Search

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: 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 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: 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: 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: Underwater wireless sensor network (UWSN) connects the real world to the hidden resources available deep into the sea using sensor nodes deployed sparsely and are interconnected to gather information for any movement/change in the ocean. The computed information on nodes obtained from concurrently running UWSN's applications will be meaningful, only if the location of this change will be identified. Nevertheless, it is always difficult to obtain the exact location coordinates of any misadventure like tectonic plate's movement but using localization algorithms in UWSN helps to obtain the coordinates. Since localization algorithms for terrestrial networks are not feasible for UWSN because of environmental challenges. Moreover, GPS systems do not work after 15 m of depth and the radio frequency gets suppressed. So, the requirement arises to analyze existing localization approaches in the deep sea where acoustic signals are used for communication and data transfer. In this paper, along with describing the UWSN's applications and challenges, the underwater localization schemes are reviewed to present, summarize, and mention the scope of improvement. Further, classification into range‐based and range‐free categories of these schemes is depicted with implementation in the NS2.30 simulation environment of some of the recent techniques to showcase the reasons for better performance. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a compact (20 × 20 × 1.59 mm3), (0.34λg × 0.34λg × 0.01λg at 2.4 GHz) triple band monopole antenna for Internet of Things (IoT) applications. The geometry of the antenna consists of (i) U‐shape monopole with feed line, (ii) stubs, and (iii) modified ground to achieve three independent operating bands ranging from (i) 2.4–2.48 GHz for industrial, scientific, and medical (ISM)/3G; (ii) 3.4–3.6 GHz for 4G/5G; and (iii) 5.725–5.825 GHz for 5.8G Wi‐Fi/extended 5G. Various parameters, input impedance, and current distribution of the proposed structure are carried out to investigate the effects of stub, ground plane and analyze the radiation. The measured result covers the three individual bands in range of 2.38–2.48 GHz, 3.41–3.61 GHz, and 5.76–6.04 GHz, respectively, and exhibits stable omnidirectional radiation patterns, acceptable antenna gain, and efficiency in the operating bands. The experimental result of the fabricated antenna closely matches with the simulated result. Therefore, satisfactory result ensures its ability to work for 3G/4G/5G/extended 5G IoT portable applications. [ABSTRACT FROM AUTHOR]

Summary: In this growing world, the field of communication is developing quickly. The research community's attention is shifting to the design of networks beyond fifth generation (5G) and the sixth generation (6G) due to the rapid spread of the 5G mobile networks. Due to that, there will be a lot of traffic in the area, which will interfere with the channels and maybe produce noise. Nonorthogonal multiple access (NOMA) contributes to streamlined communication and a decrease in the possibility of interference. The communication will be encoded and decoded from transmitter to receiver utilizing the superposition coding (SC) and successive interference cancelation (SIC) approaches, ensuring the message's secrecy. This paper considers a simultaneous wireless information and power transfer (SWIPT)‐based cooperative NOMA system, where the nearby user to the source node acts as a relay to assist the far user. To further enhance the system performance, an improved golden search optimization (IGSO) is used to determine the best power allocation and power splitting coefficients by decreasing the outage probability. The existing models are compared with the proposed model in the results section regarding outage probability and throughput, where the proposed model outperformed all other existing models. [ABSTRACT FROM AUTHOR]

Summary: The main goal of this strategy is to detect the occurrence of wild animals in farmsteads and human habitats using an improved deep learning in order to avoid loss of life and damage to agricultural crops. This paper formulates a technique for recognizing wild animals in an Internet of Things (IoT) environment utilizing deep batch normalized exponential linear unit AlexNet (DbneAlexnet) and the Gazelle Hunting Optimization Algorithm (GHOA). The IoT‐Multimedia Sensor Networks (WMSN) network is first simulated, with the IoT nodes capturing the images needed for wild animal identification. The suggested GHOA is used for routing, which sends the captured images to the base station (BS). The input wild animal image is transmitted to image preprocessing at the BS, where it is processed using the Weiner filter (WF) to remove undesirable noise from the image. The denoised output is sent into salient map extraction, which extracts salient map to determine the regions that are conspicuous or noticeable at every place in the field of vision and guides the selection of attended location. Finally, the saliency map is forwarded to DbneAlexnet for detecting the wild animals, where the DbneAlexnet is trained using proposed GHOA. The suggested GHOA algorithm is created by combining the Gazelle Optimization Algorithm (GOA) and the deer hunting optimization algorithm (DHOA). Furthermore, detection is appraised by precision, recall, and f1‐score, which provide values of 90.2%, 89.1%, and 89.6%, respectively. [ABSTRACT FROM AUTHOR]

Summary: The underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate. [ABSTRACT FROM AUTHOR]

