scholarly journals RSS-Based Target Localization in Underwater Acoustic Sensor Networks via Convex Relaxation

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2323 ◽  
Author(s):  
Shengming Chang ◽  
Youming Li ◽  
Yucheng He ◽  
Yongqing Wu
Keyword(s):  
Sensor Networks ◽  
Weighted Least Squares ◽  
Convex Relaxation ◽  
Target Localization ◽  
Superior Performance ◽  
Transmit Power ◽  
Underwater Acoustic ◽  
Acoustic Environment ◽  
First Case ◽  
Second Order Cone

The received signal strength (RSS) based target localization problem in underwater acoustic wireless sensor networks (UWSNs) is considered. Two cases with respect to target transmit power are considered. For the first case, under the assumption that the reference of the target transmit power is known, we derive a novel weighted least squares (WLS) estimator by using an approximation to the RSS expressions, and then transform the originally non-convex problem into a mixed semi-definite programming/second-order cone programming (SD/SOCP) problem for reaching an efficient solution. For the second case, there is no knowledge on the target transmit power, and we treat the reference power as an additional unknown parameter. In this case, we formulate a WLS estimator by using a further approximation, and present an iterative ML and mixed SD/SOCP algorithm for solving the derived WLS problem. For both cases, we also derive the closed form expressions of the Cramer–Rao Lower Bounds (CRLBs) on root mean square error (RMSE). Computer simulation results show the superior performance of the proposed methods over the existing ones in the underwater acoustic environment.

scholarly journals An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1731
Author(s):  
Marcelo Salgueiro Costa ◽  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
Sensor Networks ◽  
Computational Cost ◽  
Three Dimensional ◽  
Target Localization ◽  
Inner Product ◽  
Relaxation Techniques ◽  
Angle Of Arrival ◽  
Transmit Power ◽  
Measurement Models ◽  
Second Order Cone

This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small N. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.

scholarly journals 3-D RSS-AOA Based Target Localization Method in Wireless Sensor Networks Using Convex Relaxation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 106901-106909
Author(s):  
Shengming Chang ◽  
You Zheng ◽  
Peng An ◽  
Jianyu Bao ◽  
Jun Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Convex Relaxation ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Method

scholarly journals Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1159 ◽  
Author(s):  
SeYoung Kang ◽  
TaeHyun Kim ◽  
WonZoo Chung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Covariance Matrix ◽  
Weighted Least Squares ◽  
Target Position ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Error Covariance Matrix ◽  
Error Covariance

We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.

Collision Based Time Synchronization for Underwater Sensor Networks

2012 ◽  
Vol 246-247 ◽  
pp. 806-810 ◽  
Author(s):  
Ying Guo ◽  
Yu Tao Liu
Keyword(s):  
Sensor Networks ◽  
Energy Cost ◽  
Time Synchronization ◽  
Underwater Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Environment ◽  
Simulation Results ◽  
Synchronization Scheme ◽  
First Time ◽  
Current Systems

To synchronize nodes’ time in mobile underwater sensor networks, a collision based time synchronization scheme called “Coll-Sync” is proposed, which is a novel time synchronization approach for underwater acoustic environment. Collision has been identified as a major challenge to communication, and current systems implement sophisticated solutions to detect and avoid it. Coll-Sync shows that collision contains highly useful information that can be leveraged to improve time synchronization greatly. It is the first time synchronization algorithm that does not suffer but benefit from collision in underwater acoustic channel. Simulation results show that Coll-Sync has high precision and low energy cost. It significantly outperforms existing time synchronization schemes.

scholarly journals Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6519
Author(s):  
Song Han ◽  
Luo Li ◽  
Xinbin Li
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Cooperative Transmission ◽  
Superior Performance ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks ◽  
Joint Strategy

Cooperative transmission is a promising technology for underwater acoustic sensor networks (UASNs) to ensure the effective collection of underwater information. In this paper, we study the joint relay selection and power allocation problem to maximize the cumulative quality of information transmission in energy harvesting-powered UASNs (EH-UASNs). First, we formulate the process of cooperative transmission with joint strategy optimization as a Markov decision process model. In the proposed model, an effective state expression is presented to better reveal interactive relationship between learning and environment, thereby improving the learning ability. Then, we further propose a novel reward function which can guide nodes to adjust power strategy adaptively to balance instantaneous capacity and long-term quality of service (QoS) under the dynamic unpredictable energy harvesting. More specifically, we propose a deep Q-network-based resource allocation algorithm for EH-UASNs to solve the complex coupled strategy optimization problem without any prior underwater environment information. Finally, simulation results verify the superior performance of the proposed algorithm in improving the cumulative network capacity and reducing outages.

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