Relaibility in Underwater Wireless Sensor Networks

Relaibility in Underwater Wireless Sensor Networks

Gaurav Sharma, Shilpi Harnal, Neha Miglani, Savita Khurana
Copyright: © 2021 |Pages: 23
DOI: 10.4018/978-1-7998-3640-7.ch015
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Abstract

Underwater wireless sensor networks (UWSNs) have gained importance as well as diverted attention of many researchers, domain experts to a great extent in recent past. The devices used for UWSN deployment are resource-constrained like storage issue, low processing speed, as well are vulnerable to a wide class of security threats and malicious attacks, which affect reliable communication. For reliable data delivery, a system should include packet delivery ratio, battery life, delays incurred, and energy consumption, etc. Numerous reliability models for underwater networks have been designed to incorporate the parameters and performance metrics in optimized manner. The chapter deals with focusing on such models and their efficiency in terms of battery life, packet loss, error handling mechanism, and network delays. Further, it is also explained how and why the error-controlled schemes should be designed and implemented in order to incorporate reliable data delivery in limited resources-constraints of UWSN along with the consideration of efficiency and performance concerns.
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Introduction

Nowadays the wireless networking has become the one of the foremost domains in the research world (Rawan et al.,2019). Further categorization of wireless sensor networks can be done as Terrestrial Sensor Networks and Underwater Sensor Networks. Moreover, the two categories belong to entirely different domains with respect to numerous factors such as communication cost, latency and error likelihood of all the nodes. UWSNs incur high cost, latency and error rate in comparison to TWSNs. Though Underwater sensor networks, along with being a buzz word, also is a promising and an emergent domain in the technology world yet is not explored completely. UWSNs comprise numerous sensor nodes in the network (Erol et al., 2007). Sensor nodes can effectively sense the information stored and hence, are capable enough to proceed forward in the direction of upper surface sink node. Based upon the application domain, packets forwarded towards the sink nodes are manipulated accordingly. Error ratio hugely decides an accuracy of information packets. Limitation lies in the fact that an ocean comprises various noise factors as well as dynamic and time-dependent link properties (Sozer et al., 2000), which eventually descends the performance and accuracy of data transmission. Moreover, water has an absorbent properties, specifically for radio waves henceforth, UWSNs utilizes acoustic signals, which further leads to an optimum performance (Maqsood et al., 2015; Wahid and Kim, 2012).

Figure 1 depicts the model for underwater sensor networks. The network comprises m sensor nodes lying underwater and one underwater sink node. Each node has been assigned unique ID [1,2,..m]. Moreover, an underwater sink node has an ID value of 0 and the parent node of the sink node is allocated the value of -1. These IDs will help in sending the notifications to underwater sink node at the time of deployment.Wireless communication is used for building connections between sensor nodes and underwater sink on land. Also, serial ports are used to make connection of sensor nodes with a server system (SS) on land. Henceforth, SS manages the data reception from underwater sensor nodes on land and commands are sent via sensor nodes on land along with the data display on the monitor/ screen. Afterwards, the collected information is uploaded on an Internet server which can be utilized by researchers in their problem domains.Unlike underwater sensor nodes, underwater sink nodes consists of other concerns as well such as channel distribution, route maintenance and generation, thus, congestion control is required for sink nodes in order to provide reliable network. Occurrence of node failure is comparatively high in underwater sensor networks in comparison to ground-based networks due to harsh conditions and environment in underwater scenario.

Figure 1.

Underwater sensor network models

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