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Special medical supplies such as blood and vaccines have strict temperature requirements in medical cold storage. In order to improve the stability of the temperature monitoring system in the medical cold storage and solve the problem of low measurement accuracy caused by node failure or abnormal data interference, we propose an algorithm based on classification adaptive estimation (CAEWF). The algorithm first monitors and classifies the data collected by the nodes, and uses the mutual support matrix between the data to filter out the validity abnormal data and transmit them to the cluster head. Then use the classification adaptive estimation weighted fusion algorithm to estimate the weighted fusion of the data. The simulation results show that the accuracy of the CAEWF algorithm is better than the arithmetic mean and batch estimation fusion algorithm, which can meet the temperature accuracy requirements of medical cold storage. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the information collected from wireless sensor network nodes has a great similarity and some errors exist in the data results, this study proposes a data fusion algorithm based on Kalman filter for wireless sensor network, which improves the efficacy and accuracy of uploaded data by filtering invalid data and shrunk data packets. The algorithm uses the Kalman filter algorithm with high real-time performance to integrate the data in the wireless sensor network according to the time series. On the basis of time data fusion, according to the characteristics of spatial distribution, the data fusion of multi-sensor at the gateway layer is further carried out according to the weight. In view of the characteristics of real-time changes of different position errors, the gateway layer uses spatial data as the basis to dynamically adjust the weight of each node using an adaptive weighting algorithm. Simulation experiments show that the algorithm is easy to implement, can effectively remove redundant information, and improve data accuracy and reliability. Compared with the improved batch estimation and adaptive weighting method, the root mean square error is reduced by about 7.9% and the accuracy is improved by 2.% after using this method. [ABSTRACT FROM AUTHOR]

In recent years, the infrastructure of Wireless Internet of Sensor Networks (WIoSNs) has been more complicated owing to developments in the internet and devices' connectivity. To effectively prepare, control, hold and optimize wireless sensor networks, a better assessment needs to be conducted. The field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data analysis. This study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine (RTS-DELM) implemented to wireless Internet of Things (IoT) enabled sensor networks for the detection of any intrusion activity. Data fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy, as well as for the maximizing of wireless sensor networks lifespan. We also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more effective. By using the Real Time Sequential Deep Extreme Learning Machine (RTSDELM) methodology, an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is accomplished. Simulation results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed approach. Eventually, threats and a more general outlook are explored. [ABSTRACT FROM AUTHOR]

The wireless sensor network is an integral part of the physical information system. Disperse sensors through a set of special spaces track and record the natural state of the environment and manage the information collected in a central location. The sensors use wireless connections to create their own networks. Wireless sensor network technology has the advantages of flexible deployment and convenient use and has played an important role in the field of user behavior recognition. By deploying wireless sensor network technology, users can collect daily information, capture users' behavior habits, and analyze users' health status. In the deployment and application of this type of technology, it is very important to build an effective model of the logical sequence relationship of the monitored person's behavior. The sensor data can be sent to the target user through wireless transmission. Action recognition is often based on a single feature for learning and judgment, so there are many difficulties in practical applications. This article aims to study motion shake awareness and action prediction algorithms based on wireless sensor networks. Aiming at the research of human pose recognition algorithm, to optimize the overall performance of the model, this article suggests the use of multimodal input, uses a 2D and 3D network structure, and finally, proposes two network weighted fusion strategies. Aiming at the research of pedestrian motion discrimination, this article offers a behavior prediction algorithm based on multifeature joint learning. The algorithm adds the feature vectors output by gesture recognition and mask prediction and uses a cross-entropy cost function to jointly learn and predict classification. The results of the survey show that the pedestrian gesture recognition and motion recognition algorithm based on the wireless sensor network proposed in this paper has good performance and can be widely used in real scenes such as video surveillance. The accuracy of the gesture recognition algorithm in the UCF101 dataset and the HMDB51 dataset was 96% and 72%, respectively. [ABSTRACT FROM AUTHOR]

The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node's limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy. [ABSTRACT FROM AUTHOR]

With the promotion of the latest technologies and the new requirement of humanitarian, the wireless multi-sensor system is applied broadly. This paper studies the data fusion of the industrial wireless sensor networks (IWSNs), in order to acquire more thoughtful data for the prognosis and diagnosis of the monitored device. These authors propose a combination of back propagation neural network (BP NN) and Wavelet Packet algorithm for data fusion. This proposed algorithm is based on each cluster head, which is modelled with a three layers NN. A case study using the ball bearing test data, which is from the Bearing Data Center of the Case Western Reserve University, and to verify the effectiveness of the proposed algorithm. With MATLAB 2016b version, the raw data feature is extracted by the Wavelet Packet and the feature fusion is based on BP NN at sink node. The simulation results show that the proposed algorithm is effective in fault diagnosis of wind turbine. [ABSTRACT FROM AUTHOR]

