Search

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance. [ABSTRACT FROM AUTHOR]

The growing interest in precision livestock farming is prompted by a desire to understand the basic behavioural needs of the animals and optimize the contribution of each animal. The aim of this study was to develop a system that automatically generated individual animal behaviour and localization data in sheep. A sensor-fusion-system tracking individual sheep position and detecting sheep standing/lying behaviour was proposed. The mean error and standard deviation of sheep position performed by the ultra-wideband location system was 0.357 ± 0.254 m, and the sensitivity of the sheep standing and lying detection performed by infrared radiation cameras and three-dimenional computer vision technology were 98.16% and 100%, respectively. The proposed system was able to generate individual animal activity reports and the real-time detection was achieved. The system can increase the convenience for animal behaviour studies and monitoring of animal welfare in the production environment. [ABSTRACT FROM AUTHOR]

The augmentation navigation system based on multi-source information fusion can significantly improve position accuracy, and the multi-source information is usually transmitted through VHF Data Broadcast. Aiming at the burst characteristics of VHF Data Broadcast, this article proposed a novel demodulation algorithm based on open-loop structure. When a VHF Data Broadcast burst is detected, the timing recovery should be finished first, and the value of cross-correlation between the timing-recovered signal and the local training symbol is calculated to complete the frame synchronization. Then, the data-aided and non-data-aided algorithms are used to estimate the frequency offset. Finally, the phase offset is estimated and the carrier synchronization is accomplished. The simulation results demonstrate that the proposed algorithm can quickly accomplished carrier synchronization without using feedback-loop structure, and the bit error rate is less than 10−4 when the signal-to-noise ratio is greater than 17 dB, which satisfy the requirement of receiving VHF Data Broadcast signals in augmentation navigation system. Therefore, the proposed algorithm can be used for receiving VHF Data Broadcast signals. [ABSTRACT FROM AUTHOR]

The distributed detection fusion formulation (DDFF) in ideal multisensor systems has been studied over the last two decades. If some local sensors cannot work normally, the detection performance of system may reduce significantly. It is meaningful to design fault-tolerant detection fusion rules which can guarantee the performance of system no matter whether the fusion center and local sensors work well or not. A new distributed detection fusion strategy is thus proposed by minimizing a weighted sum of risk at the fusion center and risks at the local sensors, and then a fault-tolerant distributed detection fusion formulation (FT-DDFF) is derived. Some numerical examples illustrate the performance of the proposed formulation. If the whole system is perfect, compared with the DDFF, the FT-DDFF has both small risks of local sensors and a little small risk of fusion center with an appropriately selected parameter. While some local sensors cannot work, the FT-DDFF would perform better than the DDFF at fusion center in average. [ABSTRACT FROM AUTHOR]

We newly propose a query-by-singing/humming (QbSH) system considering both the preclassification and multiple classifier-based method by combining linear scaling (LS) and quantized dynamic time warping (QDTW) algorithm in order to enhance both the matching accuracy and processing speed. This is appropriate for the QbSH of high speed in the huge distributed server environment. This research is novel in the following three ways. First, the processing speed of the QDTW is generally much slower than the LS method. So, we perform the QDTW matching only in case that the matching distance by LS algorithm is smaller than predetermined threshold, by which the entire processing time is reduced while the matching accuracy is maintained. Second, we use the different measurement method of matching distance in LS algorithm by considering the characteristics of reference database. Third, we combine the calculated distances of LS and QDTW algorithms based on score level fusion in order to enhance the matching accuracy. The experimental results with the 2009 MIR-QbSH corpus and the AFA MIDI 100 databases showed that the proposed method reduced the total searching time of reference data while obtaining the higher accuracy compared to the QDTW. [ABSTRACT FROM AUTHOR]