Summary: An evaluation of various transmit precoding schemes is proposed in this thesis on basis of spectral efficiency (SE) of massive MIMO (m‐MIMO) system in 2 GHz spectrum. The present generation of wireless networks must be rethought due to the rapidly rising user base, huge volume data transfer, wide coverage requirement, and exponential rise in data rate. 5G mobile network includes a multi‐fold increase in network capacity, energy conservation, decreased latency, higher coverage probability, and a seamless connection. m‐MIMO has provided good solution for enhancing the data rate with the coverage, overcoming the effects of multipath fading and has also increased diversity of the system. Massiveness of MIMO system along with 5G network is the key technology to address the issue of SE, throughput, improvement of QoS (quality of service), coverage probability, power delay profile, diversity simulation, energy efficiency, and improvement in bit error rate. Parameters such as SE, throughput, and coverage probability are important for any communication system, and their improvement can increase overall capacity of cellular network system and could meet the requirement of upcoming demand for mobile network. The research paper is focused on performance analysis of m‐MIMO with reference to the SE of various transmit precoding schemes. Simulation results show that SE provided by M‐MMSE (multi‐cell minimum mean‐squared error) technique of precoding ranks highest for the same number of antennas and pilot reuse factor as compared to other schemes. [ABSTRACT FROM AUTHOR]

Summary: Mobile Ad Hoc Network (MANETs) are indeed autonomous, fast‐deployable wireless networks that are ideal for communications in areas with limited radio infrastructure, outdoor events, military operations, and disaster relief. But the primary problem is with routing because of the nodes' mobility. This paper proposes a cluster‐based routing under energy prediction via deep learning techniques. Here, proposes a new model termed as Concatenation of Convolutional with Max‐Avg Pooling layer in Deep Convolutional Neural Network (CCMAP‐DCNN) for energy prediction, in which additional layers are inserted into the extant DCNN structure to ensure the effectiveness of energy prediction. As a result, clusters are created by grouping the nodes together. Constraints like energy, trust, distance, and latency are taken into account while choosing the cluster leader. For cluster head selection and optimal routing, proposes a new hybrid Namib Beetle Upgraded Jellyfish Search Optimization (NBUJSO) algorithm that utilizes the NBO strategy to the JSO algorithm making the selection process more optimal. The best route is then chosen by the optimum routing method, which takes into account variables like network quality and mobility to send data packets from source to destination as efficiently as possible. Finally, the data aggregation process is followed for eliminating the redundant transmission. [ABSTRACT FROM AUTHOR]

Summary: A unique MIMO antenna with circular polarization characteristics that works similarly for skin, brain and heart phantoms has been proposed in this paper. The twin rectangular X‐shaped slot that serves as the basic structure of the proposed antenna is entirely planar and low‐profile but has a defective ground plane. For three different phantoms, the proposed antenna resonates at 2.4–2.45 GHz ISM band. With a dimension of 15.5×9×1.27mm3, the antenna exhibits compactness. Rogers RT/duroid 6010 substrate material has been used to fabricate the antenna in order to validate the designed antenna structure. Utilizing the high dielectric material for both substrate and superstrate, both skin‐mimicking gel and minced hog meat were employed in the experiment setup at 2.45 GHz ISM band. At ISM band, −28.3 dBi has been observed has realized peak gain. The obtained fractional bandwidth (FBW) and Axial Ratio Bandwidth (ARBW) values are 30.67% and 45%, respectively. Also, the maximum specific absorption rate (SAR) values are found to be in the acceptable limit regulated by IEEE. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth in wireless communication technology and the proliferation of multimedia applications, specifically Voice over Wireless Fidelity (VoWiFi), necessitate the exploration of innovative techniques for improving network performance and quality‐of‐service (QoS). This research paper presents an innovative approach to enhance VoWiFi cell capacity using aggregate medium access control protocol data unit (A‐MPDU) frame aggregation for variable bit rate (VBR) traffic. In this research article, we investigated the effect that the retransmission of voice packets has on the cell capacity of a wireless local area network (WLAN) standard that provides VoWiFi service while taking into account the A‐MPDU approach. When taking into consideration the constant bit rate (CBR) and VBR traffics, we compared the results obtained using WiFi 6 (i.e., IEEE 802.11ax) with older WLAN standards such as IEEE 802.11b/g/n/ac. [ABSTRACT FROM AUTHOR]

Summary Middleware has become an essential element in the construction of distributed Internet of Things (IoT) applications. While it plays a central role in hiding the complexities of distribution, middleware systems have also been responsible for dealing with the uncertainties in IoT environments, such as changes during operation (e.g., inaccuracies in sensor data collection) and fluctuations in resource availability, for example, the battery. These uncertainties demand attention as they can result in application failures or, even worse, jeopardize the safety of applications. Existing middleware systems are being enhanced with self‐adaptive capabilities to address these uncertainties. It means they can make runtime adjustments to the middleware and applications (built atop them) without complete shutdowns. Despite the variety of available adaptive solutions, IoT applications often face uncertainties, each requiring a distinct adaptive action. For instance, the need to fine‐tune a thing's workload due to battery consumption is a common challenge. Furthermore, these applications are susceptible to changes occurring at various layers, presenting a complex challenge of managing them simultaneously. This paper introduces Middleware Extendify (MEx), a solution for building and executing IoT adaptive middleware systems. MEx simplifies the implementation of middleware and provides an underlying environment that executes the middleware and supports a range of adaptation mechanisms. This approach ensures that the middleware meets the evolving demands of applications and copes with changes at runtime. The evaluation of MEx encompasses different adaptive middleware implementations to measure the impact of the proposed adaptation mechanisms. The results indicate that adaptation comes with acceptable performance costs while offering the ability to fine‐tune middleware functionality or align IoT applications more effectively. [ABSTRACT FROM AUTHOR]

