In this paper, we propose SAGENet that utilizes only binaural echoes (i.e., for scenarios when vision perception seriously degrades) for scene depth estimation. Unlike previous methods that implicitly learn spatial features from echoes, which may cause shape and scale drift, SAGENet explicitly extracts spatial cues, effectively enhancing depth estimation accuracy. First, we leverage signal processing to generate coarse 2D geometric cues, which contain scene scale and shape information, as additional input for the 3D depth estimation network. This approach aids the network in better reconstructing depth information from the scene. Given the substantial noise in the 2D geometric cues, we design a geometric cue consistency denoising loss function to help the network accurately interpret the scale and shape information embedded in the features. Second, we initialize learnable queries with angular spectrum peaks and fuse them with audio features via self-attention to guide the network to focus on the first few reflections echo dominant feature, while effectively suppressing interference from reverberation. Finally, Our experimental results on the Replica and real-world BatVision datasets show that the proposed method outperforms the existing binaural echo-based methods (including BatVision) by more than 5% and 10% in absolute relative error, respectively. To benefit the community, we open-source the code at https://github.com/zjuersdsd/SAGENet.git.

Acoustic indoor positioning technologies are gaining attention for their low cost and high accuracy. However, most ranging-based localization technologies suffer from significant distance measurement errors caused by Non-Line-of-Sight (NLOS) signal propagation, making the accurate identification of NLOS signals crucial for achieving precise localization. Existing NLOS identification methods either rely on carefully crafted handcrafted features or perform poorly in cross-scenario environments. This paper presents a novel NLOS identification algorithm that automatically extracts key features from dynamic acoustic signals for end-to-end recognition. Dual-channel data is collected using a smartphone’s microphones, and a trainable fractional Fourier transform (FrFT) is applied for feature extraction, followed by NLOS identification using a convolutional neural network (CNN) based on the ResNet architecture. Experimental results show that the proposed method outperforms existing algorithms, achieving the best performance metrics in cross-scenario experiments with an average accuracy of 99.34%.

High-accuracy time of arrival (TOA) estimation is essential for indoor acoustic localization, but traditional TOA estimation methods often fail to obtain accurate and robust TOA estimation performance due to severe multipath challenges, such as peak extraction by general cross correlation (GCC). To address the problem, this article presents a novel acoustic TOA estimation technique called FDME-RATE, a high-accuracy acoustic ranging system. The proposed method utilizes GCC to determine the coarse signal arrival time, followed by an enhanced frequency modulated continuous wave (FMCW) technique to convert the various arrival paths into distinct frequencies within the spectrum. In addition, we employ an adaptive multiple signal classification (AMUSIC) algorithm to achieve high-resolution TOA estimation. Through simulations and experiments conducted in a reverberant corridor, the results indicate that our method achieves an average TOA ranging error of just 0.16 and 0.19 m at the 95% confidence level across a 30-m span. Within a 20-m range, there is only a centimeter-level error. Additionally, in non line-of-sight (NLOS) scenarios, our proposed method further enhances TOA-based ranging accuracy by an additional 0.3 m over the same distance. The experimental results also demonstrate that our method outperforms traditional methods and recent research in terms of both estimation accuracy and robustness.

High-accuracy Time of Arrival (TOA) estimation is a crucial requirement for indoor acoustic localization. However, conventional approaches to TOA estimation based on Generalized Cross Correlation (GCC) suffer from insufficient resolution in severe multipath environments, which hinders their ability to achieve robust ranging. In this paper, we propose a novel and robust acoustic TOA estimation method based on Frequency Modulated Continuous Wave (FMCW) that selects the first-path signal. Our method transforms multipath signals into the frequency domain using several novel signal processing techniques of FMCW, which enhances the resolution of multipath identification and makes the estimation more stable. Simulation and experimental results show that, compared to other methods, our method can achieve centimeter-level TOA ranging estimation over a distance range of 16 m. Furthermore, our proposed method can enhance the accuracy of TOA estimation-based ranging by approximately 4.2 cm in non-line-of-sight (NLOS) scenarios.

Compressed sensing (CS) has been identified as a good candidate for user detection in grant-free non-orthogonal multiple access (NOMA) by exploiting the inherent sparsity of user activity. However, most of the existing CS-based user detection schemes do not fully utilize the temporal correlation of user activity in NOMA and rely heavily on the unrealistic assumption that the number of active users is known in advance. To address these issues, we propose a temporal correlation enhanced multiuser detection scheme to achieve efficient and pragmatic multiuser detection. First, using 1-bit memory to piggyback the information on whether the active users still have data to transmit, the base station can realize that the active users in the current time slot will turn to be silent or remain active. Then, to make explicit use of the temporal correlation of active user sets, a cross validation based adaptive subspace pursuit (CVASP) algorithm is developed by utilizing the reported information on prior active users. The proposed CVASP is a highly practical algorithm that does not require any prior knowledge of the number of active users or the noise level, as the cross validation technique could properly determine the stopping condition. Extensive simulation results demonstrate that the proposed mechanism could achieve almost the same performance as compared to the existing state of art CS-based multiuser detection algorithms while eliminating the need for any prior knowledge.

