In underwater wireless sensor networks, sensor position information has important value in network protocols and collaborative detection. However, many challenges were introduced in positioning sensor nodes due to the complexity of the underwater environment. Aiming at the problem of the stratification effect of underwater acoustic waves, the long propagation delay of messages, as well as the mobility of sensor nodes, a mobile target localization scheme for underwater wireless sensor network is proposed based on iterative tracing. Four modules are established in the mobile target localization based on iterative tracing: the data collection and rough position estimation, the estimation and compensation of propagation delay, the node localization, and the iteration. The deviation of distance estimation due to the assumption that acoustic waves propagate along straight lines in an underwater environment is compensated by the mobile target localization based on iterative tracing, and weighted least squares estimation method is used to perform linear regression. Moreover, an interacting multiple model algorithm is put forward to reduce the positioning error caused by the mobility of sensor nodes, and the two services of node time synchronization and localization assist each other during the iteration to improve the accuracy of both parties. The simulation results show that the proposed scheme can achieve higher localization accuracy than the similar schemes, and the positioning errors caused by the above three problems can be reduced effectively.
Positioning by wireless sensor network is one of its main functions and has been widely used in many fields. However, when signal propagation is hindered, serious errors, non-line-of-sight errors, occur. In order to solve this problem, this article proposes an improved particle filter algorithm, which introduces the idea of residual analysis to improve reliability. The algorithm assigns weights to the particles based on the residuals and selects the appropriate particles. In addition, the non-line-of-sight error parameter a is introduced, and the second selection is made according to a, which considers the influence of non-line-of-sight error. The non-line-of-sight error is greatly reduced after two selections. The simulation is performed under several different non-line-of-sight errors, and results show that the algorithm is superior to Kalman filter and particle filter.
The communication network of autonomous vehicles is composed of multiple sensors working together, and its dynamic topology makes it vulnerable to common attacks such as black hole attack, gray hole attack, rushing attack, and flooding attack, which pose a threat to the safety of passengers and vehicles; most of the existing safety detection mechanisms for a vehicle can only detect attacks but cannot intelligently defend against attacks. To this end, an efficient protection mechanism based on self-adaptive decision (SD-EPM) is proposed, which is divided into the offline phase and the online phase. The online phase consists of two parts: intrusion detection and efficient response. Attack detection and defense in the vehicular ad hoc networks (VANETs) are performed in terms of the attack credibility value (AC), the network performance attenuation value (NPA), and the list of self-adaptive decision. The simulation results show that the proposed mechanism can correctly identify the attack and respond effectively to different attack types. And, the negative impact on VANETs is small.
With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.
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