International Journal of Distributed Sensor Networks The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. SensorHUB: An IoT Driver Framework for Supporting Sensor Networks and Data Analysis Thu, 30 Jul 2015 13:07:34 +0000 The Internet of Things (IoT) is transforming the surrounding everyday physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage and is driven by various sensors. Sensors detect and measure changes in position, temperature, light, and many others; furthermore, they are necessary to turn billions of objects into data-generating “things” that can report on their status and often interact with their environment. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. This paper introduces the SensorHUB framework that utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and service development. SensorHUB is both a method and an environment to support IoT related application and service development; furthermore, it supports the data monetization approach, that is, provides a method to define data views on top of different data sources and analyzed data. The framework is available in a Platform as a Service (PaaS) model and has been applied for the vehicle, health, production lines, and smart city domains. László Lengyel, Péter Ekler, Tamás Ujj, Tamás Balogh, and Hassan Charaf Copyright © 2015 László Lengyel et al. All rights reserved. Low-Power Platform and Communications for the Development of Wireless Body Sensor Networks Thu, 30 Jul 2015 06:43:37 +0000 Although the roles of body sensor networks (BSNs) are similar to those carried out by the generic wireless sensor networks (WSNs), new solutions must be established to optimize communications for true pervasive biomedical monitoring transparent to the user. In this paper, a proposal of a hardware and software platform for biomedical sensors is performed, which is specially designed to minimize energy consumption in BSNs through a modular processing scheme based on the detection of events and information abstraction. The data flow is implemented through a novel communications protocol that enhances the performances of consumption and time delay of the platform. A novel aspect of the protocol is the explicit incorporation of an additional level of communications to support the distributed processing architecture that allows the execution of multiple applications in parallel within the smart sensors. The results obtained with an implementation of a smart sensor for fall detection demonstrate its feasibility as well as the viability of the communication protocol for the development of energy-efficient BSNs. David Naranjo-Hernández, Laura M. Roa, Javier Reina-Tosina, Miguel A. Estudillo-Valderrama, and Gerardo Barbarov Copyright © 2015 David Naranjo-Hernández et al. All rights reserved. A Hybrid Algorithm of GA + Simplex Method in the WSN Localization Wed, 29 Jul 2015 11:56:53 +0000 Localization provides the key support for wireless sensor networks (WSNs). In order to solve the large-error problem in the third phase and the poor position accuracy of the least square method in the weighted DV-Hop algorithm, a hybrid algorithm of GA + simplex method was proposed in this paper. The weighted DV-Hop position algorithm was applied to estimate the distance between the unknown node and the anchor node in the first and second phase. In the third phase, a hybrid genetic algorithm with the simplex method was proposed to optimize the coordinates of the unknown nodes. In the hybrid genetic algorithm, a fitness function which combined the cost function with the penalty function was built, and the simplex method was used to increase the local search ability of the algorithm. Experiments show that both of the localization accuracy and network coverage rate are improved significantly, and the hybrid algorithm of GA + simplex method is suitable for the WSN localization. Feng Wang, Cong Wang, ZiZhong Wang, and Xue-ying Zhang Copyright © 2015 Feng Wang et al. All rights reserved. Fast Channel Selection Strategy in Cognitive Wireless Sensor Networks Mon, 27 Jul 2015 13:35:34 +0000 In order to meet the practical requirement for Cognitive Wireless Sensor Networks applications, this paper proposes innovative fast channel selection algorithm to solve the shortcomings of original Experience-Weighted Attraction algorithm’s complexity, higher energy consuming, and the nodes’ hardware restrictions of real-time data processing capabilities. Research is conducted by comparing channel selection differences and timeliness with traditional