A Metadata Reconstruction Algorithm Based on Heterogeneous Sensor Data for Marine Observations

Vast amounts of heterogeneous data on marine observations have been accumulated due to the rapid development of ocean observation technology.Several state-of-art methods are proposed to manage the emerging Internet of Things(IoT)sensor data.However,the use of an inefficient data management strategy during the data storage process can lead to missing metadata;thus,part of the sensor data cannot be indexed and utilized(i.e.,‘data swamp’).Researchers have focused on optimizing storage procedures to prevent such disasters,but few have attempted to restore the missing metadata.In this study,we propose an AI-based algorithm to reconstruct the metadata of heterogeneous marine data in data swamps to solve the above problems.First,a MapReduce algorithm is proposed to preprocess raw marine data and extract its feature tensors in parallel.Second,load the feature tensors are loaded into a machine learning algorithm and clustering operation is implemented.The similarities between the incoming data and the trained clustering results in terms of clustering results are also calculated.Finally,metadata reconstruction is performed based on existing marine observa-tion data processing results.The experiments are designed using existing datasets obtained from ocean observing systems,thus verifying the effectiveness of the algorithms.The results demonstrate the excellent performance of our proposed algorithm for the metadata recon-struction of heterogenous marine observation data.

AN INFORMATION FUSION METHOD FOR SENSOR DATA RECTIFICATION

In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.

A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement

For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.

Inversion of Evaporation and Water Vapor Transport Using HY-2 Multi-Sensor Data

HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Proposal of ZigBee Systems for the Provision of Location Information and Transmission of Sensor Data in Medical Welfare

This paper proposes a scheme to obtain location and vital health information using ZigBee system. ZigBee systems are wireless communication systems defined by IEEE 802.154. In the proposed scheme, location information is obtained using the Link Quality Indication (LQI) function of a ZigBee system, which represents the received signal strength. And, the vital health information are collected from the electrocardiogram monitor, the pulse and blood pressure device, attached to the patient’s body. This information is then transmitted to an outside network by ZigBee systems. In this way, vital health information can be transmitted as ZigBee sensor data while patients with the ZigBee terminal are moving. In the experiments using actual ZigBee devices, the proposed scheme could obtain accurate location and vital health information from the sensor data. Moreover, to achieve high reliability in the actual service, the collected amount of sensor data was confirmed by the theoretic calculation, when a ZigBee terminal passed through ZigBee routers. These results indicate that the proposed scheme can be used to detect the accurate location of the ZigBee terminal. And over 99% of the sensor data on vital health information was obtained when the ZigBee terminal passed through approximately four ZigBee routers.

STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.

Application of Multiple Sensor Data Fusion for the Analysis of Human Dynamic Behavior in Space: Assessment and Evaluation of Mobility-Related Functional Impairments

The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hier-archical segments in which each lower segment tends to transfer its motion to the other superimposed segments. This segmental chain enables the derivation of both conscious perception and sensory control of action in space. This applied systems analysis approach involves the measurements of the complex motor behavior in order to elucidate the fusion of multiple sensor data for the reliable and efficient acquisition of the kinetic, kinematics and electromyographic data of the human spatial behavior. The acquired kinematic and related kinetic signals represent attributive features of the internal recon-struction of the physical links between the superimposed body segments. In-deed, this reconstruction of the physical links was established as a result of the fusion of the multiple sensor data. Furthermore, this acquired kinematics, kinetics and electromyographic data provided detailed means to record, annotate, process, transmit, and display pertinent information derived from the musculoskeletal system to quantify and differentiate between subjects with mobility-related disabilities and able-bodied subjects, and enabled an inference into the active neural processes underlying balance reactions. To gain insight into the basis for this long-term dependence, the authors have applied the fusion of multiple sensor data to investigate the effects of Cerebral Palsy, Multiple Sclerosis and Diabetic Neuropathy conditions, on biomechanical/neurophysiological changes that may alter the ability of the human loco-motor system to generate ambulation, balance and posture.

A Proposal of Sensor Data Collection System Using Mobile Relay Nodes

In recent years, as embedded devices become smaller, cheaper and more diverse, the demand for urban sensing systems that present valuable information to users is increasing. However, in achieving urban sensing systems, the communication channel from the sensors to the data centers pose a problem, especially in respect to the cost of furnishing IP/mobile networks for each and every one of the sensor nodes. Many existing researches attempt to tackle this problem, but they generally limit either the types of sensors used or the distances among the sensors. In this paper, we propose a new sensor data collection system model in which mobile relay nodes transport the sensor data to the data center. We ran simulations under conditions imitating the real world to verify the practicality of the proposed system. This simulation uses data accumulated from traffic surveys to closely imitate pedestrians in the real world. We evaluated that the proposed system has sufficient ability to use in urban sensing systems that are not under the real-time constraint.

DPCM-based vibration sensor data compression and its effect on structural system identification

Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.

Sensor data compression based on MapReduce

A compression algorithm is proposed in this paper for reducing the size of sensor data. By using a dictionary-based lossless compression algorithm, sensor data can be compressed efficiently and interpreted without decompressing. The correlation between redundancy of sensor data and compression ratio is explored. Further, a parallel compression algorithm based on MapReduce [1] is proposed. Meanwhile, data partitioner which plays an important role in performance of MapReduce application is discussed along with performance evaluation criteria proposed in this paper. Experiments demonstrate that random sampler is suitable for highly redundant sensor data and the proposed compression algorithms can compress those highly redundant sensor data efficiently.

