Coverage Reliability Evaluation of Wireless Sensor Network Considering Common Cause Failures Based on D–S Evidence Theory

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

In this paper we consider the problem of modeling and evaluating the infrastructure communication reliability (ICR) of wireless sensor networks (WSN). Our approach is progressive and based on reduced ordered binary decision diagrams (ROBDD). Due to the nature of the ROBDD and the reduction scheme used in the progressive process, the approach is computationally efficient and is easy to implement. We also incorporate the consideration of common cause failures (CCF) into the reliability analysis of WSN. We illustrate the application and advantages of our approach by working through the analysis of an example WSN

Reliability evaluation can help to minimize costs and maximize reliability of wireless sensor networks (WSN). To evaluate the reliability of WSN, a mathematical model and an enhanced factoring (EF) algorithm are presented in this paper. Being different from traditional factoring algorithm, the node contraction is enhanced by source expansion and the isomorphic sub-networks are identified. These operations can help to decrease the number of repeated computations and make the reliability computing fast. The traditional factoring algorithm and EF are compared in experiments, and the results show EF is more efficient than traditional factoring for large size network.

Electromagnetic interferences and other disturbances restrict the coverage area and reliability of wireless sensor networks (WSN) in industrial environment. The need for empirical data to provide evidence of these disturbances has also been addressed. This paper presents results of screening tests performed in the steam boiler process to find out the effect of some factors on jitter variation. The effect of factors is estimated from coefficients of a partial least squares model, which was fitted to on the basis of a statistical quantity calculated from jitter variation data. The quantity used, Pearson mode skewness of histogram frequencies, was selected by comparing different statistical techniques. The selected quantity seems to be able to identify some candidates for more detailed tests, namely distance from nodes to the gateway, mutual interference, sampling interval, number of nodes, frequency converter/pumping installation and the block. The results show that the effect of the single Bluetooth and WLAN transmission is insignificant compared to other factors.

This study proposed a novel way to calculate Basic Probability Assignment(BPA), which is crucial in the data fusion. The study mapped sensing signal with a linear discriminant function analysis and assessed the sizes with time. This was beneficial to get a clue for context inference with using the Dempster-Shafer Theory and to determine BPA based on the size changing of mapping data in time intervals. The study provides with the way of context inference for fast detecting a local event that affects the whole area. Data fusion process with multi-sensors has caught increasingly more attention nowadays. Fuzzy Theory (FT) and Dempster-Shafer Theory (DST) have been used much as a method to process heterogeneous signals and data fusion although these theories were designed originally to express the ambiguity and uncertainty of events occurring in the real world. Multi-sensors data fusion process is applied to acquire information with high quality by using some kinds of sensors in biometrics, geographic information, network security, robot control, etc. Multi-sensors data fusion is also important to acquire context information with high quality in the Ubiquitous Sensor Network(USN) data process. Multi-sensors data fusion for acquiring context information in the sensor network has become big data process in the data streaming conditions. The studies in the data streaming conditions first consider searching, classifying, and clustering the characteristics of continuous data that are not saved in a server. However, it is necessary to infer the changing context in real time through the data patterns acquired from the real time data. For example, we need to infer situations urgently and efficiently without holding to analyze when a blaze in a large area occurs, objects move around the road, or some risk factors threat the security in our residential environment. To do so, it is necessary to infer the context based on the rapid analysis of data patterns by time intervals in the data stream. We could obtain a clue of the simple and early context inference from the changing patterns with time of the sensing data detected and reported by sensors. The study presents a way to infer context information by discriminating event information reported to a host through sink nodes in the data streaming environment. It is also suggested that the results from discriminating event information are applied to BPA determination with using DST. It is reasonable to determine BPA based on the detection of patterns appeared in

An innovative Bayesian updating method for laminated composite structures under evidence uncertainty

Gas safety evaluation has always been vital for coal mine safety management. To enhance the accuracy of coal mine gas safety evaluation results, a new gas safety evaluation model is proposed based on the adaptive weighted least squares support vector machine (AWLS-SVM) and improved Dempster–Shafer (D-S) evidence theory. The AWLS-SVM is used to calculate the sensor value at the evaluation time, and the D-S evidence theory is used to evaluate the safety status. First, the sensor data of gas concentration, wind speed, dust, and temperature were obtained from the coal mine safety monitoring system, and the prediction results of sensor data are obtained using the AWLS-SVM; hence, the prediction results would be the input of the evaluation model. Second, because the basic probability assignment (BPA) function is the basis of D-S evidence theory calculation, the BPA function of each sensor is determined using the posterior probability modeling method, and the similarity is introduced for optimization. Then, regarding the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, using the idea of assigning weights, the importance of each evidence is allocated to weaken the effect of conflicting evidence on the evaluation results. To prevent the loss of the effective information of the original evidence followed by modifying the evidence source, a conflict allocation coefficient is introduced based on fusion rules. Ultimately, taking Qing Gang Ping coal mine located in Shaanxi province as the study area, a gas safety evaluation example analysis is performed for the assessment model developed in this paper. The results indicate that the similarity measures can effectively eliminate high-conflict evidence sources. Moreover, the accuracy of D-S evidence theory based on enhanced fusion rules is improved compared to the D-S evidence theory in terms of the modified evidence sources and the original D-S evidence theory. Since more sensors are fused, the evaluation results have higher accuracy. Furthermore, the multisensor data evaluation results are enhanced compared to the single sensor evaluation outcomes.