Experience-Weighted Attraction learning. Though not as stable as traditional Experience-Weighted Attraction learning, fast channel selection algorithm has effectively reduced the complexity of the original algorithm and has superior performance than Q learning. Yong Sun and Jian-sheng Qian Copyright © 2015 Yong Sun and Jian-sheng Qian. All rights reserved. Multifloor Wi-Fi Localization System with Floor Identification Mon, 27 Jul 2015 13:20:41 +0000 Indoor localization is of great importance in pervasive applications and RSS fingerprint is known as a quite effective indoor location method. Floor attenuation might not give enough margin discrepancy to classify two neighboring floors, such as windows nearby or ring structure. Fingerprint location using the nearest Euclidean distance to the reference point can be interfered by the neighboring floor. In this paper, a multifloor localization framework with floor identification is proposed. The discriminative floor model is trained to maximize between-class scatter and floor identification is triggered by stair walk and elevator events. In experiments, a real dataset is collected in the building of six floors to evaluate our method. The results show that our method can identify accurate location in multifloor environment. Lin Sun, ZengWei Zheng, Tao He, and Fei Li Copyright © 2015 Lin Sun et al. All rights reserved. Design of Wireless Nanosensor Networks for Intrabody Application Mon, 27 Jul 2015 08:06:15 +0000 Emerging nanotechnology presents great potential to change human society. Nanoscale devices are able to be included with Internet. This new communication paradigm, referred to as Internet of Nanothings (IoNT), demands very short-range connections among nanoscale devices. IoNT raises many challenges to realize it. Current network protocols and techniques may not be directly applied to communicate with nanosensors. Due to the very limited capability of nanodevices, the devices must have simple communication and simple medium sharing mechanism in order to collect the data effectively from nanosensors. Moreover, nanosensors may be deployed at organs of the human body, and they may produce large data. In this process, the data transmission from nanosensors to gateway should be controlled from the energy efficiency point of view. In this paper, we propose a wireless nanosensor network (WNSN) at the nanoscale that would be useful for intrabody disease detection. The proposed conceptual network model is based on On-Off Keying (OOK) protocol and TDMA framework. The model assumes hexagonal cell-based nanosensors deployed in cylindrical shape 3D hexagonal pole. We also present in this paper the analysis of the data transmission efficiency, for the various combinations of transmission methods, exploiting hybrid, direct, and multi-hop methods. Suk Jin Lee, Changyong (Andrew) Jung, Kyusun Choi, and Sungun Kim Copyright © 2015 Suk Jin Lee et al. All rights reserved. Performance Evaluation of DDS-Based Middleware over Wireless Channel for Reconfigurable Manufacturing Systems Mon, 27 Jul 2015 07:46:19 +0000 Reconfigurable manufacturing systems (RMS) are rapidly becoming choice of production and manufacturing industry due to their quick adaptability to the ever-changing market demands while maintaining the quality and cost of the products. Such systems are usually decentralized in their monitoring and control and consist of heterogeneous components. Therefore, need arises for an interface that can mask the heterogeneity and provide smooth communication among these dissimilar components. Data Distribution Service (DDS) is a data-centric middleware standard based on Real-Time Publish/Subscribe (RTPS) protocol that fulfills the job of such interface in distributed systems. In this work, we present the idea of using DDS-based middleware over commonly used wireless channels like Bluetooth and Industrial WiFi to facilitate data communication in distributed control systems. A simulation model is developed to quantify various performance measures like latency, jitter, and throughput and to examine the suitability of aforementioned wireless channels in distributed monitoring and control environments. The model explores various communication scenarios based upon a practical case study. Obtained results serve as an empirical proof of concept that DDS can ensure reliable and timely data communication in firm real-time distributed control systems using common wireless channels and offer extensive control over various aspects of data transmission