Journal of Sensors The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks Thu, 29 Sep 2016 12:41:09 +0000 Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively. Iman Almomani, Bassam Al-Kasasbeh, and Mousa AL-Akhras Copyright © 2016 Iman Almomani et al. All rights reserved. Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network Thu, 29 Sep 2016 11:19:44 +0000 In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF), longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network) to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects. Haorui Liu, Juan Yang, Heli Yang, and Fengyan Yi Copyright © 2016 Haorui Liu et al. All rights reserved. Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion Wed, 28 Sep 2016 14:09:52 +0000 Displacement responses of a bridge as a result of external loadings provide crucial information regarding structural integrity and current conditions. Due to the relative characteristic of displacement, the conventional measurement approach requires reference points to firmly install the transducers, while the points are often unavailable for bridges. In this paper, a displacement estimation approach using Kalman filter-based data fusion is proposed to provide a practical means for displacement measurement. The proposed method enables accurate displacement estimation by optimally utilizing acceleration and strain in combination that have high availability and are free from reference points for sensor installation. The Kalman filter is formulated using a state-space model representing the double integration of acceleration and model-based strain-displacement relationship. The validation of the proposed method is conducted successfully by a numerical simulation and a field experiment, which shows the efficacy and accuracy of the proposed approach in bridge displacement measurement. Soojin Cho, Jong-Woong Park, Rajendra P. Palanisamy, and Sung-Han Sim Copyright © 2016 Soojin Cho et al. All rights reserved. Real-Time Distributed Strain Monitoring of a Railway Bridge during Train Passage by Using a Distributed Optical Fiber Sensor Based on Brillouin Optical Correlation Domain Analysis Wed, 28 Sep 2016 10:44:38 +0000 This study demonstrates the monitoring of distributed strain of rail and girder of a railway bridge occurring during train passage over the bridge’s entire section on a real-time basis by applying a developed distributed optical fiber sensor based on Brillouin Optical Correlation Domain Analysis (BOCDA). The distributed optical fiber sensor system and an algorithm to control as well as to analyze Brillouin gain spectrum signals were also developed. A single-mode optical fiber was attached in the longitudinal direction on the rail and the lower flange of the girder to be used as a sensing fiber of the BOCDA system. Changes in the girder’s strain at the center point of the bridge during the passage of a commercial train were measured at 9 Hz, and the accuracy of this measurement was validated by comparing the measured data with the data from strain gauge. In addition, the distributed strain of a girder and the rail with a length of 40.26 m was measured in real time with a spatial resolution of 31.1 cm. Based on the results of the rail’s strain distribution, the study could identify the location where excessive strain occurred due to an influence of unsupported sleepers on girder of the bridge. Hyuk-Jin Yoon, Kwang-Yong Song, Chanyong Choi, Hee-Seoung Na, and Jung-Seok Kim Copyright © 2016 Hyuk-Jin Yoon et al. All rights reserved. Detection and Tracking of Road Barrier Based on Radar and Vision Sensor Fusion Mon, 26 Sep 2016 13:01:40 +0000 The detection and tracking algorithms of road barrier including tunnel and guardrail are proposed to enhance performance and reliability for driver assistance systems. Although the road barrier is one of the key features to determine a safe drivable area, it may be recognized incorrectly due to performance degradation of commercial sensors such as radar and monocular camera. Two frequent cases among many challenging problems are considered with the commercial sensors. The first case is that few tracks of radar to road barrier are detected due to material type of road barrier. The second one is inaccuracy of relative lateral position by radar, thus resulting in large variance of distance between a vehicle and road barrier. To overcome the problems, the detection and estimation algorithms of tracks corresponding to road barrier are proposed. Then, the tracking algorithm based on a probabilistic data association filter (PDAF) is used to reduce variation of lateral distance between vehicle and road barrier. Finally, the proposed algorithms are validated via field test data and their performance is compared with