Summary Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

Summary This paper presents two different configurations of ±45° dual‐wideband dual‐polarized (DWDP) antennas. The antenna comprises eight end‐loaded broadband dipoles grouped into two sets based on the bands of operation. The high‐frequency dipoles are strategically placed within the low‐frequency ones to form the elementary unit. Further optimization has been performed on the elementary unit in terms of the radiator placement to suppress sidelobe levels for array formation. Both configurations are meticulously fabricated using an all‐metal structure, demonstrating durability and the simplicity of fabrication. The antenna designs achieve ±45° polarization, high gain, satisfactory cross‐polarization discrimination (XPD), and wide impedance bandwidth covering 0.67–1.04 GHz and 1.54–3 GHz bands. Despite the nested configuration, mutual coupling is well within acceptable limits. Formation of the array based on the optimum elementary unit has been investigated by simulation, revealing reasonable gain enhancement and low sidelobe levels across all bands. This comprehensive approach yields a robust, high‐performing antenna configuration suitable for indoor distributed antenna system (IDAS) applications. [ABSTRACT FROM AUTHOR]

Summary In this paper, a decoupling mechanism is discussed for different mode excitations in the cylindrical dielectric resonator antenna (cDRA) by utilizing absorbing surface wall. In the cDRA, the fundamental radiating mode is HE11δ. Another higher order mode for radiation purpose in cDRA is HE12δ. Both HE11δ and HE12δ create broadside radiation. To analyze multiple‐input multiple‐output (MIMO) performance, two different dual‐port DRAs are proposed. Each antenna structure consists of two DRA with dual similar ports supporting the same mode. Therefore, two different antenna structures are proposed for two different modes. For each antenna, two 2 × 3 absorber array walls are designed, and they are placed in between the DRA to reduce the mutual coupling. When the absorber array walls are placed in between the antenna elements, they blocked the free‐space electromagnetic (EM) field interaction in between the DRA. Due to the reduction of free‐space radiation interaction, the mutual coupling between the ports is improved tremendously. For both HE11δ and HE12δ modes, the mutual coupling is reduced by more than 10 dB. To analyze efficacy of the proposed method, the antenna structure featuring the HE11δ mode is fabricated and experimentally tested. [ABSTRACT FROM AUTHOR]

Summary It is well known that hardware impairments (HWIs) can worthy reduce the wireless system performance at high carrier frequencies by showing random effects. Most current researches for 5 GB systems assume that transmitters and receivers (transceivers) are perfectly equipped. But wireless transceivers (TxRx$$ {T}_x{R}_x $$) are affected by HWIs in practice. Considering the previous studies in the literature, it is reported that HWIs have devastating effects on the performance of OFDM and OFDM‐index modulation (IM) systems with fading channels. In this paper, in‐phase and quadrature phase imbalance (IQI), which is the one of most HWIs between transmitter and receiver in wireless communication systems, is examined on OFDM‐IM system over Rayleigh and Nakagami‐m$$ m $$ fading channels. Two well‐known detectors, the maximum likelihood (ML) detector and the log‐likelihood ratio (LLR) detector are used under the effect of the IQI at TxRx$$ {T}_x{R}_x $$. Error performance analyzes over fading channels of the IQI effect on OFDM‐IM system are realized first theoretically and then by computer simulations. Results obtained for the presence of IQI at TxRx$$ {T}_x{R}_x $$ show that a performance evaluation based only on the presence of IQI in the receiver would be optimistic and misleading in terms of the performance of real‐life OFDM‐IM systems. [ABSTRACT FROM AUTHOR]

Summary Space information network (SIN) is difficult to fully utilize the limited on‐board resource due to its dynamic and heterogeneous nature, while the traditional resource management methods cannot adapt to the increasingly diverse task requirements. Space cloud network architecture is an effective technology to reduce the pressure on satellite resources. To effectively manage the space cloud network resources, we design a resources allocation strategy based on resource clustering. Firstly, we propose the space cloud network architecture. Then, we propose a genetic algorithm to cluster the space cloud resources. Finally, we propose a dynamic resource allocation method based on reinforcement learning for the dynamic characteristics of space cloud resources. The method improves the reinforcement learning algorithm through dynamic objective optimization to complete the optimization of multiple objectives in the process of space cloud resources allocation. The simulation results show that the algorithm proposed in this paper reduces the task execution delay by an average of 10.5% compared with the original DQN algorithm and increases the execution success rate by 2.17%. [ABSTRACT FROM AUTHOR]

