375 research outputs found
An accurate RSS/AoA-based localization method for internet of underwater things
Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm
Wireless sensor networks require accurate target localization, often achieved
through received signal strength (RSS) localization estimation based on maximum
likelihood (ML). However, ML-based algorithms can suffer from issues such as
low diversity, slow convergence, and local optima, which can significantly
affect localization performance. In this paper, we propose a novel localization
algorithm that combines opposition-based learning (OBL) and simulated annealing
algorithm (SAA) to address these challenges. The algorithm begins by generating
an initial solution randomly, which serves as the starting point for the SAA.
Subsequently, OBL is employed to generate an opposing initial solution,
effectively providing an alternative initial solution. The SAA is then executed
independently on both the original and opposing initial solutions, optimizing
each towards a potential optimal solution. The final solution is selected as
the more effective of the two outcomes from the SAA, thereby reducing the
likelihood of the algorithm becoming trapped in local optima. Simulation
results indicate that the proposed algorithm consistently outperforms existing
algorithms in terms of localization accuracy, demonstrating the effectiveness
of our approach
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