that of road barrier measured by lidar. Taeryun Kim and Bongsob Song Copyright © 2016 Taeryun Kim and Bongsob Song. All rights reserved. A Study on Water Pollution Source Localization in Sensor Networks Mon, 26 Sep 2016 12:43:00 +0000 The water pollution source localization is of great significance to water environment protection. In this paper, a study on water pollution source localization is presented. Firstly, the source detection is discussed. Then, the coarse localization methods and the localization methods based on diffusion models are introduced and analyzed, respectively. In addition, the localization method based on the contour is proposed. The detection and localization methods are compared in experiments finally. The results show that the detection method using hypotheses testing is more stable. The performance of the coarse localization algorithm depends on the nodes density. The localization based on the diffusion model can yield precise localization results; however, the results are not stable. The localization method based on the contour is better than the other two localization methods when the concentration contours are axisymmetric. Thus, in the water pollution source localization, the detection using hypotheses testing is more preferable in the source detection step. If concentration contours are axisymmetric, the localization method based on the contour is the first option. And, in case the nodes are dense and there is no explicit diffusion model, the coarse localization algorithm can be used, or else the localization based on diffusion models is a good choice. Jun Yang and Xu Luo Copyright © 2016 Jun Yang and Xu Luo. All rights reserved. Validation Techniques for Sensor Data in Mobile Health Applications Sun, 25 Sep 2016 13:51:55 +0000 Mobile applications have become a must in every user’s smart device, and many of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output. Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, and Natalia Díaz Rodríguez Copyright © 2016 Ivan Miguel Pires et al. All rights reserved. Development of a Decision Making Algorithm for Traffic Jams Reduction Applied to Intelligent Transportation Systems Sun, 25 Sep 2016 09:27:16 +0000 This paper is aimed at developing a decision making algorithm for traffic jams reduction that can be applied to Intelligent Transportation Systems. To do so, these algorithms must address two main challenges that arise in this context. On one hand, there are uncertainties in the data received from sensor networks produced by incomplete information or because the information loses some of the precision during information processing and display. On the other hand, there is the variability of the context in which these types of systems are operating. More specifically, Analytic Hierarchy Process (AHP) algorithm has been adapted to ITS, taking into account the mentioned challenges. After explaining the proposed decision making method, it is validated in a specific scenario: a smart traffic management system. David Gómez, José-Fernán Martínez, Juana Sendra, and Gregorio Rubio Copyright © 2016 David Gómez et al. All rights reserved. Supporting Business Privacy Protection in Wireless Sensor Networks Tue, 20 Sep 2016 09:22:41 +0000 With the pervasive use of wireless sensor networks (WSNs) within commercial environments, business privacy leakage due to the exposure of sensitive information transmitted in a WSN has become a major issue for enterprises. We examine business privacy protection in the application of WSNs. We propose a business privacy-protection system (BPS) that is modeled as a hierarchical profile in order to filter sensitive information with respect to enterprise-specified privacy requirements. The BPS aims at solving a tradeoff between metrics that are defined to estimate the utility of information and the business privacy risk. We design profile, risk assessment, and filtration agents to implement the BPS based on multiagent technology. The effectiveness of our proposed BPS is validated by experiments. Nan Feng, Zhiqi Hao, Sibo Yang, and Harris Wu Copyright © 2016 Nan Feng et al. All rights reserved. Improving the Lane Reference Detection for Autonomous Road Vehicle Control Tue, 20 Sep 2016 09:08:18 +0000 Autonomous road vehicles are increasingly becoming more important and there are several techniques and sensors that are being applied for vehicle control. This paper presents an alternative system for maintaining the position of autonomous vehicles without adding additional elements to the standard sensor architecture, by using a 3D laser scanner for continuously detecting a reference element in situations in which the GNSS receiver fails or provides accuracy below the required level. Considering