Fusion positioning technology requires stable and effective positioning data, but this is often challenging to achieve in complex Non-Line-of-Sight (NLoS) environments. This paper proposes a fusion positioning method that can achieve stable and no hop points by adjusting parameters and predicting trends, even with a one-sided lack of fusion data. The method combines acoustic signal and Inertial Measurement Unit (IMU) data, exploiting their respective advantages. The fusion is achieved using the Kalman filter and Bayesian parameter estimation is performed for tuning IMU parameters and predicting motion trends. The proposed method overcomes the problem of fusion failure caused by long-term unilateral data loss in traditional fusion positioning. The positioning trajectory and error distribution analysis show that the proposed method performs optimally in severe NLoS experiments.

Incentive mechanisms are essential for stimulating adequate worker participation to achieve good truth discovery performance in mobile crowdsensing (MCS) systems. However, most of existing incentive mechanisms only consider compensating workers’ sensing cost, while the cost incurred by potential privacy leakage has been largely neglected. Moreover, none of existing privacy-preserving incentive mechanisms has incorporated workers’ different privacy preferences to provide personalized payments for them. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, named Paris-TD, which provides personalized payments for workers as a compensation for privacy cost while achieving accurate truth discovery. The basic idea is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses to sign a contract which specifies a privacy-preserving degree (PPD) and the corresponding payment the worker will receive if she submits perturbed data with that PPD. Specifically, we respectively design a set of optimal contracts analytically under both full and incomplete information models, which maximize the truth discovery accuracy under a given budget, while satisfying the individual rationality and incentive compatibility properties. The feasibility and effectiveness of Paris-TD are validated through experiments on both synthetic and real-world datasets.

Truth discovery is an effective tool to unearth truthful answers in crowdsourced question answering systems. Incentive mechanisms are necessary in such systems to stimulate worker participation. However, most of existing incentive mechanisms only consider compensating workers' resource cost, while the cost incurred by potential privacy leakage has been rarely incorporated. More importantly, to the best of our knowledge, how to provide personalized payments for workers with different privacy demands remains uninvestigated thus far. In this paper, we propose a contract-based personalized privacy-preserving incentive mechanism for truth discovery in crowdsourced question answering systems, named PINTION, which provides personalized payments for workers with different privacy demands as a compensation for privacy cost, while ensuring accurate truth discovery. The basic idea is that each worker chooses to sign a contract with the platform, which specifies a privacy-preserving level (PPL) and a payment, and then submits perturbed answers with that PPL in return for that payment. Specifically, we respectively design a set of optimal contracts under both complete and incomplete information models, which could maximize the truth discovery accuracy, while satisfying the budget feasibility, individual rationality and incentive compatibility properties. Experiments on both synthetic and real-world datasets validate the feasibility and effectiveness of PINTION.

It is a fundamental issue to achieve efficient information collection in Internet of Things (IoT), where random (channel) access plays an indispensable role, especially when coordination among IoT end nodes is unachievable. Compressive sensing (CS) has been widely used in random access to facilitate energy efficient and accurate data collection. However, a joint sparsity structure, which commonly exists among signals acquired by different end nodes, has been long ignored by existing CS-based random access schemes, leading to insufficient energy efficiency and accuracy. In this article, capitalizing on this joint sparsity structure, we propose a structured compressive random access (SCRA) mechanism in order to achieve maximum energy efficiency with accuracy guarantee for data collection. Specifically, we first model the data loss induced by packet collisions during random access as an independent CS measurement process for each node, where the corresponding CS projection matrix is determined by the data loss pattern. Furthermore, in order to control the amount of data transmitted in the channel and alleviate the packet collisions, we employ the concept of sensing probability to perform random subsampling at each end node before transmission, where the optimal sensing probability is derived. Finally, we propose to jointly recover the set of original signals at all nodes based on the concept of group sparsity by formulating the data collection process as a single-measurement-vector problem in CS. The evaluation results validate the effectiveness of SCRA in utilizing the joint sparsity structure to obtain superior performance compared to the benchmark methods.

