Air-to-Sea Integrated Maritime Internet of Things: History
Please note this is an old version of this entry, which may differ significantly from the current revision.

Future generation communication systems are exemplified by 5G and 6G wireless technologies, and the utilization of integrated air-to-sea (A2S) communication infrastructure is employed to extend network coverage and enhance data throughput to support data-driven maritime applications. These ground-breaking techniques have promoted the rapid development of the maritime internet of things (MIoT). MIoT is a special kind of IoT system and is very different from the IoT on land in terms of connection methods, device types, data transmission, and so on. The complexity and variability in the marine environment make the construction and application of the MIoT more extensive and has far-reaching significance. 

  • air base stations (ABSs)
  • air-to-sea (A2S) integrated communication
  • future generation communication systems
  • maritime internet of things

1. Key Technologies of the Maritime Internet of Things

Due to the heterogeneity of maritime communication network segments, in order to better monitor, collect, and disseminate relevant information, different sensor technologies (STs) and communication technologies (CTs) are often used to adapt to different environments and propagation characteristics [1]. IoT technology integrates the sensor technology, communication technology, embedded technology, and other technologies. The internet of things (IoT) also has its own unique methods in relevant data processing and storage. Researchers will focus on the relevant functions of some key technologies for the maritime internet of things (MIoT), and analyze the security challenges that exist in order to solve the processing [2].
(1)
 Sensor technology (ST)
The MIoT is a system based on ST which is used for monitoring and collecting marine environmental data. Sensors are installed in various locations and devices in the ocean to sense different parameters and conditions [3]. These parameters can include ocean temperature, salinity, pressure, water quality, light, and more [4]. At the same time, ocean sensors have a variety of types and functions for monitoring and collecting marine environmental data. Here are some common ocean sensor types and their features:
  • Temperature sensor: Used to measure the temperature distribution of the ocean [5]. Common STs include resistive temperature sensors and thermal conductivity sensors.
  • Salinity sensor: Measures the salinity distribution of the ocean. A conductivity sensor is one of the commonly used salinity STs [6].
  • Pressure sensor: Used to measure the depth and pressure distribution of water in the ocean. Pressure sensors are widely used in marine scientific research and marine engineering.
  • Light sensor: Measures light intensity and spectral distribution in the ocean. This is important for understanding phytoplankton distribution, photosynthesis, and ecosystem function [7].
  • Water quality sensor: Used to monitor dissolved oxygen, chemical concentration, nutrients, toxic substances, etc., in water. They help to assess the state of marine water quality and environmental health [8].
  • Sonar sensor: Uses sound waves to measure and detect marine life, seafloor terrain, and obstacles. These sensors are commonly used in marine ecological research and ocean navigation [9].
  • Oxygen sensors: Used to measure the amount of dissolved oxygen in the ocean, which is essential for biological survival and biogeochemical processes [10].
  • Video and image sensors (VAISs): Video and images in the ocean are recorded using camera equipment for monitoring marine life, the marine substrate, and human activities [11].
Overall, MIoT ST can help us to better understand and monitor the marine environment, providing important support for marine scientific research, resource management, and marine environmental protection.
(2)
 Communication technology (CT)
With the increasing frequency of maritime activities, the development of maritime CT has an important strategic position [12]. However, due to the complex and variable marine environment, inconsistent standards of communication systems, and other factors, the development of marine communication lags behind land-based communication systems. Therefore, in order to adapt the marine communication to different environments and communication characteristics, different CTs are usually adopted. At the same time, the CT of the MIoT plays a crucial role in transmitting sensor data and realizing the functions of the MIoT. Below, researchers introduce the communication protocols and technologies used for the MIoT.
  • Cellular network: A cellular network is a wireless CT that enables communication with the internet by connecting MIoT devices to BSs on land. This communication can use different communication protocols such as 2G, 3G, 4G, and 5G. Cellular networks feature wide coverage and high-speed data transmission for a wide range of MIoT applications [13].
  • SC: SC is a CT widely used in maritime dense communication environments. MIoT devices can communicate with networks on land via satellite. SC has wide area coverage and global characteristics, suitable for MIoT applications far from land and ocean navigation.