Wireless body sensor network (WBSN) is also known as wearable sensors with transmission capabilities, computation, storage and sensing. In this paper, a supervised learning based decision support system for multi sensor (MS) healthcare data from wireless body sensor networks (WBSN) is proposed. Here, data fusion ensemble scheme is developed along with medical data which is obtained from body sensor networks. Ensemble classifier is taken the fusion data as an input for heart disease prediction. Feature selection is done by the squirrel search algorithm which is used to remove the irrelevant features. From the sensor activity data, we utilized the modified deep belief network (M-DBN) for the prediction of heart diseases. This work is implemented by Python platform and the performance is carried out of both proposed and existing methods. Our proposed M-DBN technique is compared with various existing techniques such as Deep Belief Network, Artificial Neural Network and Conventional Neural Network. The performance of accuracy, recall, precision, F1 score, false positive rate, false negative and true negative are taken for both proposed and existing methods. Our proposed performance values for accuracy (95%), precision (98%), and recall (90%), F1 score (93%), false positive (72%), false negative (98%) and true negative (98%). [ABSTRACT FROM AUTHOR]

Wireless sensor network has been widely used in various types of industries, such as water quality detection. Due to all kinds of device faults and transmission faults, there are some outliers during water quality monitoring. In a large monitoring area, the difference between monitoring parameter values exists due to uneven distribution. The monitoring data in a single location is unsuitable to represent the real situation of the whole monitoring area. The data fusion method is used to fuse data in multiple locations. Traditional methods in water quality data fusion have problems of low accuracy and efficiency for limited to poor generalization and complex calculations. In response to requirements of high-precision for sensor data in water quality detection, a novel data fusion method based on a new support function IDTW-SF (Improved Dynamic Time Warping Distance Optimized Support Function) was proposed in this study. Based on the importance of dissolved oxygen in various water quality parameters, it was used as an example to study this research in this study. The purpose of data fusion was for correcting outliers to obtain high-quality data. Firstly, the consistency detection of sensor data improved the quality of the fusion data. With high computing complexity, the traditional Gaussian support function was a defective method in data fusion. The dissolved oxygen content was used for example to study the new data fusion method. An improved dynamic time warping distance IDTW (Improved Dynamic Time Warping Distance) was used to optimize a new support function SF (Support Function), thus calculated the support degree value between water quality time series data. Unlike the Gaussian support function, the SF function obtained mutual support degree of sensors without the exponent calculation. The weighted algorithm was used to complete the data fusion process. Based on Grey correlation analysis, the IDTW-SF combined the dynamic time warping distance. time segment strategy and Mahalanobis distance together. DTW algorithm was applied to replace the Euclidean distance and compute the distance between time series. Time series segmentation strategy was utilized to reduce the computation dimension of the DTW algorithm. To prove the validity of this fusion algorithm, various experiments were carried out on a water quality monitoring platform of aquaculture pond from May 24th, 2017 to May 29th, 2017 in the Freshwater Fisheries Research Center, Jiangsu Province. Different distance measures were applied to optimized the SF function and construct new support functions, such as Cos-SF (Cosine angle optimized SF) and DTW-SF. Meanwhile, existed support functions were realized in this study, such as the G function (Gaussian function). These functions were all used as a comparison. The experimental results showed that the mean absolute error value of the proposed fusion method was 0.279 2%. Compared with DTW-SF, Cos-SF, and SF, the mean absolute error value was reduced by 6.308 7%, 54.214 5%, and 16.306 9%, respectively. And the fusion effect of dissolved oxygen was improved obviously. The run time of the IDTW-SF was just 0.021 7 s. The fusion accuracy of the IDTW-SF support degree function was higher than the counterpart existed functions, and its efficiency was also high. Meanwhile, the residual value of IDTW-SF also had a distinct advantage over the other contrast functions. It was obvious that the combination of the IDTW algorithm and the SF method was reasonable and effective. To summarize, the proposed fusion algorithm in this study fully obtained the correlation of monitoring data and overcame shortcomings of traditional function, thus improved the accuracy and efficiency of fusion results. High fusion accuracy could meet the needs of water quality monitoring in the real world. [ABSTRACT FROM AUTHOR]

A reasonable clustering structure can make data aggregation methods run efficiently in most wireless sensor networks (WSNs). However, compared with other WSNs, the energy imbalance problem of long and linear WSNs is more serious, and the delay is higher. It is still impossible to achieve efficient data aggregation by optimizing the clustering structure. Therefore, this letter proposes a novel data aggregation tree based on a clustering structure. First, through the optimization of cluster head selection and multihop path selection, this letter proposes a long and linear nonuniform clustering structure to improve the energy balance. Furthermore, taking the minimum delay as the objective function and the number of fusion nodes and energy balance as the constraint, an aggregation node selection mechanism based on a swarm optimization algorithm is proposed to build a data aggregation tree based on a long and linear nonuniform clustering structure. The simulation results show that the proposed method can effectively reduce the delay and energy consumption and that it is suitable for long and linear WSNs. [ABSTRACT FROM AUTHOR]

Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions. [ABSTRACT FROM AUTHOR]