through its rich set of QoS policies. Basem Almadani, Muhammad Naseer Bajwa, Shuang-Hua Yang, and Abdul-Wahid A. Saif Copyright © 2015 Basem Almadani et al. All rights reserved. Restoration Strategy Based on Optimal Relay Node Placement in Wireless Sensor Networks Wed, 22 Jul 2015 07:55:41 +0000 In wireless sensor networks (WSNs), connecting disjoint segments is significant for network restoration, especially in some mission critical applications. However, the variability of distances between disjoint segments has tremendous influence on relay nodes deployment. In fact, finding the optimal solution for connecting disjoint segments in terms of the number and positions of relay nodes is NP-hard. To address this issue, plenty of heuristics, such as STP-MSP (Cheng et al., 2008), MST-1tRN (Lloyd et al., 2007), and CORP (Lee and Younis, 2010) are deeply pursued. In this paper, we propose a distributed restoration algorithm based on optimal relay node placement (simply, ORNP). It aims at federating separated segments by populating the minimum number of relay nodes in a WSN that has suffered a significant damage. In addition, both of complexity and upper bound of the relay count for ORNP are explored. The simulation results show that ORNP performs better than STP-MSP, MST-1tRN, and CORP in terms of relay count and the connectivity of resulting topology. Xiaoding Wang, Li Xu, and Shuming Zhou Copyright © 2015 Xiaoding Wang et al. All rights reserved. Advanced Big Data Management and Analytics for Ubiquitous Sensors Wed, 22 Jul 2015 06:40:01 +0000 Praveen Rao, Joonho Kwon, Sangjun Lee, and L. Venkata Subramaniam Copyright © 2015 Praveen Rao et al. All rights reserved. A Multiple Feature-Based Image-Switching Strategy in Visual Sensor Networks Tue, 21 Jul 2015 14:23:43 +0000 Generally, one fixed camera is used to take still or dynamic images and extract proper information from the captured images. However, the process of analyzing images through the use of one camera is very sensitive to neighboring environmental factors, such as illumination, background, and noise; thus, it is hard to guarantee precision. To extract proper information from images more precisely in visual sensor networks, this paper proposes an image-switching strategy where, among different types of installed cameras, the one camera best suited to neighboring circumstances is chosen. The proposed strategy is to first receive initial images as input data and then extract multiple features representing neighboring circumstances from the input images. Subsequently, it is to define the neighboring circumstances metric, which is the weighted sum of the extracted features, and to dynamically switch cameras to obtain images in accordance with the neighboring circumstances. The results of the experiment show that the proposed dynamic switching strategy reliably chooses, from among different cameras, the one camera that is best suited to the neighboring circumstances. Seok-Woo Jang and Gye-Young Kim Copyright © 2015 Seok-Woo Jang and Gye-Young Kim. All rights reserved. Distributed Box Particle Filtering for Target Tracking in Sensor Networks Tue, 21 Jul 2015 08:54:31 +0000 Distributed target tracking is a significant technique and is widely used in many applications. Combined with the interval analysis, box particle filtering (BPF) has been proposed to solve the problem of Bayesian filtering when the uncertainties in the measurements are intervals; that is, the measurements are interval-based vectors. This paper is targeted for extending the existing BPF based on a single sensor to a distributed sensor network. We propose a distributed BPF (d-BPF) that each sensor communicates with its direct neighbors to collaboratively estimate the states of the target. The feasibility of the proposed distributed BPF is justified, and some numerical simulations are presented to show its effectiveness in target tracking. Ying Liu and Hao Liu Copyright © 2015 Ying Liu and Hao Liu. All rights reserved. Approximation Algorithms for Maximum Link Scheduling under SINR-Based Interference Model Thu, 16 Jul 2015 13:55:55 +0000 A fundamental problem in wireless networks is the maximum link scheduling (MLS) problem. In this problem, interference is a key issue and past researchers have shown that determining reception using Signal-to-Interference plus Noise Ratio (SINR) is more realistic than graph-based interference models. Unfortunately, the MLS problem has been proven to be NP-hard for SINR interference models. To date, several approximation