In recent years, with the development of wireless sensor networks (WSN), it has been applied in more and more areas. However, energy consumption and outlier detection have been always the hot topics in WSN. In order to solve the above problems, this paper proposes a timely anomaly detection algorithm which is based on the data fusion algorithm. This algorithm firstly employs the piecewise aggregate approximation (PAA) to compress the original data so that the energy consumption can be reduced. It then combines an improved unsupervised detection algorithm of K-Means and artificial immune system (AIS) to classify the compressed data to normal and abnormal data. Finally, relevant experiments on virtual and actual sensor databases show that our algorithm can achieve a high outlier detection rate while the false alarm rate is low. In addition, our detection algorithm can effectively prolong the life because it is based on data fusion algorithm. [ABSTRACT FROM AUTHOR]

Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K-ary input and K-ary output channel is presented to model the data transfer channel, where K-ary is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms. [ABSTRACT FROM AUTHOR]

In the application of opportunistic networking in wireless sensor network, the technology of target recognition is very important. However, since the sensor reports are typically inconsistent, incomplete, or fuzzy, the technology of target recognition whereby sensor reports is a major challenge. In this paper, based on the minimization of inconsistencies among the sensor reports, a new optimization model of target recognition is presented by using a convex quadratic programming (QP) formulation. Firstly, the description method of sensor report is introduced and then we talk about how to set up this new optimization model of target recognition by using the wireless sensor network reports and how to calculate the solution of this new optimization model. Finally, theory analysis and numeric simulation indicate that this optimization model can generate reasonable fusion results, which is similar to the Dempster-Shafer (D-S) evidence inference model. Furthermore, in contrast to D-S evidence inference model, this optimization model can fuse sensor reports of the form more general than that allowed by the D-S evidence inference model without additional processes. Meantime, it can deal with the high conflict sensor reports. [ABSTRACT FROM AUTHOR]

We consider the soil moisture monitoring problem and propose a WSN associated clustering strategy based on spatiotemporal data correlation, which ensures that the nodes within each cluster can share a good data correlation and consequently makes the cluster head do the data fusion more efficiently. As a result, the energy of each node will be saved and the lifetime of the whole sensor network will be extended. In the associated clustering strategy, the different clusters can be divided by the correlation characteristics of nodes data, which is based on a dynamic model and a correlation characteristics model after the correlation coefficient analysis. Simulation results show that our proposed associated clustering strategy works very well in soil moisture measurement. Moreover, as compared with the traditional random clustering, the associated clustering strategy based on data correlation achieves better performance for each cluster, and will be more efficient in data fusion at the cluster heads. [ABSTRACT FROM AUTHOR]

We present a low cost battery-powered 6-degree-of-freedom wireless wand for 3D modeling in free space by tri-axis Magnetic, Angular Rate, Gravity (MARG) and vision sensor fusion. Our approach has two stages of sensor fusion, each with different algorithms for finding 3D orientation and position. The first stage fusion algorithm, a complementary filter, utilizes MARG sensors to compute 3D orientation relative to the direction of gravity and earth's magnetic field in a quaternion format, which was adjusted with compensations for magnetic distortion. The second stage fusion algorithm, a Kalman filter, utilizes accelerometer data and IR marker velocity to compute 3D position. In order to compute the IR marker linear velocity along the optical axis (the z-axis), we present a simple and efficient image-based technique to find the distance of the object from the camera using blob area pixels in the image. Our fusion (inside-in and outside-in) approach efficiently solves short time occlusion, needs of frequent calibration, and unbounded drift problems involved in numerical integration of inertial sensors data and improves the degrees of freedom at low cost without compromising accuracy. The results are compared with a leading commercial magnetic motion tracking system to demonstrate the performance of the wand. [ABSTRACT FROM AUTHOR]

Pedestrian tracking is one of the bases for many ubiquitous context-aware services, but it is still an open issue in indoor environments or when GPS estimations are not optimal. In this paper, we propose two novel different data fusion algorithms to track a pedestrian using current positioning technologies (i.e., GPS, received signal strength localization from Wi-Fi or Bluetooth networks, etc.) and low cost inertial sensors. In particular, the algorithms rely, respectively, on an extended Kalman filter (EKF) and a simplified complementary Kalman filter (KF). Both approaches have been tested with real data, showing clear accuracy improvement with respect to raw positioning data, with much reduced computational cost with respect to previous high performance solutions in literature. The fusion of both inputs is done in a loosely coupled way, so the system can adapt to the infrastructure that is available at a specific moment, delivering both outdoors and indoors solutions. [ABSTRACT FROM AUTHOR]