As an Industrial Wireless Sensor Network (IWSN) is usually deployed in a harsh or unattended environment, the privacy security of data aggregation is facing more and more challenges. Currently, the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating, alternately, the aim at enhancing the security of data. The performances of the secure data aggregation protocols are the trade-off of several metrics, which involves the transmission/fusion, the energy efficiency and the security in Wireless Sensor Network (WSN). Unfortunately, there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether in IWSN or in WSN. In consideration of IWSN, we firstly review the security requirements and techniques in WSN data aggregation in this paper. Then, we give a holistic overview of the classical secure data aggregation protocols, which are divided into three categories: hop-by-hop encrypted data aggregation, end-to-end encrypted data aggregation and unencrypted secure data aggregation. Along this way, combining with the characteristics of industrial applications, we analyze the pros and cons of the existing security schemes in each category qualitatively, and realize that the security and the energy efficiency are suitable for IWSN. Finally, we make the conclusion about the techniques and approach in these categories, and highlight the future research directions of privacy preserving data aggregation in IWSN. [ABSTRACT FROM AUTHOR]

A multi-source fusion positioning system (MFPS) adopts a data fusion technique for hybrid positioning. This technique fully utilizes several types of positioning sources in contrast to the conventional positioning methods, which use a single positioning source. Sensor data collection and processing are essential components in MFPSs. A framework for a practical center-server-based MFPS is proposed in this paper. A big sensor data preprocessing (BSDP) scheme, which is composed of data extraction (DE-BSDP), data gathering (DG-BSDP), and data transmission (DT-BSDP), is further proposed under this framework. DE-BSDP attempts to remove useless positioning data from fusion sources. The big sensor data are further compressed in DG-BSDP, in which a compressive sensing technique is adopted to realize data compression before data transmission. After the data gathering phase, the compressed data are transmitted to the fusion center for reconstruction in DT-BSDP. The proposed BSDP method can reduce the data collection amount significantly and improves the data transmission efficiency with a slight reduction on positioning accuracy. Experiments and simulations verify the effectiveness of the proposed BSDP scheme. [ABSTRACT FROM AUTHOR]

Highlights • A mixed strategy game-based malicious nodes detection is proposed. • A data fusion method NetF is proposed to fuse data obtained from different networks. • An excellent performance is shown by NetF in experiments. • Our algorithm can plan the best defense strategy to nodes at different times. Abstract Advanced persistent threats (APTs) pose a grave threat in cyberspace because of their long latency and concealment. In this paper, we propose a hybrid strategy game-based dynamic defense model to optimally allocate constrained secure resources for the target network. In addition, values of profits of players in this game are computed by a novel data-fusion method called NetF. Based on network protocols and log documents, the NetF deciphers data packets collected from different networks to natural language to make them comparable. Using this algorithm, data observed from the Internet and wireless sensor networks (WSNs) can be fused to calculate the comprehensive payoff of every node precisely. The Nash equilibrium can be computed using the value to detect the possibility of a node being a malicious node. Using this method, the dynamic optimal defense strategy can be allocated to every node at different times, which enhances the security of the target network obviously. In experiments, we illustrate the obtained results via case studies of a cluster of heterogeneous networks. The results guide planning of optimal defense strategies for different kinds of nodes at different times. [ABSTRACT FROM AUTHOR]

Aimed at the WSN' s problems, such as limited node' s energy, low precision of measurement and short effective lifetime of network, etc., this paper proposed a data fusion algorithm based on abnormal data preprocessing and adaptive estimation, ADAEWF. In order to improve the algorithm' s reliability, this paper firstly proposed the data preprocessing mechanism based on abnormal data detection,simple majority principle and node's comprehensive support function. To reduce the measurement error' s effect on the fusion precision, this paper proposed an adaptive estimation weighted data fusion algorithm. Then, this paper established the WSN' s simulation model and obtained the mean square error and network 's effective lifetime under ADAEWF, AFWDF and arithmetic mean value algorithm, respectively. Simulation shows that, in terms of both fusion accuracy and network ' s effective lifetime, ADAEWF outperforms AFWDF and arithmetic mean value algorithm, and ADAEWF algorithm has superiority in improving the effectiveness of data fu sion, network lifetime and fusion accuracy. [ABSTRACT FROM AUTHOR]

Highlights • First node decides by fusing measurements, then a fusion center fuses the decisions. • Detection performance of sensor networks is improved by considering correlation. • Nodes consider the correlation among their sensors' measurements by using copulas. • Fusion center adds a summation term to its statistic for resolving the correlation. • Applying the method on a fire detector network improves its detection performance. Abstract Detection is one of the main tasks of wireless sensor networks (WSN) in many applications. The data of network nodes are usually assumed to be statistically independent since considering dependence results in intractable decision rules. However, detection performance degrades when the correlation rises. In this paper, a practically popular configuration of WSNs is considered where homogeneous nodes - each of them consisting of several heterogeneous sensors - send their processed data to a fusion center (FC). Here, data fusion should be implemented in two levels: 1. Nodes fuse the data of their sensors. 2. The FC takes the final decision by fusing the data of the nodes. We propose to implement a copula-based local decision rule in nodes. At the FC, it is shown that the correlation among nodes' decisions can be resolved by simply adding the logarithm of the sum of nodes' decisions to the statistic of the FC. The proposed distributed detection scheme is evaluated in a practical example of fire detection. Simulations show that considering correlation by implementing the proposed methods would improve detection performance. [ABSTRACT FROM AUTHOR]

