Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decis...
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Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decision-making and network efficiency. This study explores localization in the context of WSNs, focusing on the 6LoWPAN and Zigbee protocols. These protocols are vital for integrating WSNs into the Internet of Things (IoT). We highlight the significance of spatial node distribution and WSNs' challenges, such as resource limitations and signal interference. We emphasize range-based methods due to their accuracy. We propose the Adaptive Mean Center of Mass Particle Swarm Optimizer (AMCMPSO) to address these. Inspired by the center of mass principle, this algorithm adapts parameters for enhanced localization on regular and irregular surfaces. AMCMPSO leverages the principle of the center of mass and mean values to enhance the efficiency of sensor node localization. The algorithm incorporates adaptive parameters, including inertia weight and acceleration coefficients, to improve search efficiency and convergence speed. Our simulations demonstrate the superior performance of AMCMPSO, with an average improvement rate of 99.86%. Moreover, the localization error is consistently below 1.34 cm, ensuring precise spatial awareness. In 3D environments, AMCMPSO consistently delivers coverage rates exceeding 87%, even in challenging scenarios.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
This study introduces a new technique for achieving dual-band functionality in a printed dipole antenna with integrated balun feeding without additional elements. By employing an optimization algorithm, the fundamenta...
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This study introduces a new technique for achieving dual-band functionality in a printed dipole antenna with integrated balun feeding without additional elements. By employing an optimization algorithm, the fundamental parameters of the antenna are extracted, resulting in an antenna design that operates effectively in two distinct frequency bands of 2.4-2.5 and 5-6 GHz for WLAN/ISM and biomedical applications. By implementing the proposed design in a two-element array structure, the antenna successfully achieved a fan-beam radiation pattern in both frequencies. The array antenna exhibits fan-beam radiation patterns and peak gains of 6.90 dB and 7.84 dB, respectively, in two frequency bands: 2.06-2.52 GHz and 5.09-5.93 GHz. According to the proposed design, the array antenna has measured half-power beamwidths (HPBWs) of 38.34 degrees and 25.14 degrees in the two frequencies specified. The overall dimensions of the antenna are 1.100 lambda x 0.563 lambda x 0.004 lambda. This research design offers advantages such as smaller dimensions, higher gain, and a more straightforward structure compared to dualband array antennas with fan beam radiation patterns.
The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Ran...
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The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.
Wireless communication tower placement arises in many real-world applications. This paper investigates a new emerging wireless communication tower placement problem, namely, continuous space wireless communication tow...
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Wireless communication tower placement arises in many real-world applications. This paper investigates a new emerging wireless communication tower placement problem, namely, continuous space wireless communication tower placement. Unlike existing wireless communication tower placement problems, which are discrete computational problems, this new wireless communication tower placement problem is a continuous space computational problem. In this paper, we formulate the new wireless communication tower placement problem and propose a hybrid simulated annealing algorithm that can take advantage of the powerful exploration capacity of simulated annealing and the strong exploitation capacity of a local optimization procedure. We also demonstrate through experiments the effectiveness of this hybridization technique and the good performance and scalability of the hybrid simulated annulling in this paper.
Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy freque...
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Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy frequency bands below 6 GHz. With the global deployment of the first commercial installations of 5G, the availability of multi-Gbps wireless connections in the mmWave frequency band becomes closer to reality and opens up some unique uses for 5G. Although, mmWave communication is expected to enable high-power radio links and broadband wireless intranet, its main challenges are inherent poor propagation conditions and high transmitter-receiver coordination requirement, which prevent it from realizing its full potential. When smart reflective surfaces are used in mmWave communication, channel state information becomes complex and imprecise. In this study, a hybrid intelligent reflecting surface consisting of a large number of passive components and a small number of RF circuits is proposed as a solution. Then, an improved deep neural network (DNN)-based technique is proposed to estimate the effective channel. The proposed technique provides better channel estimation performance according to the simulation results and improves the quality of service.
In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-perform...