  • Radio-frequency identification (RFID): RFID technology uses radio-frequency signals to identify and communicate with items or devices. In MIoT, RFID tags can be attached to goods, equipment, or individuals to transmit relevant information to data collection systems by interacting with RFID readers [14]. This CT is widely used in supply chain management (SCM), ship tracking, and item security [15].
  • Wireless sensor network (WSN): A WSN is a technology that enables the widespread deployment of sensor devices in the MIoT. The WSN uses wireless communication protocols such as Wi-Fi or Bluetooth to connect sensor devices to a network for communication and data transmission among devices. These sensor devices can self-organize into networks that transmit data via repeaters to BSs or data collection systems.
  • Submarine optical cable (SOC): An SOC is a CT that enables communication among devices by transmitting optical signals in an undersea cable. This CT is characterized by a high data transmission speed and reliability, and is suitable for MIoT applications that require large bandwidth and long-distance communication. SOCs are commonly used to connect MIoT devices to terrestrial networks and enable data transmission and remote control [16].
The application of these communication technologies in the MIoT depends on the specific needs and environment, and can be selected and combined according to the actual needs to meet the communication needs of the MIoT.
(3)
 Data processing and storage
Data acquisition, processing, and storage in the MIoT are complex and critical processes. MIoT systems typically consist of sensors and devices that monitor and collect various maritime environmental data, such as meteorological conditions, marine life, vessel status, etc. [17].
Data collection is the first step in the process. Sensors and equipment measure and monitor environmental parameters at sea and transmit these data to a central data processing system [18]. These sensors can be mounted on boats, buoys, pontoons, and even marine organisms to enable comprehensive data collection [19].
Once the data has been collected, the next step is data processing. This includes cleaning, validating, converting, and correcting the original data. The cleansing process helps remove noise and outliers from the data, ensuring the data’s accuracy and reliability [20]. The checksum remediation process is used to handle erroneous data caused by sensor failure or other problems. The conversion process may include data format conversion, unit conversion, and data standardization for further analysis and application.
Once data processing is complete, the next step is data storage. Data in the MIoT are often large-scale and real-time, requiring robust storage systems to house and manage these data [21]. Cloud computing platforms and distributed storage systems are among the commonly used solutions, which can provide highly scalable storage and processing power [22]. In addition, data backup and redundancy mechanisms are also indispensable to ensure the security and durability of data [23].
In MIoT systems, data collection, processing, and storage need to consider factors such as resource constraints, data transmission, and security. In order to achieve effective data utilization, data analysis, and mining, visualization and monitoring operations are also needed [24]. The combination of these steps can help to achieve a comprehensive understanding of the offshore environment and support decisions.
(4)
 Security and hidden dangers
When it comes to the MIoT, security and privacy issues are important issues to be seriously considered. The following lists some challenges and privacy issues that may be faced in the MIoT and provides some solutions, which can be seen in Table 1 [25].
(a)
 Security challenges:
  • Authentication and device security: Ensure that only authorized devices can connect to the network and take appropriate measures to prevent unauthorized access.
    Solution: Adopt strong authentication mechanisms, such as two-factor authentication, and use data encryption to secure devices and communications [26].
  • Data integrity and confidentiality: Ensure that data are not tampered with or stolen during transmission [27].
    Solution: Use end-to-end encryption to protect the data transmission and implement effective data-integrity checking mechanisms.
  • Physical security: Protect the device from malicious damage, physical intrusion, or damage to the device.
    Solution: Provide appropriate physical protection, such as security facilities and monitoring measures, to ensure the physical security of equipment and infrastructure [28].
(b)
 Privacy issues:
  • Personal privacy: Ensure that data collected, transmitted, and stored do not reveal personally identifiable or sensitive information [29].
    Solution: Use data masking techniques to minimize the collection of personally identifiable information, and take appropriate access control and privilege management measures.
  • Location tracking and behavior monitoring: Tracking and recording an individual’s location and behavior can be a privacy violation [30].
    Solution: Clearly inform users that data are being collected and provide users with options to opt in, such as authorizing or disabling location tracking.
  • Data sharing: Ensure that data collected from the MIoT is only shared where necessary and with appropriate anonymization and security safeguards in place [31].
    Solution: Establish clear data sharing policies and legal frameworks, limit the collection and use of data, and ensure that data sharing complies with privacy regulations.