Applying geometry to the analysis and interpretation of basic probability assignment (BPA) is a unique research direction in evidence theory. Among them, the visualization of BPA helps to intuitively analyze the geometric properties and characteristics of BPA, which is an important research content in this direction, but there are currently few related studies. In response to this problem, we proposed a new BPA visualization method based on the vector representation of the BPA to illustrate the image of BPA directly. First, the basic point and the uncertain vectors could be obtained by the given BPA, and then we connected these components to construct the image of BPA. Through the image of BPA, we can effectively analyze the interaction effect of focal elements in BPA and observe the potential characteristics of BPA directly. For example, the uncertainty of focal elements can be expressed by geometric area. Moreover, the geometric meanings of parameters in the vector representation of the BPA can be explained. Finally, numerical examples are illustrated to demonstrate the advantages and related applications of the proposed method.

To classify data with missing values, we propose a method exploiting autoencoders and evidence theory. We augment the complete data by deleting each feature once and imputing it using the nearest neighbor to a set of predefined points generated using a new scheme. We train an autoencoder with the complete data set to get a latent space representation of the input. The network is retrained with the augmented data to get a better latent space representation. Now for each class, we train a support vector machine (SVM) with a one-vs-all strategy using the latent space representation of the complete data set. For an r-class problem, the output of each of the r SVMs is used to define a Basic Probability Assignment (BPA). The BPAs are combined using Dempster’s rule of combination to make the final decision. Now to classify any test instance with missing values, we make an initial guess of the missing values using the nearest neighbor rule. We take the latent space representation of that imputed instance and pass it through each trained SVM. As done earlier, using each SVM output, we generate a BPA and the r BPAs are aggregated to get a composite BPA. The class label of the test point is then determined using the Pignistic probabilities. We have compared the proposed method with four state-of-the-art techniques using three experiments with artificial and real datasets. The proposed method is found to perform better.

In this study a model for data reliability in wireless sensor networks is proposed, in which machine learning methods are used. Proposed framework includes data modelling, missing data prediction, anomaly detection, data fusion and trust mechanism phases. Thus, temporal analysis is performed on the preprocessed sensor data and missing data are predicated. Then outliers on collected data are detected on the cluster head nodes by using Eta one-class Support Vector Machines. If an event is detected data are fused and then send to sink. If an anomaly is detected for a node's data, the trust weight of the node is decreased.

Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k-means++ cluster method is proposed to determine basic probability assignment. At first, k-means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster’s combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.

Dempster-Shafer evidence theory (D-S) is an effective instrument for merging the collected pieces of basic probability assignment (BPA), and it exhibits superiority in achieving robustness of soft computing and decision making in an uncertain and imprecise environment. However, the determination of BPA is still uncertain, and merely applying evidence theory can sometimes lead to counterintuitive results when lines of evidence conflict. In this paper, a novel BPA generation method for binary problems called as the base algorithm is designed based on the kernel density estimation to construct the probability density function models, using the pairwise learning method to establish binary classification pairs. By means of the new BPA generation method, a new decision-making algorithm based on D-S evidence theory, fuzzy preference relation and nondominance criterion is effectively designed. The strength of the proposed method is presented in applying pairwise learning, which transforms the original complex problem into simple subproblems. With this process, the complexity of the problem to be solved is greatly reduced, which increases the feasibility for industrial applications. Furthermore, the fuzzy computing technique is used to aggregate the output for each single subproblem, and the nondominance degree of each class is determined from the fuzzy preference relation matrix, which can be directly used for the determination of the input instance. Based on several industrial-based classification experiments, the proposed BPA generation method and decision-making algorithm present the effectiveness and improvement in terms of precision and Cohen’s kappa.

As the extension of the Fuzzy sets (FSs) theory, the Interval-valued Pythagorean Fuzzy Sets (IVPFS) was introduced which play an important role in handling the uncertainty. The Pythagorean fuzzy sets (PFSs) proposed by Yager in 2013 can deal with more uncertain situations than intuitionistic fuzzy sets because of its larger range of describing the membership grades. How to measure the distance of Interval-valued Pythagorean fuzzy sets is still an open issue. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. In order to efficiently deal with uncertainty in practical applications, this paper proposes a new divergence measure of Interval-valued Pythagorean fuzzy sets,which is based on the belief function in Dempster–Shafer evidence theory, and is called IVPFSDM distance. It describes the Interval-Valued Pythagorean fuzzy sets in the form of basic probability assignments (BPAs) and calculates the divergence of BPAs to get the divergence of IVPFSs, which is the step in establishing a link between the IVPFSs and BPAs. Since the proposed method combines the characters of belief function and divergence, it has a more powerful resolution than other existing methods.

Dempster-Shafer (D-S) evidence theory has been widely used in various fields. However, how to measure the degree of conflict (similarity) between the bodies of evidence is an open issue. In this paper, in order to solve this problem, firstly we propose a modified cosine similarity to measure the similarity between vectors. Then a new similarity measure of basic probability assignment (BPAs) is proposed based on the modified cosine similarity. The new similarity measure can achieve the reasonable measure of the similarity of BPAs and then efficiently measure the degree of conflict among bodies of evidence. Numerical examples are used to illustrate the effectiveness of the proposed method. Finally, a weighted average method based on the new BPAs similarity is proposed, and an example is used to show the validity of the proposed method.

A new definition of entropy of belief functions in the Dempster–Shafer theory