A lot of facts show that many researches just place emphasis on data aggregation or data fusion, which is not beneficial to analyze the sensed data thoroughly and will lead to the aggregation results' not being used fully; worse yet, the actual networks are always existed with lossy links; many now available aggregation algorithms are based on ideal network models and not any further analysis and fusion about aggregation results are done. Thus, we propose the concept of data fusaggregation so as to support processing sensed data while transmitting in large-scale probabilistic wireless sensor networks and propose a reliability-oriented data fusaggregation algorithm (RODFA) to assist users to get the monitoring information from the monitored geographic environment and measure the reliability of the information they get. RODFA also facilitates network administrator to improve the system sensing performance for large-scale probabilistic WSNs. In RODFA, the parameter t], which could reflect the reliability of aggregation result intuitively, is defined and calculated and it plays an important part in helping users to process aggregation result further. In our experiment, the validity of RODFA is verified by our simulation results, and the influence of network sizes and network performances on data fusaggregation is analyzed. [ABSTRACT FROM AUTHOR]

Optimizing the design of tracking system under energy and bandwidth constraints in wireless sensor networks (WSN) is of paramount importance. In this paper, the problem of collaborative target tracking in WSNs is considered in a framework of quantized measurement fusion. First, the measurement in each local sensor is quantized by probabilistic quantization scheme and transmitted to a fusion center (FC). Then, the quantized messages are fused and sequential importance resampling (SIR) particle filtering is employed to estimate the target state. In the FC, quantized measurement fusion via both augmented approach and weighted approach is investigated. For both approaches, the closed-form solution to the optimization problem of bandwidth scheduling is given, where the total energy consumption is minimized subject to a constraint on the fusion performance. Finally, posterior Cramer-Rao lower bounds (CRLBs) on the tracking accuracy using quantized measurement fusion are derived. Simulation results reveal that both approaches perform very closely to the posterior CRLB while obtaining average communication energy saving up to 72.8% and 45.1%, respectively. [ABSTRACT FROM AUTHOR]

The reliability of cooperative spectrum sensing (CSS) can be severely degraded by the falsified spectrum sensing reports provided by malicious secondary users (SUs). However, in existing works, the problem of secure CSS in mobile cognitive radio networks (CRNs) has not been well considered yet. The detection of abnormal sensing reports in existing works does not evaluate both sensing reports and locations of SUs simultaneously. And the mobility pattern of all SUs is assumed to be similar, which is impractical in real mobile CRNs. In this paper, spatial correlation of the received signal strength among SUs is exploited to get the evidence whether the received signal strength is consistent with the location from where it is generated. And Dempster-Shafer theory is used to filter out abnormal reports by combining the evidence collected from the spatial correlation algorithm in each sensing period. To mitigate the adverse effects caused by the SUs' mobility pattern, a fusion scheme based on SUs' reputation is proposed. In comparison to the existing schemes, simulation results demonstrate that the proposed secure CSS scheme improves the primary user detection rate by 10% at false alarm rate of 0.1 when the mobility pattern of the SUs is different. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the characteristics of the openness and vulnerability can lead to communication failure and node capture more easily. To cope with the deviation problem caused by the compromised nodes, this paper proposes a situation data fusion mechanism which can enhance reliability and integrality. It makes a reliability assurance scheme, which not only establishes a project distinguishing purposive and purposeless acts, but also builds a trust awareness rule based on historical trust and the correlations among current nodes. Furthermore, the data integrality is ensured in three stages, respectively. Firstly, in event detection stage, the proposed mechanism detects events and pretreats data to improve accuracy through most trust majority rule after collecting data. Moreover, during data fusion stage, data filtration is adopted to extract credible data. Additionally, the fusion weights of transactions depend on trust, so that they will change with the passage of time. As a result, the final fusion data will be closer to real value. Finally, in consistency detection stage, the detection rule with lower communication traffic is utilized to judge whether the centers are credible. Theory analyses and simulation results verify that the new mechanism can reduce the deviation. Meanwhile, reliability and stability have been validly enhanced. [ABSTRACT FROM AUTHOR]

