Localization Schemes and Its Challenges in Underwater Wireless Sensor Networks

Gupta, Osho; Kumar, Manni; Mushtaq, Aadil; Goyal, Nitin

doi:10.1166/jctn.2020.9116

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…The proposed Hybrid PSO‐CSO algorithm is compared with Adaptive Energy‐Efficient Localization Algorithm (AEELA), 14 Mobility Prediction, and a Particle Swarm Optimization algorithm (MP‐PSO), 7 Crow Search Optimization (CSO) and Localization and Detection of Targets using Distance and Angle based algorithms (LDTDA) 18 . In this experiment, number of nodes varies, and these are taken as 25, 50, 75, 100, 125, and 150.…”

Section: Resultsmentioning

confidence: 99%

“…12 Localization is a motivating research area in UWSN for the researchers because of non-functioning of Global Positioning System (GPS) below the water and mobility of nodes that results from movement of water currents. 13,14 Rather than utilizing GPS to define the un-localized nodes' positions, the present underwater localization systems use the spatio-temporal association amid an unlocalized node and little reference nodes. On the other hand, UWSNs are frequently arranged sporadically to decrease the inclusive employment charge.…”

Section: Introductionmentioning

confidence: 99%

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A range based node localization scheme with hybrid optimization for underwater wireless sensor network

Nain

Goyal

Awasthi

et al. 2022

Int J Communication

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A great interest among researchers to establish Underwater Wireless Sensor Networks (UWSN) with mobile nodes have been created in recent past because of numerous potential applications in aquatic science. In comparison of radio communication, acoustic communication has lower bandwidth and larger propagation delay. This enacts additional restrictions on any localization algorithm. Existing range-based approaches involve increased localization latency and error even with large deployment of anchor nodes. Some of the optimized localization techniques involve higher energy consumption and cost. In order to solve these issues, a hybrid optimized localization technique is proposed that can accurately localize mobile nodes in UWSN. In this algorithm, a fitness function is concluded based on the number of hops from the anchor node, Time of Arrival (ToA) distance estimation error and transmission delay. In comparison of existing localization techniques, proposed algorithm helps in reducing the energy consumption, localization error, localization delay and localization cost and experiment results proved the same.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

Nain

Goyal

Awasthi

et al. 2022

Int J Communication

Self Cite

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“…WSNs have been applied in the field of localization which mainly depends on RSSI [22], such as search and rescue environment [23], and underwater scenes [24]. Generally, the geometric and fingerprint approaches are commonly used in RSS indoor localization systems.…”

Section: Wsn-assisted Localizationmentioning

confidence: 99%

Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

Ge¹,

Qin²,

Chen³

2023

Computers, Materials &Amp; Continua

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Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task. In an indoor environment where the global positioning system signal fails or becomes weak, the wireless sensor network (WSN) or simultaneous localization and mapping (SLAM) scheme gradually becomes a research hot spot. WSN method uses received signal strength indicator (RSSI) values to determine the position of the target signal node, however, the orientation of the target node is not clear. Besides, the distance error is large when the indoor signal receives interference. The laser SLAM-based method usually uses a 2D laser Lidar to build an occupancy grid map, then locates the robot according to the known grid map. Unfortunately, this scheme only works effectively in those areas with salient geometrical features. The traditional particle filter always fails for areas with similar structures, such as a long corridor. To solve their shortcomings, this paper proposes a novel coarse-to-fine paradigm that uses WSN to assist mobile robot localization in a geometrically similar environment. Firstly, the fingerprints database is built in the offline stage to get reference distance information. The distance data is determined by the statistical mean value of multiple RSSI values. Secondly, a hybrid map with grid cells and RSSI values is constructed when the mobile robot moves from a starting point to the ending place. Thirdly, the RSSI values are thought of as a basic reference to get a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results demonstrate that our approach is effective and robust for global localization. The localization success rate reaches 97.0% and the average moving distance is only 0.74 meters, while the traditional method always fails. In addition, the method also works well when the mobile robot is kidnapped to another position in the environment.

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“…The clustering and routing algorithms of WSNs cannot directly apply to UWSNs due to acoustic signals and different layers of communications. Thus, designing either clustering-based or route formation algorithms by considering the ocean properties of UWSNs gained significant attention from researchers [18][19][20][21]. But UWSNs challenges mainly related achieve trade-off among energy-efficient, higher PDR, and minimum communication delay parameters.…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization using Swarm Intelligence for IoT-Authorized Underwater Wireless Sensor Networks

Gavali¹,

Vaze

Ubale

2021

Preprint

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The technology advancement in the Internet of Things (IoT) enables a variety of smart monitoring applications assisted by networks like Wireless Sensor Networks (WSNs) and Underwater WSNs (UWSNs). The IoT-UWSNs supported a wide range of applications such as underwater data collection, underwater equipment monitoring, underwater imaging, etc. The acoustic signals have been utilized for communication in IoT-UWSNs over radio signals and optical signals. Data transmission using acoustic signals is suffering from lower throughput, excessive energy consumption, long transmission delay, and lower network lifetime. Several data forwarding and clustering algorithms have recently been proposed to enhance UWSN's performances. This paper proposed a novel routing solution for energy and QoS-efficient data transmission from the underwater sensor node to the surface sink using Swarm Intelligence (SI). This protocol called Energy Optimization using Routing Optimization (EORO) protocol. To optimize the UWSNs performance, we used Effective Fitness Function-based Particle Swarm Optimization (EFF-PSO) to select the best forwarder node for data transmission. In EORO, forwarding relay nodes discovered by the intended source node using location information firstly. Then EFF-PSO algorithm is applied to select the optimal relay node considering the rich set of parameters. Four parameters of each forwarder node used for fitness computation as residual energy, packet transmission ability, node connectivity, and distance. These parameters are intelligently selected to avoid packet collisions to achieve energy consumption and delay reduction with higher throughput. An experimental result shows that the EORO protocol outperformed underlying routing techniques using throughput, energy consumption, delay, and Packet Delivery Ratio (PDR).

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Localization Schemes and Its Challenges in Underwater Wireless Sensor Networks

Cited by 13 publications

References 18 publications

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

Energy Optimization using Swarm Intelligence for IoT-Authorized Underwater Wireless Sensor Networks

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