Radio-frequency identification (RFID) has pervasive applications in building ultralow-power ubiquitous networks, where backscatter communication during tag identification is neither reliable nor efficient. Inspired by the sparsity that only a few RFID tags communicate with the reader simultaneously, many compressed sensing (CS)-based schemes have been proposed to exploit the colliding tag responses to facilitate efficient tag identification. However, most of them suffer from huge ID search space and signature collision during the CS recovery process. To address these issues, we propose SCRIC, a novel sparsity-controlled RFID identification scheme using a random signature assignment, which achieves a faster and more robust identification performance. Specifically, to tackle identification failure caused by severe signature collision, we assign each active tag an access probability to control the sparsity and reduce signature collision. Theoretical analysis is given to prove that the signature collision probability of the proposed scheme is reduced compared with the existing random signature scheme and an optimal access probability is derived. Moreover, considering that conventional CS recovery algorithm relies heavily on the unpractical assumption that the active tag number is known precisely in advance, we integrate the cross validation (CV) into CS recovery algorithms and propose a greedy algorithm called the CV-based orthogonal matching pursuit (OMP-CV), which can reduce the tag identification false alarm rate without any prior knowledge. Extensive experimental results show that the proposed mechanism significantly outperforms the existing CS-based tag identification methods in terms of identification speed and robustness to noise.

Base stations deployed for localization are required to be location aware. Traditional techniques for calibrating base stations need to be done manually, which is time-consuming and inconvenient. To address this problem, Semidefinite Programming (SDP) -based approaches have been developed, which can achieve high precision. However, existing approaches mostly focus on the 2-dimensional (2-D) scenarios where base stations must be restricted to be of the same height. In this paper, we propose a 3-dimensional (3-D) SDP-based method to determine the positions of base stations given inaccurate even incomplete distance information and apply it to large indoor environments. The corresponding Cramer-Rao lower bounds (CRLB) are derived to ensure localization quality. We investigate the performance of this method on problems with sensing range. Simulation results verify that our method can achieve superior performance in terms of accuracy and robustness compared with traditional noncooperative localization techniques.

Compressive sensing (CS) has been widely used in the Internet of Things (IoT) to achieve efficient information collection. However, existing works have mainly focused on utilizing CS to lower the sampling rate or reduce the number of transmissions, without explicitly accounting for the heterogeneity of energy consumption in IoT environments. In this paper, we propose a CS-based prejudiced random sensing strategy (PRSS) that explicitly considers the heterogeneous energy consumption of IoT sensor nodes at different locations, in order to accurately attain a desired tradeoff between the overall energy consumption and the sensing accuracy. Specifically, each sensor node participates in sensing via distributed random access based on an assigned sensing probability, which is determined by its energy consumption in sending the sensed data, data collision rate and its contribution to recovery accuracy. We employ the statistical restricted isometry property as a practical indicator of the recovery accuracy and derive a sufficiently good recovery error bound based on it. Accordingly, we devise a novel convex optimization framework to find the most energy-efficient sensing probability assignment strategy with accuracy guarantee. We evaluate the PRSS using real-world sea surface temperature data traces. Comparative simulations corroborate that the PRSS can significantly reduce energy consumption and prolong network lifetime without sacrificing sensing accuracy.

We present a novel Fast Direct Source Localization (FDSL) approach for acoustic isotropic sensor array applications. Unlike previous approximate maximum likelihood (AML) approach, the proposed FDSL focuses on the phase shift caused by time delays among sensors and obtains an analytical result without exhaustive search for all the possible locations. Using this phase shift model, conventional phase uncertainty problem can be solved by a light-weight linear search in the limited phase space, which greatly reduces the computational complexity of array processing. Theoretical analysis of the FDSL has been proposed and compared with the Cramér Rao Bound (CRB) of the AML approach. Simulations using real bird call data validate the advantage of proposed method.

We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), and field experiments using a Voxnet system validate the proposed method and demonstrate a significant performance enhancement at a low computation burden.

A one-bit joint sparse representation direction of arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization allow significant reduction of data to be transmitted to the fusion center without additional energy consumption requirement in the source coding/compression operation. Compared with existing compressive sensing-based DOA estimation methods, the superiority of the proposed approach in providing data volume reduction and performance improvement is verified by both simulations and field experiments using a prototype wireless sensor array network platform.

Using low duty-cycle is the most common technique to extend the system lifetime in WSNs. However, it also implies limited throughput and long delay and the penalty is even higher under variable traffic patterns. In this paper, we present iQueue-MAC, a hybrid CSMA/TDMA MAC that adapts to variable/bursty traffic. With light load, iQueue-MAC uses a contention-based CSMA mechanism that provides low delay with scattered transmissions. When traffic increases, detected by a forming backlog in the sender, iQueue-MAC changes to a contention-free TDMA mechanism allocating transmission slots. Thus, iQueue-MAC mitigates packet buffering and reduces packet delay, combining the best of TDMA and CSMA. In this paper we also show how iQueue-MAC can operate in both single and multi channel modes. We implemented it on SIM32W108 chips together with other reference WSN protocols for comparison. iQueue-MAC exhibits similar figures during light traffic. However, with bursty traffic its throughput can be five times that of CoSenS and Ri-MAC-MC and its delay 20 times lower. Finally, iQueue-MAC is able to effectively use multiple channels, duplicating its throughput when compared to single channel operation.

