Dynamic localization plan for underwater mobile sensor nodes using fuzzy decision support system.

Underwater mobile sensor node localization is a key enabling technology for several subsea missions. A novel scalable underwater localization scheme, called Best Suitable Localization Algorithm (BLSA), is proposed to dynamically fuse multiple position estimates of sensor nodes using fuzzy logic, aiming at improving localization accuracy and availability along the whole trajectory in missions. Numerical simulation has been conducted to demonstrate significant improvement in localization accuracy and availability by using the proposed fuzzy inference system. The proposed method provides a costeffective localization system by harnessing all available localization methods on-board

Confidence-based Underwater Localization Scheme for Large-Scale Mobile Sensor Networks

The absence of Global Positioning System in underwater environment predominates in the challenges of underwater vehicles navigation or sensor nodes tracking. Localization of single or few underwater vehicles has been fostered in recent years. However, online simultaneous tracking of large-scale mobile sensor network is still a very challenging research area due to the high cost and the very limited number of vehicles that can be simultaneously localized using Ultra-Short Base Line (USBL) system. We propose a confidence-based localization algorithm for large-scale underwater mobile sensor networks that employs high precision localized sensor nodes in neighboring sensor nodes localization. Numerical simulation shows that a swarm of 100 sensor nodes can be tracked using a single USBL system, range measurement sensors and communication modems

Multi-objective optimization of confidence-based localization in large-scale underwater robotic swarms.

Localization in large-scale underwater swarm robotic systems has increasingly attracted research and industry communities’ attention. An optimized confidence-based localization algorithm is proposed for improving localization coverage and accuracy by promoting robots with high confidence of location estimates to references for their neighboring robots. Confidence update rules based on Bayes filters are proposed based on localization methods’ error characteristics where expected localization error is generated based on measurements such as operational depth and traveled distance. Parameters of the proposed algorithm are then optimized using the Evolutionary Multi-objective Optimization algorithm NSGA-II for localization error and trilateration utilization minimization while maximizing localization confidence and Ultra-Short Base Line utilization. Simulation studies show that a wide localization coverage can be achieved using a single Ultra-Short Base Line system and localization mean error can be reduced by over 45% when algorithm’s parameters are optimized in an underwater swarm of 100 robots

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms

This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling

Energy efficient Routing Protocols for Underwater Acoustic Wireless Sensor Network

Technological advancement regarding oceanic world discovery and monitoring has led to autonomous communication, which results in the emergence of the Internet of underwater things (IoUT). Underwater acoustic wireless sensor networks have become one of the most recently researched within the IoUT. An underwater acoustic wireless sensor network consists of sensor nodes, autonomous vehicles, and remotely operated vehicles which are normally deployed to carry out a collaborative task within an underwater region. Underwater acoustic wireless sensor networks have become one of the most recently researched area which supports long transmission range. However, acoustic signals experience deformation due to factors which consist of noise, propagation delay, and low bandwidth. Sensor nodes are battery dependent which mean they are difficult to recharge or replace once deployed. Routing protocols play important role in the communication process between these sensor nodes. As a result, this research aims to develop an energy efficient routing protocol that can bring about optimal policies for energy consumption in the process of data aggregation and transmission. The developed routing protocol focused on sparse and dense network architectures by examining the popular ad-hoc routing protocol action on demand distance vector routing protocol (AODV) for sparse networks and low energy adaptive clustering hierarchy (LEACH) for dense network. For a sparse architecture this research identifies current energy and overhead challenges facing AODV which in turn modifies the protocol by creating a new energy aware and overhead friendly routing protocol called action on demand distance vector sparse underwater acoustic routing protocol (AODV-SUARP) for underwater communication. AODV-SUARP introduces the mechanism of route stability function (RSF) by colour mode to select the most energy efficient route to forwards packets. For dense architecture this research identifies the energy challenge facing the conventional LEACH routing protocol which in turn leads to its modification by creating a new energy aware routing protocol called low energy adaptive clustering hierarchy dense underwater acoustic routing protocol (LEACH-DUARP). Furthermore, for the optimal selection of eligible cluster head in a subsequent round LEACH-DUARP introduces a concept called the stability function value (SFV). The developed routing protocols (AODV-SUARP and LEACH-DUARP) were implemented in NS-3 and validated using mathematical modelling. Results obtained indicated both AODV-SUARP and LEACH-DUARP achieves a considerable result compared to other routing protocols in terms of residual energy, packet delivery ratio, and number of dead nodes

1 Mobi-Sync: Efficient Time Synchronization for Mobile Underwater Sensor Networks

Time synchronization is a critical service for distributed network systems. In this work, we investigate this problem in the context of underwater sensor networks (UWSNs). Although there are many time synchronization protocols proposed for terrestrial wireless sensor networks, none of them could be directly applied to UWSNs. This is because most of these protocols do not consider long propagation delays and sensor node mobility, which are important characteristics in UWSNs. Further, UWSNs usually have very high requirements in network lifetime and synchronization accuracy. To satisfy these needs, innovative time synchronization solutions are demanded. In this paper, we propose a novel time synchronization scheme, called “Mobi-Sync”, for mobile underwater acoustic sensor networks. Mobi-Sync novelly utilizes the spatial correlation of underwater mobile sensor nodes to estimate the long dynamic propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and energy efficiency. I