that the guidance variables are more accurately estimated when dealing with reference points in front of and behind the vehicle, an algorithm based on vehicle dynamics mathematical model is proposed to extend the detected points in cases where the sensor is placed at the front of the vehicle. The algorithm has been tested when driving along a lane delimited by New Jersey barriers at both sides and the results show a correct behaviour. The system is capable of estimating the reference element behind the vehicle with sufficient accuracy when the laser scanner is placed at the front of it, so the robustness of the control input variables (lateral and angular errors) estimation is improved making it unnecessary to place the sensor on the vehicle roof or to introduce additional sensors. Felipe Jiménez, Miguel Clavijo, José Eugenio Naranjo, and Óscar Gómez Copyright © 2016 Felipe Jiménez et al. All rights reserved. Fiber Bragg Grating Sensors-Based In Situ Monitoring and Safety Assessment of Loess Tunnel Tue, 20 Sep 2016 09:03:41 +0000 Compared with electrical strain gauges, fiber Bragg grating (FBG) sensing technology is a relatively novel method for tunnel structural health monitoring, which has a number of advantages including high accuracy, multiplexing, electromagnetic interference resistance, and good repeatability. In order to study the internal force of the tunnel liner and detect the potential safety hazards, series of strain monitoring tests of a loess tunnel, taking into account the complex stress and strain variation of the loess during tunnelling, were performed by employing the tandem linear FBG sensor arrays controlled by the wavelength division multiplexing (WDM) technology. The concrete strain has obvious linear characteristics over time in the early stage and then gradually tends to a stable value. Moreover, after the necessary temperature compensation, loess tunnel structure safety was assessed through the analysis of real-time strain and internal force of the liner concrete, and the FBG monitoring data and safety assessment results indicate that the safety factors of various liner sections all meet the code requirements, which verify the safety and stability of the tunnel liner structure. The FBG sensors-based in situ monitoring technology can be well applied in the loess tunnel structure safety assessment. Jinxing Lai, Junling Qiu, Haobo Fan, Qian Zhang, Zhinan Hu, Junbao Wang, and Jianxun Chen Copyright © 2016 Jinxing Lai et al. All rights reserved. Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images Tue, 20 Sep 2016 08:58:34 +0000 Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting robots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper heading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene due to the camera’s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features acquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to probabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the appropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple feature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was identified. Jong-Hwan Kim, Seongsik Jo, and Brian Y. Lattimer Copyright © 2016 Jong-Hwan Kim et al. All rights reserved. R-bUCRP: A Novel Reputation-Based Uneven Clustering Routing Protocol for Cognitive Wireless Sensor Networks Tue, 20 Sep 2016 08:53:53 +0000 Energy of nodes is an important factor that affects the performance of Wireless Sensor Networks (WSNs), especially in the case of existing selfish nodes, which attracted many researchers’ attention recently. In this paper, we present a reputation-based uneven clustering routing protocol (R-bUCRP) considering both energy saving and reputation assessment. In the cluster establishment phase, we adopt an uneven clustering mechanism which controls the competitive scope of cluster head candidates to save the energy of WSNs. In the cluster heads election phase, the residual energy and reputation value are used as the indexes to select the optimal cluster head, where the reputation mechanism is introduced to support reputation assessment. Simulation results show that the proposed R-bUCRP can save node energy consumption, balance network energy distribution, and prolong network lifetime. Mingchuan Zhang, Ruijuan Zheng, Ying Li, Qingtao Wu, and Liang Song Copyright © 2016 Mingchuan Zhang et al. All rights reserved. A Passenger Flow Risk Forecasting Algorithm for High-Speed Railway Transport Hub Based on Surveillance Sensor Networks Tue, 20 Sep 2016 08:49:44 +0000 Passenger flow risk forecasting is a vital task for safety management in high-speed railway transport hub. In this paper, we considered the passenger flow risk forecasting problem in high-speed railway transport hub. Based on