SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

Chen, Pengpeng; Ma, Honglu; Gao, Shengli; Huang, Yan

doi:10.3390/s151129702

Cited by 11 publications

(13 citation statements)

References 35 publications

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“…Then, the distance between the nearby nodes S1 and S2 can be calculated as shown in Equation (5). In [59,60], the Manhattan method was used to find the distance between near-far nodes. In this case, the referential object must be allocated.…”

Section: Of 24mentioning

confidence: 99%

Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks

Yum

et al. 2019

Applied Sciences

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In the last few decades, underwater communication systems have been widely used for the development of navy, military, business, and safety applications, etc. However, in underwater communication systems, there are several challenging issues, such as limitations in bandwidth, propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. Underwater communication systems work under severe channel conditions such as ambient noise, frequency selectivity, multi-path and Doppler shifts. In order to collect and transmit the data in effective ways, multi-media/multi-band-based adaptation layer technology is proposed in this paper. The underwater communication scenario comprises of Unmanned Underwater Vehicles (UUVs), Surface gateways, sensor nodes, etc. The transmission of data starts from sensor nodes to surface gateway in a hierarchical manner through multiple channels. In order to provide strong and reliable communication underwater, the adaptation layer uses a multi-band/multi-media approach for transferring data. Hence, in this paper, existing techniques for splitting the band such as Orthogonal Frequency-Division Multiple Access (OFDMA), Frequency-Division Multiple Access (FDMA), or Orthogonal Frequency-Division Multiplexing (OFDM) are used for splitting the frequency band, and the medium selection mechanism is proposed to carry the signal through different media such as Acoustic, Visible Light Communication (VLC), and Infrared (IR) signals in underwater. For the channel selection mechanism, two phases are involved: 1. Finding the distance of near and far nodes using Manhattan method, and 2. Medium selection and data transferring algorithm for choosing different media.Due to the constrained conditions underwater, communication systems face problems such as multi-path, frequency fading, doppler shifts, etc. Multi-band techniques can be used as an effective technique with respect to efficiency and throughput, and to increase the performance level. Multi-band can overcome the frequency fading problem by allotting the same information to several frequency-bands. Need of Multi-Media Communication Techniques UnderwaterIn order to get reliable communication underwater, multi-media techniques can be used. The network architecture of underwater communication includes an underwater area network, a surface area network, and a terrestrial area network. The underwater area network supports acoustic, optical, and MFAN communication; the terrestrial and surface area network consists of RF communication and the internal communication portal is TCP/UDP. In order to avoid interference and data loss, and to decrease the error rate and increase reliability, a four-in-one adaptable modem should be designed. So, different channels can be adapted in underwater communication systems. Thus, the communication system can be more effective and reliable. Also, multi-media communication systems can avoid collisions. Benefits of Multi-Media/Multi-Band Communication Techniques Underwater Acoustic Comm...

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Section: Of 24mentioning

confidence: 99%

Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks

Yum

et al. 2019

Applied Sciences

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“…In the brought together sort of systems, the plan for estimations of separations for the nonneighboring sensors must be unique in relation to the previously mentioned one. A few analysts are referenced in [11], [12], [13] and [14].…”

Section: A To Find the Shortest Communication Pathmentioning

confidence: 99%

Improved Steiner Tree Scheme Applied to Wireless Sensor Networks for Path and Energy Optimization

Zahoor¹

2019

IJEW

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This research work presents two optimization algorithms to optimize the path and energy in the wireless sensor network. Minimum Spanning Tree (MST) and Particle Swarm Optimization (PSO) algorithms both are utilized to optimize the path and energy of a system, which is connected on a fifty nodes network deployed randomly on 100x100 meters region. The proposed scheme is for the constrained improvement problem, or more explicitly, a weighted spanning tree problem and its appliance to Wireless Sensor Network (WSN) is examined here where definite exploratory discoveries on the energy improvement of the network have been exhibited.

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“…They can obtain the relative position of nodes by other information (e.g., geometric relationship and hop), to estimate the localization of unknown nodes [26,27,28,29,30]. These schemes reduce the demand for hardware and are suitable for large-scale dense deployment of sensor nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks

Niu

Yang

Gao

et al. 2016

Sensors

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Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.

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SSL: Signal Similarity-Based Localization for Ocean Sensor Networks

Cited by 11 publications

References 35 publications

Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks

Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks

Improved Steiner Tree Scheme Applied to Wireless Sensor Networks for Path and Energy Optimization

Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks

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