Strict quality of service requirements of industrial applications, challenged by harsh environments and huge interference especially in multi-vendor sites, demand incorporation of cognition in industrial wireless sensor networks (IWSNs). In this paper, a distributed protocol of light complexity for congestion regulation in cognitive IWSNs is proposed to improve the channel utilization while ensuring predetermined performance for specific devices, called primary devices. By sensing the congestion level of a channel with local measurements, a novel congestion control protocol is proposed by which every device decides whether it should continue operating on the channel, or vacate it in case of saturation. Such a protocol dynamically changes the congestion level based on variations of non-stationary wireless environment as well as traffic demands of the devices. The proposed protocol is implemented on STM32W108 chips that offer IEEE 802.15.4 standard communications. Experimental results confirm substantial performance enhancement compared to the original standard, while imposing almost no signaling/computational overhead. In particular, channel utilization is increased by 56% with fairness and delay guarantees. The presented results provide useful insights on low-complexity adaptive congestion control mechanism in IWSNs.

End-to-end delay measurement has been an essential element in the deployment of real-time services in networked systems. Traditional methods of delay measurement based on time domain analysis, however, are not efficient as the network scale and the complexity increase. We propose a novel theoretical framework to analyze the end-to-end delay distributions of networked systems from the frequency domain. We use a signal flow graph to model the delay distribution of a networked system and prove that the end-to-end delay distribution is indeed the inverse Laplace transform of the transfer function of the signal flow graph. Two efficient methods, Cramer’s rule-based method and the Mason gain rule-based method, are adopted to obtain the transfer function. By analyzing the time responses of the transfer function, we obtain the end-to-end delay distribution. Based on our framework, we propose an efficient method using the dominant poles of the transfer function to work out the bottleneck links of the network. Moreover, we use the framework to study the network protocol performance. Theoretical analysis and extensive evaluations show the effectiveness of the proposed approach.

Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user's preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by text mining, we model a user's daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users' impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.

Abstract Wormhole attack is a severe attack that can be easily mounted on a wide range of wireless networks without compromising any cryptographic entity or network node. In the wormhole attack, an attacker sniffs packets at one point in the network and tunnels them through the wormhole link to another point. Such kind of attack can deteriorate the localization procedure in wireless sensor networks. In this paper, we first analyze the impacts of the wormhole attack on the localization procedure. Then, we propose a secure localization scheme against the wormhole attacks called SLAW including three phases: wormhole attack detection, neighboring locators differentiation, and secure localization. The main idea of the SLAW is to build a so‐called conflicting set for each locator based on the abnormalities during the message exchanges, which can be used to differentiate the dubious locators to achieve secure localization. We first consider the simplified system model in which there is no packet loss and all the nodes have the same transmission range. We further consider the general system model where the packet loss exists and different types of nodes have different transmission radii. We conduct the simulations to illustrate the effectiveness of the proposed secure localization scheme and compare it with the existing schemes under different network parameters. Copyright © 2014 John Wiley & Sons, Ltd.

Node localization is a key issue for wireless sensor networks (WSNs). The triangulation method and the maximum likelihood (ML) estimator are usually adopted for angle of arrival (AOA) based node localization in WSNs. However, the localization accuracy of the triangulation is low, and the ML estimator requires a good initialization close to the true location to avoid the divergence problem. In this paper, we develop two efficient closed-form AOA based localization algorithms derived from effective auxiliary variables based method. First, we formulate the node localization problem as a linear least squares problem using auxiliary variables. Based on its closed-form solution, a new auxiliary variables based pseudo-linear estimator (AVPLE) is developed. Then, we further propose an auxiliary variables based total least square (AVTLS) estimator to improve the localization accuracy. In addition, we investigate the impact of the orientation of the unknown node on estimation performance of the new algorithms. Simulation results demonstrate that the new algorithms achieve much higher localization accuracy than the triangulation method and also avoid local minima and divergence problem in ML estimator. Moreover, the AVTLS estimator has higher localization accuracy than the AVPLE, and its localization accuracy remains robust when the orientation angle of the unknown node varies from 0 to 180 degrees.

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection), the performance of which is highly related with locations of sensor nodes. Existing work on barrier coverage mainly assume that sensor nodes have accurate location information, however, little work explores the effects of location errors on barrier coverage. In this paper, we study the barrier coverage problem when sensor nodes have location errors and deploy mobile sensor nodes to improve barrier coverage if the network is not barrier covered after initial deployment. We analyze the relationship between the true distance and the measured distance of two stationary sensor nodes and derive the minimum number of mobile sensor nodes needed to connect them with a guarantee when nodes location errors. Furthermore, we propose a fault tolerant weighted barrier graph, based on which we prove that the minimum number of mobile sensor nodes needed to form barrier coverage with a guarantee is the length of the shortest path on the graph. Simulation results validate the correctness of our analysis.