the surveillance sensor networks, a passenger flow risk forecasting algorithm was developed based on spatial correlation. Computational results showed that the proposed forecasting approach was effective and significant for the high-speed railway transport hub. Zhengyu Xie and Yong Qin Copyright © 2016 Zhengyu Xie and Yong Qin. All rights reserved. Characteristics of Eddy Current Distribution in Carbon Fiber Reinforced Polymer Tue, 20 Sep 2016 08:38:15 +0000 The paper studies the characteristics of eddy current (EC) distribution in carbon fiber reinforced polymer (CFRP) laminates so as to guide the research and operation of eddy current testing of CFRP. To this end, an electromagnetic field computation model of EC response to CFRP based on the finite element method is developed. Quantitative analysis of EC distribution in plies of unidirectional CFRP reveals that EC changes slowly along the fiber direction due to the strong electrical anisotropy of the material. Variation of EC in plies of multidirectional CFRP is fast in both directions. The attenuation of EC in the normal direction in unidirectional CFRP is faster than that in isotropic material due to faster diffusion of EC. In multidirectional CFRP, EC increases near the interfaces of plies having different fiber orientations. The simulation results are beneficial to optimizing sensor design and testing parameters, as well as damage detection and evaluation. Shaoni Jiao, Jian Li, Fei Du, Lei Sun, and Zhiwei Zeng Copyright © 2016 Shaoni Jiao et al. All rights reserved. WDARS: A Weighted Data Aggregation Routing Strategy with Minimum Link Cost in Event-Driven WSNs Tue, 20 Sep 2016 08:37:47 +0000 Realizing the full potential of wireless sensor networks (WSNs) highlights many design issues, particularly the trade-offs concerning multiple conflicting improvements such as maximizing the route overlapping for efficient data aggregation and minimizing the total link cost. While the issues of data aggregation routing protocols and link cost function in a WSNs have been comprehensively considered in the literature, a trade-off improvement between these two has not yet been addressed. In this paper, a comprehensive weight for trade-off between different objectives has been employed, the so-called weighted data aggregation routing strategy (WDARS) which aims to maximize the overlap routes for efficient data aggregation and link cost issues in cluster-based WSNs simultaneously. The proposed methodology is evaluated for energy consumption, network lifetime, throughput, and packet delivery ratio and compared with the InFRA and DRINA. These protocols are cluster-based routing protocols which only aim to maximize the overlap routes for efficient data aggregation. Analysis and simulation results revealed that the WDARS delivered a longer network lifetime with more proficient and reliable performance over other methods. Omar Adil Mahdi, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris, Ammar Abu Znaid, Yusor Rafid Bahar Al-Mayouf, and Suleman Khan Copyright © 2016 Omar Adil Mahdi et al. All rights reserved. Accelerometer Sensor Specifications to Predict Hydrocarbon Using Passive Seismic Technique Tue, 20 Sep 2016 07:42:36 +0000 The ambient seismic ground noise has been investigated in several surveys worldwide in the last 10 years to verify the correlation between observed seismic energy anomalies at the surface and the presence of hydrocarbon reserves beneath. This is due to the premise that anomalies provide information about the geology and potential presence of hydrocarbon. However a technology gap manifested in nonoptimal detection of seismic signals of interest is observed. This is due to the fact that available sensors are not designed on the basis of passive seismic signal attributes and mainly in terms of amplitude and bandwidth. This is because of that fact that passive seismic acquisition requires greater instrumentation sensitivity, noise immunity, and bandwidth, with active seismic acquisition, where vibratory or impulsive sources were utilized to receive reflections through geophones. Therefore, in the case of passive seismic acquisition, it is necessary to select the best monitoring equipment for its success or failure. Hence, concerning sensors performance, this paper highlights the technological gap and motivates developing dedicated sensors for optimal solution at lower frequencies. Thus, the improved passive seismic recording helps in oil and gas industry to perform better fracture mapping and identify more appropriate stratigraphy at low frequencies. M. H. Md Khir, Atul Kumar, and Wan Ismail Wan Yusoff Copyright © 2016 M. H. Md Khir et al. All rights reserved. A Novel Differential Log-Companding Amplifier for Biosignal Sensing