In surveillance application scenarios, like border security and area monitoring, potential targets to be detected may be either an unarmed person, a soldier carrying ferrous weapon or a vehicle. Detection is the first phase of a monitoring process, followed by the target classification phase and finally their tracking if required. This work focuses on classification step, where we introduce our classification approach not too resource-intensive, easy to implement and suitable for large scale environment. For that, we used probabilistic reasoning techniques to address multi sensing data correlation and take advantage of multi-sensor data fusion, then, based on adopted fusion architecture, we implemented our trained classification model in a fusion node, to make the classification more accurate. [ABSTRACT FROM AUTHOR]

Summary: Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method). [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of change detection through a Wireless Sensor Network (WSN) whose nodes report only binary decisions (on the presence/absence of a certain event to be monitored), due to bandwidth/energy constraints. The resulting problem can be modelled as testing the equality of samples drawn from independent Bernoulli probability mass functions, when the bit probabilities under both hypotheses are not known. Both One-Sided (OS) and Two-Sided (TS) tests are considered, with reference to: (i) identical bit probability (a homogeneous scenario), (ii) different per-sensor bit probabilities (a non-homogeneous scenario) and (iii) regions with identical bit probability (a block-homogeneous scenario) for the observed samples. The goal is to provide a systematic framework collecting a plethora of viable detectors (designed via theoretically founded criteria) which can be used for each instance of the problem. Finally, verification of the derived detectors in two relevant WSN-related problems is provided to show the appeal of the proposed framework. [ABSTRACT FROM AUTHOR]

When a new event occurs, the nodes in the neighborhood of the event sense and then send many packets to the sink node. Such circumstances need their networks to be simultaneously reliable and event-driven. Moreover, it should be remove redundant packets in order to lower the average energy consumption. A data fusion algorithm based on event-driven and Dempster-Shafer evidence theory is proposed in this paper to reduce data packet quantities and reserve energy for wireless sensor networks upon detecting abnormal data. Sampling data is compared against the set threshold, and the nodes enter the relevant state only when there are abnormal datum; at this point, cluster formation begins. All cluster members incorporate a local forwarding history to decide whether to forward or to drop recent sampling data. Dempster-Shafer evidence theory is exploited to process the data. The basic belief assignment function, with which the output of each cluster member is characterized as a weighted-evidence, is constructed. Then, the synthetic rule is subsequently applied to each cluster head to fuse the evidences gathered from cluster member nodes to obtain the final fusion result. Simulation results demonstrate that the proposed algorithm can effectively ensure fusion result accuracy while saving energy. [ABSTRACT FROM AUTHOR]

The primary objective of cooperative spectrum sensing (CSS) is to determine whether a particular spectrum is occupied by a licensed user or not, so that unlicensed users called secondary users (SUs) can utilize that spectrum, if it is not occupied. For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS is degraded. We have proposed an algorithm which uses error detection technique on sensing measurement of all SUs. Each SU is required to re-transmit the sensing data to the FC, if error is detected on it. Our proposed algorithm combines the sensing measurement of limited number of SUs. Using Proposed algorithm, we have achieved the improved probability of detection (PD) and throughput. The simulation results compare the proposed algorithm with conventional scheme. [ABSTRACT FROM AUTHOR]

We study detection performance of the majority dominance rule in anm-ary relay tree with node and link failures. The leaves of the tree represent N identical and independent sensors, the root of the tree represents a fusion center that makes the final detection decision, each of the other nodes in the tree is a relay node that combines m binary messages from its immediate child nodes and forms a new binary message using the majority dominance rule. We first provide the limit performance of the detection error probability at the fusion center for the casewhenN goes to infinity. Then, we derive upper and lower bounds for the detection error probability at the fusion center as explicit functions of N. These bounds also characterize the asymptotic decay rate of the detection error probability asN goes to infinity, and showthat the decay rate in the failure case is not faster than that in the nonfailure case. Furthermore, we derive a necessary and sufficient condition in terms of the decay rate of the local failure probability (combination of node failure probability and link failure probability) at each level to ensure that the detection error probability of the tree with node and link failures decays as fast as that of the tree without failures. [ABSTRACT FROM AUTHOR]

Energy consumption is an important performance indexe for Wireless Sensor Networks (WSNs) because the node energy is limited. Clustering structure facilitates data fusion and load balancing, thus reducing energy consumption. We propose an unequal multiple hops clustering algorithm (UMCA) for WSNs. UMCA is a multiple hops clustering algorithm. By changing the probabilities that nodes become cluster heads (CHs), UMCA selects CHs in the nodes with high residual energy. A node that has more around nodes, and is close to around nodes is more likely to become a CH. The possibility to becomes a CH also has relative with the distance to the base station (BS). There are more CHs near the BS, and less CHs away from the BS to balance the load of CHs. UMCA and other related algorithms are compared by simulation. The results show that UMCA can effectively prolong the network lifetime. [ABSTRACT FROM AUTHOR]

