A novel nomadic people optimizer-based energy-efficient routing for WBAN

In response to user demand for wearable devices, several WBAN deployments now call for effective communication processes for remote data monitoring in real time. Using sensor networks, intelligent wearable devices have exchanged data that has benefited in the evaluation of possible security hazards. If smart wearables in sensor networks use an excessive amount of power during data transmission, both network lifetime and data transmission performance may suffer. Despite the network's effective data transmission, smart wearable patches include data that has been combined from several sources utilizing common aggregators. Data analysis requires careful network lifespan control throughout the aggregation phase. By using the Nomadic People Optimizer-based Energy-Efficient Routing (NPO-EER) approach, which effectively allows smart wearable patches by minimizing data aggregation time and eliminating routing loops, the network lifetime has been preserved in this research. The obtained findings showed that the NPO method had a great solution. Estimated Aggregation time, Energy consumption, Delay, and throughput have all been shown to be accurate indicators of the system's performance

An intelligent network selection mechanism for vertical handover decision in vehicular Ad Hoc wireless networks

The design of the Vehicular Ad-hoc Network (VANET) technology is a modern paradigm for vehicular communication on movement. However, VANET's vertical handover (VHO) decision in seamless connectivity is a huge challenge caused by the network topology complexity and the large number of mobile nodes that affect the network traffic in terms of the data transmission and dissemination efficiency. Furthermore, the conventional scheme only uses a received signal strength as a metric value, which shows a lack of appropriate handover metrics that is more suitable in horizontal handover compared to VHO. Appropriate VHO decisions will result in an increase in the network quality of service (QoS) in terms of delay, latency, and packet loss. This study aims to design an intelligent network selection to minimize the handover delay and latency, and packet loss in the heterogeneous Vehicle-to- Infrastructure (V2I) wireless networks. The proposed intelligent network selection is known as the Adaptive Handover Decision (AHD) scheme that uses Fuzzy Logic (FL) and Simple Additive Weighting (SAW) algorithms, namely F-SAW scheme. The AHD scheme was designed to select the best-qualified access point (AP) and base station (BS) candidates without degrading the performance of ongoing applications. The F-SAW scheme is proposed to develop a handover triggering mechanism that generates multiple attributes parameters using the information context of vertical handover decision in the V2I heterogeneous wireless networks. This study uses a network simulator (NS-2) as the mobility traffic network and vehicular mobility traffic (VANETMobiSim) generator to implement a topology in a realistic VANET mobility scenario in Wi-Fi, WiMAX, and LTE networks technologies. The proposed AHD scheme shows an improvement in the QoS handover over the conventional (RSS-based) scheme with an average QoS increased of 21%, 20%, and 13% in delay, latency and packet loss, while Media Independent Handover based (MIH-based) scheme with 12.2%, 11%, and 7% respectively. The proposed scheme assists the mobile user in selecting the best available APs or BS during the vehicles’ movement without degrading the performance of ongoing applications

Network selection mechanism for telecardiology application in high speed environment

The existing network selection schemes biased either to cost or Quality of Service (QoS) are not efficient enough for telecardiology application in high traveling speed environment. Selection of the candidate network that is fulfilling the telecardiology service requirements as well as user preference is a challenging issue. This is because the preference of telecardiology user might change based on the patient health condition. This research proposed a novel Telecardiology-based Handover Decision Making (THODM) mechanism that consists of three closely integrated algorithms: Adaptive Service Adjustment (ASA), Dwelling Time Prediction (DTP) and Patient Health Condition-based Network Evaluation (PHCNE). The ASA algorithm guarantees the quality of telecardiology service when none of the available networks fulfils the service requirements. The DTP algorithm minimizes the probability of handover failure and unnecessary handover to Wireless Local Area Network (WLAN), while optimizing the connection time with WLAN in high traveling speed environment. The PHCNE algorithm evaluates the quality of available networks and selects the best network based on the telecardiology services requirement and the patient health condition. Simulation results show that the proposed THODM mechanism reduced the number of handover failures and unnecessary handovers up to 80.0% and 97.7%, respectively, compared with existing works. The cost of THODM mechanism is 20% and 85.3% lower than the Speed Threshold-based Handover (STHO) and Bandwidth-based Handover (BWHO) schemes, respectively. In terms of throughput, the proposed scheme is up to 75% higher than the STHO scheme and 370% greater than the BWHO scheme. For telecardiology application in high traveling speed environment, the proposed THODM mechanism has better performance than the existing network selection schemes

Optimizing multi-antenna M-MIMO DM communication systems with advanced linearization techniques for RF front-end nonlinearity compensation in a comprehensive design and performance evaluation study

The study presented in this research focuses on linearization strategies for compensating for nonlinearity in RF front ends in multi-antenna M-MIMO OFDM communication systems. The study includes the design and evaluation of techniques such as analogue pre-distortion (APD), crest factor reduction (CFR), multi-antenna clipping noise cancellation (M-CNC), and multi-clipping noise cancellation (MCNC). Nonlinearities in RF front ends can cause signal distortion, leading to reduced system performance. To address this issue, various linearization methods have been proposed. This research examines the impact of antenna correlation on power amplifier efficiency and bit error rate (BER) of transmissions using these methods. Simulation studies conducted under high signal-to-noise ratio (SNR) regimes reveal that M-CNC and MCNC approaches offer significant improvement in BER performance and PA efficiency compared to other techniques. Additionally, the study explores the influence of clipping level and antenna correlation on the effectiveness of these methods. The findings suggest that appropriate linearization strategies should be selected based on factors such as the number of antennas, SNR, and clipping level of the system

Implement DNN technology by using wireless sensor network system based on IOT applications

The smart Internet of Things-based system suggested in this research intends to increase network and application accuracy by controlling and monitoring the network. This is a deep learning network. The invisible layer's structure permits it to learn more. Improved quality of service supplied by each sensor node thanks to element-modified deep learning and network buffer capacity management. A customized deep learning technique can be used to train a system that can focus better on tasks. The researchers were able to implement wireless sensor calculations with 98.68 percent precision and the fastest execution time. With a sensor-based system and a short execution time, this article detects and classifies the proxy with 99.21 percent accuracy. However, we were able to accurately detect and classify intrusions and real-time proxy types in this study, which is a significant improvement over previous research

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research

Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of Deep Bayesian Neural Network (DBNN) for the personalized treatment of leukemia cancer has shown a significant tested accuracy for the model. DBNNs used in this study was able to classify images with accuracy exceeding 98.73%. This study depicts that the DBNN can classify cell cultures only based on unstained light microscope images which allow their further use. Therefore, building a bayesian‐based model to great help during commercial cell culturing, and possibly a first step in the process of creating an automated/semiautomated neural network‐based model for classification of good and bad quality cultures when images of such will be available

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

An Energy-Efficient and Reliable Data Transmission Scheme for Transmitter-based Energy Harvesting Networks

Energy harvesting technology has been studied to overcome a limited power resource problem for a sensor network. This paper proposes a new data transmission period control and reliable data transmission algorithm for energy harvesting based sensor networks. Although previous studies proposed a communication protocol for energy harvesting based sensor networks, it still needs additional discussion. Proposed algorithm control a data transmission period and the number of data transmission dynamically based on environment information. Through this, energy consumption is reduced and transmission reliability is improved. The simulation result shows that the proposed algorithm is more efficient when compared with previous energy harvesting based communication standard, Enocean in terms of transmission success rate and residual energy.This research was supported by Basic Science Research Program through the National Research Foundation by Korea (NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A3012227)