Although human activity recognition (HAR) has been studied extensively in the past decade, HAR on smartphones is a relatively new area. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. Realizing this goal is challenging, however. Firstly, these devices are low on resources, which limits the number of sensors that can be utilized. Secondly, to achieve optimum performance efficient feature extraction, feature selection and classification methods are required. This work implements a smartphone-basedHARscheme in accordance with these requirements. Time domain features are extracted from only three smartphone sensors, and a nonlinear discriminatory approach is employed to recognize 15 activities with a high accuracy. This approach not only selects the most relevant features from each sensor for each activity but it also takes into account the differences resulting from carrying a phone at different positions. Evaluations are performed in both offline and online settings. Our comparison results show that the proposed system outperforms some previous mobile phone-based HAR systems. [ABSTRACT FROM AUTHOR]

Decision fusion has been adopted in a number of sensor systems to deal with sensing uncertainty and enable the sensors to collaborate with each other. It can distribute computation workload and significantly reduces the communication overhead. However, some variants of decision rules such as Voting, Bayes Criterion, and Neyman-Pearson require a priori knowledge on the probability of targets presence which is still an open issue in detection theory. In this paper, we propose a binary decision fusion scheme that reaches a global decision by integrating local decisions made by fusion members .The optimal local thresholds and global threshold are derived by using the Minimax criterion based analysis while they are ensuring false alarm rate constraint, without a pre-estimated target appearance probability. Simulation results show that our scheme can improve the system performance under certain constraints, which can guide the threshold selection for implementing WSN systems in mission-critical applications. [ABSTRACT FROM AUTHOR]

We present a monitoring and tracking system for storage and transportation of chemicals. In accord with the standards and regulations of transport vehicles for hazardous chemicals, an overall structure-function diagram for vehicle monitoring and a pre- warning system were proposed by integrating various technologies such as computer modeling, control theory, multisensor data fusion, communication science, space positioning, and safety management science. Transportation experiments performed on sea and land demonstrated that the monitoring and tracking system is feasible with high precision, in which an amount of system state data was collected by different kinds of sensors group. Once sensor-derived signals were detected, the signals would be fused and transmitted to the master monitor center via a high precision and large-scale wireless communication network. The designed tracking and localizing system displayed good monitoring ability and adequately satisfied technical requirements. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimation fusion in a distributed wireless sensor network (WSN) under the following conditions: (i) sensor noises are contaminated by outliers or gross errors; (ii) process noise and sensor noises are correlated; (iii) cross-correlation among local estimates is unknown. First, to attack the correlation and outliers, a correlated robust Kalman filtering (coR2KF) scheme with weighted matrices on innovation sequences is introduced as local estimator. It is shown that the proposed coR2KF takes both conventional Kalman filter and robust Kalman filter as a special case. Then, a novel version of our internal ellipsoid approximation fusion (IEAF) is used in the fusion center to handle the unknown cross-correlation of local estimates. The explicit solution to both fusion estimate and corresponding covariance is given. Finally, to demonstrate robustness of the proposed coR2KF and the effectiveness of IEAF strategy, a simulation example of tracking a target moving on noisy circular trajectories is included. [ABSTRACT FROM AUTHOR]

This paper is concerned with a reliable solution to the distributed estimation and control issues over hybrid two-tier industrial wireless sensor and actuator networks (IWSANs). It aims at applying wireless technology to industrial automation control domains in hash industrial environment. A main challenge in these application domains is that wireless communication channels must satisfy strict requirements of real time and reliability. Therefore, first, we propose a new interacting dual model (IDM) adaptive estimation algorithm to identify the unreliable wireless communication channels, and, second, to further enhance the accuracy and fault tolerance of state estimation for controlled plant, a federated multisensor estimation fusion approach is presented. Furthermore, considering that the IWSANs need commonly to be deployed over a vast geographical area, a distributed collaborative control strategy is adopted. Finally, an example of temperature control illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a multirate network coding scheme to improve the energy efficiency of wireless sensor networks (WSNs). Firstly, a five-layer network model is introduced, in which a perfect solution can always be achieved. Then, a network topology which belongs to the five-layer network model will be established from the given sensor networks so that the receivers can receive data at a higher rate without introducing excessive additional transmissions, which improves the energy efficiency as a result. The performance of the proposed scheme is evaluated in a simulated instance, and the result shows that 9% of transmission overhead can be saved compared with traditional scheme. [ABSTRACT FROM AUTHOR]