In the field of vehicular wireless sensor networks-based structural health monitoring, the structural damage identification is achieved by two structural features, namely natural frequencies and mode shapes. The kind of data fusion-based routing algorithm in specific applications needs to meet time synchronization requirements and meet certain constraints, such as the single-hop communication between cluster head node and each node in cluster, the overlap between different clusters and so on. To meet the special constraints for data fusion-based routing algorithm in structural health monitoring, this paper proposed a new method based on an improved flooding time synchronization protocol, which is called time synchronization and enhanced greedy algorithm based on D(v) (TSDEGA) routing algorithm. The TSDEGA method can achieve the minimum connected cover by node's own degree D(v), and it can also meet the structural health monitoring routing constraints. The simulation experiments show that TSDEGA has better energy resistance and longer network lifetime, and it is superior to the traditional greedy algorithms. The proposed algorithm can effectively eliminate interference of outliers and improve the accuracy in order to meet time synchronization requirements in structural health monitoring applications. [ABSTRACT FROM AUTHOR]

The Lamb wave phased array structural health monitoring method is effective in structural damage monitoring. In this method, the damage scattering signal can be obtained by comparing the damage structural response signal with health structural response signal, and it can be used for structural damage identification. But in the structural health monitoring based on wireless sensor networks, this method has some inevitable defects in data transmission. A large number of sampling data of damage response signal will cause huge wireless communication burden. To solve this problem, we proposed a phased array image method based on compressive sampling and data fusion for wireless structural damage monitoring. First, compressive sampling signal by compressive sampling method was collected. Then, data fusion for multisensor's damage response signal was implemented in phased array. Finally, the Lamb wave phased array damage identification method based on compressive sampling and data fusion was proposed. Experimental results on carbon composite structure show that the proposed method can largely save network bandwidth and energy. Thismethod can also realize the damage identification accurately on the aviation aluminum plate and keep the detection error within 0.82 mm. [ABSTRACT FROM AUTHOR]

Distributed wireless networks often employ voting to perform critical network functions such as fault-tolerant data fusion, cooperative sensing, and reaching consensus. Voting is implemented by sending messages to a fusion center or via direct message exchange between participants. However, the delay overhead of message-based voting can be prohibitive when numerous participants have to share the wireless channel in sequence, making it impractical for time-critical applications. In this paper, we propose a fast PHY-layer voting scheme called PHYVOS, which significantly reduces the delay for collecting and tallying votes. In PHYVOS, wireless devices transmit their votes simultaneously by exploiting the subcarrier orthogonality of OFDM and without explicit messaging. Votes are realized by injecting energy to pre-assigned subcarriers. We show that PHYVOS is secure against adversaries that attempt to manipulate the voting outcome. Security is achieved without employing cryptography-based authentication and message integrity schemes. We analytically evaluate the voting robustness as a function of PHY-layer parameters. We extend PHYVOS to operate in ad hoc groups, without the assistance of a fusion center. We discuss practical implementation challenges related to multi-device frequency and time synchronization and present a prototype implementation of PHYVOS on the USRP platform. We complement the implementation with larger scale simulations. [ABSTRACT FROM AUTHOR]

Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes. [ABSTRACT FROM AUTHOR]

The sustainable exploitation of natural resources is nowadays an important challenge for governments and institutions, considering the expected increase of the world population. In order to respond to this emergent criticality, the principles of green economy have been introduced in the European policy discussion to achieve a good compromise between the sustainability and the profitability of productions by increasing the efficiency of farming operations. Such approach poses some technical and financial challenges for small-sized enterprises because they generally do not possess adequate internal knowledge, nor they can acquire external expertise due to their budget restrictions. Decision Support Systems (DSS) can be an effective solution to overcome such difficulties, since they can involve experts’ knowledge and complex mathematical elaborations on contextual data, thus helping managers in taking more effective decisions. Based on these motivations, the paper proposes a methodological approach to the development of a DSS in the specific context of Integrated Pest Management (IPM) applied to intensive (greenhouse) production, and an experimental validation based on real data. The DSS involves a rule-based decision approach based on referenced mathematical models applied to the information gathered by a sensor network. The multi sensor decision fusion methodology proposed represents an innovative contribution to the literature and an easy-to use and low cost solution to reduce the use of pesticides and fertilizers in protected crops. [ABSTRACT FROM AUTHOR]

Underwater sensor position information is very important in Underwater Wireless Sensor Networks (UWSNs). The sensor collected information is only meaningful with the correct positions. While current method only processes underwater nodes individually, which causes the localization precision for each nodes varying severely. This problem is very common in both centralized network and distributed network. The varying localization precision causes real trouble especially in data fusion, aided navigation, and signal processing. To solve this problem and achieve a consistent localization precision in the network, we propose an error control and adjustment method to make all the sensor localization precision refined. We use the distance information between nodes to establish a network error adjustment model, which can improve the localization precision and make the network localization error consistent. The simulation and actual experiment proves that this method can make all the node positioning more precise, the localization precision can be lower than decimetres. This method can be widely used in UWSNs and show a favourable potential in ocean engineering applications. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. [ABSTRACT FROM AUTHOR]