Summary The handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN. [ABSTRACT FROM AUTHOR]

Summary This paper introduces a concept for designing a polarization reconfigurable transmitarray (PRT). The unit cell of the transmitarray utilizes Huygen elements, with dimensions measuring 0.6λ0×0.6λ0$$ {\lambda}_0\times 0.6{\lambda}_0 $$ (λ0$$ {\lambda}_0 $$ corresponds to a frequency of 15 GHz). An equivalent circuit model is proposed for the unit cell based on the two‐port Z‐matrix. The proposed unit cells have a very low insertion loss of 0.5 dB at 15 GHz. The PRT is illuminated by a linearly polarized (LP) microstrip‐printed Yagi antenna. The transmitarray is designed using the 13×13$$ 13\times 13 $$ proposed unit cells with a two‐level phase quantization. The collimated LP radiation emitted by the transmitarray subsequently traverses a polarization reconfigurable converter (PRC). The resultant radiation from the PRC can be either LP or circularly polarized (CP), contingent on the configuration of the PIN diodes employed within the PRC. The proposed PRT demonstrates a measured gain of 16 dBi when operating in the LP mode while achieving a peak gain of 15 dBic in the CP mode. The overall size of the proposed PRT is compact owing to a small f/d ratio (0.58). The practicality of the design is validated through fabrication and measurement. This innovative antenna design holds potential applications in the realm of Ku‐band uplink satellite communication. [ABSTRACT FROM AUTHOR]

Summary Cognitive Radio Ad Hoc Networks (CRAHNs) are an essential method for resolving conflicts between extreme spectrum scarcity and rapid traffic increase while maintaining high‐quality service for consumers. However, the coexistence of primary and secondary users represents a critical challenge for reasonable resource allocation in order to provide a sustaining system performance. Many approaches have been developed to efficiently allocate resources; however, these methods are currently limited by things like user collision, strange traffic networks, and high data transmission error rates. To address these constraints, this paper proposes a policy‐configured reinforcement learning‐based ad hoc network (AHN) model. To obtain the ideal policy configuration for the network, the system first models the cognitive radio (CR) network, in which nodes are initialised and grouped employing the Link Reliability K‐Means clustering Algorithm (LR‐KMA). The available spectrum was then detected and separated into multiple bands utilizing coherent‐based detection (CBD) and signal source identification employing the Parzen–Rosenblatt Window‐based Restricted Boltzmann Machine (PRW‐RBM). Next, the learning model for the resource allocation process employs the Weibull Distribution‐based Blue Monkey Optimization (WD‐BMO) approach to pick the relevant bands. Finally, the experimental results were analyzed in order to evaluate the proposed resource allocation model's performance in CRAHNs. When compared with previous findings, the proposed method improves resource utilization by 5%, the proposed model achieves a 7% higher throughput, and the PRW‐RBM's accuracy improves classification accuracy by 1.07%. [ABSTRACT FROM AUTHOR]

Summary: In the case of future access networks, such as 6G‐based applications, the free‐space optical communication (FSO) technology is an efficient solution. FSO in 6G is extremely popular because of its unique properties, which include ease of installation, high bandwidth, high security, license‐free long range, and interference resistance. However, environmental disturbances have a negative impact on the FSO system's functioning. Because of these atmospheric turbulences, the optical information gets restricted, which in turn reduces the link reliability, power density, and distance. This paper proposes a hybrid FSO strategy for improving system efficiency to address these problems. Signal creation is fundamental to every successful communication system. The FSO system consists of three components: the transmitter, the channel medium, and the receiver. The transmitter section includes a continuous wave laser, a Mach–Zehnder modulator, a nonreturn to zero transpose Walsh code generator, and a nonreturn to zero pulse generator. Wavelength division multiplexing‐optical code division multiple access‐spectrum slicing (WDM‐OCDMA‐SS) is used to facilitate efficient data transfer after signals have been generated by alternate mark inversion (AMI). In order to boost a weak signal, fused Raman erbium amplifiers (Fuse‐RE) are used. Q factor and log bit error rate (BER) are used to assess the performance of the suggested approach. The proposed model has obtained a log BER of −15.3291 for clear air and a Q factor of 8.2922, whereas the performances are implemented using Python. The proposed approach achieves better performance when compared to the existing methodologies. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Summary: This study presents a novel method for reducing return loss and achieving lower side lobe levels in small planar antenna arrays (PAAs). Quantum particle swarm optimisation (QPSO) findings are utilised for antenna design modelling and performance assessment utilising the MATLAB‐based moment's code approach and MATLAB‐HFSS interface. The mutual coupling between antenna components is considered and computed using the method of moments (MoM). Changing the array elements' length and spacing will accomplish the abovementioned objectives. The Matlab‐based moments code calculates the antenna components' length and spacing, and the antennas' length and spacing are used to produce the radiation pattern via the Matlab‐HFSS interface. Four examples of small PAA designs are given to illustrate how well the applied methodology works. The method discussed in this paper is generic and can be employed in the future for different sizes of the array elements and the different antenna array structure designs. [ABSTRACT FROM AUTHOR]

