scholarly journals A novel method for asynchronous source localisation based on time of arrival measurements

2021 ◽  
Vol 17 (10) ◽  
pp. 155014772110537
Author(s):  
Huijie Zhu ◽  
Sheng Liu ◽  
Zhiqiang Yao ◽  
Moses Chukwuka Okonkwo ◽  
Zheng Peng
Keyword(s):  
Sensor Networks ◽  
Computational Complexity ◽  
Trust Region Method ◽  
Initial Position ◽  
High Signal ◽  
Time Of Arrival ◽  
Signal Source ◽  
Step Method ◽  
Source Localisation ◽  
Maximum Minimum

Source localisation is an important component in the application of wireless sensor networks, and plays a key role in environmental monitoring, healthcare and battlefield surveillance and so on. In this article, the source localisation problem based on time-of-arrival measurements in asynchronous sensor networks is studied. Because of imperfect time synchronisation between the anchor nodes and the signal source node, the unknown parameter of start transmission time of signal source makes the localisation problem further sophisticated. The derived maximum-likelihood estimator cost function with multiple local minimum is non-linear and non-convex. A novel two-step method which can solve the global minimum is proposed. First, by leveraging dimensionality reduction, the maximum (minimum) distance maximum (minimum) time-of-arrival matching-based second-order Monte Carlo method is applied to find a rough initial position of the signal source with low computational complexity. Then, the rough initial position value is refined using trust region method to obtain the final positioning result. Comparative analysis with state-of-the-art semidefinite programming and min–max criterion-based algorithms are conducted. Simulations show that the proposed method is superior in terms of localisation accuracy and computational complexity, and can reach the optimality benchmark of Cramér–Rao Lower Bound even in high signal-to-noise ratio environments.

scholarly journals Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8017
Author(s):  
Nurfazrina M. Zamry ◽  
Anazida Zainal ◽  
Murad A. Rassam ◽  
Eman H. Alkhammash ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Principal Component ◽  
Support Vector ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Memory Utilization

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with (𝑛𝑑) memory utilization and no communication overhead.

scholarly journals Convolutional technique for enhancing security in wireless sensor networks against malicious nodes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Turki Ali Alghamdi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Convolutional Codes ◽  
Time Instant ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Malicious Nodes ◽  
Security Issues ◽  
Network Operation

Abstract Wireless sensor networks (WSNs) comprise tiny devices known as sensors. These devices are frequently employed in short-range communications and can perform various operations such as monitoring, collecting, analyzing, and processing data. WSNs do not require any infrastructure, are reliable, and can withstand adverse conditions. Sensor networks are autonomous structures in which the sensor nodes can enter or leave the network at any time instant. If the entering node is attacker node it will monitor the network operation and can cause security issues in the network that can affect communication. Existing literature presents security improvements in such networks in the form of cryptography, asymmetric techniques, key distribution, and various protocols. However, these techniques may not be effective in the case of autonomous structures and can increase computational complexity. In this paper, a convolutional technique (CT) is proposed that generates security bits using convolutional codes to prevent malicious node attacks on WSNs. Different security codes are generated at different hops and the simulation results demonstrate that the proposed technique enhances network security and reduces computational complexity compared to existing approaches.

Gas leakage acoustic source localisation with compressed sensing method in sensor networks

2020 ◽  
Vol 15 (4) ◽  
pp. 206
Author(s):  
Liyi Zhang ◽  
Jinzhao Li ◽  
Yu Shi ◽  
Qi Chen ◽  
Tong Wang ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Compressed Sensing ◽  
Acoustic Source ◽  
Source Localisation ◽  
Gas Leakage

Gas leakage acoustic source localisation with compressed sensing method in sensor networks

2020 ◽  
Vol 15 (4) ◽  
pp. 206
Author(s):  
Yong Zhang ◽  
Tong Wang ◽  
Qi Chen ◽  
Yu Shi ◽  
Jinzhao Li ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Compressed Sensing ◽  
Acoustic Source ◽  
Source Localisation ◽  
Gas Leakage

scholarly journals Anchor-Node-Based Distributed Localization with Error Correction in Wireless Sensor Networks

2012 ◽  
Vol 8 (4) ◽  
pp. 975147 ◽  
Author(s):  
Taeyoung Kim ◽  
Minhan Shon ◽  
Mihui Kim ◽  
Dongsoo S. Kim ◽  
Hyunseung Choo
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Initial Position ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Localization Accuracy ◽  
Message Transmission ◽  
Transmission Cost ◽  
Anchor Nodes

This paper proposes a scheme to enhance localization in terms of accuracy and transmission overhead in wireless sensor networks. This scheme starts from a basic anchor-node-based distributed localization (ADL) using grid scan with the information of anchor nodes within two-hop distance. Even though the localization accuracy of ADL is higher than that of previous schemes (e.g., DRLS), estimation error can be propagated when the ratio of anchor nodes is low. Thus, after each normal node estimates the initial position with ADL, it checks whether the position needs to be corrected because of the insufficient anchors within two-hop distance, that is, the node is in sparse anchor area. If correction needs, the initial position is repositioned using hop progress by the information of anchor nodes located several hops away so that error propagation is reduced (REP); the hop progress is an estimated hop distance using probability based on the density of sensor nodes. Results via in-depth simulation show that ADL has about 12% higher localization accuracy and about 10% lower message transmission cost than DRLS. In addition, the localization accuracy of ADL with REP is about 30% higher than that of DRLS, even though message transmission cost is increased.

scholarly journals A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-24 ◽  
Author(s):  
Maximo Cobos ◽  
Fabio Antonacci ◽  
Anastasios Alexandridis ◽  
Athanasios Mouchtaris ◽  
Bowon Lee
Keyword(s):  
Sensor Networks ◽  
Source Localization ◽  
Spatial Location ◽  
Time Of Arrival ◽  
Suitable Model ◽  
Acoustic Sensor ◽  
Acoustic Sensing ◽  
General Terms ◽  
Acoustic Sensor Networks ◽  
Acoustic Sources

Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field.

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