(5) 
Channel modeling
In the A2S integrated MIoT, channel modeling is important for network deployment and the accurate prediction of wireless conditions, especially considering the existence of aerial nodes [32]. Specifically, the A2S integrated MIoT represents an advanced network architecture that tightly integrates the realms of the sky and the ocean. The successful implementation of such networks requires establishing reliable communication connections in different environments [33]. Channel modeling plays a crucial role in this context by studying the mathematical models of signal transmission processes to optimize channel transmission performance, enhance data transfer reliability, and adapt to changes in diverse environments [34].
  • First, channel modeling in A2S integrated MIoT contributes to understanding and analyzing the channel characteristics in the atmosphere and the ocean. The atmospheric and oceanic environments exert unique influences on the propagation of electromagnetic waves, involving phenomena such as multi-path propagation, fading, and scattering. By establishing accurate channel models, researchers can better comprehend the propagation characteristics of signals in these complex environments, providing robust theoretical support for network design.
  • Second, channel modeling aids in optimizing the communication system parameters of A2S integrated MIoT. Through simulation and analysis of channel transmission performance, the optimal parameters for modems, power control strategies, and spectrum allocation schemes can be determined, enhancing the efficiency and performance of the communication system. This optimization ensures that the network maintains high-quality communication connections under different atmospheric and oceanic conditions.
  • Third, channel modeling is crucial for the security of A2S integrated MIoT. By modeling the propagation paths of signals, potential sources of interference and security threats can be identified. This helps in designing and implementing effective security protocols and protection mechanisms, ensuring that the network remains stable and reliable in the face of malicious attacks.
In summary, channel modeling plays a vital role in the A2S integrated MIoT. A deep understanding and accurate modeling of channel characteristics provides the foundation for network design and optimization, ensuring that the network can deliver efficient, reliable, and secure communication services even in extreme atmospheric and oceanic conditions.

2. Air-to-Sea Integrated Technology

In an A2S integrated network, the communication equipment built by UAVs is used as the ABS, which has the characteristics of being low cost, easy to deploy, and being capable of on-demand deployment, and is widely used in military rescue, emergency communication, commercial aviation, and other fields. An ABS is a device used to provide wireless communication services, usually installed on an aircraft or other aerial platforms. An ABS usually consists of an antenna, a transmission device, a frequency converter, a processor, and a communication interface [35]. Its main function is to connect mobile communication devices (such as mobile phones) with ground BS or SC networks, allowing users to maintain communication connections during flight. The following introduces the categories of A2S BSs, their applications in various fields, and the role and advantages of ABSs in MIoT [36].
(1)
 Classification:
  • Mobile communication BS: This is the most common type of ABS used to provide mobile communication services. It connects the aircraft’s mobile communications equipment with ground BS or SC networks, enabling passengers and crew to make voice calls, send text messages, and use the internet during flight, among other things [37].
  • Other dedicated ABSs: In addition to mobile communication BSs, there are also some ABSs specifically designed for specific purposes. For example, military aircraft may be equipped with military communication BSs for military command and control communications; public safety BSs on aircraft may be used for emergency communications and rescue operations [38].
  • Satellite communication BS: This BS provides air communication connection through a satellite network. SC installs satellite dishes and terminal equipment on the aircraft for communication with satellite communication systems, thus enabling long-distance communication in flight [39].
  • UAV communication BS: UAVs can also be equipped with communication BSs for communication and control with ground control stations or other UAVs. This BS enables two-way communication between the drone and the operator, and supports DT and sensor information sharing [40].
(2)
 Application of ABSs in various fields:
  • Commercial aviation: ABSs play an important role in commercial aviation. They enable passengers to maintain mobile phone signals during the flight, make voice calls, send text messages, and use the internet. This provides a better passenger experience and communication connectivity, while also providing value-added communication services for airlines.
  • Military and security applications: ABSs play a key role in the military and security fields. ABSs on military aircraft provide military communications capabilities, support command and control, and share battlefield information. This is essential for coordination and decision-making in military operations.
  • Emergency rescue and disaster relief: ABSs provide critical communications support during emergency rescue and relief operations. They allow rescuers to maintain contact with ground command centers and share rescue information. This helps to improve rescue efficiency and the safety of rescue operations [41].