Leakage monitoring is different from sudden incident monitoring because most of the leakage cases involve a slow process that lasts for a long time. During this case monitoring, sensors suffer long exposure to erosion and may lead to errors in the measurement. An approach is proposed to make use of a soft-decision fusion approach accordingto the Neyman-Pearson criterion to accumulate auxiliary data from multiple sensors. The proposed method optimizes the soft-function and adjusts its range of sensors, which provide auxiliary data to improve the fusion center confidence for making a global decision. The new method encompasses the collection of useful data and weights and combines them according to the corresponding confidence level to make a global decision. In the simulation case of Rayleigh-distributed observations of leakage monitoring, it is proved that the proposed method has a good performance. [ABSTRACT FROM AUTHOR]

Determining basic probability assignment (BPA) is essential in multisensor data fusion by using Fussy Theory or Dempster-Shafer Theory (DST). The study presented a method to determine BPA through sensor data only reported by sensors without depending on preset information data modeled prior to actual events. This was used to determine BPA for multi-sensor data fusion so that a pedestrian, who walked or moved, could recognize a moving object. The method resulted from the study was to evaluate the changes of each sensor measurement as time passed. Each BPA of each focal element was normalized to evaluate the aspects of the changes by time and to meet the basic characteristics of BPA in DST. That is, BPA of each focal element after evaluating sensor data was ranged between 0 and 1, and the total amount of all focal elements was 1. The study showed that a pedestrian could recognize a moving object with the method of determining BPA through multi-sensor data fusion conducted in the study. [ABSTRACT FROM AUTHOR]

Because of the special nature of wireless sensor networks, data fusion process is vulnerable to attacks by the destroyer, which is useful for reducing the network communication overhead and improving the data transmission efficiency Therefore, it is very essential to provide transmission with high security and low energy consumption for sensitive data fusion in wireless sensor network. In this paper, a data fusion algorithm of privacy protection based on Query of Server (Qos) and hierarchical multilayers was put forward, which divided the required privacy protection levels according to different safety requirements, and set up hierarchical network models. In data fusion process, delay constraint was added to guarantee service quality of Qos, and this would reduce energy consumption overhead. Meanwhile, it would guarantee the accuracy of the data and reduce the probability of the whole network information exposed. [ABSTRACT FROM AUTHOR]

Due to the low-cost and low-power requirement in an individual sensor node, the available computing resources turn out to be very limited like small memory footprint and irreplaceable battery power. Sensed data fusion might be needed before being transmitted as a tradeoff between procession and transmission in consideration of saving power consumption. Even worse, the application program needs to be complicated enough to be self-organizing and dynamically reconfigurable because changes in an operating environment continue even after deployment. State-driven operating system platform offers numerous benefits in this challenging situation. It provides a powerful way to accommodate complex reactive systems like diverse wireless sensor network applications. The memory usage can be bounded within a state transition table. The complicated issues like concurrency control and asynchronous event handling capabilities can be easily achieved in a well-defined behavior of state transition diagram. In this paper, we present an efficient and effective design of the state-driven operating system for wireless sensor nodes. We describe that the new platform can operate in an extremely resource constrained situation while providing the desired concurrency, reactivity, and reconfigurability We also compare the executing results after comparing some benchmark test results with those on TinyOS. [ABSTRACT FROM AUTHOR]

A pair of sensing shoes for measuring foot pressure was developed. This system aims at recognizing human movement in unlimited environments. The multipressure sensor network of seven sensors on one insole was set up. Analysis for discriminating the user's movements from foot pressure distribution was carried out, considering the movements of standing, walking, going upstairs, and going downstairs. These actions were discriminated using characteristics extracted from the data of sensors. The classifier based on SVM showed highly accurate movement recognition. Specifically, to improve the classification performance, PCA based dimensionality reduction and channel reduction based data fusion were introduced. Experimental outcomes verified the testing speed of the classification function which was improved without affecting the accuracy rate. The results confirmed that this discriminant analysis can be employed for automatically recognizing human moving pattern based on foot pressure signal. [ABSTRACT FROM AUTHOR]

