International Journal of Sensor Networks (14 papers in press)
Recent Advances in Wireless Sensor Networks with Environmental Energy Harvesting
by Lei Shu, Wanjiun Liao, Jaime Lloret, Lei Wang
Joint Topology Control and Routing Design for Reconfigurable Ring-Tree Networks
by Chih-Min Yu, Chun-Chyuan Chen
Abstract: This article presents a joint topology control and routing design of Reconfigurable Ring-Tree (RRT) topology for Bluetooth non-uniform networks. The non-uniform network consists of one dense and many other sparse regions. In the dense area, the RRT builds a ring-shaped topology as a backbone subnet in a distributive manner, which is expanded by a tree-shaped topology to other, more sparse areas. For various sizes of networks, the size of the ring subnet is controlled by the trade-off between the network performance and the construction cost. Because corresponding nodes in the ring subnet do not procure the global computation situations, obtaining the optimal ring size is an NP-complete problem. In seeking to finalize the optimum ring size, an empirical max-search strategy is provided to attain the preferred cost-performance ratio. The max-search strategy is a methodical decision policy, carried out by three working elements: the topology construction, the packet routing and the maximum decision elements. The topology construction element engenders the ring-tree topology, the packet routing element processes the routing performance with a uniform traffic model, and the maximum decision element utilizes a decision-making criterion to discover the optimum ring size. Experimental values demonstrate that the optimum ring size can be resolved by the max-search scheme for various sizes of networks, and the RRT delivers a better throughput performance than that of the conventional BlueHRT and Bluetree networks.
Keywords: Bluetooth; Sensor networks; Topology configuration; Routing scheme.
Target Detection and Tracking Based on Information Geometry for Sensor Networks
by Hao Xu, Huafei Sun, Yongqiang Cheng
Abstract: Information geometry is becoming an important research field due to the various applications in statistical inferences, signal processing, neural networks and so on. In this paper, the application of information geometry is explored in anrnattempt to gain a better understanding of sensor system issues for target resolution and tracking in ground-to-air sensor networks. In particular, the Fisher information distance between two targets is used to measure the target resolvability in the region covered by the sensor system and is approximately calculated when it is close enough. And then, the accumulative information is proposed to analyze the resolution of the target on the same detection cellrnwith respect to one measurement model. Furthermore, the single step tracking method is presented based on Fisher information with single bearings-only sensor. The preliminary analysis results presented in this paper provide evidence that information geom- etry is able to offer consistent but more comprehensive means to understand and solve sensor network problems which are difficult to deal with via conventional analysis methods.
Keywords: Information geometry; information resolution; target detection; accumulative information; target tracking.
Design of Human Behavior Recognition Algorithm based on Wearable IMU Sensor
by Wei Zhuang, Yi Chen, Jian Su, Baowei Wang, Chunming Gao
Abstract: In recent years, with the rapid development of Inertial Measurement Unit (IMU) technology, wireless body area network and pattern recognition theory, human motion recognition based on wearable technology has gradually gained the attention of researchers. In this paper, the human behavior recognition method based on wearable sensor motion information fusion is studied. On the existing wearable system platform, the time domain analysis and frequency domain analysis of human motion information are used to distinguish the daily behavior of the human body, and based on the human motion data acquisition experiment, time domain features, frequency domain features and attitude angles of the human motion data are used as identification features. On the basis of it, multi-classification behavior recognition algorithm based on support vector machine is proposed and human motion pattern recognition is carried out. The experimental results show that the system can accurately identify the daily behavior of the human body.
Keywords: IMU; Wearable technology; Attitude angle estimation; Posture recognition; Support vector machine.
Local Outlier Detection Based on Information Entropy Weighting
by Lina Wang
Abstract: As a key research area in data mining technologies, outlier detection can expose data inconsistent with the majority in the dataset and therefore is applicable in extensive areas. The addition of entropy weighting to the spatial local outlier measure (SLOM) and local distance-based outlier factor (LDOF) algorithms in outlier data mining, i.e. the adoption of entropy in the calculation of weighted distance is taken into consideration, leads to enhanced accuracy of outlier detection and produces more expense of time. The algorithm of entropy-weighted LDOF is more optimized than that of entropy-weighted SLOM in terms of detection accuracy. The superiority of the entropy-weighted algorithm is verified through experimental results.
Keywords: Local outlier; detection; information entropy weighting; SLOM; LDOF.
