Yaping Zhu

SDN-Based Anchor Scheduling Scheme for Localization in Heterogeneous WSNs

In this letter, the anchor scheduling scheme for localization in heterogeneous wireless sensor networks is studied. In order to minimize the number of actively participating anchors to prolong the network lifetime, we propose a centralized anchor scheduling scheme on the basis of the software-defined networking (SDN) paradigm. First, an expression evaluating the connectivity degree of an agent is derived and used to judge if this agent has desired number of connected anchors for its localization. Then, the state of each anchor is determined by the SDN controller through a flow table via sensor OpenFlow. Simulations show that the proposed anchor scheduling scheme reduces the number of active anchors and prolongs the network lifetime. It can also be shown that this scheme ensures the desired number of anchors for the localization, and can tradeoff the localization accuracy for energy by ensuring a better balance of energy consumption among minimum number of active anchors.

RSS-Based Localization in WSNs Using Gaussian Mixture Model via Semidefinite Relaxation

Energy-based source localization methods are normally developed according to the channel path-loss models in which the noise is generally assumed to follow Gaussian distributions. In this letter, we represent the practical additive noise by the Gaussian mixture model, and develop a localization algorithm based on the received signal strength to achieve a maximum likelihood location estimator. By using Jensen’s inequality and semidefinite relaxation, the initially proposed nonlinear and nonconvex estimator is relaxed into a convex optimization problem, which can be efficiently solved to obtain the globally optimal solution. Besides, the corresponding Cramer–Rao lower bound is derived for performance comparison. Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks.

A Software-Defined Network Based Node Selection Algorithm in WSN Localization

Localization technologies in wireless sensor networks have been suffering from great energy consumption problem due to the energy-constrained characteristic of the networks. Existing power allocating solutions are mostly distributed for the lack of global network knowledge. In this paper, we investigate localization algorithm with the support of software-defined network (SDN) technique and propose a localization node selection algorithm based on the cramer-rao lower bound (CRLB). By making use of the global network knowledge provided by the SDN controller, we formulate the issues into a 0-1 programming problem on the premise of energy satisfaction. Simulation results show that significant improvement in localization performance can be achieved with our proposed SDN based algorithm.

A hybrid indoor positioning system based on UWB and inertial navigation

The paper illustrates to integrate UWB technology with inertial navigation, and introduces the design of the hybrid indoor positioning system including server, gateway and sensor nodes. A linear regression matrix model is proposed to modify inertial navigation path. Least squares method is used to determine equations relating UWB trajectories to inertial navigation trajectories. Experimental results are shown to compare error distributions of hybrid algorithm and pure UWB solution.

Energy-efficient radio resource allocation in software-defined wireless sensor networks

The software-defined wireless sensor networks (SDWSNs) have been proposed recently to solve the energy limitation of sensor nodes and extend the lifetime of the wireless sensor networks by fast node reconstruction and dynamical resource allocation. In this study, the authors investigate an energy-efficient resource allocation algorithm in SDWSNs, in which radio resource allocation could be handled at central controllers with powerful storage and computation capacity. In this algorithm, the authors formulate an optimisation problem to minimise the energy consumption, under the individual constraint of quality of service. Then, the initial optimisation problem is transformed using semidefinite relaxation, to achieve centralised adaptive bandwidth and power allocation (CABPA). Additionally, two special cases are derived to reveal the performance of the CABPA. Furthermore, an OpenFlow-based scheme is proposed for information exchanging and updating to realise the centralised resource allocation. Meanwhile, a distributed scheme with limited information about the whole network is developed to serve as a performance benchmark for the CABPA in the SDWSN. Finally, the simulation results reveal that the proposed CABPA performs better than the other algorithms, and it balances the power and bandwidth utilisation.

