MétaCan
Menu
Back to cohort
Record W4230323069 · doi:10.1002/wcm.667

Underwater sensor networks: architectures and protocols

2008· article· en· W4230323069 on OpenAlexaff
Jun Zheng, Nirwan Ansari, Cheng Li, Baoxian Zhang

Bibliographic record

VenueWireless Communications and Mobile Computing · 2008
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsMemorial University of NewfoundlandUniversity of Ottawa
Fundersnot available
KeywordsUnderwaterUnderwater acoustic communicationComputer scienceWireless sensor networkTelecommunicationsReal-time computingComputer networkOceanographyGeology

Abstract

fetched live from OpenAlex

The ocean, which covers about two-third of the Earth surface, is a largely unexplored world that has fascinated humans since the beginning of human history. Over a long period of time, there is a great interest in exploring the ocean and other underwater environments (e.g., rivers, lakes, and reservoirs) for scientific, environmental, commercial, and military purposes. With the increasing demand for acquiring localized, precise and real-time knowledge of the harsh underwater environments, traditional underwater exploration technologies such as SONAR or other remote sensing technologies can no longer meet such demands. Underwater sensor networks are an emerging network paradigm which provides a promising solution to exploring the ocean and underwater environments. An underwater sensor network consists of a number of underwater sensor nodes with sensing, data processing, and communication capabilities, which are deployed in a region of interest and collaborate to accomplish a common task such as underwater environmental monitoring, mine reconnaissance, and military surveillance. Driven by a broad range of potential applications in both civilian and military areas as well as rapid technological advances in microelectronics, wireless communications, and embedded processing, underwater sensor networks have recently received much attention from both academia and industry. Distinct from terrestrial sensor networks, an underwater sensor network has some unique characteristics that need to be particularly addressed such as low communication bandwidth, large propagation delay, harsh geographical environment, and floating node mobility. These unique characteristics present many challenges in the design of underwater sensor networks, which have recently motivated a growing interest and a considerable amount of research activities in this emerging area. This special issue includes a collection of eight outstanding research papers, which cover a diversity of topics on the design of network architectures and protocols for underwater sensor networks. The issue begins with an invited paper, ‘Prospects and Problems of Wireless Communication for Underwater Sensor Networks,’ contributed by Jun-Hong Cui et al. This paper reviews the physical fundamentals and engineering implementations for efficient information exchange via wireless communications using physical waves as the carrier among nodes in an underwater sensor network. It also makes recommendations for the selection of the communication carrier for underwater sensor networks with engineering countermeasures that can possibly enhance the communication efficiency in specified underwater environments. In the second paper, ‘Coverage and Connectivity in Three-Dimensional Underwater Sensor Networks,’ Alam and Haas studied the node deployment problem in a 3D underwater sensor network and provided a solution to the coverage and connectivity problem with limited and full communication redundancy requirements. In the third paper, ‘Placement of Multiple Mobile Data Collectors in Underwater Acoustic Sensor Networks,’ Alsalih et al. studied the placement problem of mobile data collectors in underwater sensor networks and proposed two routing and placement schemes. One is delay-tolerant placement and routing (DTPR), which can maximize the network lifetime without any delay consideration. The other is delay-constrained placement and routing (DCPR), which can maximize the network lifetime with an upper bound on the maximum delay. The fourth paper, ‘Target Tracking Based on a Distributed Particle Filter in Underwater Sensor Networks,’ by Huang et al. proposes two algorithms for tracking mobile targets in cluster-based underwater sensor networks based on a distributed particle filter. One of them can achieve higher tracking accuracy while the other can significantly reduce the communication cost, energy cost, and tracking response time. In the fifth paper, ‘Utilizing Acoustic Propagation Delay to Design MAC Protocols for Underwater Wireless Sensor Networks,’ Guo et al. proposed an efficient MAC protocol for underwater sensor networks, which makes use of the propagation delay to avoid collisions, thus reducing control overhead and energy consumption. In the sixth paper, ‘Path Unaware Layered Routing Protocol (PULRP) With Non-Uniform Node Distribution for Underwater Sensor Networks,’ Gopi et al. proposed a PULRP for 2D underwater sensor networks with mobile nodes, which has been demonstrated to have better throughput and delay performance as compared to the underwater diffusion (UWD) algorithm. In the seventh paper, ‘PAS: Probability and Sub-Optimal Distance (SOD)-Based Lifetime Prolonging Strategy for Underwater Acoustic Sensor Networks,’ Dou et al. proposed a couple of lifetime prolonging strategies for underwater sensor networks: probability-based energy-balancing (PEB) strategy and SOD-based data transmission strategy. They showed through simulation results that both strategies can efficiently save energy consumption and thus prolong the network lifetime. In the last paper, ‘Development of Routing Protocols for the Solar-Powered Autonomous Underwater Vehicle (SAUV) Platform,’ Bartos et al. presented a summary of the experience obtained in the development, evaluation, and field testing of two routing protocols for the SAUV platform. Useful suggestions based on field experience are also presented for improving the design and evaluation of routing protocols for a harsh underwater environment. We thank all the authors who submitted their papers to this special issue. Owing to the limitation of space, we can include only eight papers in the issue. We are grateful to all the reviewers for their time and efforts in carefully reviewing all the papers and providing valuable review comments. We also thank the Editor-in-Chief, Mohsen Guizani, for his continuous support for this special issue, and all the publication staff for their support during the publishing process. It is our hope that the papers included in this special issue present a good snapshot of the latest research progress in the design of network architectures and protocols for underwater sensor networks and become an important reference for researchers and practitioners in the area. Finally, we hope that the readers will find this special issue timely and informative.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.257
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueWireless Communications and Mobile ComputingSame topicUnderwater Vehicles and Communication SystemsFrench-language works237,207