Public auditing for real‑time medical sensor data in cloud‑assisted HealthIIoT system

With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as HealthIIoT.Considering the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and maintenance.However,there are still some serious security issues within outsourcing medical sensor data to the cloud.One of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and treatment.To meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT system.Specifcally,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data proof.We formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art ones.The results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.

The use of animal sensor data for predicting sheep metabolisable energy intake using machine learning

The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems.The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake(MEI)of sheep by using temperature,pitch angle,roll angle,distance,speed,and grazing time data obtained directly from wearable sensors on the sheep.A Deep Belief Network(DBN)algorithm was used to predict MEI,which to our knowledge,has not been attempted previously.The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone.The mean square error(MSE)values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets,respectively.We also evaluated the influential sensor data variables,i.e.,distance and pitch angle,for predicting the MEI.Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data.We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management,as wearable sensors become widely used by livestock producers.

Data fusion and machine learning for ship fuel efficiency modeling:Part Ⅲ-Sensor data and meteorological data

Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.

A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendation is a major bottleneck for general Io Tbased applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.

Mining Sensor Data in Cyber-Physical Systems

A Cyber-Physical System （CPS） integrates physical devices （i.e.,sensors） with cyber （i.e.,informational） components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations.Such a system has wide applications in the scenarios of traffic control,battlefield surveillance,environmental monitoring,and so on.A core element of CPS is the collection and assessment of information from noisy,dynamic,and uncertain physical environments integrated with many types of cyber-space resources.The potential of this integration is unbounded.To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time.Therefore,CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber.The various properties of the physical world must be addressed in information management and knowledge discovery.This paper discusses the problems of mining sensor data in CPS：With a large number of wireless sensors deployed in a designated area,the task is real time detection of intruders that enter the area based on noisy sensor data.The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data.IntruMine first analyzes the trustworthiness of sensor data,then detects the intruders＇ locations,and verifies the detections based on a graph model of the relationships between sensors and intruders.

New multi-layer data correlation algorithm for multi-passive-sensor location system

Under the scenario of dense targets in clutter, a multi-layer optimal data correlation algorithm is proposed. This algorithm eliminates a large number of false location points from the assignment process by rough correlations before we calculate the correlation cost, so it avoids the operations for the target state estimate and the calculation of the correlation cost for the false correlation sets. In the meantime, with the elimination of these points in the rough correlation, the disturbance from the false correlations in the assignment process is decreased, so the data correlation accuracy is improved correspondingly. Complexity analyses of the new multi-layer optimal algorithm and the traditional optimal assignment algorithm are given. Simulation results show that the new algorithm is feasible and effective.

Efficient Packet Scheduling Technique for Data Merging in Wireless Sensor Networks

Wireless Sensor Networks(WSNs) has become a popular research topic due to its resource constraints. Energy consumption and transmission delay is crucial requirement to be handled to enhance the popularity of WSNs. In order to overcome these issues, we have proposed an Efficient Packet Scheduling Technique for Data Merging in WSNs. Packet scheduling is done by using three levels of priority queue and to reduce the transmission delay. Real-time data packets are placed in high priority queue and Non real-time data packets based on local or remote data are placed on other queues. In this paper, we have used Time Division Multiple Access(TDMA) scheme to efficiently determine the priority of the packet at each level and transmit the data packets from lower level to higher level through intermediate nodes. To reduce the number of transmission, efficient data merge technique is used to merge the data packet in intermediate nodes which has same destination node. Data merge utilize the maximum packet size by appending the merged packets with received packets till the maximum packet size or maximum waiting time is reached. Real-time data packets are directly forwarded to the next node without applying data merge. The performance is evaluated under various metrics like packet delivery ratio, packet drop, energy consumption and delay based on changing the number of nodes and transmission rate. Our results show significant reduction in various performance metrics.

A New Energy Efficient Data Gathering Approach in Wireless Sensor Networks

Data gathering in wireless sensor networks is one of the important operations in these networks. These operations require energy consumption. Due to the limited energy of nodes, the energy productivity should be considered as a key objective in design of sensor networks. Therefore the clustering is a suitable method that used in energy consumption management. For this purpose many methods have been proposed. Between these methods the LEACH algorithm has been attend as a basic method. This algorithm uses distributed clustering method for data gathering and aggregation. The LEACH-C method that is the improvement of LEACH, which performs the clustering in centralized mode. In this method, collecting the energy level of information of every node directly in each period increases the energy cost. Also the phenomenon that is seen by sensor nodes continually change over time. Thereby the information received by nodes is correlated. Sending time correlated data in the network cause to energy dissipation. TINA method and its improvement have been proposed in order to not sending correlated data. These approaches have reported errors. In this paper, the idea of not sending time correlated data of nodes has been considered by using the time series function. Also, a model to estimate the remaining energy of nodes by the base station has been presented. Finally, a method has been proposed to aware the base station from the number of correlated data in each node as the estimation of energy will be more precise. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results showed that the proposed methods had a better performance in energy consumption and the lifetime of the network in comparison with similar methods.

Stream Segmentation-A Data Fusion Approach for Sensor Networks

Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.