Summary: Optical millimeter‐wave generation is a predominant approach for the generation of high‐quality carrier signals for future wireless transmissions. This paper proposes an optical frequency 12‐tupling millimeter‐wave generation scheme using three cascaded‐MZMs assisted by polarization multiplexing for carrier suppression. A three‐stage MZM architecture produces the desired sixth‐order sidebands along with the undesired carrier signal. The proper adjustment of polarization direction and angle of polarization rotation lead to the effective suppression of the undesired optical carrier signal. A comprehensive mathematical analysis has been accomplished to evaluate the system's performance, and the obtained results are also validated through simulation findings. The derived results demonstrate that the proposed system is capable of operating over a broad modulation index range extending from 1.5 to 4.5, thereby relaxing the stringent requirements for a constant modulation index. The proposed system generates high‐purity mm‐wave signals with an OSSR of 62.06 dB and an RFSSR of 54.79 dB at a modulation index of 2.6. Moreover, it is also validated that even if the system parameters like modulation index, polarizer rotation angle, and phase difference among RF driving signals deviate from their ideal values to a certain degree, the system performance remains acceptable. The involvement of polarization technique, omission of wavelength‐dependent devices, and a large modulation index operating range make this methodology readily appealing for implementation in radio‐over‐fiber transmissions. [ABSTRACT FROM AUTHOR]

Summary Wireless Sensor Networks (WSNs) find applications in diverse fields such as environmental monitoring, healthcare, and military surveillance. Nonetheless, one of the primary challenges encountered by WSNs is congestion. Congestion arises in WSNs when there is a high volume of traffic on the network, leading to significant repercussions. These repercussions encompass packet loss, heightened latency durations, and diminished network efficiency. This paper presents an innovative congestion control mechanism named Dual Indicator Random Early Detection (DIRED). DIRED leverages two indicators, namely queue length and packet loss rate, to dynamically adjust the dropping probability of packets, thereby mitigating congestion and enhancing network performance. For the successful execution of the DIRED model, a congestion control algorithm is introduced. This strategy efficiently prevents the deterioration of network performance that can commonly arise from extremely low packet drop probabilities. Simulations are conducted to evaluate the proposed DIRED model, comparing it with the KACO and KFOA techniques. The results demonstrate that DIRED outperforms KACO and KFOA in terms of network performance, achieving a more optimal balance between performance metrics and packet‐dropping probability. [ABSTRACT FROM AUTHOR]

Summary Localization algorithms are essential for tracking, fault tolerance, and adding location context to Internet of Things (IoT) networks. Among the various range‐free techniques, DV‐Hop stands out for its lower computational complexity. Despite efforts to minimize manufacturing costs for sensors, they often find themselves deployed in remote and hard‐to‐access locations, making battery replacement a challenging prospect. These sensors can be utilized in a hostile environment, such as a field of battle, fires, floods, and earthquakes. In these types of scenarios, the sensors can become dead and stop communicating with each other. Hence, there is a critical need to improve algorithms that can tolerate and detect faults in network nodes. The faults of the sensor can lower the quality of the algorithm if they are not replaced. In such situations, we need to distribute supplementary target nodes to replace faulty nodes. This paper presented an improved DV‐Hop based method for localization of additional target nodes using grey wolf optimization (GWO). The proposed method will only localize the position of target nodes and minimizes the localization time, which makes the system more energy efficient. Simulation results conclude that the proposed GWO‐based DV‐Hop localization performed well as compared with traditional DV‐hop, genetic DV‐Hop, and Adaptive genetic DV‐hop. [ABSTRACT FROM AUTHOR]

Summary In wireless sensor networks (WSNs), routing based on cluster construction is highly preferred as it greatly supports reliable data communication, load balancing, and fault tolerance with extended network lifetime. In specific, metaheuristic approach‐based dynamic cluster heads (CHs) selection has the possibility of enhancing the lifespan of network and at the same time is capable in reducing the energy consumption. In this paper, hybrid grasshopper and Harris hawk optimization algorithm‐based energy efficient routing protocol (HGHHOA) is propounded for optimal CH selection. This proposed HGHHOA approach adopted a fitness function that incorporated the factors of residual energy, distance between CH and cluster members, distance between selected CHs and the sink, node centrality, and node degree into account. The fitness function values of optimality facilitate a potential CH selection with significant cost‐effective routing. It is proposed with primary objective of improving the network lifespan through optimized selection of CHs that balances the available energy in a predominant way. It is proposed with significance of handling premature convergence with minimized energy consumption and network lifetime through the possibility of establishing an ideal balance between number of alive and dead nodes. The results of this proposed HGHHOA approach with varying rounds of implementation exhibited better results in throughput and residual energy which is 23.98% and 29.21%, better than the bassline CH selection mechanisms. [ABSTRACT FROM AUTHOR]