The joint sparse representation of array covariance matrices (JSRACM) approach in direct position transforms the source position estimation problem into a spatial sparse signal representation (SSSR) optimization problem. With a novel binary sparse indicative vector (SIV) representing the support of joint SSSR of array covariance matrices, this SIVR-JSRACM algorithm presents high resolution, low computing complexity without knowing the number of sources and initial source positions estimates in advance. However, its performance degrades significantly along with the number of sources increases. To overcome this shortcoming, we proposed an improved joint sparse representation of array covariance matrices (IJSRACM) algorithm. The main contribution of this paper is that we estimate the elements in K-sparse covariance matrix of the potential sources. Thus we could get a new dictionary for SIV. The simulation results demonstrate that the SIVR-IJRSACM algorithm has superior localization accuracy and strong robust to noise under different numbers of sources and also remains the advantages of the SIVR-JSRACM algorithm listed above.

In this work, compressive sensing (CS) is applied to facilitate efficient wireless information transmission over lossy communication links. Inherently sparse data packets are transmitted without compression or error protection. The packet loss during transmission is modeled as a random sampling process of the transmitted data. The original signal then is reconstructed based on correctly received data packets using CS-based reconstruction method. No computations for source, channel coding or random measurement sampling will be required at the transmitter side. Thus, this method is suitable for applications where transmitters have extreme low power constraints such as wireless sensor networks. Compared with traditional error protection technique(automatic repeat request, data interleaving and interpolation), the proposed method delivers higher quality of sparse signal while significantly reducing energy consumption at transmitter as well as transmission latency.

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection) where directional sensors (e.g., cameras) are becoming more popular than omni-directional scalar sensors (e.g., microphones). However, barrier coverage cannot be guaranteed after initial random deployment of sensors, especially for directional sensors with limited sensing angles. In this paper, we study how to efficiently use mobile sensors to achieve \(k\) -barrier coverage. In particular, two problems are studied under two scenarios. First, when only the stationary sensors have been deployed, what is the minimum number of mobile sensors required to form \(k\) -barrier coverage? Second, when both the stationary and mobile sensors have been pre-deployed, what is the maximum number of barriers that could be formed? To solve these problems, we introduce a novel concept of weighted barrier graph (WBG) and prove that determining the minimum number of mobile sensors required to form \(k\) -barrier coverage is related with finding \(k\) vertex-disjoint paths with the minimum total length on the WBG. With this observation, we propose an optimal solution and a greedy solution for each of the two problems. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithms.

Along with the rapid increasing energy consumption, the energy cost of Internet data centers (IDCs) has been skyrocketing. A novel scheme of geographical load balancing was proposed to reduce electricity bills for service providers. However, one important challenge faced by service providers has not been considered properly. In service systems, the service delay faced by consumers includes the queuing delay and the transmission delay. While existing work only consider the queuing delay, the transmission delay introduced by geographical load balancing has been overlooked. It is one of the most important factors affecting the quality of service for real-time service systems. In this paper, we take the transmission delay into our design consideration and formulate a mixed-integer nonlinear programming (MINLP) problem with coupled constraint to achieve the optimal load balancing and energy cost management for IDCs while meeting the service-level agreements (SLA) of consumers. A novel heuristic based branch and bound with feedback (HBBF) algorithm is proposed to decouple the MINLP problem with coupled constraint efficiently. Extensive performance evaluations based on real electricity price data and site-to-site transmission delay data demonstrate the effectiveness of our proposed algorithm.

We present a new subband information fusion (SIF) method for wideband direction-of-arrival (DOA) estimation using single sparse signal representation of multiple frequency-based measurement vectors. The problem of wideband DOA estimation using SIF method is to jointly utilize all the frequency bin information to recover a single sparse indicative vector (SIV). The SIF method belongs to the sparse signal representation domain and therefore it will suffer from two cases of ambiguity: algebraic aliasing and spatial aliasing. We show that these two categories of ambiguity can be reduced by combining all the frequency components. The SIF algorithm is then proposed and the SIV is recovered iteratively. The numerical simulations are performed to illustrate that the SIF method has superior performances.

In this paper, a new Cramér-Rao lower bound (CRLB) is derived for passive source localization based on angles-of-arrival (AOAs), gain ratios of arrival (GROAs) and time differences of arrival (TDOAs) in a wireless sensor array network. The derived CRLB using AOA-GROA-TDOA (AGT) is reduced to the one using AOA-GROA if no coherence exists across the arrays and lower than the CRLB using AOA-only. When the coherence is considered, the CRLB using AGT measurements is consistently lower than the other known bounds using AOA-only, TDOA-only and AOA-TDOA.