  • UAV applications: ABSs are also widely used in UAV applications. The BS equipped with the UAV can realize the communication and control between the UAV and the ground control station, as well as the collaborative operation among UAVs. This allows UAVs to play an important role in surveillance, logistics, agriculture, security, and other fields [42].
  • Satellite communications: ABSs are used in conjunction with SC systems to provide long-range communication connectivity in aviation services. Through SC, ABS can realize broadcasting, DT, and remote control on a global scale to meet the communication needs of aircraft and passengers [43].
(3)
 The role and advantages of ABSs in the MIoT
  • ABSs can be used as wireless communication BSs to provide stable networking connectivity for MIoT devices. By incorporating radio equipment and antennas, they enable efficient communication with IoT devices far from shore [44].
  • Air-based warfare can provide greater coverage. Their height and flexibility allow them to provide the signal coverage over a wider range than land BSs or SCs. As a result, more MIoT devices can be connected, enabling a wider range of data transmission and communication.
  • Air-based warfare also offers the advantages of rapid deployment and flexibility. They can move from one place to another at a relatively fast pace, providing communication support in different areas at sea depending on demand [45]. This flexibility can play an important role in situations of emergency, temporary assignment, or specific needs [46].
In summary, the role of ABSs in the MIoT is to provide stable communication connectivity, extended coverage, rapid deployment, and flexibility. These advantages make them an important part of enabling efficient MIoT communications.

3. Air-to-Sea Integrated MIoT Architecture

Figure 1 depicts an illustrative A2S integrated MIoT architecture. Specifically, the A2S integrated MIoT is based on the internet and connects a network of various objects and devices in the ocean, land, and air. Its architecture is a system architecture for realizing marine environment perception and DT [47]. It connects sensor devices in the air and on the ground, data centers, communication networks, and MIoT devices, and enables data acquisition, processing, and distribution through IoT platforms [20]. Below, researchers analyze the main architectural parts shown in the diagram, as well as the success stories of A2S integrated networks in the MIoT.
Figure 1. A2S integrated MIoT architecture.
(1)
 Perception layer: The perception layer refers to sensor nodes and IoT devices. Sensor nodes include various environmental detectors, position and attitude sensors, cameras, etc., which are connected to the MIoT through IoT devices and collect data and upload the data to the next layer of the network. It consists of various sensor devices deployed in the air, on the ground, and at sea, such as meteorological sensors, ocean sensors, position sensors, etc. These sensor devices transmit the collected data to the data center via wireless communication [48].
(2)
 Transport layer: The transport layer refers to the interaction of various communication networks. Including satellite positioning system, SC of the wireless network, etc., used to connect the various components. These communication networks provide reliable data transmission channels that support real-time marine environmental awareness and data exchange [49].
(3) 
Processing layer: The marine environmental data obtained from the perception layer are centrally stored and processed for cloud computing. A cloud-based service enablement platform is used to manage and operate the entire MIoT system [50]. The platform provides the equipment management, data management, and other functions, and supports the real-time monitoring, analysis, and sharing of the marine environmental data. At the same time, the IoT platform can also be integrated with other systems, such as shipborne navigation systems, maritime supervision systems, etc. Data centers are typically equipped with HPCAS devices and utilize data processing algorithms to analyze, mine, and visualize ocean data.
(4)
 Application layer: This refers to various specific application scenarios and business requirements, such as smart ports, marine environmental monitoring, ship safety, fisheries resource management, etc., which exchange data and share information through the MIoT. Based on the MIoT, various marine environment application services, such as marine early warning, marine resource management, route planning, etc. These application services use the marine environmental data provided by the IoT platform to provide decision support and information services for users in maritime, fisheries, energy, tourism, and other fields [51].
(5)
 Space segment: This segment aims to establish a security monitoring system, real-time monitoring of the security status and abnormal conditions of the IoT system, security protection and privacy protection, and timely warning and appropriate protective measures [52].
(6)
 Specific cases: Through the A2S integrated MIoT architecture, researchers can achieve comprehensive perception and monitoring of the marine environment, improve the safety and efficiency of maritime transportation, promote the sustainable development and utilization of marine resources, and provide decision-making support and services for relevant industries and government departments [53].