Summary While wavelength division multiplexing (WDM) technology combines several wavelengths onto a single waveguide, the technology of mode division multiplexing (MDM) allows many orthogonal modes of the same wavelength to operate simultaneously without interchannel crosstalk. Thus, the hybrid WDM and MDM network in which the two above‐mentioned techniques cooperate could give a several‐fold increase in the overall network capacity. Constructing this network requires hybrid wavelength‐and‐mode multiplexers, especially ones with high integration and complementary metal‐oxide‐semiconductor (CMOS) compatibility. In this paper, we propose a design of a four‐mode triple‐band multiplexer that is capable of multiplexing up to 12 separate optical signal flows by utilizing four eigenmodes (TE0, TE1, TE2, and TE3) and three‐wavelength windows, which center at 1310, 1490, and 1550 nm. The device is on silicon‐on‐insulator (SOI) platform, consisting of four butterfly‐shaped multimode interference (MMI) couplers, four directional couplers, and a 4×1$$ 4\times 1 $$ cascaded asymmetric Y‐junction coupler. Via numerical simulations, the proposed design is verified to be able to operate effectively on the three aforementioned bandwidth slots with an optical conversion efficiency of over 93% in all functions. Moreover, it exhibits insertion loss less than 1.5 dB and crosstalk smaller than −16 dB within 25 nm bandwidth at each wavelength window. These results can affirm the success of wavelength–mode combination, which leads to a massive improve in the channel capacity on the same optical multiplexing system for optical telecommunications and photonics on‐chip interconnections. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a received signal strength indicator (RSSI)‐based weighted centroid localization (WCL) method for indoor corridor scenarios is developed and tested. The contribution of the proposed system is that, to scope the area of an unknown target position, the possible corridor area where the target node should be located is automatically selected, and only reference nodes deployed in such an area are applied for position estimation. Additionally, to improve the estimation precision, distance values converted from measured RSSIs and used for the WCL are also compensated to alleviate the RSSI variation problem caused by physical environments. Therefore, our methods can save a number of reference nodes to be used, while localization accuracy can also be achieved. Experiments in the corridor environment using a 2.4 GHz IEEE 802.15.4 wireless sensor network with four reference nodes deployed in the test field dimension of 22 m × 9.3 m have been performed. Experimental results demonstrate that the proposed method, which requires only two reference nodes, outperforms the original WCL method with four reference nodes by 53.053%, as indicated by average localization errors. The results also reveal that the proposed method provides error distances lower than the WCL method for all test target positions, while all estimated positions fall within the corridor areas by 100%. [ABSTRACT FROM AUTHOR]

Summary: This paper analyzes the performance of a cooperative nonorthogonal multiple access (NOMA) in an underlay cognitive radio network aided by an energy harvesting relay. A secondary source transmits signal for two users, where a near user acts as a relay for the far user. The far user applies the selection combining (SC) approach on the signals which were relayed by the near user and received via direct path from the secondary source. We analytically derive the outage probability (OP) of each user separately, the overall system OP, and the throughput of the system. The impact of the power allocation coefficient of NOMA and energy harvesting parameters on outage is indicated. Further, the performance of the network is investigated with imperfection in successive interference cancellation (SIC), maximal ratio combining (MRC) at relay, and Nakagami‐m fading. The results derived analytically are supported by simulation in MATLAB. [ABSTRACT FROM AUTHOR]

Summary: The orthogonal time frequency space (OTFS) transmission scheme was shown to outperform orthogonal frequency division multiplexing (OFDM) under the doubly dispersive channel. In this paper, the linear precoding is studied for multiple‐input and multiple‐out (MIMO) OTFS systems, in which a spatial‐Doppler domain singular value decomposition (SVD) precoding scheme is proposed. At the transmitter, the Doppler domain symbols from different spatial streams are precoded before projected onto multiple antennas for transmission. At the receiver, multipath components of the transmitted symbols in the delay‐Doppler grid are combined by using maximal ratio combining (MRC) strategy, so as to achieve the multipath diversity gain and increase the reception reliability. The achievable rate and complexity of the proposed scheme are analyzed, revealing that it can increase the achievable rate while reducing the detector complexity as well. The simulation results confirm that the SVD‐based precoding significantly enhances the error performance of the MIMO‐OTFS with MRC‐based detector. [ABSTRACT FROM AUTHOR]