In this paper, we present simulation and experimental studies of multiple bird source separation based on the Voxnet acoustic array node. The Approximate Maximum Likelihood (AML) method is used to estimate blindly the direction-of-arrivals(DOAs) of the sources to generate the steering vectors in order to separate the sources via beamforming. Simulation and measured data confirmed the proper operations of the AML beamforming algorithm and the Voxnet hardware node.

A novel sparse representation based multi-source localization method is presented in this work. We envision a wireless network infrastructure containing multiple phase arrays of acoustic sensors. With multiple arrays, direct estimation of a set of source locations is achieved using a new joint sparse representation of array covariance matrices (JSRACM). This representation transforms the source location estimation problem into a spatial sparse signal representation (SSSR) optimization problem. To mitigate the high computation complexity of JSRACM, a novel binary sparse indicative vector (SIV) is introduced to represent the support of joint SSSR of array covariance matrices. As such, the multiple source locations may be estimated by solving an unconstrained optimization problem of the SIV vector using existing FOCUSS-like algorithms. The resulting SIVR-JSRACM algorithm does not require prior information of the number of sources nor initial source location estimates. It promises super-resolution, robustness to noise, and low computing complexity which is independent of the number of sensor phase arrays. Simulation results demonstrate superior performance of the proposed algorithm.

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection) where directional sensors (e.g., cameras) are becoming more popular and advantageous than omni-directional scalar sensors for the extra dimensional information they provide. However, barrier coverage can not be guaranteed after initial random deployment of sensors, especially for directional sensors with limited sensing angles. In this paper, we study how to efficiently achieve barrier coverage in hybrid directional sensor networks by moving mobile sensors to fill in gaps and form a barrier with stationary sensors. In specific, we introduce the notion of directional barrier graph to model the barrier coverage formation problem. We prove that the minimum number of mobile sensors required to form a barrier with stationary sensors is the length of the shortest path from the source node to the destination node on the directional barrier graph. We then formulate the problem of minimizing the cost of moving mobile sensors to fill in the gaps on the shortest path as a minimum cost bipartite assignment problem, and solve it in polynomial time using the Hungarian algorithm. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithm.

We present AWSAN, a adjustable wireless sensor array network based target monitoring system. It is universal for different scenarios and convenient for deploymen- t. A compressed sampling scheme is introduced to greatly reduce the data exchange volume, moving most processing load to fusion center. It provides similar performance to the traditional wireless or fixed array while using low-cost & low-power nodes.

Compressive Sensing (CS) theory witnesses great breakthrough in signal acquisition and processing. In this paper we focus on the application of direction of arrival (DoA) and proposed a compressive sensing based direct DoA estimation framework (CS-DDoA) for wireless sensor array network. Unlike the former signal reconstruction-processing scheme, CS-DDoA utilize the spatial and spectral sparsity of target, then reformulates them into a global problems. It eliminates the error propagation between this two independent procedure and provides a enhanced DoA performance. Under this framework, array data transmission volume can be greatly reduce without distinct performance decline, which has great potential for wireless sensor array network with limited resources. At last theoretical analysis and prototype system are provided to validate this framework.

Duty-cycling technique has been widely adopted in MAC protocols for wireless sensor networks to conserve energy. However, low duty-cycle also leads to limited throughput in most of existing solutions. In this paper, we propose iQueue-MAC to provide immediate yet energy-efficient throughput enhancement for dealing with burst or heavy traffic. Combined with CSMA/CA, iQueue-MAC makes use of queue length of each sensor node and allocates suitable TDMA slots to them for packets transmission. During light traffic period, no extra slots will be allocated; iQueue-MAC acts like other low duty-cycle MACs to conserve power. While in burst or heavy traffic period, iQueue-MAC senses the build up of packet queues and dynamically schedules adequate number of slots for packet transmission. We have implemented iQueue-MAC on STM32W108 chips that offer IEEE 802.15.4 standard communication. We set up several real-world experimental scenarios, including a 46 nodes multi-hop test-bed for simulating a general application, and conducted numerous experiments to evaluate iQueue-MAC, in comparison with other traffic adaptive duty-cycle protocols, such as multi-channel version RI-MAC and CoSenS. Results clearly show that iQueue-MAC outperforms multi-channel version of RI-MAC and CoSenS in terms of packet delay and throughput.

