Fault-resilient localization for underwater sensor networks

Das, Anjana P.; Thampi, Sabu M.

doi:10.1016/j.adhoc.2016.09.003

Cited by 55 publications

(28 citation statements)

References 20 publications

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“…Initially, n sensors are deployed in the cuboid where the sensor positions can follow any type of distribution, such as uniform distribution, Poisson distribution, and normal distribution, as shown in Figure 1. Similarly to most previous works in underwater sensor deployment, we assume that all the deployed sensors are able to identify their current locations by existing underwater localization algorithms, 9,34 and each sensor equipped with propelled equipment, for example, autonomous underwater vehicles (AUVs), 35,36 is able to relocate itself from its initial position to another specified position at a maximum speed of V max (m=s) in underwater environment. We assume an ideal 0/1 sphere sensing model that an object within (outside) a sensor's sensing sphere is detected by the sensor with probability 1 (0).…”

Section: Network Modelmentioning

confidence: 99%

“…For each sensor, the key to initial movement is to find the centerline closest to it. According to the previous analysis, the centerlines are perpendicular to the base layer and pass through the hexagon center points in each layer, where the positions (x, f y (w, r, i), f z (w, r, j)) of the hexagon center points can be obtained via equations (6) and (9). In the following, we describe how to obtain the centerline closest to a sensor s i .…”

Section: Details Of the Proposed Algorithmmentioning

confidence: 99%

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A fully distributed deployment algorithm for underwater strong k-barrier coverage using mobile sensors

Shen

Zhang

et al. 2019

International Journal of Distributed Sensor Networks

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Sensor barrier coverage has been recognized as an appropriate coverage model for intrusion detection, and many achievements have been obtained in two-dimensional terrestrial wireless sensor networks. However, the achievements based on two-dimensional assumption cannot be directly applied in three-dimensional application scenarios, for example, underwater wireless sensor networks. In this article, we aim to devise a fully distributed deployment algorithm for constructing maximum-level underwater strong k-barrier coverage with available mobile sensors in underwater environment to satisfy the requirement of underwater security-related applications. We first analyze how to form underwater strong one-barrier coverage with minimum mobile sensors, based on which we obtain the minimum number of sensors required for constructing underwater strong one-barrier coverage and the corresponding optimal final positions of these sensors. Then, we propose a fully distributed deployment algorithm for constructing maximum-level underwater strong k-barrier coverage with available mobile sensors in three-dimensional underwater environment. We show that the proposed algorithm has a guaranteed termination after a finite time and is able to self-heal the underwater strong k-barrier coverage to deal with sudden sensor failures. Experimental results validate our analysis and show that the proposed algorithm outperforms both Hungarian and HungarianK in terms of duration and achieves performance close to them with respect to several performance metrics.

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Section: Network Modelmentioning

confidence: 99%

Section: Details Of the Proposed Algorithmmentioning

confidence: 99%

A fully distributed deployment algorithm for underwater strong k-barrier coverage using mobile sensors

Shen

Zhang

et al. 2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…It detects the physical layer failures occurring due to bad channel conditions although it increases bandwidth and memory storage. Das and Thampi have proposed a fault‐resilient localization scheme that offers good localization accuracy with minimal communication overhead. The scheme predicts fault location for medium‐range UWSNs.…”

Section: Related Workmentioning

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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“…Even though the information extracted from the sensors, but the reliability data still remain a challenge. Flood-related data can be obtained in the respective duration but it often performs poor performance due to the sensor faults conditions [3].…”

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confidence: 99%

An Intelligent Crisis-Mapping Framework for Flood Prediction

2019

IJRTE

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This paper proposes a new framework for crisis-mapping with flood prediction model based on the crowdsourcing data. Crisis-mapping is still at infancy stage development and offers opportunities for exploration. In fact, the application of the crisis-mapping gives fast information delivery and continuous updates for crisis and emergency evacuation using sensors. However, current crisis-mapping is more to the information dissemination of flood-related information and lack of flood prediction capability. Therefore, this paper applied artificial neural network for flood prediction model in the proposed framework. Sensor data from the crowdsourcing platform can be used to predict the flood-related measures to support continuous flood monitoring. In addition, the proposed framework makes used of the unstructured data from the Twitters to support the flood warnings dissemination to locate flood area with no sensor installation. Based on the results of the experiment, the fitted model from the optimization process gives 90.9% of accuracy performance. The significance of this study is that we provide a new alternative in flood warnings dissemination that can be used to predict and visualized the flood occurrence. This prediction is significant to agencies and authorities to identify the flood risk before its occurrence and crisis-maps can be used as an analytics tool for future city planning

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Fault-resilient localization for underwater sensor networks

Cited by 55 publications

References 20 publications

A fully distributed deployment algorithm for underwater strong k-barrier coverage using mobile sensors

A fully distributed deployment algorithm for underwater strong k-barrier coverage using mobile sensors

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

An Intelligent Crisis-Mapping Framework for Flood Prediction

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