Novel Approach using Robust Routing Protocol in Underwater Acoustic Wireless Sensor Network with Network Simulator 2: A Review

In recent year wireless sensor network has been an emerging technology and promising technology in unveiling the riddle of the marine life and other underwater applications. As it is a permutation of computation, sensing and communication. In the 70% of the earth a huge amount of unexploited resources lies covered by oceans. To coordinate interact and share information among themselves to carry out sensing and monitoring function underwater sensor network consists number of various sensors and autonomous underwater vehicles deployed underwater. The two most fundamental problems in underwater sensor network are sensing coverage and network connectivity. The coverage problem reflects how well a sensor network is tracked or monitored by sensors. An underwater wireless sensor networks is the emerging field that is having the challenges in each field such as the deployment of nodes, routing, floating movement of sensors etc. This paper is concerned about the underwater acoustic wireless sensor network of routing protocol applications and UW-ASNs deployments for monitoring and control of underwater domains

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

An energy-aware architecture : a practical implementation for autonomous underwater vehicles

Energy awareness, fault tolerance and performance estimation are important aspects for extending the autonomy levels of today’s autonomous vehicles. Those are related to the concepts of survivability and reliability, two important factors that often limit the trust of end users in conducting large-scale deployments of such vehicles. With the aim of preparing the way for persistent autonomous operations this work focuses its efforts on investigating those effects on underwater vehicles capable of long-term missions. A novel energy-aware architecture for autonomous underwater vehicles (AUVs) is presented. This, by monitoring at runtime the vehicle’s energy usage, is capable of detecting and mitigating failures in the propulsion subsystem, one of the most common sources of mission-time problems. Furthermore it estimates the vehicle’s performance when operating in unknown environments and in the presence of external disturbances. These capabilities are a great contribution for reducing the operational uncertainty that most underwater platforms face during their deployment. Using knowledge collected while conducting real missions the proposed architecture allows the optimisation of on-board resource usage. This improves the vehicle’s effectiveness when operating in unknown stochastic scenarios or when facing the problem of resource scarcity. The architecture has been implemented on a real vehicle, Nessie AUV, used for real sea experiments as part of multiple research projects. These gave the opportunity of evaluating the improvements of the proposed system when considering more complex autonomous tasks. Together with Nessie AUV, the commercial platform IVER3 AUV has been involved in the evaluating the feasibility of this approach. Results and operational experience, gathered both in real sea scenarios and in controlled environment experiments, are discussed in detail showing the benefits and the operational constraints of the introduced architecture, alongside suggestions for future research directions