Target tracking is a typical and important application of wireless sensor networks (WSNs). Existing target tracking protocols focus mainly on energy efficiency, and little effort has been put into network management and real-time data routing, which are also very important issues for target tracking. In this paper, we propose a scalable cluster-based target tracking framework, namely the hierarchical prediction strategy (HPS), for energy-efficient and real-time target tracking in large-scale WSNs. HPS organizes sensor nodes into clusters by using suitable clustering protocols which are beneficial for network management and data routing. As a target moves in the network, cluster heads predict the target trajectory using Kalman filter and selectively activate the next round of sensors in advance to keep on tracking the target. The estimated locations of the target are routed to the base station via the backbone composed of the cluster heads. A soft handoff algorithm is proposed in HPS to guarantee smooth tracking of the target when the target moves from one cluster to another. Under the framework of HPS, we design and implement an energy-efficient target tracking system, HierTrack, which consists of 36 sensor motes, a sink node, and a base station. Both simulation and experimental results show the efficiency of our system.

We present the design, implement and evaluation of a sensing platform for on-line multi-target tracking based on acoustic array networks, named Integrated Acoustic Array Sensor Network Tracker (IAASNT). To provide on-line multi-target tracking service, a well-designed system structure is proposed, composed by supporting components and associations between each part. Among these, IAASNT's multi-level low-power management and integrated tracking frame set it different from other related platforms. The integrated tracking frame is the core of the system and has been carefully designed, to achieve a self-acting tracking service. Finally, a series of experiments on system have been done to evaluate the performance of IAASNT. The tracking experiments on system show a perfect tracking performance in both noise-free and noisy environment, and the tracking precision can be within 5.8m in 300*300m area.

We propose a new low complexity direction-of-arrival (DOA) estimation method based on sparse variable projection (SVP) optimization. This method estimates an indicative sparse vector that indicates the locations of DOA from each visual sources corresponding to DOA sampling space and is particular useful to simplify the multiple measurement vector (MMV) problem as a single indicative sparse vector recovery problem. The indicative sparse vector can be recovered by adding additional sparsity measure information. We use ℓp(p ≤ 1) norm and smoothed approximate ℓ0norm to regularize the SVP function, so that we can formulate the SVP optimization as an unconstrained optimization problem and we solve it efficiently using quasi-Newton method. The experimental results demonstrate that our method has much lower complexity by comparing with a standard Regularized M-FOCUSS algorithm.

With the development of social networks, it has been increasingly easier to make friends on the Internet. However, it may not be as easy to automatically find a friend with "similar interests". In this paper, we develop a novel system that allows users with similar interests to be quickly introduced based on the similarity of pictures they took. A real online system, named Friendbook, is implemented on a smartphone network. Due to the limited resources on a smartphone as well as privacy issues, instead of directly comparing the original pictures for similarity measure, Friendbook uses "feature-based" picture comparison. By comparing features extracted from pictures taken by people who want to make friends, their similarity in interests can be automatically inferred based on the content of these pictures. We refer to friends made through Friendbook as "S-friend" for "Semantic-friend". The system also demonstrates the difference between S-friend matching with geographic-based G-friend matching.

Target tracking is a typical and important application of wireless sensor networks (WSNs). A lot of target tracking protocols have been proposed, whereas few of them are implemented into real systems. In this paper, we develop a real target tracking system, called HieTrack, for energy efficient and real-time target tracking in WSNs. The system relies on the cluster-based network architecture and contends for network cost minimization by selectively activating appropriate senor nodes according to the prediction of target trajectory. The estimated locations of the target can be routed to the base station via the backbone composed by the cluster heads. A graphical user interface (GUI) is also designed complementarily to facilitate users while monitoring the status of the sensor network. The efficiency of our system is validated by experiments.

With the advance of automation technology, the scale of industrial communication networks at field level is growing. Guaranteeing real-time performance of these networks is therefore becoming an increasingly difficult task. This paper addresses the optimization of device allocation in industrial Ethernet networks with real-time constraints (DAIEN-RC). Considering the inherent diversity of real-time requirements of typical industrial applications, a novel optimization criterion based on relative delay is proposed. A hybrid genetic algorithm incorporating a reduced variable neighborhood search (GA-rVNS) is developed for DAIEN-RC. Experimental results show that the proposed novel scheme achieves a superior performance compared to existing schemes, especially for large scale industrial networks.

The recent rapid development of industrial Ethernet requires addressing the topic of network design from a new perspective. The design of industrial Ethernet networks must typically consider real-time constraints and robustness against network failures. In this paper, the topology design of industrial Ethernet networks problem is firstly modelled considering the requirements of industrial applications. Considering the increasing scale of industrial Ethernet networks, computational complexity of network optimization schemes becomes a more and more important issue. A fast algorithm based on a heuristic extension of a genetic algorithm is presented and shown to provide an effective solution and lower computation complexity as compared to a pure genetic algorithm.

