An Energy-Efficient Mobile-Sink Path-Finding Strategy for UAV WSNs

Data collection using a mobile sink in a Wireless Sensor Network (WSN) has received much attention in recent years owing to its potential to reduce the energy consumption of sensor nodes and thus enhancing the lifetime of the WSN. However, a critical issue of this approach is the latency of data to reach the base station. Although many data collection algorithms have been introduced in the literature to reduce delays in data delivery, their performances are affected by the flight trajectory taken by the mobile sink, which might not be optimized yet. This paper proposes a new path-finding strategy, called Energy-efficiency Path-finding Strategy (EPS) in the Air-Ground Collaborative Wireless Sensor Network (AGCWSN). The proposed approach is able to greatly enhance the efficiency of data collection. The performance of the proposed strategy is simulated and compared with the existing strategies over several parameters. The simulation results show that the mobile sink with EPS can collects data with lower data delivery delay as compared to other existing strategies. The number of data retransmissions between sensor nodes and mobile sink in EPS is also the lowest in EPS among several existing strategies. The data delivery delay is 66% and 120% lower than Rest Center Tractor Scanning (RCTS) and Non-stop Center Tractor Scanning (NCTS) in irregular and grid topology respectively. The data delivery delay is 62% lower than Two Row Scanning (TRS) in grid topology and 120% lower than RkM in irregular topology. The packet loss of EPS-2 is 1.3% lower than RkM.     

Application of Time Serial Model in Water Quality Predicting

Water resources are an indispensable and valuable resource for human survival and development. Water quality predicting plays an important role in the protection and development of water resources. It is difficult to predict water quality due to its random and trend changes. Therefore, a method of predicting water quality which combines Auto Regressive Integrated Moving Average (ARIMA) and clustering model was proposed in this paper. By taking the water quality monitoring data of a certain river basin as a sample, the water quality Total Phosphorus (TP) index was selected as the prediction object. Firstly, the sample data was cleaned, stationary analyzed, and white noise analyzed. Secondly, the appropriate parameters were selected according to the Bayesian Information Criterion (BIC) principle, and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting. Thirdly, the relationship between the precipitation and the TP index in the monitoring water field was analyzed by the K-means clustering method, and the random incremental characteristics of precipitation on water quality changes were calculated. Finally, by combining with the trend component characteristics and the random incremental characteristics, the water quality prediction results were calculated. Compared with the ARIMA water quality prediction method, experiments showed that the proposed method has higher accuracy, and its Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) were respectively reduced by 44.6%, 56.8%, and 45.8%.     

Efficient monitoring approach for reputation system-based trust-aware routing in wireless sensor networks

Trust-aware routing in wireless sensor networks (WSNs) is a crucial problem that has drawn the attention of researchers. The motivation for tackling this problem arises directly from the highly constrained nature of a WSN and its easy exposure to insecure conditions. In this regard, reputation-based solutions are used to provide trust-aware routing. However, this approach requires that a node needs to continuously monitor its environment to detect misbehaviour events. This is considered to be a costly operation for WSN nodes because of its resource scarcity. Here, the authors propose a reputation system-based solution for trust-aware routing, which implements a new monitoring strategy called an efficient monitoring procedure in a reputation system (EMPIRE). EMPIRE is a probabilistic and distributed monitoring methodology that tries to reduce the monitoring activities per node while maintaining the ability to detect attacks at a satisfactory level. The proposed procedure has been evaluated using the Monte Carlo simulation. New evaluation methodologies are introduced to test and explore the efficiency of our proposed procedure. Simulation results of the reputation system show that reducing monitoring activities with EMPIRE does not have a significant impact on system performance in terms of security.     

Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

The wireless sensor network (WSN), as the terminal data acquisition system of the 5G network, has attracted attention due to advantages such as low cost and easy deployment. Its development is mainly restricted by energy. The traditional transmission control scheme is not suitable for WSNs due to the significant information interaction. A switchable transmission control scheme for WSNs based on a queuing game (SQGTC) is proposed to improve network performance. Considering that sensor nodes compete for the resources of sink nodes to realize data transmission, the competitive relationship between nodes is described from the perspective of a game. Different types of sensor node requests require a sink node to provide different service disciplines. Mathematical models of social welfare are established for a sink node under the service disciplines of first-come, first-served (FCFS), egalitarian processor sharing (EPS), and shortest service first (SSF). The optimal service strategies are obtained by maximizing social welfare. The sensor nodes provide the expected benefits and satisfy the service requirements of the requests, and the sink node switches the transmission control strategy for the service. Simulation results show that the proposed scheme improves the data transmission efficiency of WSNs and achieves the optimal allocation of resources.     

