We proposed a novel approach based on a complementary split-ring resonator metamaterial in a two-port MIMO antenna, giving high gain, multiband results with miniature size. We have also analyzed a circular disk metasurface design. The designs are also defected using ground structure by reducing the width of the ground plane to 8 mm and etching all other parts of the ground plane. The electric length of the proposed design is 0.5λ × 0.35λ × 0.02λ. The design results are also investigated for a different variation of complementary split-ring resonator ring sizes. The inner and outer ring diameters are varied to find the optimized solution for enhanced output performance parameters. Good isolation is also achieved for both bands. The gain and directivity results are also presented. The results are compared for isolation, gain, structure size, and the number of ports. The compact, multiband, high gain and high isolation design can apply to WiMAX, WLAN, and satellite communication applications.

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies.

Carbon nanotubes (CNTs) and graphene are extensively studied materials in the field of sensing technology and other electronic devices due to their better functional and structural properties. Additionally, more attention is given to utilize these materials as a filler to reinforce the properties of other materials. However, the role of weight percentage of CNTs in the piezoresistive properties of these materials has not been reported yet. In this work, CNT-graphene composite-based piezoresistive pressure samples in the form of pellets with different weight percentages of CNTs were fabricated and characterized. All the samples exhibit a decrease in the direct current (DC) resistance with the increase in external uniaxial applied pressure from 0 to 74.8 kNm⁻². However, under the same external uniaxial applied pressure, the DC resistance exhibit more decrease as the weight percentage of the CNTs increase in the composites.

In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs subsequently transmit packets to neighboring RPs. The unique determination of this study is the shortest path to reach RPs. As the mobile sink (MS) concept has emerged as a promising solution to the energy consumption problem in WSNs, caused by multi-hop data collection with static sinks. In this study, we proposed two novel hybrid algorithms, namely” Reduced k-means based on Artificial Neural Network “(RkM-ANN) and “Delay Bound Reduced k-means with ANN” (DBRkM-ANN) for designing a fast, efficient, and most proficient MS path depending upon rendezvous points (RPs). The first algorithm optimizes the MS’s latency, while the second considers the designing of delay-bound paths, also defined as the number of paths with delay over bound for the MS. Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage. In addition, a method of using MS scheduling for efficient data collection is provided. Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

The design of the modern computing paradigm of heuristics is an innovative development for parameter estimation of direction of arrival (DOA) using sparse antenna arrays. In this study, the optimization strength of the flower pollination algorithm (FPA) is exploited for the DOA estimation in a low signal to noise ratio (SNR) regime by applying coprime sensor arrays (CSA). The enhanced degree of freedom (DOF) is achieved with FPA by investigating the global minima of highly nonlinear cost function with multiple local minimas. The sparse structure of CSA demonstrates that the DOF up to $O\left(MN\right)$ is achieved by employing $M+N$ CSA elements, where M and N are the numbers of antenna elements used to construct the CSA. Performance analysis is conducted for estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of root mean square error (RMSE), variability analysis of RMSE, cumulative distribution function (CDF) of RMSE over Monte Carlo runs and the comparative studies of particle swarm optimization (PSO). These reveal the worth of the proposed methodology for estimating DOA.

Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using an Artificial Neural Network (ANN) and Bayesian Regularization Algorithm (BRA). LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs, mitigating inefficiencies from random CH selection. The ANN further enhances CH selection and routing processes, effectively reducing data transmission overhead and idle listening. Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols. This framework offers a promising solution to the energy efficiency challenges in WSNs, paving the way for more sustainable and reliable network deployments.