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In the big data era, mode division multiplexing, as a technology for extended channel capacity, demonstrates potential in enhancing parallel data processing capability. Consequently, developing a compact, high-performance mode converter through efficient design methods is an urgent requirement. However, traditional design methodologies for these converters face significant computational complexities and inefficiencies. Addressing this challenge, this paper introduces a novel topology optimization design method for mode converters employing a Dynamic Adjustment of Update Rate (DAUR). This approach markedly reduces computational overhead, accelerating the design process while ensuring high performance and compactness. As a proof-of-concept, an ultra-compact dual-mode converter was designed. The DAUR method demonstrated an 80% reduction in computational time compared to traditional methods, while maintaining a compact design (only 1.4 mu m x 1.4 mu m) and an insertion loss under 0.68 dB across a wavelength range of 1525 nm to 1575 nm. Meanwhile, simulated inter-mode crosstalk remained below - 24 dB across a 40 nm bandwidth. A comprehensive comparison with traditional inverse design algorithms is presented, demonstrating our method's superior efficiency and effectiveness. Our findings suggest that DAUR not only streamlines the design process but also facilitates exploration into more complex micro-nano photonic structures with reduced resource investment.
Network planning technology could be used to represent project plan management, such Critical Path Method (CPM for short) and Performance Evaluation Review Technique (PERT for short) etc. Aiming at problem that how to...
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Network planning technology could be used to represent project plan management, such Critical Path Method (CPM for short) and Performance Evaluation Review Technique (PERT for short) etc. Aiming at problem that how to find hypo-critical path in network planning, firstly, properties of total float. free float and safety float are analyzed, and total float theorem is deduced on the basis of above analysis; and secondly, simple algorithm of finding the hypo-critical path is designed by using these properties of float and total theorem, and correctness of the algorithm is analyzed. Proof shows that the algorithm could realize effect of whole optimization could be realized by part optimization. Finally, one illustration is given to expatiate the algorithm.
Image compression is one of the essential requirements for the efficient use of storage space and bandwidth. A new technique based on fractal theory is proposed for encoding the image;it is known as fractal image comp...
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Image compression is one of the essential requirements for the efficient use of storage space and bandwidth. A new technique based on fractal theory is proposed for encoding the image;it is known as fractal image compression. In the procedure of encoding, the mechanism of search is considered as one of the main problems of this technique. In this work, an attempt to speed up the encoding process with minimal loss of the compressed image quality is adopted based on the Scatter Search algorithm. It is a sibling of Tabu search based on similar origins. The experimental results show a significant reduction in the computation time, where the mean square error measures be-tween blocks are decreased after comparing them to full search methods. Consequently, the decoding process evinced that the reconstructed images were of high quality.
In gas condensate reservoirs, gas flow at large velocities enhances the gas permeability due to gas-liquid positive coupling which results in near-miscible flow condition. On the other hand, augmented pressure drop du...
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In gas condensate reservoirs, gas flow at large velocities enhances the gas permeability due to gas-liquid positive coupling which results in near-miscible flow condition. On the other hand, augmented pressure drop due to non-Darcy flow, reduces the gas permeability. Models for the Positive Coupling or non-Darcy flow include several parameters, which are rarely known from reliable lab special core analysis. We offer a good alternative for tuning of these parameters in which the observed production history data are reproduced from the readjusted simulation model. In this study, history matching on observed production data was carried out using evolutionary optimization algorithms including genetic algorithms, neighborhood algorithm, differential evolution algorithm and particle swarm optimization algorithm, where a faster convergence and lower misfit value were obtained from a genetic algorithm. Then, the Neighborhood algorithm-Bayes was used to perform Bayesian posterior inference on the history matched models and create the posterior cumulative probability distributions for all uncertain parameters. Finally, Bayesian credible intervals for production rate and wellhead pressure were computed in the long-range forecast. Our new approach enables to not only calibrate the gas effective permeability parameters to dynamic reservoir data, but allows to capture the uncertainty with parameter estimation and production forecast.
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