A novel similarity measure towards effective recommendation using Matusita coefficient for Collaborative Filtering in a sparse dataset

Collaborative Filtering (CF) is a prominent approach to ensure personalized recommendations to active online users. An efficient CF is the memory-based strategy that finds nearest neighbours to an active user using conventional similarity measures. Most such measures deal with a co-rated item rated by a pair of users and hence they are not appropriate to provide an effective recommendation to a sparse dataset having less co-rated items. This study proposes a novel similarity measure, Matusita coefficient in CF (MCF), which considers all ratings given by a user to estimate nearest neighbours. MCF considers local and global rating information provided by users on different rating scales. The performance of the proposed measure is examined and checked by comparing it to conventional measures using popular benchmark datasets like MovieLens and Netflix. The recommendation results demonstrate that the proposed measure outperforms conventional similarity measures on various performance metrics like Mean Absolute Error, Root Mean Squared Error, accuracy, precision, recall and coverage.     

Hybrid BWO-IACO Algorithm for Cluster Based Routing in Wireless Sensor Networks

Wireless Sensor Network (WSN) comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region. As the nodes in WSN operate on inbuilt batteries, the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime. To enhance energy efficiency and network longevity, clustering and routing techniques are commonly employed in WSN. This paper presents a novel black widow optimization (BWO) with improved ant colony optimization (IACO) algorithm (BWO-IACO) for cluster based routing in WSN. The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads (CHs). The BWO algorithm derives a fitness function (FF) using five input parameters like residual energy (RE), inter-cluster distance, intra-cluster distance, node degree (ND), and node centrality. In addition, IACO based routing process is involved for route selection in inter-cluster communication. The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm (KHA). The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network. The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime. The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects. A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.     

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters. Besides, the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance. Moreover, the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN. The design of EAOA for CH election in the WSN depicts the novelty of work. In order to exhibit the enhanced efficiency of EAOA-CHS technique, a set of simulations are applied on 3 distinct conditions dependent upon the place of base station (BS). The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.     

A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

Phasor Measurement Units (PMUs) provide Global Positioning System (GPS) time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system. Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs’ principal work are essential to the network operators to enhance the grid quality and the operating expenses. This paper introduces a proposed method that led to low-cost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter. It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point. The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian. The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model. The results were tested using Mean Square Error (MSE). The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into the PMU structure to clarify the significance of the proposed new PMU.     

A Mobile Host Approach for Wireless Powering and Interrogation of Structural Health Monitoring Sensor Networks

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can provide the data collection necessary for rapid structural assessment after an event such as a natural disaster puts the reliability of civil infrastructure in question. Technical challenges affecting deployment of such a network include ensuring power is maintained at the sensor nodes, reducing installation and maintenance costs, and automating the collection and analysis of data provided by a wireless sensor network. In this work, a new "mobile host" WSN paradigm is presented. This architecture utilizes nodes that are deployed without resident power. The associated sensors operate on a mechanical memory principle. A mobile host, such as a robot or unmanned aerial vehicle, is used on an as-needed basis to charge the node by wireless power delivery and subsequently retrieve the data by wireless interrogation. The mobile host may be guided in turn to any deployed node that requires interrogation. The contribution of this work is the first field demonstration of a mobile host wireless sensor network. The sensor node, referred to as THINNER, capable of collecting data wirelessly in the absence of electrical power was developed. A peak displacement sensor capable of interfacing with the THINNER sensor node was also designed and tested. A wireless energy delivery package capable of being carried by an airborne mobile host was developed. Finally, the system engineering required to implement the overall sensor network was carried out. The field demonstration took place on an out-of-service, full-scale bridge near Truth-or-Consequences, NM.     

