Underwater sensor networks: architectures and protocols
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".