One of the successful examples of the A2S integrated MIoT is China’s “Nanhai No. 1” research ship. The ship is equipped with a large number of sensors and monitoring equipment that can monitor and collect data in real time on the marine environment, weather conditions, and the status of the ship. At the same time, the “Nanhai No.1” research ship can also upload data to ground BSs or central servers for processing and analysis through SC systems and its own data processing equipment [54]. These data can provide a scientific basis for related fields, such as marine environmental protection, fishery resource management, etc.
Another example is the port of Zhuhai in China. Zhuhai Port adopts A2S integrated MIoT technology and is equipped with a large number of sensors and monitoring equipment, which can conduct real-time monitoring and management of vessels, cargo, traffic conditions, environment, and other aspects in the port [55]. BSs and wireless network facilities are also set up in the port to facilitate the collection and transmission of information. These data can be used to optimize vessel entry and exit processes, improve the efficiency of cargo transportation, protect the environment, and more [56].
In addition, there are many other cases of A2S integrated MIoT applications around the world, such as the “Cape Town Underwater Cable” project in the United States, the “MOSES” project in Europe, and the “Marine Resource Utilization System” in Japan. These cases demonstrate the potential of the A2S integrated MIoT to improve the efficiency of marine resource utilization, improve transportation safety, and protect the marine environment.

This entry is adapted from the peer-reviewed paper 10.3390/jmse12010011

References

  1. Chen, Y.; Zhang, Z.; Li, B. Research on secure communication technology in collaborative NOMA system based on non-perfect channel information. J. Univ. Electron. Sci. Technol. China 2020, 49, 674–679.
  2. Liu, G. Thinking on the construction of digital oilfield driven by Internet of Things technology. China Manag. Informatiz. 2020, 23, 78–79.
  3. Mu, Q.; Chai, Y.; Song, P. Research on development status and standard system of edge computing. Inf. Commun. Technol. 2022, 14, 23–30.
  4. Jiang, L.; Chang, X.; Yang, R. Model-Based Comparison of Cloud-Edge Computing Resource Allocation Policies. Comput. J. 2020, 49, 302–309.
  5. Xu, X.; Gu, R.; Dai, F. Multi-objective computation offloading for Internet of Vehicles in cloud-edge computing. Wirel. Netw. J. Mob. Commun. Comput. Inf. 2020, 26, 12–15.
  6. Ding, T.; Chen, F.; Xie, T. Design concept of guard and service support information system of ship ambulance station based on Internet of Things technology. Southwest Def. Med. 2020, 30, 168–169.
  7. Liu, Y.; Jiang, R.; Zhang, Z. Development and application of personnel information and emergency management system for offshore production facilities based on Internet of Things technology. China Informatiz. 2019, 19, 73–75.
  8. Wang, W. Application of Internet of Things technology in ship wireless sensor network. Sci. Technol. Commun. 2018, 10, 139–140.
  9. Ma, L. Application of Internet of Things technology in fishing vessel operation management and dispatching system. Ship Sci. Technol. 2018, 40, 172–174.
  10. Xu, L. Efficient ship dispatching system based on Internet of Things technology. Ship Sci. Technol. 2017, 39, 158–160.
  11. Xiao, Z. Application of Internet of Things technology in maritime surveillance system. Ship Sci. Technol. 2015, 37, 203–206.
  12. Yang, Q. Ship intelligent fire protection system based on Internet of Things technology. J. Zhejiang Inst. Water Resour. Hydropower 2015, 27, 67–71.
  13. He, Y.; Huang, F.; Wang, D.; Zhang, R.; Gu, X.; Pan, J. A NOMA and MRC enabled framework in drone-relayed vehicular networks: Height/trajectory optimization and performance analysis. IEEE Internet Things J. 2023, 10, 22305–22319.
  14. He, Y.; Wang, D.; Huang, F.; Zhang, R.; Min, L. A D2I and D2D collaboration framework for resource management in ABS-assisted post-disaster emergency networks. IEEE Trans. Veh. Technol. 2023; Early Access.
  15. Chen, M.; Chang, U.; Saad, W. Artificial neural networks-based machine learning for wireless networks:a tutorial. Southwest Def. Med. 2022, 78, 302–313.
