Sequential emitter identification method based on D-S evidence theory

This paper proposes a novel sequential identification method for enhancing the anti-jamming performance and for accurate recognition rate of the emitters’ individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information

Data Credence in IoR: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

Data Credence in IoT: Vision and Challenges

As the Internet of Things permeates every aspect of human life, assessing the credence or integrity of the data generated by "things" becomes a central exercise for making decisions or in auditing events. In this paper, we present a vision of this exercise that includes the notion of data credence, assessing data credence in an efficient manner, and the use of technologies that are on the horizon for the very large scale Internet of Things

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)

Efficacy of Decentralized CSS Clustering Model Over TWDP Fading Scenario

Cognitive Radio technology, which lowers spectrum scarcity, is a rapidly growing wireless communication technology. CR technology detects spectrum holes or unlicensed spectrums which primary users are not using and assigns it to secondary users. The dependability of the spectrum-sensing approach is significantly impacted from two of the most critical aspects, namely fading channels and neighboring wireless users. Users of non-cooperative spectrum sensing devices face numerous difficulties, including multipath fading, masked terminals, and shadowing. This problem can be solved using a cooperative- spectrum-sensing technique. For the user, CSS enables them to detect the spectrum by using a common receiver. It has also been divided into distributed CSS and centralized CSS. This article compares both ideas by using a set of rules to find out whether a licensed user exists or not. This thought was previously used to the conventional fading channels, such as the Rician, Rayleigh and the nakagami-m models. This work focused on D-CSS using clustering approach over TWDP fading channel using two-phase hard decision algorithms with the help of OR rule as well as AND rule. The evaluation of the proposed approaches clearly depicted that the sack of achieve a detection-probability of greater than 0.8; the values SNR varies between -14 dB to -8 dB. For all two-phase hard decision algorithms using proposed approach and CSS techniques, the detection probability is essentially identical while the value of signal to noise ratio is between -12 dB to -8dB. Throughout this work, we assess performance of cluster-based cooperative spectrum-sensing over TWDP channel with the previous findings of AWGN, Rayleigh, and wei-bull fading channels. The obtained simulation results show that OR-AND decision scheme enhanced the performance of the detector for the considered range of signal to noise ratios

Bearing Fault Diagnosis using Multi-sensor Fusion based on weighted D-S Evidence Theory

This paper has presented a novel method for bearing fault diagnosis using a multi-sensor fusion approach based on an improved weighted Dempster-Shafer (D-S) evidence theory combined with Genetic Algorithm (GA). Vibration measurements are collected from an industrial multi-stage centrifugal air compressor using three wireless acceleration sensors. Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE) is applied to extract the complexity changes of vibration data sets. Then, the extracted feature vectors produced by F2CMPE via multiple scales are fed into Back Propagation Neural Network (BPNN) for fault classification. The normalized probability outputs of BPNN are considered now as inputs of the proposed weighted D-S evidence theory for multi-sensor information fusion. The measurements collected from real industrial equipment are analyzed using the proposed diagnosis method, and the experimental validation has demonstrated its efficiency to identify rolling bearing conditions, the results of which have also shown higher accuracy compared to those using individual sensor signal analysis

DFIOT: Data Fusion for Internet of Things

In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data fusion is one of the current research trends in this arena that is considered in this paper. We particularly consider typical IoT scenarios where the sources measurements highly conflict, which makes intuitive fusions prone to wrong and misleading results. This paper proposes a taxonomy of decision fusion methods that rely on the theory of belief. It proposes a data fusion method for the Internet of Things (DFIOT) based on Dempster–Shafer (D–S) theory and an adaptive weighted fusion algorithm. It considers the reliability of each device in the network and the conflicts between devices when fusing data. This is while considering the information lifetime, the distance separating sensors and entities, and reducing computation. The proposed method uses a combination of rules based on the Basic Probability Assignment (BPA) to represent uncertain information or to quantify the similarity between two bodies of evidence. To investigate the effectiveness of the proposed method in comparison with D–S, Murphy, Deng and Yuan, a comprehensive analysis is provided using both benchmark data simulation and real dataset from a smart building testbed. Results show that DFIOT outperforms all the above mentioned methods in terms of reliability, accuracy and conflict management. The accuracy of the system reached up to 99.18 % on benchmark artificial datasets and 98.87 % on real datasets with a conflict of 0.58 %. We also examine the impact of this improvement from the application perspective (energy saving), and the results show a gain of up to 90 % when using DFIOT