Small Antennas for Wearable Sensor Networks: Impact of the Electromagnetic Properties of the Textiles on Antenna Performance

The rapid development of wearable wireless sensor networks (W-WSNs) has created high demand for small and flexible antennas. In this paper, we present small, flexible, low-profile, light-weight all-textile antennas for application in W-WSNs and investigate the impact of the textile materials on the antenna performance. A step-by-step procedure for design, fabrication and measurement of small wearable backed antennas for application in W-WSNs is also suggested. Based on the procedure, an antenna on a denim substrate is designed as a benchmark. It demonstrates very small dimensions and a low-profile, all while achieving a bandwidth (|S₁₁| < −6 dB) of 285 MHz from 2.266 to 2.551 GHz, radiation efficiency more than 12% in free space and more than 6% on the phantom. Also, the peak 10 g average SAR is 0.15 W/kg. The performance of the prototype of the proposed antenna was also evaluated using an active test. To investigate the impact of the textile materials on the antenna performance, the antenna geometry was studied on cotton, polyamide-elastane and polyester substrates. It has been observed that the lower the loss tangent of the substrate material, the narrower the bandwidth. Moreover, the higher the loss tangent of the substrate, the lower the radiation efficiency and SAR.

Framework for the Simulation of Sensor Networks Aimed at Evaluating In Situ Calibration Algorithms

The drastically increasing availability of low-cost sensors for environmental monitoring has fostered a large interest in the literature. One particular challenge for such devices is the fast degradation over time of the quality of their data. Therefore, the instruments require frequent calibrations. Traditionally, this operation is carried out on each sensor in dedicated laboratories. This is not economically sustainable for dense networks of low-cost sensors. An alternative that has been investigated is in situ calibration: exploiting the properties of the sensor network, the instruments are calibrated while staying in the field and preferably without any physical intervention. The literature indicates there is wide variety of in situ calibration strategies depending on the type of sensor network deployed. However, there is a lack for a systematic benchmark of calibration algorithms. In this paper, we propose the first framework for the simulation of sensor networks enabling a systematic comparison of in situ calibration strategies with reproducibility, and scalability. We showcase it on a primary test case applied to several calibration strategies for blind and static sensor networks. The performances of calibration are shown to be tightly related to the deployment of the network itself, the parameters of the algorithm and the metrics used to evaluate the results. We study the impact of the main modelling choices and adjustments of parameters in our framework and highlight their influence on the results of the calibration algorithms. We also show how our framework can be used as a tool for the design of a network of low-cost sensors.

A Method for Sensor-Based Activity Recognition in Missing Data Scenario

Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.

Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment

Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only. In some practical scenarios of systems, there are certain interruptions in addition to these faults. These interruptions may occur in the form of noise. However it is expected that the decision about the sensor data is difficult in the presence of noise. This is because the noise adversely affects the communication amongst sensors and the processing unit. More complexity is expected when there are faults and noise simultaneously. To deal with this problem, in addition to existing fault detection and isolation schemes, the Kalman filter is employed. Here, a generic discussion is provided, which is equally applicable to other situations. This work addresses various faults in the presence of noise for decentralized tracking systems. Local single faults and multiple faults in the presence of noise are the core issues addressed in this paper. The proposed work is comprised of a general scenario for a decentralized tracking system followed by a case study of a target tracking scenario with and without noise. The presented schemes are also tested for different types of faults. The proposed work presents effective tracking in the presence of noise and faults. The results obtained demonstrate the acceptable performance of the scheme of this work.

Impact of Leadership and Mobility on Consensus-Building in Sensor Networks

Introducing leadership and mobility is known to benefit wireless sensor networks in terms of consensus-building and collective decision-making. However, these benefits are neither analytically proven nor quantified in the literature. This paper fills this gap by investigating the mobility dynamics in wireless sensor networks analytically. The results of the analytical investigation are presented as a set of theorems and their proofs. This paper also establishes a natural synergy between the leader-follower model and its bipartite graph representation. It demonstrates the advantages of the leader-follower model for consensus-building over others in terms of improved convergence rate. It presents a strategy for choosing leaders from among the agents participating in the consensus-building process using the well-known graph-coloring solution. Then, it shows how the leader-follower model helps improve the convergence rate of consensus-building. Finally, it shows that the convergence rate of the consensus-building process can be further improved by making the leaders mobile.