Summary: This paper presents Relay node selection scheme and Deep sleep period for power management in Energy Harvesting Wireless Sensor Networks (RD‐EHWSN), a new energy‐saving scheme founded on asynchronous duty cycling. RD‐EHWSN reduces sensor node energy consumption and guarantees equilibrium energy use between sensor nodes in WSN with the energy harvesting capacity by adjusting these sensor nodes duty cycles more drastically and deeply by according to the estimated value of its residual energy on the basis of future‐presented harvested energy, and this is done through the use of a new proposed energy threshold policy. RD‐EHWSN also grips the benefit of transmitter initiated using the low power listening (LPL) technique with short preamble messages and uses a new relay node selection procedure to achieve the load balancing in WSN. We implemented RD‐EHWSN by using OMNeT++/MiXiM. For evaluation, we compared it with PS‐EHWSN, under multiple concurrent multihop traffic flows scenarios and scenarios in which nodes can harvest different energy harvesting rate. In all experiments, RD‐EHWSN significantly outperformed the PS‐EHWSN scheme; the results of simulation demonstrate that our scheme enhances the general yielding of WSN thru lessening the energy consumption and the mean latency, as well as raising the packet delivery ratio and the throughput. Moreover, RD‐EHWSN improves the WSN lifetime and ensures it operates in good condition in the case where the energy harvesting rate is lower by comparing it with the PS‐EHWSN scheme. [ABSTRACT FROM AUTHOR]

Summary: Opportunistic network (OppNet) belongs to the category of Mobile Ad‐hoc Networks (MANETs), a kind of Delay Tolerant Network (DTN), where the wireless nodes are completely mobile and the data transmission routes are dynamic. The major challenge in developing a routing model for such a network is the unpredictable nature of the movement of the nodes. In this paper, a spatiotemporal prediction model based on human mobility pattern is proposed using Bayesian posterior probability (BPPR) where several clusters are identified within the network and the day and time duration of nodes visiting those clusters are recorded. The Bayesian posterior probability is then used to determine the probability of the neighbor node visiting the destination's cluster. If the calculated probability for that node is higher than a specified threshold, the packet will be forwarded. A comparison of the results obtained on simulation is made with benchmark models—Epidemic, Prophet, HBPR, EDR, NexT, and EBC, to name a few, where it is found that on average the proposed model outperforms the other models in terms of delivery probability, hop count and number of messages dropped by around 23.89%, 24.8%, 24.4%, 37%, 11%, and 42% respectively, with varying number of nodes, TTL, message generation interval, and buffer size. Similar improvements have been observed in terms of the other two metrics. In terms of overhead ratio, the proposed model outperforms Epidemic, Prophet, HBPR, NexT, and EBC. However, as the number of nodes and TTL are varied, BPPR performs better than NexT by around 9% and 12%, respectively, in terms of average latency. [ABSTRACT FROM AUTHOR]

Summary: Fractal antenna arrays are usually used to tune multiband frequencies. However, these types of iteratively constructed antennas are associated with undesirable high sidelobe levels and low directivities. In this paper, an optimization procedure based on the genetic algorithm is used to find the optimal weights of the array elements such that the corresponding array patterns have low sidelobe levels and good directivity. Moreover, the fractal nature in the proposed arrays is maintained regardless of the optimized weights. Thus, the proposed fractal‐shaped array maintains its capability in performing multiband frequency operation. These good radiation features make the proposed fractal‐shaped array more appropriate for the current and future wireless communication applications. Simulation results confirm the superiority of the presented linear and planar fractal‐shaped array structures with compared to the conventional fractal cantor linear array and the standard Sierpinski carpet planar array. For the proposed fractal cantor linear array, the sidelobe level has been reduced to more than −20 dB at different operating frequencies, and the directivity has been improved by more than 8 dB, while the modified Sierpinski carpet planar array has achieved −30 dB depressions in the sidelobe level and 6 dB improvement in the directivity. [ABSTRACT FROM AUTHOR]

Summary In the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively. [ABSTRACT FROM AUTHOR]

Summary In a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA. [ABSTRACT FROM AUTHOR]

Summary Non‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively. [ABSTRACT FROM AUTHOR]

Summary India has developed its own regional navigation satellite system called the Indian Regional Navigation Satellite System (IRNSS), also known as Navigation with Indian Constellation (NavIC). IRNSS/NavIC provides standard positioning and timing services for civilians and restricted services for authorized users. In this paper, single point positioning is implemented on the NavIC receiver data. Estimation techniques of the least squares method and extended Kalman filter are implemented after applying broadcast‐based clock corrections, orbit computation, relative time correction, and ionosphere delay correction for single and dual frequency. A data extraction and storage module is developed to store computed parameters at every intermediate stage in separate files. This is followed by an error plotter module to plot the extracted parameters for each satellite and to separately analyze their performance and the obtained errors. The estimated receiver position is visualized on Google Earth. The absolute error in both estimation techniques ranges between 5 and 15 m throughout the period. [ABSTRACT FROM AUTHOR]

