Rigidity Driven Underwater Sensor Self-Organized Deployment

Xia, Na; Zheng, Yanping; Du, Huazheng; Xu, Chaonong; Zheng, Rong

doi:10.3724/sp.j.1016.2013.00494

Cited by 6 publications

(4 citation statements)

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“…It also acts as a recycle measure to get back the fatal nodes. Later [17] presents a joint approach for providing optimality in determining the depth node. Some IDS schemes proposed [18][19][20] provides the effective detection of malicious nodes for defending the underwater sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient intrusion detection system for secure acoustic communication in under water sensor networks

2023

KSII TIIS

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Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with

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Section: Related Workmentioning

confidence: 99%

Energy-efficient intrusion detection system for secure acoustic communication in under water sensor networks

2023

KSII TIIS

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“…Xia et al [ 28 ] established joint optimization objectives for underwater sensor network coverage and connectivity following a strict theoretical basis, and proposed a corresponding moving algorithm. This algorithm enables networks to improve coverage and connectivity.…”

Section: Related Workmentioning

confidence: 99%

Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks

Jiang

Wang

Jiang

2015

Sensors

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Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments.

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“…Xia et al introduced a rigid theory and defined rigidity-coverage value as the evaluation criteria for the positions of underwater sensors [21]. They established a novel underwater self-organization deployment mechanism based on rigidity-driven mobile strategy.…”

Section: Analysis Of Deployment Algorithmsmentioning

confidence: 99%

A Survey on Deployment Algorithms in Underwater Acoustic Sensor Networks

Han

Zhang

Shu

et al. 2013

International Journal of Distributed Sensor Networks

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Node deployment is one of the fundamental tasks for underwater acoustic sensor networks (UASNs) where the deployment strategy supports many fundamental network services, such as network topology control, routing, and boundary detection. Due to the complex deployment environment in three-dimensional (3D) space and unique characteristics of underwater acoustic channel, many factors need to be considered specifically during the deployment of UASNs. Thus, deployment issues in UASNs are significantly different from those of wireless sensor networks (WSNs). Node deployment for UASNs is an attractive research topic upon which a large number of algorithms have been proposed recently. This paper seeks to provide an overview of the most recent advances of deployment algorithms in UASNs while pointing out the open issues. In this paper, the deployment algorithms are classified into three categories based on the mobility of sensor nodes, namely, (I) static deployment, (II) self-adjustment deployment, and (III) movement-assisted deployment. The differences of the representative algorithms in aspects of sensor node types, computation complexity, energy consumption, deployment objectives, and so forth, are discussed and investigated in detail.

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Rigidity Driven Underwater Sensor Self-Organized Deployment

Cited by 6 publications

References 0 publications

Energy-efficient intrusion detection system for secure acoustic communication in under water sensor networks

Energy-efficient intrusion detection system for secure acoustic communication in under water sensor networks

Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks

A Survey on Deployment Algorithms in Underwater Acoustic Sensor Networks

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