MobiL: A 3-dimensional localization scheme for Mobile Underwater Sensor Networks

Ojha, Tamoghna; Misra, Sudip

doi:10.1109/ncc.2013.6488033

Cited by 28 publications

(13 citation statements)

References 23 publications

(19 reference statements)

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“…LDB [9,10], AAL [20,21], DNRL [22,23], and MobiL [24] focus on how anchors provide their positions to the sensor nodes and assume that the anchors are mobile and equipped with GPS to obtain their locations. Their disadvantages include high localization delays and high cost as the accuracy of the location relies on a large number of mobile beacons, especially for large-scale UWSNs.…”

Section: Distributed Localization Techniquesmentioning

confidence: 99%

“…In each period , the unlocalized or location-expired sensor nodes that received more than three location messages perform self-localization by multilateration [24,25,27] and time synchronization by Mobi-Sync [14]. The successfully localized and time synchronized sensor nodes start to observe data and then transmit data packets with their location information.…”

Section: Node Localizationmentioning

confidence: 99%

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Hybrid Localization Approach for Underwater Sensor Networks

Tsai

Wang

2017

Journal of Sensors

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Underwater Wireless Sensor Networks (UWSNs) are widely used to collect data in the marine environment. Location and time are essential aspects when sensors collect data, particularly in the case of location-aware data. Many studies on terrestrial sensor networks consider sensor locations as the locations where data is collected and focus on sensor positioning when sensors are fixed. However, underwater sensors are mobile networks and the sensor locations change continuously. Localization schemes designed for static sensor networks need to run periodically to update locations and consume considerable sensor power and increase the communication overhead; hence, they cannot be applied to UWSNs. This paper presents a hybrid localization approach with data-location correction, called Data Localization Correction Approach (DLCA), which positions data without additional communication overhead and power consumption on sensors. Without loss of generality, we simulate the ocean environment based on a kinematic model and meandering current mobility model and conduct extensive simulations. Our results show that DLCA can significantly reduce communication costs, while maintaining relatively high localization accuracy.

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Section: Distributed Localization Techniquesmentioning

confidence: 99%

Section: Node Localizationmentioning

confidence: 99%

Hybrid Localization Approach for Underwater Sensor Networks

Tsai

Wang

2017

Journal of Sensors

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“…Considering an underwater scenario, the node at depth experienced underwater current velocity V = (V ( ), V ( ), 0), where V and V are the velocities of the node in and directions, respectively. Due to spatial correlation in underwater networks, mobile sensor nodes show a group-like behavior [2]. So, by using the group movement property as shown in (1), we can find the velocity of the node.…”

Section: Comparatively High Packet Acceptance Ratio High Energy Consumentioning

confidence: 99%

“…For this purpose various attempts in the literature have been proposed. In MobiL [2], anchor nodes deployed on water surface are used as reference nodes for the localization of ordinary nodes. However, nodes that are not in the transmission range of these anchor nodes are localized as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Localization Based Cooperative Routing Protocol for Underwater Wireless Sensor Networks

Javaid

Maqsood

Abdul

et al. 2017

Mobile Information Systems

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Localization is one of the major aspects in underwater wireless sensor networks (UWSNs). Therefore, it is important to know the accurate position of the sensor node in large scale applications like disaster prevention, tactical surveillance, and monitoring. Due to the inefficiency of the global positioning system (GPS) in UWSN, it is very difficult to localize a node in underwater environment compared to terrestrial networks. To minimize the localization error and enhance the localization coverage of the network, two routing protocols are proposed; the first one is mobile autonomous underwater vehicle (MobiL-AUV) and the second one is cooperative MobiL (CO-MobiL). In MobiL-AUV, AUVs are deployed and equipped with GPS and act as reference nodes. These reference nodes are used to localize all the nonlocalized ordinary sensor nodes in order to reduce the localization error and maximize the network coverage. CO-MobiL is presented in order to improve the network throughput by using the maximal ratio combining (MRC) as diversity technique which combines both signals, received from the source and received from the relay at the destination. It uses amplify-and-forward (AF) mechanism to improve the signal between the source and the destination. To support our claims, extensive simulations are performed.

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“…In the last few years, a lot of research work on jamming in WSNs emerged . Some of the existing literature are discussed in this section.…”

Section: Related Workmentioning

confidence: 99%

Topology control in the presence of jammers for wireless sensor networks

Bhavathankar

Mondal

Misra

2017

Int J Communication

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In this paper, the problem of ensuring packet delivery ratio and high network lifetime in wireless sensor networks in the presence of single or multiple jammers is studied using single-leader-multiple-followers Stackelberg game theory. A topology control scheme is proposed, in which the sink node, which acts as the leader, identifies the set of jamming affected nodes. On the other hand, the sensor nodes, which act as followers, need to decide an optimum transmission power level, while ensuring an optimal set of neighbor nodes covered. A scheme, named TC-JAM, for ensuring packet delivery ratio, while avoiding jammers and increasing network lifetime in wireless sensor networks, is proposed. In existing literatures, the sensor nodes are envisioned to be equipped with multiple interfaces, while having access for multiple channels. However, in TC-JAM, the sensor nodes have simple hardware with single interface for communication, ie, the sensor nodes have single channel for communication. Additionally, in the proposed scheme, TC-JAM, each sensor node has a provision to vary its transmission power according to the chosen strategies. Using TC-JAM, the energy consumption of the overall network reduces by up to 62%, and the network lifetime increases by 56% to 73%. KEYWORDSDynamic transmission power level model, jammer, network lifetime, stackelberg game, topology control, wireless sensor network 1 Int J Commun Syst. 2017;30:e3289.wileyonlinelibrary.com/journal/dac

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MobiL: A 3-dimensional localization scheme for Mobile Underwater Sensor Networks

Cited by 28 publications

References 23 publications

Hybrid Localization Approach for Underwater Sensor Networks

Hybrid Localization Approach for Underwater Sensor Networks

A Localization Based Cooperative Routing Protocol for Underwater Wireless Sensor Networks

Topology control in the presence of jammers for wireless sensor networks

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