The wormhole attack is a severe attack that can be easily mounted on a wide range of wireless networks without compromising any cryptographic quantity or network node. In the wormhole attack, an attacker sniffs packets at one point in the network, tunnels the packets through a wired or wireless link to another point. Such kind of attack can cause severe problems in wireless sensor networks, especially deteriorate the routing process and the localization process. In this paper, we propose a secure localization scheme against wormhole attacks, which includes three phases: wormhole attack detection, neighboring locators differentiation and secure localization. The main idea of the proposed secure localization scheme is to build a so-called conflicting set for each locator according to the abnormalities of message exchanges among neighboring locators, which is used to differentiate the dubious locators from valid locators for the secure localization. The simulation results show that the proposed scheme outperforms the existed schemes under different network parameters.

ZigBee/IEEE 802.15.4 beacon-enabled mode allows low duty-cycle operation with high energy saving in the wireless sensor network. However, low duty cycles introduce higher end-to-end delay in event propagation and generally lower reactivity of the network with respect to the actual sensing. In target tracking systems, both qualities should be satisfied, namely, low energy consumption while no target is being tracked and low end-to-end delay in target tracking mode. Hence, we need to define a trade-off between the energy consumption and the end-to-end delay. In this paper, we propose a dynamic dual-rate beacon scheduling method with two rate switching schemes modifying ZigBee/IEEE 802.15.4 to enhance its real-time property but still maintain the advantage of low energy consumption. We evaluate our scheme through a simulation using the OPNET simulator. The results show that our scheme exhibits low energy consumption while presenting reduced end-to-end delay, outperforming usual single rate working modes.

Node localization becomes an important issue in the wireless sensor network as its broad applications in environment monitoring, emergency rescue and battlefield surveillance, etc. Basically, the DV-Hop localization mechanism can work well with the assistance of beacon nodes that have the capability of self-positioning. However, if the network is invaded by a wormhole attack, the attacker can tunnel the packets via the wormhole link to cause severe impacts on the DV-Hop localization process. The distance-vector propagation phase during the DV-Hop localization even aggravates the positioning result, compared to the localization schemes without wormhole attacks. In this paper, we analyze the impacts of wormhole attack on DV-Hop localization scheme. Based on the basic DV-Hop localization process, we propose a label-based secure localization scheme to defend against the wormhole attack. Simulation results demonstrate that our proposed secure localization scheme is capable of detecting the wormhole attack and resisting its adverse impacts with a high probability.

The recent developments of industrial Ethernet are brushing aside people's doubt whether industrial Ethernet could fulfill all of their requirements to real-time performance and reliability. Although designing a highly reliable network with strict real-time constraints is becoming possible, no method has been proposed to help network design systematically considering reliability and real-time constraints. A risk metric is proposed in this paper for evaluating the relationship between reliability mechanisms of an industrial Ethernet network and its real time performance. By simulation of an example network with ring topology the metric is shown to be a suitable basis for effective network optimization.

A two-hop neighborhood information-based routing protocol is proposed for real-time wireless sensor networks. The approach of mapping packet deadline to a velocity is adopted as that in SPEED; however, our routing decision is made based on the novel two-hop velocity integrated with energy balancing mechanism. Initiative drop control is embedded to enhance energy utilization efficiency, while reducing packet deadline miss ratio. Simulation and comparison show that the new protocol has led to lower packet deadline miss ratio and higher energy efficiency than two existing popular schemes. The result has also indicated a promising direction in supporting real-time quality-of-service for wireless sensor networks.

Many routing protocols have been proposed for wireless sensor networks in recent years. For some special applications, not only energy aware but link reliable is needed. Historical link status should be captured while making routing decisions. In this paper, we design a reliable link quality estimation based routing protocol (LQER), which integrates the approach of minimum hop field and (m, k). The performance of LQER is evaluated by simulation experiments to be more energy-aware, with lower loss rate and better scalability than MHFR (Z. Ma and Y. Sun, 2004) and MCR (F. Ye et al., 2001). Thus the whole network may obtain longer lifetime and better link quality.

A CPN based IEC FB model and its application are introduced in this paper. The model not only can analyze the internal procedure of IEC FB, especially the parameter's status propagation and the mode's switch, but also can be integrated into modeling IEC FB application. The latter is explained with an FB application, FB based boiler water level control system.

Modern manufacturing companies need flexible manufacturing control systems (FMCSs) to compete in today's unpredictable and ever-changing global markets. Traditional methodologies, however, can't meet the requirements of FMCS design. The function block (FB) concept in IEC61499 may provide a promising approach. In this present work, a FB based design pattern has been introduced to systematize the task of FMCS design in the IEC61499 context, which is enhanced by adopting formal reference models in the design cycle. The FB based framework and engineering methodology have been proposed, together with requirements specification of CASE tools to facilitate the development process. An illustrative example using the proposed design pattern is presented. When properly applied, it can substantially reduce the time and cost of systems engineering, while enhancing the flexibility, modularity, extensibility, reusability and interoperability of FMCSs.