Mobile Agent (MA) technology has been recently proposed in Wireless Sensors Networks (WSNs) literature to answer the scalability problem of client/server model in data fusion applications. Herein we present CBID, a novel algorithm that calculates near-optimal routes for MAs that incrementally fuse the data as they visit the Sensor Nodes (SNs) while also enabling fast updates on the designed itineraries upon changes of network topology. CBID dispatches in parallel a number of MAs that sequentially visit sensor nodes arranged in tree structures and upon visiting an SN with two or more child SNs, the MAs (master MAs) clone of themselves with each clone (slave MA) visiting a tree branch. When all slave MAs return to that SN, they deliver their collected data to the master MA and are then disposed of. This results in a significant reduction of the overall energy expenditure and response time. Simulation results prove the high effectiveness of CBID in data fusion tasks compared to other alternative algorithms. [ABSTRACT FROM AUTHOR]

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm. [ABSTRACT FROM AUTHOR]

Fault diagnosis of equipment is a key issue in the industrial field, and it is essential to keep abreast of equipment status. However, previous studies either considered fault data at a single moment or gave the same weight to data over a period of time. In view of the problems above, fault diagnosis method based on time domain weighted data aggregation and information fusion is proposed in this article. First, the monitored data of sensors loaded by the equipment are aggregated utilizing the linear decaying weights. Then, Gaussian models of each fault type under different fault features are established based on aggregated data. And the basic probability assignments are generated by matching aggregated testing samples with the constructed Gaussian model. At last, the basic probability assignments generated under each fault feature are fused by Dempster combination rule. The proposed method is verified and the results show that the total fault recognition rate can reach 97.5%, which increased by 1.9% compared with the method that Gaussian model constructed by original data. [ABSTRACT FROM AUTHOR]

Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other. Recent researches show that weighting the evidences with the consideration of its corresponding credibility is an efficient methodology. As a result, how to determine the weight is an important issue. In this article, a new divergence measure of BPA is proposed based on geometric mean of Deng relative entropy. The weight of each evidence is determined by the proposed divergence measure and information volume. Compared with the existing belief Jensen–Shannon divergence, the proposed method has a better performance in the convergence to the correct target. The result shows that the proposed method outperforms other related methods, giving the highest belief value 98.98% to the correct target. [ABSTRACT FROM AUTHOR]

Particle filtering algorithm has found an increasingly wide utilization in many fields at present, especially in non-linear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. The estimation process of particle filtering algorithm is a series of weighted calculation processes, which can be regarded as weighted data fusion. This article proposed an improved particle filtering algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in MATLAB, it discovers that the proposed algorithm provides better accuracy in comparison with particle filtering, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filter in Gaussian mixture noise. A practical seven-dimensional harmonic model is also implemented in the simulation. After comparing the performances of different algorithms, we found that the proposed method had more accuracy than the widely used extended Kalman filtering algorithm. [ABSTRACT FROM AUTHOR]

It is a great challenge to offer a fine-grained and accurate PM2.5 monitoring service in urban areas as required facilities are very expensive and huge. Since PM2.5 has a significant scattering effect on visible light, large-scale user-contributed image data collected by the mobile crowdsensing bring a new opportunity for understanding the urban PM2.5. In this article, we propose a fine-grained PM2.5 estimation method based on random forest with data announced by meteorological departments and collected from smartphone users without any PM2.5 measurement devices. We design and implement a platform to collect data in the real world including the image provided by users. By combining online learning and offline learning, the method based on random forest performs well in terms of time complexity and accuracy. We compare our method with two kinds of baselines: subsets of the whole data sets and six classical models (such as logistic, naive Bayes). Six kinds of evaluation indexes (precision, recall, true-positive rate, false-positive rate, F-measure, and receiver operating characteristic curve area) are used in the evaluation. The experimental results show that our method achieves high accuracy (precision: 0.875, recall: 0.872) on PM2.5 estimation, which outperforms the other methods. [ABSTRACT FROM AUTHOR]