LWDSA: light-weight digital signature algorithm for wireless sensor networks

The essential security mechanism in wireless sensor networks (WSNs) is authentication, where nodes can authenticate each other before transmitting a valid data to a sink. There are a number of public key authentication procedures available for WSN in recent years. Due to constraints in WSN environment there is a need for light-weight authentication procedure that consumes less power during computation. This proposed work aims at developing a light-weight authentication protocol using MBLAKE2b with elliptic curve digital signature algorithm (ECDSA). The proposed protocol is also tested using the protocol verification tool Scyther and found to be secure in all claims and roles. This proposed algorithm increases the network life time and reduces the computation time, which is essential for the constrained environment like WSNs.     

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters, such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) of 0.9451 and root-mean-square error of prediction (RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.     

Rssi- and Lqi-based clustering: analysis and implementation of multihop EOP-LEACH for WSN using Sun SPOT

Network energy is the main constraint that affects the practical design of wireless sensor networks (WSNs) as the nodes have limited resource capabilities. This aticle presents a novel EOP-LEACH (Efficient Optimized Practical-LEACH) that is proposed to overcome limitations of conventional low energy adaptive clustering hierarchy (LEACH) protocol to improve the life time and reduce the energy consumption of the WSN. The proposed enhancement is achieved by inserting novel factors in the threshold equation of conventional LEACH in order to choose the optimum node to be Cluster Head (CH).. The novel proposed parameters to be inserted are the Received Signal Strength (RSSI) which is related to the communication pass distance and link quality indication (LQI) that reflect the effect of communication channel noise and interference. Multihop routing, based mainly on RSSI values of neighbor nodes, is another proposed improvement to conventional LEACH to decrease distance of transmission which leads to savings in network energy. The simulation of the proposed protocols was done using MATLAB software. Comparison between the performance of proposed protocols and conventional LEACH shows that the WSN performance is improved using the proposed protocols.     

REEP: data-centric, energy-efficient and reliable routing protocol for wireless sensor networks

Owing to the growing demand for low-cost 'networkable' sensors in conjunction with recent developments of micro-electro mechanical system (MEMS) and radio frequency (RF) technology, new sensors come with advanced functionalities for processing and communication. Since these nodes are normally very small and powered with irreplaceable batteries, efficient use of energy is paramount and one of the most challenging tasks in designing wireless sensor networks (WSN). A new energy-aware WSN routing protocol, reliable and energy efficient protocol (REEP), which is proposed, makes sensor nodes establish more reliable and energy-efficient paths for data transmission. The performance of REEP has been evaluated under different scenarios, and has been found to be superior to the popular data-centric routing protocol, directed-diffusion (DD) (discussed by Intanagonwiwat et al. in `Directed diffusion for wireless sensor networking? IEEE/ACM Trans. Netw., 2003, 11(1), pp. 2?16), used as the benchmark.     

Application of an EWMA combining technique to the prediction of currency exchange rates

Financial forecasting is an important and challenging task for both academic researchers and business practitioners. The recent trend to improve the prediction accuracy is to combine individual forecasts using a simple average or weighted average where the weight reflects the inverse of the prediction error. In the existing combining methods, however, the errors between actual and predicted values are equally reflected in the weights regardless of the time order in a forecasting horizon. In this paper, we propose a new approach where the forecasting results of Generalized AutoRegressive Conditional Heteroskedastic (GARCH), neural network, and random walk models are combined based on a weight that reflects the inverse of the exponentially weighted moving average of the Mean Absolute Percentage Error (MAPE) of each individual prediction model. The results of an empirical study indicate that the proposed method has a better accuracy than the GARCH, neural network, and random walk models, and also combining methods based on using the MAPE for the weight.     