algorithms have been proposed to solve MLS under the SINR-based interference model. However, most of these works do not have either an approximation bound or a distributed version. To this end, we present a novel scheduling method with a constant approximation ratio which is much simpler and only 1/28 of it in past research. The improvement of constant also offers a better MLS set. In addition, based on our centralized method, we present a polynomial time, randomized, distributed algorithm, which only requires estimates of the number of links, and maximum and minimum link lengths. We prove its correctness and show that it can compute a MLS with time complexity of , where is an estimate of the number of links. Zi-Ao Zhou and Chang-Geng Li Copyright © 2015 Zi-Ao Zhou and Chang-Geng Li. All rights reserved. Path Prediction Method for Effective Sensor Filtering in Sensor Registry System Thu, 16 Jul 2015 09:21:54 +0000 The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and management, semantic interpretation and processing, and sensor searching and filtering in Wireless Sensor Networks (WSNs). Also, as the number of sensors in an IoT environment increases significantly, sensor filtering becomes more important. Many sensor filtering techniques have been researched. However most of them do not consider real-time searching and efficiency of mobile networks. In this paper, we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS). SRS is a sensor platform to register and manage sensor information for sensor filtering. We also propose a method for learning and predicting user paths based on the Collective Behavior Pattern. To improve prediction accuracy, we consider a time feature to measure weights and predict a path. We implement the method and the implementation and its evaluation confirm the improvement of time and accuracy for processing sensor information. Sukhoon Lee, Dongwon Jeong, Doo-Kwon Baik, and Dae-Kyoo Kim Copyright © 2015 Sukhoon Lee et al. All rights reserved. Energy-Aware Sink Node Localization Algorithm for Wireless Sensor Networks Thu, 16 Jul 2015 06:33:29 +0000 Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime. Mohamed Mostafa Fouad, Vaclav Snasel, and Aboul Ella Hassanien Copyright © 2015 Mohamed Mostafa Fouad et al. All rights reserved. Optimizing Sensor Locations in a Multisensor Single-Object Tracking System Thu, 16 Jul 2015 06:20:39 +0000 Tracking a mobile object presents many challenges, especially when the tracked object is autonomous or semiautonomous and may move unpredictably. The use of autonomous mobile sensor systems allows for greater opportunity to track the mobile object but does not always yield an estimate of the tracked object’s location that minimizes the estimation error. This paper presents a methodology to optimize the sensor system locations, given a single object and a fixed number of sensor systems, to achieve a position estimate that minimizes the estimation error. The tracking stations may then be controlled to achieve and maintain this optimal position, under position constraints. The theory predicts that given sensor systems and one object there is a sensor system configuration that will yield a position estimate that minimizes the estimation error. A mathematical basis for this theory is presented and simulation and experimental results for two and three sensor system cases are shown to illustrate the effectiveness of the theory in the laboratory. Jasmine Cashbaugh and Christopher Kitts Copyright © 2015 Jasmine Cashbaugh and Christopher Kitts. All rights reserved. Outage Analysis of Transmit Beamforming and Relay Selection with Outdated Channel Estimates over Nakagami- Fading Channels Tue, 14 Jul 2015 11:22:12 +0000 We investigate the joint effects of feedback delay and channel estimation errors (CEE) over Nakagami- fading channels for two-hop amplify-and-forward (AF) relaying systems with transmit beamforming (TB) and relay selection (RS). We derive new closed-form expressions for the system outage performance including the exact analysis and informative high SNR asymptotic approximations, which indicate that TB feedback delay reduces the achievable diversity order to one, regardless of Nakagami- fading parameters. Whereas RS delay reduces the diversity order to a case without relay selection and CEE merely result in coding gain loss, numerical results verify the theoretical analysis and illustrate that the outage performance is more sensitive to the TB feedback delay. Lei Wang, Yueming Cai, Liang Zhang, and