Single Anchor Node Based Localization in Mobile Underwater Wireless Sensor Networks

Das, Anjana P.; Thampi, Sabu M.

doi:10.1007/978-3-319-27161-3_69

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…S-Nodes in a particular cluster are localized with the aid of the A-Node in that cluster. Figure 2 illustrates the steps in UASN localization procedure introduced in [8]. An A-Node sends its location data to all S-Nodes within its coverage periodically.…”

Section: Uasn Localizationmentioning

confidence: 99%

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Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic

2019

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Underwater acoustic sensor network (UASN) offers a promising solution for exploring underwater resources remotely. For getting a better understanding of sensed data, accurate localization is essential. As the UASN acoustic channel is open and the environment is hostile, the risk of malicious activities is very high, particularly in time-critical military applications. Since the location estimation with false data ends up in wrong positioning, it is necessary to identify and ignore such data to ensure data integrity. Therefore, in this paper, we propose a novel anomaly detection system for UASN localization. To minimize computational power and storage, we designed separate anomaly detection schemes for sensor nodes and anchor nodes. We propose an auto-regressive prediction-based scheme for detecting anomalies at sensor nodes. For anchor nodes, a fuzzy inference system is designed to identify the presence of anomalous behavior. The detection schemes are implemented at every node for enabling identification of multiple and duplicate anomalies at its origin. We simulated the network, modeled anomalies and analyzed the performance of detection schemes at anchor nodes and sensor nodes. The results indicate that anomaly detection systems offer an acceptable accuracy with high true positive rate and F-Score.

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Section: Uasn Localizationmentioning

confidence: 99%

“…As rangefree techniques provide ambiguous location estimates, range-based techniques attract more attention. Most of the range-based schemes are anchor node based [8,16]. SenLin et al [44] proposed a localization scheme that does not require an even distribution of anchors.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic

2019

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“…But still, this area has not been explored well enough to exploit all the resources and their potential applications. Even though the Wireless Sensor Network Protocols and Algorithms ISSN 1943-3581 2016 Network (WSN) technologies have evolved a lot, there are practical difficulties in incorporating the state of the art WSN technologies into UWSN because of the obvious domain characteristics such as constrained environment, error prFone acoustic channel, and mobility of nodes [1,2,3,4,5]. Hence, these unique features of underwater environment have motivated research activities in UWSN.…”

Section: Introductionmentioning

confidence: 99%

Simulation Tools for Underwater Sensor Networks: A Survey

Das¹,

Thampi

2017

NPA

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In underwater sensor network(UWSN) research, it is highly expensive to deploy a complete test bed involving complex network structure and data links to validate a network protocol or an algorithm. This practical challenge points to the need of a simulation environment which can reproduce the actual underwater scenario without the loss of generality. Since so many simulators are proposed for UWSN simulation, the selection of an appropriate tool based on the research requirement is very important in validation and interpretation of results. This paper provides an in-depth survey of different simulation tools available for UWSN simulation. We compared the features offered by each tool, pre-requirements, and provide the run time experiences of some of the open source tools. We conducted simulation of sample scenarios in some of the open source tools and compared the results. This survey helps a researcher to identify a simulation tool satisfying their specific research requirements.

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“…To avoid the energy waste in falsely triggered CH recovery process, it is important to accurately detect the failure of a CH. Thus, it is necessary to develop a qualitative approach and a reliable mechanism that has characteristics of higher delivery ratio along with lower energy consumption …”

Section: Introductionmentioning

confidence: 99%

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

Goyal

Dave

Verma

2017

Int J Communication

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The performance of underwater wireless sensor network gets affected by the working of a cluster in the network. The cluster head (CH) or cluster member (CM) fails because of energy depletion or hardware errors that increase delay and message overhead of the network. To recover the affected cluster, a technique is required to identify the failed CH or CM. We propose a fault detection and recovery technique (FDRT) for a cluster-based network in this paper. Primarily, while selecting the CH, a backup cluster head (BCH) is selected using fuzzy logic technique based on parameters such as node density, residual energy, load, distance to sink, and link quality. Then, failure of CH, BCH, and CM is detected. If fault is detected at CH, then the BCH will start performing the task of failed CH. Simultaneously, when BCH failed, any other CM will be elected as BCH. If any of the CM appears to be nonperforming, then CH will detect the communication failure and request BCH to transfer the data from the failed CM to CH. The comparison of proposed FDRT is performed with existing FDRTs EDETA, RCH, and SDMCGC on the basis of packet drop, end-to-end delay, energy consumption, and delivery ratio of data packets. By simulation results, it is shown that FDRT for cluster-based underwater wireless sensor network results in quicker detection of failures and recovery of the network along with the reduction in energy consumption, thereby increasing the lifespan of the network.KEYWORDS backup cluster head, cluster head, cluster member, fault detection, fault recovery, UWSN

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Single Anchor Node Based Localization in Mobile Underwater Wireless Sensor Networks

Cited by 8 publications

References 9 publications

Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic

Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic

Simulation Tools for Underwater Sensor Networks: A Survey

A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

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