Mon, 19 Sep 2016 10:05:09 +0000 We proposed a new method for designing the CMOS differential log-companding amplifier which achieves significant improvements in linearity, common-mode rejection ratio (CMRR), and output range. With the new nonlinear function used in the log-companding technology, this proposed amplifier has a very small total harmonic distortion (THD) and simultaneously a wide output current range. Furthermore, a differential structure with conventionally symmetrical configuration has been adopted in this novel method in order to obtain a high CMRR. Because all transistors in this amplifier operate in the weak inversion, the supply voltage and the total power consumption are significantly reduced. The novel log-companding amplifier was designed using a 0.18 μm CMOS technology. Improvements in THD, output current range, noise, and CMRR are verified using simulation data. The proposed amplifier operates from a 0.8 V supply voltage, shows a 6.3 μA maximum output current range, and has a 6 μW power consumption. The THD is less than 0.03%, the CMRR of this circuit is 74 dB, and the input referred current noise density is . This new method is suitable for biomedical applications such as electrocardiogram (ECG) signal acquisition. Zigang Dong, Xiaolin Zhou, and Yuanting Zhang Copyright © 2016 Zigang Dong et al. All rights reserved. Routing Algorithm with Uneven Clustering for Energy Heterogeneous Wireless Sensor Networks Sun, 18 Sep 2016 08:50:10 +0000 Aiming at the “hotspots” problem in energy heterogeneous wireless sensor networks, a routing algorithm of heterogeneous sensor network with multilevel energies based on uneven clustering is proposed. In this algorithm, the energy heterogeneity of the nodes is fully reflected in the mechanism of cluster-heads’ election. It optimizes the competition radius of the cluster-heads according to the residual energy of the nodes. This kind of uneven clustering prolongs the lifetime of the cluster-heads with lower residual energies or near the sink nodes. In data transmission stage, the hybrid multihop transmission mode is adopted, and the next-hop routing election fully takes account of the factors of residual energies and the distances among the nodes. The simulation results show that the introduction of an uneven clustering mechanism and the optimization of competition radius of the cluster-heads significantly prolonged the lifetime of the network and improved the efficiency of data transmission. Ying Zhang, Wei Xiong, Dezhi Han, Wei Chen, and Jun Wang Copyright © 2016 Ying Zhang et al. All rights reserved. Corrigendum to “A Novel Improved Probability-Guided RANSAC Algorithm for Robot 3D Map Building” Sun, 18 Sep 2016 07:29:45 +0000 Songmin Jia, Ke Wang, Xiuzhi Li, and Tao Xu Copyright © 2016 Songmin Jia et al. All rights reserved. Design of Tunnel Magnetoresistive-Based Circular MFL Sensor Array for the Detection of Flaws in Steel Wire Rope Thu, 15 Sep 2016 13:37:53 +0000 Tunnel magnetoresistive (TMR) devices have superior performances in weak magnetic field detection. In this study, TMR devices were first employed to form a circular magnetic flux leakage (MFL) sensor for slight wire rope flaw detection. Two versions of this tailor-made circular TMR-based sensor array were presented for the inspection of wire ropes with the diameters of 14 mm and 40 mm, respectively. Helmholtz-like coils or a ferrite magnet-based magnetizer was selected to provide the proper magnetic field, in order to meet the technical requirements of the TMR devices. The coefficient of variance in the flaw detection performance of the sensor array elements was experimentally estimated at 4.05%. Both versions of the MFL sensor array were able to detect multiple single-broken wire flaws in the wire ropes. The accurate axial and circumferential positions of these broken wire flaws were estimated from the MFL scanning image results. In addition, the proposed TMR-based sensor array was applied to detect the MFL signal induced by slight surface wear defects. A mutual correlation analysis method was used to distinguish the signals caused by the lift-off fluctuation from the MFL scanning image results. The MFL sensor arrays presented in this study provide inspiration for the designing of tailor-made TMR-based circular sensor arrays for cylindrical ferromagnetic structural inspections. Liu Xiucheng, Wang Yujue, Wu Bin, Gao Zhen, and He Cunfu Copyright © 2016 Liu Xiucheng et al. All rights reserved. Positioning Error Analysis of Ranging-Mode Using AIS Signals in China Thu, 15 Sep 2016 07:42:36 +0000 In order to provide resilient position, navigation, and time (PNT) information for -Navigation, the ranging-mode (R-Mode) positioning using automatic identification system (AIS) signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK) demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP) for time of arrival (TOA) and time difference of arrival (TDOA) used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation. Kai Zheng, Qing Hu, and Jingbo Zhang Copyright © 2016 Kai Zheng et al. All rights reserved. Faulty Line Selection Method for Distribution Network Based on Variable Scale Bistable System Tue, 06 Sep 2016 11:06:53 +0000 Since weak fault signals often lead to the misjudgment and other problems for faulty line selection in small current to ground system, this paper proposes a novel faulty line selection method based on variable scale bistable system (VSBS). Firstly, VSBS is adopted to analyze the transient zero-sequence current (TZSC) with different frequency variety scale ratio and noise intensity, and the results show that VSBS can effectively extract the variation trends of initial stage of TZSC. Secondly, TZSC is input to VSBS for calculation with Runge-Kutta equations, and the output signal is chosen as the characteristic currents. Lastly, correlation coefficients of every line characteristic current are used as the index to a novel faulty line selection criterion. A large number of simulation experiments prove that the proposed method can accurately select the faulty line and extract weak fault signals in the environment with strong noise. Xiaowei Wang, Jie Gao, Guobing Song, Qiming Cheng, Xiangxiang Wei, and Yanfang Wei Copyright © 2016 Xiaowei Wang et al. All rights reserved. Dual-Layer Density Estimation for Multiple Object Instance Detection Mon, 05 Sep 2016 13:12:56 +0000 This paper introduces a dual-layer density estimation-based architecture for multiple object instance detection in robot inventory management applications. The approach consists of raw scale-invariant feature transform (SIFT) feature matching and key point projection. The dominant scale ratio and a reference clustering threshold are estimated using the first layer of the density estimation. A cascade of filters is applied after feature template reconstruction and refined feature matching to eliminate false matches. Before the second layer of density estimation, the adaptive threshold is finalized by multiplying an empirical coefficient for the reference value. The coefficient is identified experimentally. Adaptive threshold-based grid voting is applied to find all candidate object instances. Error detection is eliminated using final geometric verification in accordance with Random Sample Consensus (RANSAC). The detection results of the proposed approach are evaluated on a self-built dataset collected in a supermarket. The results demonstrate that the approach provides high robustness and low latency for inventory management application. Qiang Zhang, Daokui Qu, Fang Xu, Kai Jia, and Xueying Sun Copyright © 2016 Qiang Zhang et al. All rights reserved. The Fuzzy Feedback Scheduling of Real-Time Middleware in Cyber-Physical Systems for Robot Control Thu, 01 Sep 2016 16:04:41 +0000 Cyber-physical systems for robot control integrate the computing units and physical devices, which are real-time systems with periodic events. This work focuses on CPS task scheduling in order to solve the problem of slow response and packet loss caused by the interaction between each service. The two-level fuzzy feedback scheduling scheme is designed to adjust the task priority and period according to the combined effects of the response time and packet loss. Empirical results verify the rationality of the cyber-physical system architecture for robot control and illustrate the feasibility of the fuzzy feedback scheduling method. Feng Tang, Ping Zhang, and Fang Li Copyright © 2016 Feng Tang et al. All rights reserved. WildSense: Monitoring Interactions among Wild Deer in Harsh Outdoor Environments Using a Delay-Tolerant WSN Thu, 01 Sep 2016 16:03:54 +0000 Biologists and ecologists often monitor the spread of disease among deer in the wild by using tracking systems that record their movement patterns, locations, and interaction behavior. The existing commercial systems for monitoring wild deer utilize collars with GPS sensors, deployed on captured and rereleased deer. The GPS sensors record location data every few hours, enabling researchers to approximate the interaction behavior of tracked deer with their GPS locations. However, the coarse granularity of periodically recorded GPS location data provides only limited precision for determining deer interaction behavior. We have designed a novel system to monitor wild deer interaction behavior more precisely in harsh wilderness environments. Our system combines the functionalities of both GPS and RF-radio sensors with low-cost and minimal-resource