Weiwei Yang Copyright © 2015 Lei Wang et al. All rights reserved. A DCT Regularized Matrix Completion Algorithm for Energy Efficient Data Gathering in Wireless Sensor Networks Tue, 14 Jul 2015 10:05:59 +0000 This paper presents a novel matrix completion algorithm to enable energy efficient data gathering in wireless sensor networks. The algorithm takes full advantage of both the low-rankness and the DCT compactness features in the sensory data to improve the recovery accuracy. The time complexity of the algorithm is analyzed, which indicates it has a low computational cost. Moreover, the recovery error is carefully analyzed and a theoretical upper bound is derived. The error bound is then validated by experimental results. Extensive experiments are conducted on three datasets collected from two testbeds. Experimental results show that the proposed algorithm outperforms state-of-the-art methods for low sampling rate and achieves a good recovery accuracy even if the sampling rate is very low. Kefu Yi, Jiangwen Wan, Tianyue Bao, and Lei Yao Copyright © 2015 Kefu Yi et al. All rights reserved. Model-Based Adaptive Iterative Hard Thresholding Compressive Sensing in Sensor Network for Volcanic Earthquake Detection Mon, 13 Jul 2015 11:50:09 +0000 Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for volcanic eruption detection, where the volcano-seismic signals were collected and processed by sensor nodes. However, it is faced with the limitation of energy resources and the transmission bottleneck of sensors in WSN. In this paper, a Model-Based Adaptive Iterative Hard Thresholding (MAIHT) compressive sensing scheme is developed, where a large number of inexpensive sensors are used to collect fine-grained, real-time volcano-seismic signals while a small number of powerful coordinator nodes process and pick arrival times of primary waves (i.e., P-phases). The paper contribution is two-fold. Firstly, a sparse measurement matrix with theoretical analysis of its restricted isometry property (RIP) is designed to simplify the acquisition process, thereby reducing required storage space and computational demands in sensors. Secondly, a compressive sensing reconstruction algorithm with theoretical analysis of its error bound is presented. Experimental results based on real volcano-seismic data collected from a volcano show that our method can recover the original seismic signal and achieve accurate P-phase picking based on the reconstructed seismic signal. Guojin Liu, Qian Zhang, Yuyuan Yang, Zhenzhi Yin, and Bin Zhu Copyright © 2015 Guojin Liu et al. All rights reserved. Compressed Sensing Based Apple Image Measurement Matrix Selection Mon, 13 Jul 2015 09:46:08 +0000 The purpose of this paper is to design a measurement matrix of apple image based on compressed sensing to realize low cost sampling apple image. Compressed sensing based apple image sampling method makes a breakthrough to the limitation of the Nyquist sampling theorem. By investigating the matrix measurement signal, the method can project a higher dimensional signal to a low-dimensional space for data compression and reconstruct the original image using less observed values. But this method requires that the measurement matrix and sparse transformation base satisfy the conditions of RIP or incoherence. Real time acquiring and transmitting apple image has great importance for monitoring the growth of fruit trees and efficiently picking apple. This paper firstly chooses sym5 wavelet base as apple image sparse transformation base, and then it uses Gaussian random matrices, Bernoulli random matrices, Partial Orthogonal random matrices, Partial Hadamard matrices, and Toeplitz matrices to measure apple images, respectively. Using the same measure quantity, we select the matrix that has best reconstruction effect as the apple image measurement matrix. The reconstruction PSNR values and runtime were used to compare and contrast the simulation results. According to the experiment results, this paper selects Partial Orthogonal random matrices as apple image measurement matrix. Ying Xiao, Wanlin Gao, Ganghong Zhang, and Han Zhang Copyright © 2015 Ying Xiao et al. All rights reserved. Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model Sun, 12 Jul 2015 09:38:27 +0000 Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge. Recently, with high classification precision and affordable complexity, one-class quarter-sphere support vector machine (QSSVM) has been introduced to deal with the online and adaptive outlier detection in WSN. Regarding the one-sided consideration of optimization or iterative updating algorithm for QSSVM model within current techniques, we have proposed comprehensive outlier detection methods in WSN based on the QSSVM algorithm. To reduce the complexity of optimization algorithm for QSSVM model in existing techniques, a fast optimization algorithm based on average Euclidean distance has been developed and employed to the comprehensive outlier detection method. Evaluated by real and synthetic WSN data sets, our methods have shown an excellent outlier detection performance, and they have been proved to meet the requirements of online adaptive outlier detection in the case of nonstationary detection tasks of WSN. Haiqing Yao, Heng Cao, and Jin Li Copyright © 2015 Haiqing Yao et al. All rights reserved. Trust, Security, and Privacy in Next-Generation Wireless Sensor Networks 2014 Sun, 12 Jul 2015 09:25:34 +0000 Muhammad Khurram Khan, Yang Xiang, Shi-Jinn Horng, and Hsiao-Hwa Chen Copyright © 2015 Muhammad Khurram Khan et al. All rights reserved. Development and Evaluation of Game-Based Learning System Using the Microsoft Kinect Sensor Sun, 12 Jul 2015 08:48:14 +0000 This paper investigates the capability of Kinect sensor as interactive technology and discusses how it can assist and improve teaching and learning. The Kinect sensor is a motion sensor that provides a natural user interface. It was implemented by Microsoft for the Xbox 360 video-game console to create a new control-free experience for the user without any intermediary device. The Kinect sensor can enhance kinesthetic pedagogical practices to profit learners with strong bodily-kinesthetic intelligence (body smart). As a learning tool, the Kinect sensor has potential to create interactive games, to increase learner motivation, and to enhance learning efficiency via its multimedia and multisensory capacity. Many students must learn spatial skills to improve learning achievement in science, mathematics, and engineering. This paper will focus on developing the Kinect sensor-assisted game-based learning system with ARCS model to provide kinesthetic pedagogical practices for learning spatial skills, motivating students, and enhancing students’ effectiveness. The responses to the System Usability Scale indicated that our system demonstrated usability and learnability. We conclude that the Kinect sensor-assisted learning system promotes the development of students’ spatial visualization skills and encourages them to become active learners. Chih-Hsiao Tsai, Yin-Hao Kuo, Kuo-Chung Chu, and Jung-Chuan Yen Copyright © 2015 Chih-Hsiao Tsai et al. All rights reserved. Spectrum-Efficient Wireless Sensor Networks Sun, 12 Jul 2015 07:13:45 +0000 Shensheng Tang, Yan Zhang, Liqiang Zhang, and Rong Yu Copyright © 2015 Shensheng Tang et al. All rights reserved. Algorithmic Methods in Wireless Sensor Network 2014 Sun, 12 Jul 2015 06:56:18 +0000 Haigang Gong, Dajin Wang, Shigeng Zhang, and Nianbo Liu Copyright © 2015 Haigang Gong et al. All rights reserved. Design and Experimentation of Piezoelectric Crystal Sensor Array for Grain Cleaning Loss Thu, 09 Jul 2015 09:24:19 +0000 In order to improve the accuracy and reliability for detection of grains cleaning loss, a piezoelectric crystal sensor array was proposed in full width of distribution to realize the online multipoints detection. The dynamic model for grain collision sensor array was evaluated by the method of experimental model analysis. The distribution of low-level vibration and deformation was tested for the sensitive elements of detection array using dynamic signal tester. The location and quantity of the piezoelectric crystal units were determined. The simulation was done based on the software ANSYS for the impacts of grains on the sensitive elements. A quantitative analysis was developed to correct the positions of the piezoelectric crystal units and the construction was adjusted according to the deformation contours. The exact positions were quantitatively analyzed and corrected based on the numerical results for the deformation placement, which was used for optimizing the array construction. Then, the grain impact experiment was established on the test bench. The results showed that the performance was stable for the piezoelectric sensor array, and output signal amplitude was about 2.5 V, with uniform sensitivity in the whole range. The present array sensor helps to provide a technical foundation for detection of grain cleaning loss. Jun Ni, Hanping Mao, Fangrong