This paper considers the problem of cooperative spectrum sensing in multiuser multiple-input multiple-output cognitive radio networks considering the presence of uncertainty in the channel state information (CS!) of the secondary user channels available at the fusion center. Several schemes are proposed that employ cooperative decision rules based on local sensor decisions transmitted to the fusion center by thecooperating nodes over an orthogonal multiple access channel. First, fusion rules are derived under perfect CS! at the fusion center for both antipodal and nonantipodal signaling. Then, a robust detector, termed the uncertainty statisticsbased likelihood ratio test, which optimally combines the decisions of different secondary users, is obtained for scenarios with CS! uncertainty. A generalized likelihood ratio test based robust detector is also derived for this scenario. Closed-form expressions are obtained to characterize the probabilities of false alarm (PFA) and detection (PD) at the fusion center. Simulation results are presented to compare the performance of the proposed schemes with that of the conventional uncertainty agnostic detectors and also to corroborate the analytical expressions developed. [ABSTRACT FROM PUBLISHER]

The paper describes a Kalman filtering technique for dynamic displacement estimation using accelerometer and laser sensor measurements. Data fusion of measurements from multiple sensors can give the more accurate results because of different advantages of sensors. Since the acceleration and displacement have different sampling rates, the multi-rate Kalman filter is applied. The filter is expanded with the fixed interval smoother to improve reconstruction accuracy of displacements. A modelled signal consisting of two sinus functions and Gaussian distributed noise is used to validate developed state-space model. [ABSTRACT FROM AUTHOR]

The cowshed environment has significant impacts on the yield, diseases and behaviors of dairy cows. Heat stress, in particular, has a great impact on yield. The cowshed environment monitoring system based on wireless sensor network can accurately sense the temperature and other environmental parameters in real time and provide basis for manual environmental intervention and control. Energy constraint is one of the important problems that affect the long-term stable monitoring by the dairy cow wireless sensor network. So, the weighted Markov chain method is used to predict the time series of cowshed temperature. Replacing the actual values with the predicted values at the cluster head can effectively reduce data traffic in the cluster, thereby reducing network power consumption. Test data show that, the average variance of the cowshed environment temperature predicted by the method proposed in this paper is 0.185, and the average power consumption is reduced by about 40% when the compression ratio is 0.3, which effectively prolongs the network lifetime. In addition to that, the cowshed environment prediction can also help make prejudgments for environmental control, reduce or avoid the heat stress of dairy cows after the environmental parameters exceed the thresholds and provide the basis for the multi-source data fusion for dairy cow. [ABSTRACT FROM AUTHOR]

Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). However, as a result of the temporal and spatial variability of acoustic channels, underwater acoustic communications suffer from an extremely limited bandwidth. In order to reduce network congestion, it is important to shorten the length of the data transmitted from local sensors to the fusion center by quantization. Although quantization can reduce bandwidth cost, it also brings about bad tracking performance as a result of information loss after quantization. To solve this problem, this paper proposes an optimal quantization-based target tracking scheme. It improves the tracking performance of low-bit quantized measurements by minimizing the additional covariance caused by quantization. The simulation demonstrates that our scheme performs much better than the conventional uniform quantization-based target tracking scheme and the increment of the data length affects our scheme only a little. Its tracking performance improves by only 4.4% from 2- to 3-bit, which means our scheme weakly depends on the number of data bits. Moreover, our scheme also weakly depends on the number of participate sensors, and it can work well in sparse sensor networks. In a 6×6×6 sensor network, compared with 4×4×4 sensor networks, the number of participant sensors increases by 334.92%, while the tracking accuracy using 1-bit quantized measurements improves by only 50.77%. Overall, our optimal quantization-based target tracking scheme can achieve the pursuit of data-efficiency, which fits the requirements of low-bandwidth UWSNs. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim's algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms. [ABSTRACT FROM AUTHOR]

Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed cophasing (DCP) technique in which multiple sensor nodes (SNs) communicating their observations to a fusion center (FC) need to transmit different classes of data requiring different levels of error protection. To achieve this, we propose a variant of the DCP technique with autonomous constellation selection at the different SNs. In the first stage of the two-stage time division duplexed cophasing scheme, the SNs obtain estimates of the channels to the FC using pilot symbols transmitted by the latter. Following this, the SNs simultaneously transmit their data symbols prerotated according to the estimated channel phases to combine coherently at the FC. The symbols transmitted by different SNs are drawn from possibly different constellations selected based on the estimated instantaneous channel gains. We show that this scheme is equivalent to transmitting symbols from hierarchical constellations. Based on the properties of hierarchical constellations, we develop recursive expressions for the BER of the proposed system. Following this, we use the properties of the effective channel coefficients to show that it is possible to recover the transmitted data bits from the signal received at the FC blindly, without requiring explicit pilot symbols to be sent by the power-starved SNs. We develop three blind channel estimation and data detection schemes for the presented system model. Using Monte Carlo simulations, we show that the proposed blind channel estimation algorithm achieves a probability of error performance close to that with genie aided perfect channel state information (CSI) at the FC, while using only a moderate number of unknown data symbols for channel estimation. [ABSTRACT FROM PUBLISHER]