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results. [ABSTRACT FROM AUTHOR]

Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown. [ABSTRACT FROM AUTHOR]

The railway is one of the most important facilities for freight logistics. This paper suggests the use of the container security devices (CSDs), which has been adopted for the marine transportation so far, to enhance the security of railway freight transportation. The proposed protocol, CSD Chain Network Protocol (CCNP), was designed for a chain-form network of CSDs, each of which is attached to a container. In CCNP, unauthorized access to freight is reliably reported to the reader deployed at the locomotive. CCNP also cares about the fairness in energy consumption. In a usual chain network, a tag closer to the reader consumes more energy since it ought to forward packets coming from rear tags as well. However, in CCNP, the tag energy consumption for packet forwarding is always the same regardless of the position in a chain owing to an efficient data fusion scheme. In our simulation using QualNet, CCNP reduces the number of disconnections between tags by 59.5% as compared to general chain networks. The energy consumption also decreases by 91.3% of other networks not using an aggregation method. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the characteristics of the openness and vulnerability can lead to communication failure and node capture more easily. To cope with the deviation problem caused by the compromised nodes, this paper proposes a situation data fusion mechanism which can enhance reliability and integrality. It makes a reliability assurance scheme, which not only establishes a project distinguishing purposive and purposeless acts, but also builds a trust awareness rule based on historical trust and the correlations among current nodes. Furthermore, the data integrality is ensured in three stages, respectively. Firstly, in event detection stage, the proposed mechanism detects events and pretreats data to improve accuracy through most trust majority rule after collecting data. Moreover, during data fusion stage, data filtration is adopted to extract credible data. Additionally, the fusion weights of transactions depend on trust, so that they will change with the passage of time. As a result, the final fusion data will be closer to real value. Finally, in consistency detection stage, the detection rule with lower communication traffic is utilized to judge whether the centers are credible. Theory analyses and simulation results verify that the new mechanism can reduce the deviation. Meanwhile, reliability and stability have been validly enhanced. [ABSTRACT FROM AUTHOR]

A lot of facts show that many researches just place emphasis on data aggregation or data fusion, which is not beneficial to analyze the sensed data thoroughly and will lead to the aggregation results' not being used fully; worse yet, the actual networks are always existed with lossy links; many now available aggregation algorithms are based on ideal network models and not any further analysis and fusion about aggregation results are done. Thus, we propose the concept of data fusaggregation so as to support processing sensed data while transmitting in large-scale probabilistic wireless sensor networks and propose a reliability-oriented data fusaggregation algorithm (RODFA) to assist users to get the monitoring information from the monitored geographic environment and measure the reliability of the information they get. RODFA also facilitates network administrator to improve the system sensing performance for large-scale probabilistic WSNs. In RODFA, the parameter η, which could reflect the reliability of aggregation result intuitively, is defined and calculated and it plays an important part in helping users to process aggregation result further. In our experiment, the validity of RODFA is verified by our simulation results, and the influence of network sizes and network performances on data fusaggregation is analyzed. [ABSTRACT FROM AUTHOR]

Possibilistic c-means clustering algorithm (PCM) has emerged as an important technique for pattern recognition and data analysis. Owning to the existence of many missing values, PCM is difficult to produce a good clustering result in real time. The paper proposes a distributed weighted possibillistic c-means clustering algorithm (DWPCM), which works in three steps. First the paper applies the partial distance strategy to PCM (PDPCM) for calculating the distance between any two objects in the incomplete data set. Further, a weighted PDPCM algorithm (WPCM) is designed to reduce the corruption of missing values by assigning low weight values to incomplete data objects. Finally, to improve the cluster speed of WPCM, the cloud computing technology is used to optimize the WPCM algorithm by designing the distributed weighted possibilistic c-means clustering algorithm (DWPCM) based on MapReduce. The experimental results demonstrate that the proposed algorithms can produce an appropriate partition efficiently for incomplete big sensor data. [ABSTRACT FROM AUTHOR]