  16. Liolis, K.; Schlueter, G.; Krause, J. Cognitive radio scenarios for satellite communications: The CoRaSat approach. IEEE Future Netw. Mob. Summit 2013, 27, 1–10.
  17. Guo, P. Application of Internet of Things based on RFID technology in remote logistics management of petroleum materials in Bohai Sea. Inf. Comput. (Theor. Ed.) 2014, 3, 180–181.
  18. Zhou, X. Internet of things technology in the application of direct flights “table”. Water Transp. China 2013, 38–39.
  19. Wang, D.; Wu, M.; Wei, Z.; Yu, K.; Min, L.; Mumtaz, S. Uplink secrecy performance of RIS-based RF/FSO three-dimension heterogeneous networks. IEEE Trans. Wirel. Commun. 2023; Early Access.
  20. Wang, D.; He, T.; Lou, Y.; Pang, L.; He, Y.; Chen, H.-H. Double-edge computation offloading for secure integrated space-air-aqua networks. IEEE Internet Things J. 2023, 10, 15581–15593.
  21. Wang, D.; Wu, M.; Chakraborty, C.; Min, L.; He, Y.; Guduri, M. Covert communications in air-ground integrated urban sensing networks enhanced by federated learning. IEEE Sens. J. 2023.
  22. Shen, X.; Liao, W.; Yin, Q. A novel wireless resource management for the 6G-enabled high-density Internet of Things. IEEE Wirel. Commun. 2022, 29, 32–39.
  23. Wang, F.; Liang, Y.; Zheng, P. Research on intelligent supervision of offshore crude oil transfer based on Internet of Things technology. J. Ningbo Univ. (Sci. Technol. Ed.) 2013, 26, 91–94.
  24. Whang, Z.; Leng, P.; Xiong, K. Allocation Strategy of Multiple Agents for Cooperative Sensing of UAV Swarms. Chin. J. Internet Things 2023, 7, 18–26.
  25. Jiang, K.; Cao, Y.; Zhou, H.; Ren, X.; Zhu, Y.; Lin, H. Connected Vehicle Edge Intelligence: Concept, Architecture, Problems, Implementation and Prospect. Chin. J. Internet Things 2023, 7, 37–48.
  26. Liao, C.; Chen, J.; Liang, G.; Xie, X.; Lu, X. Intelligent SDN Service Quality Optimization Algorithm Based on Deep Reinforcement Learning. Chin. J. Internet Things 2023, 7, 73–82.
  27. Zhang, B.; Wang, X.; Xu, Y.; Li, W.; Han, H.; Song, S. Research on Multi-domain Collaborative Anti-interference Method Based on Multi-agent Deep Reinforcement Learning. Chin. J. Internet Things 2022, 6, 104–116.
  28. Zhang, H.; Zhou, A.; Ma, H. Research on Real-time Video Flow Control and Mobile Terminal Training Method Based on Reinforcement Learning. Chin. J. Internet Things 2022, 6, 1–13.
  29. Yu, H.; Lin, Y.; Jia, L.; Li, Q.; Zhang, Y. Distributed Strategy of Communication-constrained UAV Swarm for Multi-target Rescue. Chin. J. Internet Things 2022, 6, 103–112.
  30. Luo, Z.; Jiang, C.; Liu, L.; Zheng, X.; Ma, H. Research on Intelligent Workshop Scheduling Method Based on Deep Reinforcement Learning. Chin. J. Internet Things 2022, 6, 53–64.
  31. Wang, M.; Li, Z.; Chen, Y.; Hong, G.; Su, W. Analysis and research on security authentication technology in Internet of Vehicles. Chin. J. Internet Things 2021, 5, 106–114.
  32. Wang, C.-X.; Lv, Z.; Gao, X.; You, X.; Hao, Y.; Haas, H. Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios. IEEE Trans. Veh. Technol. 2022, 71, 9159–9173.
  33. He, Y.; Wang, C.X.; Chang, H.; Feng, R.; Sun, J.; Zhang, W.; Hao, Y.; Aggoune, E.H.M. A novel 3-D beam domain channel model for maritime massive MIMO communication systems using uniform circular arrays. IEEE Trans. Commun. 2023, 71, 2487–2502.