Summary Internet of Things (IoT) networks continue to be deployed and play a crucial role in our daily life. Thus, ensuring their security is of utmost importance. This necessitates the use of cryptographic materials to maintain the confidentiality of exchanged data between IoT devices (or sensor nodes). The key component of these cryptographic materials is the encryption and decryption keys. In resource‐constrained networks like sensor and IoT networks, employing symmetric pairwise keys offers a trade‐off between resource saving and security. However, symmetric cryptosystems suffer from node compromising attacks. In addition, such networks often require the post‐deployment of new IoT devices either periodically or based on specific use cases leading to a multi‐phase IoT networks. So, key establishment is required to secure newly added node communications. This paper presents an adaPtive and rObust Key pre‐distribution (POK) that enables key establishment between deployed nodes. POK enhances the generation and pre‐loading of keys in sensor nodes. The fundamental concept of POK involves pre‐loading newly added IoT or sensor nodes with pairwise keys computed using a hash function and taking into account the expected number of future post‐deployments. Through a comparative analysis with related works, POK minimizes communication overhead, eliminates the need for time synchronization, and offers an energy‐efficient scheme. Furthermore, POK offers a resilience to node compromising attack by the self‐healing property, where compromised nodes have a limited effect on the network, and newly deployed nodes remain unaffected. [ABSTRACT FROM AUTHOR]

Summary Wireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs. [ABSTRACT FROM AUTHOR]

Summary Environmental disasters such as earthquakes, tsunamis, floods, and landslides disrupt natural resources, human casualties, and so on. Early warning and alert of such events will help for preparedness to avoid human and economic causalities. Managing a disaster or emergency scenario is a difficult endeavor. These episodes of mass devastation, whether caused by natural disasters or man‐made disasters, result in massive losses of money, property, and lives as a result of inadequate response by the government and management authorities. As a result, actions must be taken to avoid these circumstances from occurring by discovering the causes of these catastrophes in advance and providing swift rescue procedures once the crisis happens. In a disaster situation, communication plays a vital role, and a lot of people turn towards it as a source of information conveyance/delivery for controlling the situation. Even these communication networks are damaged and incur tremendous losses during disasters. All the communication networks, including mobile wireless sensor networks (WSNs) deployed for communication, also suffer huge damage and face many challenges during disasters. Nature appears to deal with these issues well. WSN is inspired by several biological systems and processes that have intrinsically attractive properties such as adaptation, self‐organization, and self‐healing. In this paper, we proposed an enhanced bio‐inspired energy‐efficient localization (EBEEL) routing for Mobile WSN. The main focus of the proposed EBEEL algorithm is to overcome the challenges faced by peer‐existing routing protocols such as data aggregation, limited bandwidth, scalability, data redundancy, energy consumption, packet drops, load balancing, and Quality of Service (QoS). [ABSTRACT FROM AUTHOR]

Summary: IoT‐assisted WSN contains various nodes, which are placed on a huge scale that increases complications. Thus, the challenges and issues of these networks fluctuate as compared to WSNs. Hence, sensor nodes are imperative unit that runs on less energy resources. Hence, devising a robust and energy‐effective protocol for increasing network lifetime is a complex task. This paper devises a novel hybrid optimization‐driven approach for selecting cluster head (CH) in IoT‐assisted WSNs. Initially, the simulation of IoT nodes is done by configuration. Thereafter, the Cluster Head selection is done using a newly devised optimization technique, namely the Taylor‐Political optimizer (Taylor‐PO). Thus, fitness is newly developed by adapting certain attributes like energy, delay, inter and intra‐cluster distance, Link Lifetime (LLT), predicted energy, and delay. Here, the multipath routing is accomplished using Tunicate Swarm gray wolf optimization (TSGWO). Thus, the proposed Taylor‐PO is offered for effective Cluster Head selection along with multipath routing using TSGWO. The proposed Taylor‐PO offered improved performance with smallest delay of 0.006 sec, highest energy of 2.368 J, highest throughput of 494.043 kbps. [ABSTRACT FROM AUTHOR]

Summary: Non‐orthogonal multiple access (NOMA) has proven to be a very effective multiple access scheme to be employed in visible light communication (VLC) systems. NOMA scheme is capable of enhancing the spectral efficiency, sum rate performance, and various other performance parameters in VLC systems as compared to traditional orthogonal multiple access schemes. However, it is not practical to apply the concept of NOMA to all the users jointly. Therefore, the concept of user pairing has been introduced in literature to implement NOMA effectively so as to reduce the decoding order of successive interference cancellation (SIC) mechanism and complexity. In this paper, we have proposed a user pairing scheme based on clustering for downlink 3D NOMA‐based VLC systems. In this user pairing scheme, clustering is performed prior user pairing. The number of clusters are selected using K‐means algorithm and Elbow method, and the user allocation in a particular cluster is validated using artificial bee colony (ABC) optimization. In order to achieve high throughput, inter‐cluster user pairing is performed by forming pairs of two or three users with maximum channel gain difference in between them. The simulation results show that, for given values of signal‐to‐noise ratio (SNR), the proposed user pairing scheme offers higher average user data rates (AUDRs) as compared to the existing user pairing schemes for downlink NOMA‐based VLC systems. The energy efficiency (EE) and the bit error rate (BER) performance of the proposed user pairing scheme have also been studied. [ABSTRACT FROM AUTHOR]