Driving behavior recognition based on orientation and position deviations
by Wei Sun, Xiaorui Zhang, Xu Zhang, Xiaozheng He, Guoce Zhang
Abstract: This paper proposes a driving behavior recognition method, which applies vehicle orientation and position deviations to warn the driver against possible dangers. We integrate a gradient reinforcement method based on the linear discriminate analysis (LDA) to reinforce lane edges. An improved Canny operator based on adaptive threshold segmentation is exploited to extract the lane edges reliably. Based on an improved Hough transform algorithm, the reinforced lane edges help the detection of polar angle and polar radius of lanes that are used to calculate the vanishing point position. After that, the proposed method predicts current-frame lane parameters based on the previous-frame parameters through using the Kalman filter. Combining deviation angle and deviation distance, the proposed method categorizes vehicle lane-keeping behavior into three states: normal, left deviation, and right deviation. Experimental results of a variety of traveling scenes show that the proposed method outperforms other existing methods in precision.
Keywords: lane detection; vanishing point; Hough transform; Kalman filter; slope angle; offset distance; fatigue driving.
Multi-scale Residual Network for Energy Disaggregation
by Wan'an Liu, Liguo Weng, Min Xia, Yiqing Xu, Ke Wang, Zhuhan Qiao
Abstract: Energy disaggregation technology is a key technology to realize real-time non-intrusive load monitoring. Current energy disaggregation methods use the same scale to extract features from the sequence, which makes part of the local features lost, resulting in lower recognition accuracy of electrical appliances with low using frequency. Aiming at the low accuracy of non-intrusive energy disaggregation with low-frequency sampling, a non-intrusive sequential energy disaggregation method based on multi-scale residual network is proposed. Multi-scale residual network extracts multi-scale feature information through multi-scale convolution, and uses residual learning to deepen the network structure to further improve the performance of the algorithm. Through multi-scale convolution, more scale features are captured, and improve the recognition accuracy of electrical appliances with low using frequency. Sequence-to-Sequence energy disaggregation method can improve the disaggregation efficiency and ensure the efficiency of the algorithm. The experimental comparison results show that the model can get a better disaggregation effect, and can effectively identify the start-stop state of electrical appliances.
Keywords: Energy disaggregation; Deep learning; Residual learning; Multi-scale convolution; Non-intrusive load monitoring.
Distributed Spectrum-Sharing in Cognitive Ad Hoc Networks using Evolutionary Game Theory
by Yifei Wei, Bo Gu, Yali Wang, Mei Song, Xiaojun Wang
Abstract: As smart portable devices are becoming increasingly popular and autonomous, decentralized ad hoc networks have been widely applied. Meanwhile, to address the critical problem of spectrum congestion and inefficiency, cognitive radio (CR) are introduced. Cognitive radio ad hoc networks (CRAHNs) consist of a group of cognitive radios nodes connected in an ad hoc manner. Due to its decentralized architecture, unstable network topology, highly fluctuated available spectrum and various transmitting requirements, CRAHNs impose unique difficulties for spectrum sharing among CR users. A challenging and open question is how CR users could share vacant spectrum reasonably in CRAHNs without centralized control. Hence, we formulate the vacant spectrum sharing among CR users in CRAHNs with Evolutionary Game Theory (EGT). In the proposed game, we define the payoff of each CR user as a function consist of the achieved transmit rate and the produced interference to primary users. And we use replicator dynamics to model the strategy adaptation process. Simulation results suggest that the evolutionary equilibrium can be obtained through strategy adaptation and convergence is sensitive to the information latency. The fairness can be guaranteed in the EGT-based spectrum sharing.
Keywords: Spectrum-Sharing; Cognitive Networks; Evolutionary Game Theory.
Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models
by Lars Mikkelsen, Hans Peter Schwefel, Tatiana Madsen
Abstract: This paper obtains an improved estimator of number of people on the bus, based on probabilistic models. The improved estimator is based on a baseline estimator for number of WLAN enabled devices present on the bus. The estimated number of devices is subject to both false positives and false negatives. The false positives are caused by detecting devices on the roadside outside the bus and not being able to distinguish them from inside bus devices. The amount of false negatives depends on probe emission frequency, message losses due to collisions, MAC address randomization, WLAN channel selection and the time a person stays on a bus. The model proposed in the paper includes the influences of these factors assuming FP and FN being binomially distributed. Distribution parameters for false positives and false negatives are found from measurements.
Keywords: WLAN probes; bus occupancy; device count; stochastic models; public transport; devices per person; maximum likelihood estimator; MAC randomization; probe frequency; probe signal strength.
Optimizing Rendezvous-based Data collection for Target Tracking in WSNs with Mobile Elements
by Jian Zhang, Tianbao Wang, Jian Tang
Abstract: Wireless sensor networks (WSNs) applications have referred to many fieldsrninvolving target tracking systems, however, energy efficiency issues in applications always suffer bottleneck and hence continuously receive significant attention for recent decades. In the literature, naturally, a mobility collector is utilized as an energy-efficient solution to prolong the network lifetime, meanwhile, data collection strategies are investigated due to the factors including the amount of contributed data and the range of transmittedrndistances. For contributed data, the quantization technology plays an important role in the sense of energy efficiency. Considering the uncertainty of sensing data for nodes, we proposed a distributed algorithm for selecting an contributed group from intra-cluster members to gather data with rigorously mathematical analysis. We formulate our design for target tracking as a selection optimization problem, maximizing the utilization of the quality of contributed data by using information matrix. As a result, we proposed an optimization algorithm named rendezvous-based data collection(RDC) which not only integrates positive factors mentioned above to track a target but also maintains WSNsrnfunctions more prior than traditional clustering. Furthermore, two stages of WSNs are analyzed for data collection, i.e., sensing data and transmitting data as far as intra-cluster and inter-cluster. Simulations verify that the proposed schemes achieve network energy saving as well as energy balance in the framework of target tracking.
