Cooperative network-coding system for wireless sensor networks

Describes a cooperative network coding system for wireless sensor networks. In this paper, we propose two practical power) and bandwidth)efficient systems based on amplify)and)forward (AF) and decode)and)forward (DF) schemes to address the problem of information exchange via a relay. The key idea is to channel encode each source’s message by using a high)performance non)binary turbo code based on Partial Unit Memory (PUM) codes to enhance the bit)error)rate performance, then reduce the energy consumption and increase spectrum efficiency by using network coding (NC) to combine individual nodes’ messages at the relay before forwarding to the destination. Two simple and low complexity physical layer NC schemes are proposed based on combinations of received source messages at the relay. We also present the theoretical limits and numerical analysis of the proposed schemes. Simulation results under Additive White Gaussian Noise, confirm that the proposed schemes achieve significant bandwidth savings and fewer transmissions over the benchmark systems which do not resort to NC. Theoretical limits for capacity and Signal to Noise Ratio behaviour for the proposed schemes are derived. The paper also proposes a cooperative strategy that is useful when insufficient combined messages are received at a node to recover the desired source messages, thus enabling the system to retrieve all packets with significantly fewer retransmission request messages

Network coding cooperation performance analysis in wireless network over a lossy channel, M users and a destination scenario

Network coding (NC), introduced at the turn of the century, enables nodes in a network to combine data algebraically before either sending or forwarding them. Random network coding has gained popularity over the years by combining the received packet randomly before forwarding them, resulting in a complex Jordan Gaussian Elimination (JGE) decoding process. The effectiveness of random NC is through cooperation among nodes. In this paper, we propose a simple, low-complexity cooperative protocol that exploits NC in a deterministic manner resulting in improved diversity, data rate, and less complex JGE decoding process. The proposed system is applied over a lossy wireless network. The scenario under investigation is as follows: M users must send their information to a common destination D and to exchange the information between each others, over erasure channels; typically the channels between the users and the destination are worse than the channels between users. It is possible to significantly reduce the traffic amon g users and destination, achieving significant bandwidth savings, by combining packets from different users in simple, deterministic ways without resorting to extensive header information before being forwarded to the destination and the M users. The key problem we try to address is how to efficiently combine the packets at each user while exploiting user cooperation and the probability of successfully recovering information from all users at D with k < 2M unique linear equations, accounting for the fact that the remaining packets will be lost in the network and there are two transmission stages. Simulation results show the behaviour for two and three transmission stages. Our results show that applying NC protocols in two or three stages decreases the traffic significantly, beside the fact that the proposed protocols enable the system to retrieve the lost packets rather than asking for ARQ, resulting in improved data flow, and less power consumption. In fact, in some protocols the ARQ dropped from the rate 10-1 to 10-4, because of the proposed combining algorithm that enables the nodes to generate additional unique linear equations to broadcast rather than repeating the same ones via ARQ. Moreover, the number of the transmitted packets in each cooperative stage dropped from M (M − 1) to just M packets, resulting to 2 M packets instead 2 (M2 −  1) when three stages of transmission system are used instead of one stage (two cooperative stages)

Researching the Impact of Parameters of the Developed Routing Models on Network Performance

Nowadays, it is hard to imagine work without applying the principle of computer networks, and every day the requirement for high-quality network maintenance is increasing. In order to have a high-quality network; always optimize QoS through the optimization of routing protocols is required. In this paper, the scientific task of optimizing routing processes in hybrid telecommunication networks with guaranteed quality of service is solved by developing models and methods of adaptive routing. To develop methods, a system of Telecommunications network (TN) mathematical models was constructed at the levels of its morphological and functional descriptions. We used a weighted oriented graph as a structural model. Formalization of the main indicators of the network operation efficiency is carried out, which are the network performance (or its derivatives – relative and normalized performances) and indicators of the degree of use of network resources – buffer memory capacities of nodes and bandwidth of the transmission paths. In this paper, an experimental study of the developed models and routing methods was carried out in order to verify their adequacy, evaluate the effectiveness, and develop practical recommendations. The scheme of experiment, focusing on the investigation of processes occurring in the network while solving routing tasks (data gathering, RT processing, distribution, and implementation), is proposed

A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices

In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE

Synthetic generation of multidimensional data to improve classification model validity

This article aims to compare Generative Adversarial Network (GAN) models and feature selection methods for generating synthetic data in order to improve the validity of a classification model. The synthetic data generation technique involves generating new data samples from existing data to increase the diversity of the data and help the model generalize better. The multidimensional aspect of the data refers to the fact that it can have multiple features or variables that describe it. The GAN models have proven to be effective in preserving the statistical properties of the original data. However, the order of data augmentation and feature selection is crucial to build robust and accurate predictive models. By comparing the different GAN models with feature selection methods on multidimensional datasets, this article aims to determine the best combination to support the validity of a classification model in multidimensional data.</p

Prediction of wear rates of UHMWPE bearing in hip joint prosthesis with support vector model and grey wolf optimization

One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness (Ra) values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments

A comprehensive study on the performance of various tracker systems in hybrid renewable energy systems, Saudi Arabia

To compensate for the lack of fossil fuel-based energy production systems, hybrid renewable energy systems (HRES) would be a useful solution. Investigating different design conditions and components would help industry professionals, engineers, and policymakers in producing and designing optimal systems. In this article, different tracker systems, including vertical, horizontal, and two-axis trackers in an off-grid HRES that includes photovoltaic (PV), wind turbine (WT), diesel generator (Gen), and battery (Bat) are considered. The goal is to find the optimum (OP) combination of an HRES in seven locations (Loc) in Saudi Arabia. The proposed load demand is 988.97 kWh/day, and the peak load is 212.34 kW. The results of the cost of energies (COEs) range between 0.108 to 0.143 USD/kWh. Secondly, the optimum size of the PV panels with different trackers is calculated. The HRES uses 100 kW PV in combination with other components. Additionally, the size of the PVs where 100% PV panels are used to reach the load demand in the selected locations is found. Finally, two sensitivity analyses (Sens) on the proposed PV and tracker costs and solar GHIs are conducted. The main goal of the article is to find the most cost-effective tracker system under different conditions while considering environmental aspects such as the CO2 social penalty. The results show an increase of 35% in power production from PV (compared to not using a tracker) when using a two-axis tracker system. However, it is not always cost-effective. The increase in power production when using vertical and horizontal trackers (HT) is also significant. The findings show that introducing a specific tracker for all locations depends on renewable resources such as wind speed and solar GHI, as well as economic inputs. Overall, for GHIs higher than 5.5 kWh/m2/day, the vertical tracker (VT) is cost-effective