In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of various security fusion algorithms, namely, data security fusion algorithm EDCSDA and approximate fusion algorithm PADSA. By analyzing the probability distribution model of the data collected by the nodes, the disturbance data is superimposed on the original data to hide the effect of the original data. A test bed system for perception layer of the Internet of things is designed and implemented. The test results prove the feasibility of the two algorithms. Meanwhile, it shows that the two algorithms can reduce the transmission overhead of the network while guaranteeing the security. Based on the above finding, it is concluded that data fusion technology is very effective for improving network efficiency and prolonging the network life cycle as one of the key technologies in the perception layer of Internet of things. [ABSTRACT FROM AUTHOR]

A wireless sensor network is characterized by resource constraint and limited computational capable sensor motes that are powered by battery. Ensuring optimal lifetime of a sensor network has always been a research question from the past decade with evidences of various ranges of solutions to mitigate them. However, till now such energy preservation solutions were not found to be effective. Another unique pattern observed from literatures is that majority of such techniques are cluster based which causes latency in data fusion mechanism. Therefore, this paper has discussed a tree-based data fusion technique using an extra module termed as core fusion node, which ensures energy aware and non-redundant data to be fused by the fusion node and then transmit to the base station using multiple-hops. Accompanied with cost effective computation technique, the proposed system (TBFT) is found to outperform conventional LEACH algorithm both with respect to energy and data fusion time, showing the effective outcome till date. [ABSTRACT FROM AUTHOR]

We present a tandem network of queues $$0,\dots , s-1.$$ Customers arrive at queue 0 according to a Poisson process with rate $$\lambda $$ . There are s independent batch service processes at exponential rates $$\mu _0, \dots , \mu _{s-1}$$ . Service process i, $$i=0, \dots , s-1$$ , at rate $$\mu _i$$ is such that all customers of all queues $$0, \dots , i$$ simultaneously receive service and move to the next queue. We show that this system has a geometric product-form steady-state distribution. Moreover, we determine the service allocation that minimizes the waiting time in the system and state conditions to approximate such optimal allocations. Our model is motivated by applications in wireless sensor networks, where s observations from different sensors are collected for data fusion. We demonstrate that both optimal centralized and decentralized sensor scheduling can be modeled by our queueing model by choosing the values of $$\mu _i$$ appropriately. We quantify the performance gap between the centralized and decentralized schedules for arbitrarily large sensor networks. [ABSTRACT FROM AUTHOR]

This paper considers the design of minimal mean square error (MMSE) transceivers in a wireless sensor network. The problem is nonconvex and challenging, and previous results (with partial solutions and/or with convergence unproved) left much to be desired. Here we propose several approaches – 2 block coordinate descent (BCD), essentially cyclic multi-block method and its variants and distributive method to solve this problem. The proposed 2-BCD approach formulates the subproblem of joint beamformer optimization as a general second-order cone programming problem, which lends itself to standard numerical solvers and which requires no extra assumptions like previous works do. The proposed essentially cyclic multi-block approach further decomposes the joint beamformer design subproblem into multiple blocks, and rigorously solves each with semi-closed-form solution. The distributive algorithm optimizes transmitters in a decentralized manner and has never been considered in existing literature. The distributive algorithm has time complexity independent of number of sensors and is especially suitable for large-scale networks. All the previous BCD-based approaches left some singularity issue unattended as well as the convergence property unaddressed, our proposals are the first to provide a complete and provably-converging analytical solution. Extensive analysis and simulations demonstrate the merits of the novel approaches relative to existing alternatives. [ABSTRACT FROM AUTHOR]

In unstructured wireless sensor networks (WSNs), which consist of a dense collection of sensor nodes deployed randomly, the communication and processing capabilities of sensor nodes can be limited owing to their small embedded batteries and available bandwidth. Power management is therefore one of the most important issues to consider in the implementation of WSNs. As a result, decentralized detection, in which the fusion center makes the final decision to use data partially processed by local nodes, is more attractive than centralized detection in unstructured WSNs. This paper proposes a more efficient and effective method for solving the power allocation problem as a constrained optimization problem: to schedule power allocation in a distributed WSN using correlated observations and amplify-and-forward local processing at sensor nodes so that the WSN detects a constant signal while maintaining a sufficient fusion error probability threshold. To accomplish this goal, this paper proposes using Deb's method, which does not require a penalty parameter when handling the constraints of the optimization problem. Additionally, representative optimization algorithms based on swarm intelligence are used, i.e., particle swarm optimization, ant colony optimization for continuous domains ( $$\hbox {ACO}_{\mathbb {R}}$$ ), and artificial bee colony. Through a simulation, their performance is compared for several different WSNs to determine the best algorithm for solving the power allocation problem. [ABSTRACT FROM AUTHOR]

