International Journal of Wireless and Ad Hoc Communication

A novel design of metamaterial structure has been developed to reduce the mutual coupling between two microstrip patch antennas. A split rectangular structure loaded between the two microstrip patches is the method used to decrease coupling. The proposed antenna is designed to operate at a frequency of 28 GHz. This construction is suited for use with mm-wave, 5G antenna systems, and mobile applications. The connection between antennas is significantly improved. The maximum isolation achieved is 29.5 dB by etching the split rectangular structure on the ground of the proposed antenna and on a floated ground between the two patches. The distance between the two elements of the antenna, from edge to edge, is 0.4λ. The design is achieved with CST Studio software.

Nowadays, the internet of things (IoT) has become increasingly common and finds application in various fields, particularly in the health care industry. However, the development and design of IoT data analysis methods face some problems such as lack of adequate training data, resource constraints, centralized framework, security, and privacy. In contrast, the increase in blockchain techniques provides a decentralized framework. It is promoted to remove centralized control and resolve the problem of AI as well as allow secured distribution of data and resources to the diverse nodes of IoT system. This paper devises a new IoT with Blockchain based Secure Image Encryption with Disease Classification model.

A proposed design and FPGA implementation of a demodulator and phase compensation system is presented. The system is simple, accurate, dissipate low power. Simulations indicate that the proposed system compensates the error in the received phase quickly which decreases the bit error rate (BER) in wireless systems. FPGA implementation of the proposed system shows a power reduction by 27.91% and the speed by 66.89% compared to Costas loop.

Today's societies couldn't function without elaborate networks of communication. Many problems remain unresolved, but novel approaches to these problems are constantly being offered. Many of the problems plaguing existing works, such as high characteristic design cost, challenging feature selection, poor real-time performance, etc., stem from their focus on a wide range of characteristics. Worse still, the difficulty in training models due to data imbalance results in a poor detection rate for aberrant samples. To achieve a more effective and robust model, we present a multi-level feature fusion (MFFusion) model that utilizes a combination of data temporal, byte, and statistical characteristics to extract relevant information from different angles. Too far, MFFusion has outperformed the state-of-the-art on several real-world network datasets in terms of prediction performance and false alarm rate. We also use MFFusion for anomaly detection in an IoT network, using the most recent IoT malicious traffic information. The experimental results demonstrate the adaptability of MFFusion and its suitability for identifying network anomalies in an IoT context with system performance.

The current wireless and communication system may be attributed to the contributions made by the Sensor Network in a significant measure. During the last decade, several efforts have been performed to examine and propose answers to challenges about the energy efficiency of wireless sensor network communications. Several different researchers has done these efforts. The challenge of constructing economical energy-use paths has not yet been overcome. Because sensors have limited computational capabilities, which are frequently coupled with energy limitations, it is rather difficult to guarantee that a sensor’s lifespan will be longer. This is because of the energy constraints often associated with these limitations. The results of this research have led to the development of a one-of-a-kind communication system for sensor networks that is not only environmentally friendly but also supported by three distinct revolutionary frameworks. The framework that has been recommended, which goes by the name Potential Energy Efficient Data Fusion (PEE-DF), is the one that is in charge of the optimization of energy. It achieves this with the aid of probabilistic approaches and clustering. The K-SOM (Korhonen self-organizing map) framework was designed using a globular topology, which aids load balancing during data fusion. K-SOM stands for "Korhonen self-organizing map." This was done to ensure we got the most out of our resources. A novel method to routing is presented by the technique, which has the potential to be used to assist in the operation of energy-efficient routing in large-scale wireless sensor networks. The framework for the Tree-Based Fusion Technique (TBFT), which has been offered, comes up with a new way for dynamic reconfiguration. This is accomplished via the introduction of the concept of routing agents. The strategy enables the system to recognise which sensor has a greater energy dissipation rate and then instantly moves data fusion work to a more energy-efficient node. This allows the system to save energy. This approach, based on thresholds, enable a sensor to act as a cluster head up until it reaches its threshold remnant energy and then as a member node once it exceeds threshold residual energy. In other words, it may play both roles simultaneously. It is possible to fulfil both of these responsibilities at the same time. The findings have been mathematically modelled using a standard radio-energy model, which has enhanced the robustness of the findings, which is highly positive. The results were encouraging because of the increased robustness of the findings. Compared to the benchmark previously established for energy-efficient strategies, the proposed system demonstrates higher performance in terms of its ability to communicate while using less energy. In contrast to LEACH, the recommended system's findings reveal an almost fifty percent decrease in energy consumption, and at the same time, a reduction in the amount of time required to carry out the operation..

The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifespan. This study introduces a novel Seeker Optimization based Energy Aware Clustering Scheme for Underwater Wireless Sensor Networks (SOEACS-UWN). The presented SOEACS-UWN model follows the operation on a collection of solutions named search population (i.e., human team) and considered optimization procedure as a searching process of optimum solutions via human teams. The SOEACS-UWN model constructs a fitness function for effectual CH choices using diverse variables namely distance, residual energy, node degree, centrality, and link quality. The simulation analysis of the SOEACS-UWN model is tested and the outcomes were investigated under diverse aspects. The experimental outcomes demonstrated the supremacy of the SOEACS-UWN model over other approaches.