Discovering Suspicious APT Behaviors by Analyzing DNS Activities

As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and real-time evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing the domain name resolution (DNS) request to the target network in an APT attack. In past work based on DNS log analyses, most of the work would simply calculate the characteristics of the request message or the characteristics of the response message or the feature set of the request message plus the response message, and the relationship between the response message and the request message was not considered. This may leave out the detection of some APT attacks in which the DNS resolution process is incomplete. This paper proposes a new feature that represents the relationship between a DNS request and the response message, based on a deep learning method used to analyze the DNS request records. The algorithm performs threat assessment on the DNS behavior to be detected based on the calculated suspicious value. This paper uses the data of 4, 907, 147, 146 DNS request records (376, 605, 606 records after DNS Data Pre-processing) collected in a large campus network and uses simulation attack data to verify the validity and correctness of the system. The results of the experiments show that our method achieves an average accuracy of 97.6% in detecting suspicious DNS behavior, with the orange false positive (FP) at 2.3% and the recall at 96.8%. The proposed system can effectively detect the hidden and suspicious DNS behavior in APT.

A Distributed Testbed for 5G Scenarios: An Experimental Study

This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.

Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O₃) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O₃ analyzer at the station along with a network of three O₃ sensing nodes was used to evaluate the spatial and temporal variability of O₃ across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O₃ site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O₃ concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R² values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O₃ concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O₃ trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site.

Factors Affecting the Synthesis of Autonomous Sensors with RFID Interface

A general view on the problem of designing atypical battery-free, autonomous semi-passive RFID transponders-sensors (autonomous sensors with RFID interfaces) is presented in this review. Although RFID devices can be created in any of the electronic technologies, the design stage must be repeated each time when the manufacturing processes are changed, and their specific conditions have to be taken into consideration when modeling new solutions. Aspects related to the factors affecting the synthesis of semi-passive RFID transponder components on the basis of which the idea of the autonomous RFID sensor was developed are reflected in the paper. Besides their general characteristics, the operation conditions of modern RFID systems and achievements in autonomous RFID sensor technology are revealed in subsequent sections—they include such issues as technological aspects of the synthesis process, designing antennas for RFID transponders, determining RFID chip and antenna parameters, creating the interrogation zone IZ, etc. It should be pointed that the universal construction of an autonomous RFID sensor, which could be use in any application of the automatic object identification system, cannot be developed according to the current state of the art. Moreover, a trial and error method is the most commonly used in the today’s process of designing new solutions, and the basic parameters are estimated on the basis of the tests and the research team experience. Therefore, it is necessary to look for new inventions and methods in order to improve implementations of RFID systems.

Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning

Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and observing physical attributes often pose issues related to data uncertainty in practice. In this paper, we propose a learning-based data management scheme where the implementation is layered between sensors attached to physical attributes and domain-specific applications, thereby mitigating the data uncertainty between them. To do so, we present a sensor data management framework, namely D2WIN, which adopts reinforcement learning (RL) techniques to manage the data quality for CPS applications and autonomous systems. To deal with the scale issue incurred by many physical attributes and sensor streams when adopting RL, we propose an action embedding strategy that exploits their distance-based similarity in the physical space coordination. We introduce two embedding methods, i.e., a user-defined function and a generative model, for different conditions. Through experiments, we demonstrate that the D2WIN framework with the action embedding outperforms several known heuristics in terms of achievable data quality under certain resource restrictions. We also test the framework with an autonomous driving simulator, clearly showing its benefit. For example, with only 30% of updates selectively applied by the learned policy, the driving agent maintains its performance about 96.2%, as compared to the ideal condition with full updates.