Keywords: Wireless sensor networks; Mobile collectors; Rendezvous nodes; Data collection; Fisher information matrix; Target tracking.
DVF-fog: Distributed Virtual Firewall in Fog Computing based on Risk analysis
by Ferdaous Kammoun-Abid, Amel Meddeb-makhlouf, Faouzi Zarai, Mohsen Guizani
Abstract: To eliminate network saturation during dada exchanges, Fog Computing is deployed as the technology that benefits from both Cloud computing and Internet of Things (IoT) paradigms. This new phenomena is called edge computing. In this study, we focus on network access control issues that are considered as grave challenges in a distributed environment such as fog/cloud computing.
Therefore, we present an architecture for distributed fog with a divided topology into Zones and implement distributed firewall and distributed Controller. This way, we can combine user-based access control and distributed network-based access control based on risk analysis and estimation. The performance of our work is evaluated via simulations using Nessi
Keywords: Fog computing; Access control; Distributed firewall; Risk analysis; Cooperative controller.
Online calibration of ultra-short baseline installation error in dynamic environment
by Liang Zhang, Tao Zhang, Jinwu Tong, Shaoen Wu
Abstract: Due to its advantages of small size and easy installation, the ultra-short base-line system is widely used in the navigation of ships and underwater vehicles. The installation error between the sensor and inertial measurement unit are the main sources of positioning inaccuracy. In high-precision navigation, the installation error is not negligible. A method based on Kalman filter is used to estimate the installation error of USBL in real time. The detailed derivation of Kalman filter for the calibration is presented in the paper. In order to show the validity of extending the installation error to the state of the filter, observability analysis is carried out. Simulation results show that the method proposed in this paper can calibrate the installation error between the ultra-short baseline and inertial measurement unit in real time and online. The positioning accuracy is improved five times by compensating the installation error. Therefore, the error calibration method proposed in this paper is effective and can greatly improve the positioning accuracy of USBL.
Keywords: Error Calibration; Kalman filter; Ultra-short baseline; Observability analysis.
A Border Surveillance System using WSN under Various Environment Characteristics
by Imen Arfaoui, Noureddine Boudriga
Abstract: Surveillance has become one of the promising application areas of wireless sensor networks (WSNs) where detecting and tracking an
intruder as it moves through a sensor network is becoming an increasingly important challenge. This motivate us to investigate the impact of some factors such as the area geography and the intruders behaviors that have influence on this application. For this,
we present in this paper an efficient surveillance system that uses
a WSN deployed inside a border area in order to detect an
intruder at least N times from its entrance. The proposed surveillance
system is examined under various environment characteristics, namely; obstacles free area, obstacles area and obstacles area with different intruders behaviors. To do this, we focus first on modeling the area into multi-thick lines architecture. Second, as a simple case study, we provide a deployment method that aims to detect the intruder from its entrance in an area without obstacles. Finally, we investigate the effect of the obstacles in the area to be monitored and we provide a general surveillance system for detection purpose that considers the intruders with different behaviors crossing an area with obstacles.
Keywords: WSNs; border surveillance; intruder; detect; multi-thick lines; obstacles.
A Tumor Perception System Based on a Multi-layer Mass-spring Model
by Xiaorui Zhang, Jiali Duan, Wei Sun, Sunil Kumar Jha
Abstract: The virtual reality (VR) has been utilized in the preoperative planning of tumor surgery because it enables surgeons to touch and operate on tumor in a virtual environment. This paper proposes a virtual tumor perception system for preoperative tumor surgery planning. The system is based on a novel multi-layer mass-spring model, which consists of connected springs in series. The basic parameters of springs are equal ratio sequence. The core parameter shear modulus of springs can be dynamically adjusted according to the biological properties of soft tissue in clinical applications to simulate tissue heterogeneity. A palpation simulation algorithm is designed to help users to perceive the tumors. Two validation experiments are carried out: model accuracy experiment and practical perception experiment. Experiments indicate that the proposed model faithfully mimics actual clinical tumor, which is more accurate compared with models simulated by other systems.
Keywords: tumor perception; virtual surgery; preoperative planning; mass-spring model; shear modulus adjusting; surgery planning; multi-layer springs; clinical application; palpation simulation; tissue heterogeneity.