two‐dimensional (2D) space. Although these algorithms can accomplish the discovery between nodes, the algorithm portability is poor for three‐dimensional (3D) platforms, and the application scenarios are limited since new communication scenarios such as unmanned aerial vehicle (UAV) groups and maritime fleets emerge where information needs to be delivered in real time and nodes are located in 3D space. This paper proposes a new deterministic directional neighbor discovery algorithm in 3D space named 3D scan‐based algorithm (3D‐SBA) to meet the needs of the line‐of‐sight (LOS) scenes. Four existing neighbor discovery algorithms, namely, SBA‐based random mode selection (SBA‐R), quorum, complete random algorithm (CRA), and SBA‐based leader election algorithm (LE), have been extended to our proposed 3D algorithm model for simulation and comparative analysis with 3D‐SBA. The simulation results show that the 3D‐SBA algorithm consumes 51.15%, 180.20%, 9.48%, and 17.49% of the time slots compared with the four existing algorithms mentioned above. However, the quorum algorithm has a very high node collision rate, up to 92.11%. Ultimately, the 3D‐SBA algorithm has the best performance considering the conflicts and the density of network nodes. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the performance of a dual‐hop mixed relay system with radio frequency (RF) and free‐space optics (FSO) communication under the effect of pointing error (PE) and atmospheric turbulence (AT). This paper considers a system where RF and FSO links are cascaded. The RF link is modeled by Nakagami‐m fading, and the FSO link is modeled as gamma–gamma (G‐G) fading channel. Both the channel models use orthogonal frequency division multiplexing (OFDM) with M‐ary quadrature amplitude modulation (QAM). The expressions for probability density function, cumulative distribution function, signal‐to‐noise ratio, and ergodic capacity are derived. The moment generating function (MGF) of fading and the bit error rate (BER) of the OFDM‐based M‐ary QAM scheme is derived in terms of Meijer's G‐function. It has been observed that, in fixed gain relay systems, the modulation scheme's BER is dominated by the SNR of the RF link. While in a variable gain relay system, the turbulence conditions of the FSO system affect the SNR and the BER of the modulation method. The feasibility of heterodyne detection and intensity modulation direct detection (IM/DD) is analyzed in terms of outage probability and ergodic capacity. The results can be used to choose the optimal modulation order and relay system for QAM‐OFDM‐based optical wireless systems. [ABSTRACT FROM AUTHOR]

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: 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: 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: 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]