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The development of underwater acoustic networks is a significant expansion of Internet-of-Things technology to underwater environments. These networks are essential for a variety of marine applications. For many practical uses, it is more efficient to collect marine data from a remote location over multiple hops, rather than direct point-to-point communications. In this article, we will focus on the underwater acoustic string network (UA-SN) designed for this type of application. We propose a Robust and Adaptive Pipeline Medium Access Control (RAP-MAC) protocol to enhance the network's transmission efficiency, adaptability, and robustness. The protocol includes a scheduling-based concurrent algorithm, online real-time configuration adjustment function, a rate mode adaptive algorithm, and a fault recovery algorithm. We conducted simulations to compare the new protocol with another representative protocol, validating the RAP-MAC protocol's adaptability and fault recovery ability. Additionally, we carried out two large-scale sea trials. The results of these experiments indicate that the RAP-MAC protocol ensures effectiveness and reliability in large-scale multihop UA-SNs. In the South China Sea, we were able to achieve a communication distance of 87 km with a throughput of 601.6 bps, exceeding the recognized upper bound of underwater acoustic communication experiment performance by 40 km·kbps. [ABSTRACT FROM AUTHOR]

The technologies most suitable for monitoring the ecosystem of inland waters are image spectrometry and electrochemical sensors. The reason is that these instruments are able to ensure accuracy in the surveillance of very large areas through reliable and frequent measurements performed remotely. Electrochemical systems provide low-cost, miniaturized, reliable sensors that can be organized, when equipped with commercial on the shelf (COTS) low-power radio components implementing LoRaWAN, Sigfox or NB-IoT communications, in a dense network of sensors achieving the aforementioned requirements. In this work, a low-cost, low-size and low-noise electrochemical sensor endowed with protocols for network configuration, management and monitoring is presented. The electronic interface of the sensor allows high reproducible responses. As proof of concept for its utilization in inland water monitoring, the device has been tested for water composition analysis, bacteria identification and frequent pollutant detection: atrazine, dichloromethane and tetrachloroethene. The results are promising, and future investigations will be oriented to unlock the true potential of a general-purpose approach exploiting the continuous fusion of distributed data in each of the three considered application scenarios. A new device, with reduced power consumption and size, has been also developed and tested; this new device should be a node of a large network for inland water monitoring. [ABSTRACT FROM AUTHOR]

A structural health monitoring (SHM) system is an approach for identifying the damages caused to various kinds of structures using different system functions and providing the necessary feedback about structure's conditions. As civil structures are the backbone of our society, to determine its daily operations is a very important issue. The performance measurement of those structures is manual whereas a computer-based monitoring system could automatically assess the structural damages and identify its exact location. Recently, wireless sensor networks (WSNs) have attracted a great deal of attention for remote sensing applications due to flexibility to measure of various activity of large scale network. Since technology is advancing day by day, the overall cost of a monitoring system is also decreased. However, the major challenging fact of a WSNs is to provide scalability for covering a large area. The main question is arisen how much capable have of a monitoring system to turn off unnecessary nodes to save energy while there are no events detected. To support the scalability required of an existing network and save the node energy for future use, we propose a topology maintenance protocol integrated with construction to address the issue of a node's energy consumption by placing it optimally and extending the monitoring system's lifetime. As per the authors' acknowledgement that, a little attention has been paid to developing such a hybrid approach. To mitigate node energy consumption issue with large scale support, an Internet of Things (IoT)-based maintenance approach is the best candidate for obtaining better system lifetime responses. Therefore, the main goal of this work is to develop an 'on-the-fly'-based topology maintenance monitoring system, which can maintain a network's infrastructure while gathering a node's information to switch its state regularly when the present network is no longer optimal. [ABSTRACT FROM AUTHOR]