Semidefinite programming-based localisation and tracking algorithm using Gaussian mixture modelling

In this study, the authors propose a semidefinite programming (SDP)-based localisation and tracking algorithm, which mitigates the non-line-of-sight (NLOS) error of range measurement and calibrates the accumulative error within the inertial sensing data. Both the range measurement in a mixed line-of-sight/NLOS environment and the step length estimated from inertial sensing information are approximated parametrically using Gaussian mixture modelling, and a maximum-likelihood estimator (MLE) is formulated to obtain the optimal position estimation. Since the Gaussian mixture models are non-linear functions of positions, the MLE is a non-convex problem, which global optimum is difficult to attain. Then, the non-convex MLE is transformed into an SDP-based localisation and tracking problem, relying on Jensens inequality and semidefinite relaxation. Thus, a sub-optimal solution to the original MLE can be achieved. Moreover, the Cramer-Rao lower bound is also derived to serve as a performance indicator for localisation errors. The simulation and experimental results demonstrate the performance of the proposed algorithm. Compared with the existing algorithms, the proposed algorithm owns the best localisation accuracy, and can achieve a sub-metre level accuracy to a root mean square error of 0.46 m in the real deployments.

Indoor Positioning and Tracking Using Particle Filters with Suboptimal Importance Density

Schemes combining Ultra-wide bandwidth (UWB) ranging technology and Inertial Measurement Unit (IMU) have been proposed for high precision positioning and tracking. However, positioning accuracy can be significantly affected by the non-line-of-sight (NLOS) UWB ranging measurements and cumulative inertial sensing error. In this paper, we model the ranging measurement error and the step length as Gaussian Mixture Model (GMM), respectively. Then, we derived a Suboptimal Importance Density (SID) for particle filters, which could resolve the degeneracy of particles and sample impoverishment. Finally, experimental results illustrate the performance gain of the particle filters with the proposed SID.

A single-anchor calibration indoor positioning system using heterogeneous sensors

This paper introduces an indoor positioning system which is built with heterogeneous sensors, i.e., inertial sensors and ultra wide band (UWB) ranging modules. It takes a hybrid approach with Inertial Navigation and UWB ranging method to explore their cooperative efforts. Besides, a novel hybrid algorithm for single-anchor positioning and calibration is proposed, which combines real-time inertial sensing data and UWB ranging measurements from single anchor. Additionally, the proposed hybrid algorithm is based on MMSE of user displacement, providing a significant deviation reducing for tracking. Finally, one experimentation is conducted to show that the hybrid algorithm can improve the accuracy of positioning by 47.2% compared to pure inertial solution, meanwhile its mean of the location errors is less than 1m. With the popularity of wearable devices with inertial sensors and wireless communication chips, such hybrid approach could be very promising for indoor positioning system.

A VLC-Based 3-D Indoor Positioning System Using Fingerprinting and K-Nearest Neighbor

In this paper, a three-dimensional (3-D) positioning system using fingerprinting and K-Nearest neighbor is proposed. The implementation of the proposed positioning system based on visible light communication (VLC) is discussed. On-off keying (OOK) modulation, Manchester coding and time division multiplexing (TDM) are utilized to obtain both lossless data and accurate received signal strength indications (RSSIs) without sacrificing the comfort of indoor illumination. The proposed 3-D positioning algorithm is conducted in three processes. Firstly, we quickly traverse all sampling points in the fingerprint database to find the nearest neighbor and choose candidate sampling points around the nearest neighbor. Secondly, we conduct an iterative search among candidate sampling points to find K-Nearest neighbors. Finally, we obtain estimated coordinates using weighted average method. Experimental result shows that, the proposed system possesses fast location and high accuracy.

A Semidefinite Relaxation Approach to Positioning in Hybrid Sensor Networks

Many applications can be benefit greatly from location- awareness information obtained by positioning and navigation system. Nowadays, a hybrid method combining Ultra-wide bandwidth (UWB) ranging modules and inertial measurement unit (IMU) has been one promising scheme for high precision positioning requirement. However, positioning errors could be terribly affected by the noise of UWB ranging measurements and accumulated errors caused by inertial sensing data. In this paper, a semidefinite programming (SDP) based node localization algorithm is proposed for such hybrid method. The positions of target sensors (TNs) can be determined using the distance estimations from location-aware anchor nodes (ANs) as well as other inertial information (e.g., acceleration and azimuth). Meanwhile, the corresponding Cramer-Rao lower bounds (CRLB) are derived as performance benchmarks. Finally, simulations are provided to illustrate the validity of our hybrid algorithm, which demonstrate that the proposed algorithm achieves superior performance and it could be very promising for high precision positioning service.