Yueyue Zhang

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.

Joint energy and spectrum detection in cooperative cognitive radio under noise uncertainty

Spectrum sensing is a method which is used to find out whether the channel is idle or not in Cognitive Radio Network (CRN). Traditional methods are not much reliable due to the uncertain noise during the detection. In this paper, a weighted cooperative spectrum sensing strategy with jointing energy and spectrum-width detection under noise uncertainty is proposed to improve the detection performance. Firstly, secondary users use an energy double-threshold detector and spectrum bandwidth detector for local detection. Then the detection results will be sent to fusion center to make a final decision by fusing these results based on signal-noise-ratio (SNR) weight factor and majority rule. Theoretical analysis and simulation results indicate that this scheme can improve the spectrum sensing performance significantly.

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.

UWB-based indoor high precision localization system with robust unscented Kalman filter

In this paper, an indoor localization system is implemented, in which the distances are measured via by the Ultra-Wideband (UWB) and are utilized to determine positions of one blind node through trilateration localization algorithm. Additionally, to overcome the measurement errors caused by complex indoor environment, an Unscented Kalman Filter (UKF) algorithm is proposed to improve the accuracy of the localization results. Finally, the developed localization system is tested and the proposed algorithm is analyzed. It shows that the indoor localization system can achieve the positioning accuracy less than 10cm, providing a promising approach for high precision localization applications.

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.