Underwater Acoustic Sensor Node Scheduling using an Evolutionary Memetic Algorithm, Journal of Telecommunications and Information Technology, 2018, nr 1

Underwater Acoustic Sensor Networks (UWASNs) play an important role in monitoring the aqueous environment which has created a lot of interest for researchers and scientists. Utilization of underwater acoustic sensor node (UASN) scheduling for transmission remains, due to the limited acoustic bandwidth available, a challenge in such an environment. One of the methods to overcome this problem is to efficiently schedule UASN data using time division multiple access (TDMA) protocols the parallel transmissions, simultaneously avoiding interference. The paper shows how to optimize the utilization of acoustic sensor node bandwidth by maximizing the possible node transmissions in the TDMA frame and also by minimizing the node's turnaround wait time for its subsequent transmissions by using an evolutionary memetic algorithm (MA). The simulation of MA-TDMA proves that as the size of the network increases, every node in UWASN transmits with an average minimal turnaround transmission time. It also proves that as the TDMA cycle repeats, the overall network throughput gets maximized by increasing the possible node transmissions in the MA-TDMA frame

Mobile Undersea Routing Protocol

The myriad barriers to underwater communication provide a new set of challenges for network protocols. Routing protocols which operate in underwater ad hoc networks must react quickly to changing conditions without significant increase in packet overhead or congestion. Dynamic Source Routing Protocol provides a framework for accomplishing these goals. In this paper we present the Mobile Undersea Routing Protocol, which implements this framework and enhances upon it. It uses a limited propagating route request which we call a Route Recovery to quickly and inexpensively recover from routing errors. A Java based network simulator was constructed in order to test and compare the protocols. Statistics were calculated based on packets delivered, total transmissions, and time to recover from a route error as measurements of protocol effectiveness

TDMA frame design for a prototype underwater RF

Very low frequency electromagnetic communication system is used in a small scale underwater wireless sensor network for coastal monitoring purposes, as recent research has demonstrated distinct advantages of radio waves compared to acoustic and optical waves in shallow water conditions. This paper describes the detailed TDMA and packet design process for the prototype sensor system. The lightweight protocol is time division based in order to fit the unique characteristics and specifications of the network. Evaluations are based on initial beach trial as well as modeling and simulations

TDMA frame design for a prototype underwater RF communication network

This document is the Accepted Manuscript version of the following article: Xianhui Che, Ian Wells, Gordon Dickers, and Paul Kear, ‘TDMA frame design for a prototype underwater RF communication network’, Ad Hoc Networks, Vol. 10 (3): 317-327, first available online 23 July 2011. The version of record is available online at doi: http://dx.doi.org/10.1016/j.adhoc.2011.07.002 © 2011 Elsevier B. V. All rights reserved.Very low frequency electromagnetic communication system is used in a small scale underwater wireless sensor network for coastal monitoring purposes, as recent research has demonstrated distinct advantages of radio waves compared to acoustic and optical waves in shallow water conditions. This paper describes the detailed TDMA and packet design process for the prototype sensor system. The lightweight protocol is time division based in order to fit the unique characteristics and specifications of the network. Evaluations are based on initial beach trial as well as modeling and simulations.Peer reviewe

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa