Xiaobing Yuan

Design of small MEMS microphone array systems for direction finding of outdoors moving vehicles.

In this paper, a MEMS microphone array system scheme is proposed which implements real-time direction of arrival (DOA) estimation for moving vehicles. Wind noise is the primary source of unwanted noise on microphones outdoors. A multiple signal classification (MUSIC) algorithm is used in this paper for direction finding associated with spatial coherence to discriminate between the wind noise and the acoustic signals of a vehicle. The method is implemented in a SHARC DSP processor and the real-time estimated DOA is uploaded through Bluetooth or a UART module. Experimental results in different places show the validity of the system and the deviation is no bigger than 6° in the presence of wind noise.

An Efficient Clustering Protocol for Wireless Sensor Networks Based on Localized Game Theoretical Approach

Game theory has emerged as a brand new approach to model and analyse several problems of wireless sensor networks, such as routing, data collection, and topology control. Recently, a novel clustering mechanism called clustered routing for selfish sensors (CROSS) has been proposed based on game theory. The sensor nodes, which are modelled as players, join in a clustering game to campaign for cluster heads with an equilibrium probability. However, the CROSS algorithm needs the global information of how many nodes participate in the game at every round. Considering that this global way introduces much more packets exchange and energy consumption, we present a Localized game theoretical clustering algorithm (LGCA). In our protocol, each node selfishly plays a localized clustering game only with its neighbours within a communication radius R c . Moreover, exactly one node can successfully bid for a position of the cluster head in one district, thus achieving an optimal payoff. Simulation results show that our method achieves a better result compared with CROSS and LEACH in terms of network lifetime.

A Practicable Method for Ferromagnetic Object Moving Direction Identification

The direction of a moving target is an important piece of information in many wireless sensor network (WSN) applications, such as in boundary security, traffic flow control, etc. Due to its robustness, the magnetic sensor can be used to detect a passing ferromagnetic object. By using the orthogonality of two perpendicularly placed sensing units in a monomer magnetic sensor, a linear algorithm based on a magnetic dipole model to identify the ferromagnetic objects moving direction is introduced in this paper. It has been successfully applied in real WSN applications to reduce the numbers of nodes, and prolong the lifetime of the network. Both simulation and field experiments show it has strong noise immunity and more than 95% correction rate in direction detecting.

A Novel Energy-Efficient Cluster Formation Strategy: From the Perspective of Cluster Members

Traditional clustering methods mostly concentrate on how to choose nodes to serve as cluster heads. As for cluster formation, most papers assume that a normal node joins a nearest cluster head. However, this is not an optimal solution to form a good cluster. It is shown in this paper that a cluster member may not response to the advertisement of the closest cluster head but join a farther cluster head in order to achieve better energy efficiency or longer co-alive lifespan. Based on our new observation, a novel cluster formation strategy is proposed. Besides, simulation results also verify the correctness of our analysis.

Maximizing precision for energy-efficient data aggregation in wireless sensor networks with lossy links

Two main factors that impact the performance of data aggregation in wireless sensor networks (WSNs) are data quality and energy efficiency. This paper exploits the tradeoff between data quality and energy consumption to maximize the data aggregation precision under heterogeneous per-node energy constraints. Unlike previous work, we explicitly account for link loss in the optimization framework. To tackle link unreliability, we need to appropriately allocate the limited energy across the incoming and outgoing links of each individual node. We present a centralized algorithm based on the Immune-Genetic heuristic to find near-optimal energy allocation strategy such that the precision of the aggregated data received by the sink is maximized. The algorithmic complexity and implementation issues are also discussed. Furthermore, we develop a localized alternative algorithm based on the Gibbs sampler, which is more scalable and can adapt to large-scale distributed WSNs. Finally, we conduct numerical simulations to demonstrate the convergence as well as the data aggregation precision performance of the proposed algorithms.

Design of a Direction-of-Arrival Estimation Method Used for an Automatic Bearing Tracking System.

In this paper, we introduce a sub-band direction-of-arrival (DOA) estimation method suitable for employment within an automatic bearing tracking system. Inspired by the magnitude-squared coherence (MSC), we extend the MSC to the sub-band and propose the sub-band magnitude-squared coherence (SMSC) to measure the coherence between the frequency sub-bands of wideband signals. Then, we design a sub-band DOA estimation method which chooses a sub-band from the wideband signals by SMSC for the bearing tracking system. The simulations demonstrate that the sub-band method has a good tradeoff between the wideband methods and narrowband methods in terms of the estimation accuracy, spatial resolution, and computational cost. The proposed method was also tested in the field environment with the bearing tracking system, which also showed a good performance.

A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

Seismic signal is widely used in ground target classification due to its inherent characteristics. However, its propagation is highly dependent on local underlying geology. It means that nearly every one geographical environment requires a unique classifier. To resolve the problem, this paper presents a robust feature extraction method Log-Sigmoid Frequency Cepstral Coefficients (LSFCC) which evolves from Mel frequency cepstral coefficients (MFCC) for ground target classification by means of geophone. With the LSFCCs, the average classification accuracy of tracked and wheeled vehicle is more than 89% in three different geographical environments by only one classifier which is trained in one of the three environments.

Acoustic Source Localization via Subspace Based Method Using Small Aperture MEMS Arrays

Small aperture microphone arrays provide many advantages for portable devices and hearing aid equipment. In this paper, a subspace based localization method is proposed for acoustic source using small aperture arrays. The effects of array aperture on localization are analyzed by using array response (array manifold). Besides array aperture, the frequency of acoustic source and the variance of signal power are simulated to demonstrate how to optimize localization performance, which is carried out by introducing frequency error with the proposed method. The proposed method for 5 mm array aperture is validated by simulations and experiments with MEMS microphone arrays. Different types of acoustic sources can be localized with the highest precision of 6 degrees even in the presence of wind noise and other noises. Furthermore, the proposed method reduces the computational complexity compared with other methods.

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

An energy-balanced multi-sensor scheduling scheme for collaborative target tracking in wireless sensor networks:

Improving tracking quality and extending network lifespan are two main objects for target tracking, which are usually contradictory due to limited energy of sensor nodes in wireless sensor networks. This article incorporates this contradiction into a problem of multi-objective optimization in tracking networks where multiple sensor nodes are scheduled for collaborative target tracking by adopting the unscented Kalman filter algorithm. We propose an effective scheme to extend the lifespan of wireless sensor networks while guaranteeing preset tracking quality. More specifically, with regard to practical circumstances, we perform analysis on the target detection probability, as well as residual energy of sensor nodes, when selecting a suitable set of candidate sensor nodes. Then, we put forward a novel energy-balanced sensor nodes scheduling algorithm, Greedy Balance Replace Heuristic Algorithm, to select a near-optimal task sensor set from the candidate sensor node set to balance tracking quality and network lifetime. In addition, we also design an efficient multi-sensor node collaborative method to track a single target and to timely report its state to the remote end. From simulation results, it is demonstrated that the proposed node scheduling scheme can not only maintain the preset tracking accuracy but also extend network lifespan with a low computation complexity.