motes. We designed and built our system to be able to operate robustly for a period of up to several months for continual tracking and monitoring of the locations and interaction behaviors of wild deer in harsh environments. We successfully deployed six deer collars on six wild deer that were captured and rereleased in the Soapstone Prairie Natural Area of northern Colorado over a one-month period. In this paper, we describe how we designed and built this system and evaluate its successful operation in a wilderness area. Junho Ahn, Akshay Mysore, Kati Zybko, Caroline Krumm, Sravan Thokala, Xinyu Xing, Ming Lian, Richard Han, Shivakant Mishra, and Thompson Hobbs Copyright © 2016 Junho Ahn et al. All rights reserved. Measurement Axis Searching Model for Terrestrial Laser Scans Registration Wed, 31 Aug 2016 16:13:57 +0000 Nowadays, terrestrial Lidar scans can cover rather a large area; the point densities are strongly varied because of the line-of-sight measurement principle in potential overlaps with scans taken from different viewpoints. Most of the traditional methods focus on registration algorithm and ignore searching model. Sometimes the traditional methods are directly used to align two point clouds; a large critically unsolved problem of the large biases will be created in areas distant from the overlaps while the local overlaps are often aligned well. So a novel measurement axis searching model (MASM) has been proposed in this paper. The method includes four steps: the principal axis fitting, the measurement axis generation, low-high-precision search, and result generation. The principal axis gives an orientation to the point cloud; the search scope is limited by the measurement axis. The point cloud orientation can be adjusted gradually until the achievement of the global optimum using low- and high-precision search. We perform some experiments with simulated point clouds and real terrestrial laser scans. The results of simulated point clouds have shown the processing steps of our method, and the results of real terrestrial laser scans have shown the sensitivity of the approach with respect to the indoor and outdoor scenes. Shaoxing Hu, Aiwu Zhang, and Sicheng Liu Copyright © 2016 Shaoxing Hu et al. All rights reserved. System-Aware Smart Network Management for Nano-Enriched Water Quality Monitoring Mon, 29 Aug 2016 09:49:48 +0000 This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management Subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI) scheme which is proposed through integrating an association rule learning algorithm with fuzzy logic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH) in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes. B. Mokhtar, M. Azab, N. Shehata, and M. Rizk Copyright © 2016 B. Mokhtar et al. All rights reserved. Low Complexity Signed Response Based Sybil Attack Detection Mechanism in Wireless Sensor Networks Sun, 28 Aug 2016 12:32:56 +0000 Security is always a major concern in wireless sensor networks (WSNs). Identity based attacks such as spoofing and sybil not only compromise the network but also slow down its performance. This paper proposes a low complexity sybil attack detection scheme, that is, based on signed response (SRES) authentication mechanism developed for Global System for Mobile (GSM) communications. A probabilistic model is presented which analyzes the proposed authentication mechanism for its probability of sybil attack. The paper also presents a simulation based comparative analysis of the existing sybil attack schemes with respect to the proposed scheme. It is observed that the proposed sybil detection scheme exhibits lesser computational cost and power consumption as compared to the existing schemes for the same sybil detection performance. M. Saud Khan and Noor M. Khan Copyright © 2016 M. Saud Khan and Noor M. Khan. All rights reserved. A UEP LT Codes Design with Feedback for Underwater Communication Sun, 28 Aug 2016 12:08:28 +0000 To satisfy the performance requirement of LT codes with Unequal Erasure Protection (UEP) in underwater environment, the Weighted Expanding Window Fountain (WEWF) code is proposed in this paper. The WEWF codes can achieve strong UEP property by nonuniformly selecting input symbols within each window. To overcome the disadvantages in terms of redundancy in the lower prioritized segments, Correlation Chain Feedback (CCFB) is also introduced to help the transmitter to precisely adjust the encoding scheme. Asymptotic analysis and simulation results demonstrate that the proposed approach can achieve lower symbol error rate and less overall redundancy in the underwater acoustic sensor networks. Danfeng Zhao, Jie Wen, and Jiaxi Si Copyright © 2016 Danfeng Zhao et al. All rights reserved.