In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum Detection (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity. [ABSTRACT FROM AUTHOR]

Systems based on wireless sensor have been presented as an alternative to location and navigation for visually impaired in environments where GPS is not effective. However, dealing with the information obtained from these sensors may present some challenges. The difficulties described by several authors are related mainly to the oscillation of the signals, which are susceptible to numerous interfering agents such as the scene itself, the presence of people, etc. It is necessary to increase the accuracy of the location in all the techniques observed. Therefore, this paper proposes to apply data fusion of wi-fi sensors with inertial sensors to allow visually impaired to location and navigation in indoor places using aspects of healthcare like sensor based systems embedded in wearable devices. The methodology adopted was associate redundant fusion and complementary fusion to construct a database obtained from several readings of the wi-fi sensors and an inertial sensor to construct a reference position to compare the data of the test readings to this reference and indicate the reliability in an indoor location. The results show that RSSI with SNR and magnetometer with gyroscope provide high error rates with raw and isolated data, but when fused, they show a more efficient location, decreasing the error rates observed in isolated mode. The error margins between the real position and the position indicated by the system were 0,386 m, 0,336 m and 0,148 m to RSSI with SNR, inertial and fusion application, respectively. [ABSTRACT FROM AUTHOR]

Mobile agent (MA)-based middleware has been thoroughly investigated in the past few years as a means to address the efficiency, scalability, and reliability issues of data fusion applications on wireless sensor networks. Deriving an efficient itinerary for each MA to follow is of high importance, because itineraries determine to a large extent the overall performance of data fusion tasks. In this article, we present a novel algorithmic approach for efficient itinerary planning of MA objects undertaking data fusion tasks. We adopt a method based on iterated local search to construct the itineraries (ie, visiting sequences of source nodes) assigned to multiple traveling MAs. We apply alternative optimization criteria which aim either at minimizing the overall energy expenditure over all derived MA itineraries or prolonging the network lifetime. Furthermore, we propose algorithmic solutions for 2 realistic settings which have not been investigated in the past: firstly, the employment of multiple sinks that share the responsibility of MA-based data fusion tasks across the sensor field, and secondly, the consideration of heterogeneous sensor networks comprising nodes powerful enough to host the runtime environment required to execute MA code as well as 'ordinary' nodes which lack these resources. Simulation tests verify the performance gain attained by our algorithmic methods against alternative itinerary planning approaches which involve multiple MAs. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced. [ABSTRACT FROM AUTHOR]

A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and the total number of detections that report '1' to the fusion center (FC) is studied for a wireless sensor network (WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic regression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a global decision about the presence/absence of the target. Both the fixed and variable numbers of decisions received by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR. The LRFA does not depend on the signal model and the priori knowledge of the local sensors' detection probabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR improves the performance of the system with low computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a data fusion technique aimed at achieving highly accurate localization in a wireless sensor network with low computational cost. This is accomplished by fusing multiple types of sensor measurement data including received signal strength and angle of arrival. The proposed method incorporates a powerful data fusion technique, one that has never before been used in low cost localization of a stationary node, known as Dempster-Shafer Evidence Theory. Many useful functions of this theory, including sampling, aggregation, and plausibility, are integrated into the localization method. From there, the algorithm determines whether a set of given measurements belong to a particular county. Motivated by the flexible nature of Dempster-Shafer Theory, a multitude of network setups and combinations of available measurement features are tested to verify the performance of the proposed method. Performance of the proposed approach is evaluated using numerical results obtained from extensive simulations. When compared with the results of existing approaches in similarly constructed scenarios, the proposed localization technique achieves up to 98% accuracy in less than a tenth of the run-time required under presently established algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), a space filling curve (SFC) refers to a path passing through all nodes in the network, with each node visited at least once. By enforcing a linear order of the sensor nodes through an SFC, many applications in WSNs concerning serial operations on both sensor nodes and sensor data can be performed, with examples including serial data fusion and path planning of mobile nodes. Although a few studies have made efforts to find such SFCs in WSNs, they primarily target 2D planar or 3D surface settings and cannot be directly applied to 3D volumetric WSNs due to considerably more complex geometric features and topology shapes that the 3D volumetric settings introduce. This article presents BLOW-UP, a distributed, scalable, and connectivity-based algorithm to construct an SFC for a 3D volumetric WSN (or alternatively to linearize the 3D volumetric network). The main idea of BLOW-UP is to decompose the given 3D volumetric network into a series of connected and closed layers, and the nodes are traversed layer by layer, incrementally from the innermost to the outermost, yielding an SFC covering the entire network, provably at least once and atmost a constant number of times. To the best of our knowledge, BLOW-UP is the first algorithm that realizes linearization in 3D volumetric WSNs. It does not require advance knowledge of location or distance information. It is also scalable with a nearly constant per-node storage cost and message cost. Extensive simulations under various networks demonstrate its effectiveness on nodes' covered times, coverage rate, and covering speed. [ABSTRACT FROM AUTHOR]