  34. Liu, Y.; Wang, C.-X.; Chang, H.; He, Y.; Bian, J. A novel non-stationary 6G UAV channel model for maritime communications. IEEE J. Sel. Areas Commun. 2021, 39, 2992–3005.
  35. Du, J.; Xue, N.; Sun, Y.; Jing, J.; Li, S.; Lu, G. Optimization Strategy of Vehicle Edge Computing Network Based on NOMA. Chin. J. Internet Things 2021, 5, 19–26.
  36. Liu, X. Research and simulation of maritime wireless data communication network based on AIS. Ship Sci. Technol. 2014, 36, 144–147.
  37. Kong, X. Research on clustering routing algorithm of wireless sensor network. Yangzhou Yangzhou Univ. 2014, 19, 34–37.
  38. Li, W.; Shen, L.; Hu, J. Adaptive Energy Saving Route Optimization Algorithm for Inter-cluster Communication of Sensor Network. J. Commun. 2012, 33, 10–19.
  39. Ji, Z. A Multi-QoS Adaptive Routing Algorithm for Real-time Network. Hangzhou Zhejiang Univ. 2004, 12, 101–104.
  40. Lei, M.; Ribas, J.; Wang, W. Rate control in DCT video coding for low delay communications. IEEE Trans. Circuits Syst. Video Technol. 1999, 9, 172–185.
  41. Lee, J.; Chiang, L.; Zhang, Y. Scalable rate control for MPEG-4 video. IEEE Trans. Circuits Syst. Video Technol. 2000, 10, 878–894.
  42. Aggarwal, S.; Kumar, N.; Tanwar, S. Blockchain-envisioned UAV communication using 6G networks: Open issues, use cases, and future directions. IEEE Internet Things J. 2021, 8, 5416–5441.
  43. Wang, H.; Li, H.; Dong, Q. Design of network video monitoring integral system onboard. Ship Sci. Technol. 2007, 29, 129–133.
  44. Zeng, G.; Xue, H.; Mao, Y. VOX Technology Based on Digital Signal Processing. Ship Sci. Technol. 2012, 34, 125–127.
  45. Jia, J.; Wang, H.; Xia, X. Research on access selection and switching control technology of air-ground integrated network. Radio Commun. Technol. 2023, 9, 1–8.
  46. Liu, J. Research on application and development of maritime satellite communication technology. China New Commun. 2023, 25, 3–5.
  47. Zi, L.; Peng, D.; Lin, L. Adaptive Data Collection and Offloading in Multi-UAV-Assisted Maritime IoT Systems: A Deep Reinforcement Learning Approach. Remote Sens. 2023, 15, 77–79.
  48. Wang, A.; Wang, Y.; Zhao, L. Application and implementation of LoRa-based technology in monitoring and tracking of offshore oil offshore material carriers. Internet Things Technol. 2022, 12, 111–113.
  49. Shen, Y.; Zhang, X.; Liu, X. Monitoring system of natural resource elements with space-ground integration and its application. Resour. Sci. 2022, 44, 1696–1706.
  50. Wang, C. Application and data analysis of offshore wind turbine tower tilt monitoring. Eng. Technol. Res. 2022, 7, 12–15.
  51. Li, J.; Sun, J. Application of Internet of Things technology in offshore drilling and completion. Oil Drill. Prod. Technol. 2022, 44, 233–240.
  52. Song, W. Navis Engineering Turns to KVH Watch Cloud Connect for Maritime IoT Solution; Dynamic positioning systems manufacturer Navis Engineering will offer KVH Watch services to enable remote monitoring of equipment. M2 Press Wire 2022, 25, 87–89.
  53. Kang, J.; Yuan, Z.; Chen, J. Design of fire IoT gateway based on STM32. Ind. Control Comput. 2021, 34, 118–119.
  54. Shabih, K.; Sadaf, H.; Muhammad, J. Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices. Energies 2021, 14, 772–778.
  55. Guo, H.; Ren, B.; Xie, Y. Research on space-ground integrated monitoring and early warning platform for forest pests in Qinling. Sci. Technol. Inf. 2021, 19, 80–82.
  56. Fu, Z.; Ji, J.; Ji, F. Marine heterogeneous IoT architecture for intelligent offshore equipment. Telecommun. Sci. 2021, 37, 34–39.
More
This entry is offline, you can click here to edit this entry!
Video Production Service