Localisation of underwater targets using sensor networks

Prabha, C.; Ananthakrishnan, V.; Supriya, M. H.; Pillai, P. R. Saseendran

doi:10.1504/ijsnet.2013.055007

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…In underwater applications, the sensor nodes deployed in water form a random three‐dimensional (3D) structure [26, 27]. There are several methods of finding the node location under the water [28–32]. In [32], the significance of accurate information of the SVP for source localisation is described, in which the real‐time measurements using CTD sensors were used to find the sound speed at different depths and a matched filter processor was used for source localisation.…”

Section: Introductionmentioning

confidence: 99%

Sound velocity profile estimation using ray tracing and nature inspired meta‐heuristic algorithms in underwater sensor networks

2019

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Section: Introductionmentioning

confidence: 99%

Sound velocity profile estimation using ray tracing and nature inspired meta‐heuristic algorithms in underwater sensor networks

2019

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“…These features also become challenges since traditional solutions for the terrestrial WSN cannot be used for the UAN directly (Mandal and De, 2015). Practical schemes have been proposed to manage the challenges, e.g., the two-step security model for time synchronisation (Hu et al, 2008), the tracking algorithms for tracking mobile targets (Huang et al, 2008), the precise prototype localiser model (Prabha et al, 2013) and the energyefficiency routing schema without location information (Al-Bzoor et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

CACA-UAN: a context-aware communication approach to efficient and reliable underwater acoustic sensor networks

Liu

Chen

Liu

et al. 2018

IJSNET

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Underwater acoustic sensor networks (UANs) have emerged as a promising technology recently, which can be applied in many areas such as military and civil, where the communication between devices is crucial and challenging owing to the unique characteristics of underwater acoustic-based environment, such as high latency and low bandwidth. In this paper, context awareness is applied to the design of an underwater communication approach, called context-aware communication approach for a UAN (CACA-UAN), which aims to improve the overall performance of the underwater communication. According to the results, the proposed CACA-UAN can increase the efficiency and reliability of the underwater communication system.

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“…Smart things communicate primarily in a wireless manner and construct a sensor network [2]. In fact, wireless sensor networks (WSNs) have become an important research topic in the recent decades and have been applied in many application scenarios like target tracking [3]. As argued in [4], WSNs are a key component at the perception layer of the IoT architecture, and serve as the data acquisition interface for IoT with the physical environment.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs

Zhou¹,

Shu

et al. 2016

IEEE Trans. Ind. Inf.

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Abstract-Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energyefficiently while prolonging the network lifetime is a challenge. To remedy this issue, we propose a three-phase energy-balanced heuristic. Specifically, the network region is first divided into grid cells with the same geo-graphical size. These grid cells are assigned to clusters through an algorithm inspired by the k-dimensional tree algorithm, such that the energy consumption of each clus-ter is similar when gathering data. These clusters are adjusted by (de)allocating grid cells contained in these clusters, while considering the energy consumption of sink movement. Consequently, the energy to be consumed in each cluster is approximately balanced considering the energy consumption of both data gathering and sink movement. Experimental evaluation shows that this technique can generate an optimal grid cell division within a limited time of iterations and prolong the network lifetime.

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Localisation of underwater targets using sensor networks

Cited by 7 publications

References 10 publications

Sound velocity profile estimation using ray tracing and nature inspired meta‐heuristic algorithms in underwater sensor networks

Sound velocity profile estimation using ray tracing and nature inspired meta‐heuristic algorithms in underwater sensor networks

CACA-UAN: a context-aware communication approach to efficient and reliable underwater acoustic sensor networks

An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs

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