EAGLE-A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio-temporal correlations. Most semantic approaches do not have spatio-temporal support. Some of them have attempted to provide full spatio-temporal support, but have poor performance for complex spatio-temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio-temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio-temporal computing in the linked sensor data context.

Optimizing 802.15.4 Outdoor IoT Sensor Networks for Aerial Data Collection

Rural IoT sensor networks, prevalent in environmental monitoring and precision agriculture, commonly operate over some variant of the IEEE 802.15.4 standard. Data collection from these networks is often challenging, as they may be deployed in remote regions where existing backhaul infrastructure is expensive or absent. With the commercial and industrial success of Unmanned Aircraft Systems (UAS), there is understandable interest in using UASs for delay tolerant data collection from 802.15.4 IoT sensor networks. In this study, we investigate how to optimize 802.15.4 networks for aerial data collection, which, unlike other wireless standards, has not received rigorous evaluation for three-dimensional aerial communication. We analyze experimental measurements from an outdoor aerial testbed, examining how factors, such as antenna orientation, altitude, antenna placement, and obstruction, affect signal strength and packet reception rate. In our analysis, we model and predict the quality of service for aerial data collection, based on these network configuration variables, and contrast that with the Received Signal Strength Indication (RSSI)—a commonly used signal strength metric. We find that network configuration plays a significant role in network quality, which RSSI, a mediator variable, struggles to account for in the presence of high packet loss. We conclude with a discussion of strategies for optimizing sensor network configuration for aerial data collection, in light of our results.

A Data-Based Framework for Identifying a Source Location of a Contaminant Spill in a River System with Random Measurement Errors

This study addresses the problem of identifying the source location of a contaminant spill in a river system when a sensor network returns observations containing random measurement errors. To solve this problem, we suggest a new framework comprising three main steps: (i) spill detection, (ii) data preprocessing, and (iii) source identification. Specifically, we applied a statistical process control chart to detect a contaminant spill with measurement errors while keeping the false alarm rate at less than or equal to a user-specified value. After detecting a spill, we generated a nonlinear regression model to estimate a breakthrough curve of the observations and derive a characteristic vector of the estimated curve. Using the characteristic vector as an input, a random forest model was constructed with the sensor raising the first alarm. The model provides output values between 0 and 1 to represent the possibility of each candidate location being the true spill source. These possibility values allow users to identify strong candidate locations for the spill. The accuracy of our framework was tested on part of the Altamaha River system in Georgia, USA.

Active Monitoring of Fatigue Crack in the Weld Zone of Bogie Frames Using Ultrasonic Guided Waves

The bogie frame is an important structure of railway vehicles, transmitting the traction, braking force, lateral force, and vertical force during the traction operation. With the development of high speeds and heavy loads, the appearance of fatigue cracks in the bogie frames is increasing, which reduces the driving life of railway vehicles and even causes serious traffic accidents. Real-time monitoring on the integrity of the bogie is an inevitable requirement for ensuring the safe operation of railway vehicles. In this paper, ultrasonic guided wave-based active structural health monitoring (SHM) was developed to identify the fatigue crack of the bogie frame. Experiments were conducted on a welded T-shape specimen with a thickness of 12 mm. A total of 10 piezoelectric lead zirconate titanate (PZT) disks were mounted around the weld zone of the specimen, five of which were used as actuators, and the other five were used as sensors. Five-peak modulation narrow-band sine waves were input into the actuators to excite the specimen. From the sensor signals, the advanced damage index (DI) was calculated to identify the propagation of the crack. The experimental results demonstrate that crack damage as small as 2 mm in the weld zone of the bogie frame can be successfully detected. Some practical issues for implementing the SHM in real applications, such as crack quantification and environmental compensation, were also discussed.

On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures

NarrowBand IoT (NB-IoT) is emerging as a promising communication technology offering a reliable wireless connection to a large number of devices employed in pervasive monitoring scenarios, such as Smart City, Precision Agriculture, and Industry 4.0. Since most of the NB-IoT transmissions occur in the uplink, the random access channel (that is the primary interface between devices and the base station) may usually become the main bottleneck of the entire system. For this reason, analytical models and simulation tools able to investigate its behavior in different scenarios are of the utmost importance for driving current and future research activities. Unfortunately, scientific literature partially addresses the current open issues by means of simplified and, in many cases, not standard-compliant approaches. To provide a significant step forward in this direction, the contribution of this paper is three-folded. First, it presents a flexible, open-source, and 3GPP-compliant implementation of the NB-IoT random access procedure. Second, it formulates an analytical model capturing both collision and success probabilities associated with the aforementioned procedure. Third, it presents the cross-validation of both the analytical model and the simulation tool, by taking into account reference applications scenarios of sensor networks enabling periodic reporting in monitoring infrastructures. Obtained results prove the remarkable accuracy, demonstrating a well-calibrated instrument, which will be also useful for future research activities.

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

Characterization of Path Loss and Large-Scale Fading for Rapid Intervention Team Communication in Underground Parking Garages

This paper reports the characterization of the 2.45-GHz-ISM-band radio wave propagation channel. Specifically, measurements were performed in an underground parking garage, with the aim of optimizing breadcrumb systems for a Rapid Intervention Team application. The effects of the high penetration loss and large reflections by the concrete reinforced building structure on the path loss and the large-scale fading were studied. Based on the analysis of the wireless channel, critical points for reliable communication between members of a Rapid Intervention Team were identified. In particular, attention was paid to dealing with large, spatially confined signal losses due to shadowing, the anticipation of corner losses and the ability of the system to operate on multiple floors.

Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands

The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology.

Tracking Clinical Staff Behaviors in an Operating Room

Inadequate staff behaviors in an operating room (OR) may lead to environmental contamination and increase the risk of surgical site infection. In order to assess this statement objectively, we have developed an approach to analyze OR staff behaviors using a motion tracking system. The present article introduces a solution for the assessment of individual displacements in the OR by: (1) detecting human presence and quantifying movements using a motion capture (MOCAP) system and (2) observing doors’ movements by means of a wireless network of inertial sensors fixed on the doors and synchronized with the MOCAP system. The system was used in eight health care facilities sites during 30 cardiac and orthopedic surgery interventions. A total of 119 h of data were recorded and analyzed. Three hundred thirty four individual displacements were reconstructed. On average, only 10.6% individual positions could not be reconstructed and were considered undetermined, i.e., the presence in the room of the corresponding staff member could not be determined. The article presents the hardware and software developed together with the obtained reconstruction performances.

Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors

Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.

Monitoring and Fault Location Sensor Network for Underground Distribution Lines

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.

Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

CMTN-SP: A Novel Coverage-Control Algorithm for Moving-Target Nodes Based on Sensing Probability Model in Sensor Networks

The non-consecutive coverage problem for the target nodes in Sensor Networks could lead to the coverage blind area and a large amount of redundant data, which causes the bottleneck phenomenon for the communication link. A novel Coverage Control Algorithm for Moving Target Nodes Based on Sensing Probability Model (CMTN-SP) is proposed in this work. Firstly, according to the probability theory, we derive the calculation method for the expectation of the coverage quality with multiple joint nodes, which aims to reduce the coverage blind area and improving network coverage rate. Secondly, we employ the dynamic transferring mechanism of the nodes to re-optimize the deployment of the nodes, which alleviates the rapid exhaustion of the proper network energy. Finally, it is verified via the results of the simulation that the network coverage quality could not only be improved by the proposed algorithm, but the proposed algorithm could also effectively curb the rapid exhaustion of the node energy.

On Metric Dimension in Some Hex Derived Networks

The concept of a metric dimension was proposed to model robot navigation where the places of navigating agents can change among nodes. The metric dimension md(G) of a graph G is the smallest number k for which G contains a vertex set W, such that |W|=k and every pair of vertices of G possess different distances to at least one vertex in W. In this paper, we demonstrate that md(HDN1(n))=4 for n≥2. This indicates that in these types of hex derived sensor networks, the least number of nodes needed for locating any other node is four.