Research On Localization Model And Algorithm For Mobile Underwater Sensor Networks

Underwater wireless sensor networks(UWSN)has been widely applied in many application fields such as underwater surveillance,pollution detection and disaster prevention.Generally,an UWSN is comprised of different types of nodes,which can be floating sensor nodes,surface buoys,autonomous underwater vehicles and other application specific devices.These nodes communicate with each other and sense underwater environments collaboratively.The sensed data is then analyzed to provide decision support for the upper applications.In this process,the locations of nodes need to be aware to interpret the sensed data meaningfully.Hence,localization is one of the critical services in underwater wireless sensor networks.Existing localization algorithms mainly focus on localizing unknown nodes(location-unaware)by measuring their distances to beacon nodes(location-aware),whereas ignoring additional challenges posed by harsh underwater environments.Especially,underwater nodes move constantly with ocean currents and measurement noises vary with distances.However,in practice,ranging packets are inevitably corrupted due to packet collisions and signal noises,resulting in degrading localization performance significantly.Aimed at the existing localization algorithms for underwater acoustic sensor networks need the presence of beacon nodes and assume that measurement noises follow Gaussian distributions,resulting in high cost and low accuracy.To address these problems,this paper proposes a self-localization algorithm based on maximum a posteriori for drifting-restricted underwater acoustic sensor networks under mixed measurement noises.In maximum a posteriori estimation,we analyze nodes' mobility patterns to obtain the priori knowledge for localization,and characterize distance measurements under the assumption of additive and multiplicative noises as the likelihood information for localization.Under the Bayesian framework,the priori and likelihood information are fused to derive localization objective function by maximum a posteriori probability.In particle swarm optimization localization,a swarm of particles are used to search the best location solution from local and global views simultaneously.Moreover,we eliminate the localization ambiguity using a novel reference selection mechanism and improve the convergence speed using a bound constraint mechanism.In the simulations,we evaluate the performance of the proposed algorithm under different settings and determine the optimal values for tunable parameters.The simulation results show that,compared with similar localization methods,the proposed method doesn't need the presence of beacon nodes,has high localization accuracy and fast convergence speed,and is robust to varying measurement noises.Aimed at the problem of underwater acoustic sensor networks localization with missing and noisy distance measurements due to packet corruptions.In this paper,we propose a packet corruption tolerant localization algorithm to address this challenge.First,we design an energy-efficient mechanism to gather inter-node distance measurements and form partially observed square distance matrix(SDM).Then,leveraging the intrinsic low-rank structure of SDM,the reconstruction of true SDM is formulated as a Frobenius-norm regularized matrix factorization problem and an improved Newton-Raphson method is designed to solve this problem.Finally,we apply Multi-Dimension Scaling technique to localize all the nodes based on the reconstructed SDM.Simulation results demonstrate that,our proposed algorithm outperforms the benchmark approaches in terms of localization accuracy,coverage and stability.