Pang, Yan Zhu, Xia Yao, and Yongchao Tian Copyright © 2015 Jun Ni et al. All rights reserved. Trajectory Data Mining in Distributed Sensor Networks Thu, 09 Jul 2015 07:04:36 +0000 Shaojie Qiao, Huidong (Warren) Jin, Yunjun Gao, Lu-An Tang, and Huanlai Xing Copyright © 2015 Shaojie Qiao et al. All rights reserved. Data Processing Techniques in Wireless Multimedia Sensor Networks Thu, 09 Jul 2015 06:45:54 +0000 Yun Liu, Qing-An Zeng, Ying-Hong Wang, and Jan Holub Copyright © 2015 Yun Liu et al. All rights reserved. A Vehicle Parking Detection Method Based on Correlation of Magnetic Signals Wed, 08 Jul 2015 11:22:03 +0000 Recently, significant research efforts have been focused on vehicle parking detection due to fuel consumption and traffic congestion. Many solutions have been successfully applied in indoor parking lots. However, due to the strong noise disturbance in outdoor parking environment, the detection accuracy for on-street parking is still a challenging task. In this paper, we propose a vehicle parking detection method by the use of normalized cross-correlation (NCC) of magnetic signals generated by magnetoresistive sensors. In the proposed method, the sensed signal is correlated with a reference. If the result is greater than a threshold, a pulse is generated. One of the primary factors that affect the accuracy of the NCC-based detection is the choice of reference which is obtained by using a k-means clustering algorithm in this paper. Compared with the-state-of-the-art vehicle detection methods, the proposed method is competitive in terms of cost, accuracy, and complexity. The proposed method is simulated and tested on the Xueyuan Boulevard, University Town of Shenzhen, Nanshan, Shenzhen, China. The experimental results show that the proposed method can provide the detection accuracy of 99.33% for arrival and 99.63% for departure. Hongmei Zhu and Fengqi Yu Copyright © 2015 Hongmei Zhu and Fengqi Yu. All rights reserved. Attendance Check System and Implementation for Wi-Fi Networks Supporting Unlimited Number of Concurrent Connections Wed, 08 Jul 2015 10:55:30 +0000 In the past, we used to call the name of members for the purpose of attendance checking by managers (or instructors). They verify the identity of member’s participation by human recognition using facial and voice matching. This approach is time-consuming because the number of members is getting increased. Moreover, they may have to recheck any of the students’ presence at the end of the period manually. In this research, we offer a convenient novel attendance checking method to take advantage of Wi-Fi 802.11x technology. Our application initiates AP mode Wi-Fi service for checking attendance of users in which a token is generated only to a person who is close to a manager. If a member has the token, the smart application of the member will connect and report to attendance server. Otherwise, the smart application of the member will report to the server that the users/students are not near the manager. By this way managers/instructors can easily check the member’s attendance. In addition, this research proposes a novel concept that unlimited number of devices can be supported. We make use of Wi-Fi scan (rather than connect) to the manager’s AP enabled smart devices, resulting in an enhanced scalability. Min Choi, Jong-Hyuk Park, and Gangman Yi Copyright © 2015 Min Choi et al. All rights reserved. A Key Management Method Based on Dynamic Clustering for Sensor Networks Wed, 08 Jul 2015 06:36:38 +0000 Many cluster-based routing protocols had been proposed which had rarely considered the network security issues so far. The existing key management methods have imperfection when they combine with cluster-based routing protocols. Normally cluster-based key management method has better performance than the distributed key management method, but most of the layer-cluster key management methods do not consider the problem of key updating and being captured for cluster heads. Considering the nodes’ capture probability, particle swarm optimization algorithm was used to optimize the clustering of sensor networks. A dynamic key management method was proposed to achieve key updating regularly and provided a security strategy for sensor networks to solve the problem of being captured for cluster heads. The simulation illustrates that the proposed key management method can achieve better security performance. Ying Zhang, Bingxin Zheng, Pengfei Ji, and Jinde Cao Copyright © 2015 Ying Zhang et al. All rights reserved.