A NEW MULTIPARAMETER APPROACH TO THE PREDICTION OF FATIGUE CRACK GROWTH

A new multiparameter approach is proposed for the prediction of the combined effects of multiple variables on fatigue crack growth. The method, which is based on multiple linear regression analysis, involves the statistical formulation of mathematical expressions for the crack growth rate, da/dN, as a function of multiple variables, e.g. stress intensity factor range, ΔK, crack closure stress intensity factor, K_cl , and stress ratio, R. A general empirical approach is proposed for the estimation of the fatigue crack growth rate as a function of the above variables. The predictive capability of the empirical approach is then verified by comparing predicted and measured fatigue crack growth and crack growth rate data obtained from tests on a quenched and tempered Q1N (HY80) pressure vessel steel. Error ranges and reliability functions are presented within a probabilistic mechanics framework, and the implications of the results are discussed for the development of generalized fatigue life prediction methods.     

A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that the cluster head (CH) selection in the network is fair and that the location of the selected CH is not concentrated within a certain range, we chose the appropriate CH competition radius. Simulation results show that, compared with LEACH, LEACH-C, and the DEEC clustering algorithm, this algorithm can effectively balance the energy consumption of the CH and extend the network life.     

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python user-defined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.     

A novel approach for development of a multivariate calibration model using a Doehlert experimental design: Application for prediction of key gasoline properties by Near-infrared Spectroscopy

Alternative methods for quality control in the petroleum industry have been obtained using Near-infrared Spectroscopy (NIRS) combined with multivariate techniques such as PLS (Partial Least-Square). The process of development and refinement of PLS models usually follows a nonsystematic and univariate procedure. The Standard Error of Cross Validation (SECV), the Standard Error of Prediction (SEP) and the determination coefficient (r²_regr.) are usually the only guides used in pursuit of the best model. In the present work, a novel approach was proposed using a Doehlert experimental design with three input variables (wavenumber range, preprocessing technique and regression/validation technique) varied at 5, 7 and 3 levels, respectively. Besides SECV, SEP and r²_regr., some additional response variables, such as the slope, r² and p_value from the external validation, as well as the number of PLS factors, were simultaneously assessed to find the optimum conditions for PLS modeling. The optimum setting for each input variable was simultaneously defined through a multivariate approach using a desirability function. With the proposed approach, the main and interaction effects could also be investigated. The methodology was successfully applied to obtain PLS models to monitor the gasoline quality through the process of product loading in trucks. To prevent product contamination or adulteration, fast prediction of key properties was obtained from FT-NIR spectra within the 7300-3900 cm^− 1 region with SECV in the range 0.04-0.63% w/w for composition (Aromatics, Saturates, Olefins and Benzene) and 0.0008 for Relative Density 20/4 °C. Each optimized PLS model was obtained with less than 40 modeling runs, demonstrating the efficiency of the proposed approach.     

Modeling surface tension of pure refrigerants using feed-forward back-propagation neural networks

In this study, a model was proposed to predict the surface tension on the basis of feed-forward back-propagation network by employing different training algorithms including Levenberg–Marquardt, Scaled Conjugate Gradient and Pola–Ribiere Conjugate Gradient. A total of 793 experimental data points from 24 different pure refrigerants were gathered from reliable literature to train, test and validate the proposed network. Temperature, critical pressure, critical temperature, and acentric factor were chosen as input variables of the developed network. The network with 1 hidden layer and 19 neurons with tan-sigmoid and purelin transfer functions in the hidden and output layers was determined to have the optimum performance. The results revealed that the proposed network has the ability to correlate and predict the surface tension accurately with an overall Mean Relative Error (MRE) value of 0.0074 and correlation coefficient (R²) of 0.9996. The obtained results were compared to different well-known correlations in the literature which demonstrated a better performance of the proposed network.     

Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system

The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and automotive sector. However, practical implementation of this technology is not at an expected speed, specifically because of lack of application-specific details. In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems. The proposed digital twin has four modules — (a) a physics-based nominal model, (b) a data collection module, (c) algorithm for real-time update of the digital twin and (d) module for predicting future state. The modules for real-time update and prediction are based on the so-called gray-box modeling approach, and utilizes both physics based and data driven frameworks; this enables the proposed digital twin to generalize and predict future responses. The gray box modeling framework used within the digital twin is developed by coupling Bayesian filtering and machine learning algorithm. Although, the proposed digital twin can be used with any machine learning regression algorithm, we have used Gaussian process in this study. Performance of the proposed approach is illustrated using two examples. Results obtained indicate the applicability and excellent performance of the proposed digital twin framework.