Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

With the rapid development of marine activities, there has been an increasing number of maritime mobile terminals, as well as a growing demand for high-speed and ultra-reliable maritime communications to keep them connected. Traditionally, the maritime Internet of Things (IoT) is enabled by maritime satellites. However, satellites are seriously restricted by their high latency and relatively low data rate. As an alternative, shore & island-based base stations (BSs) can be built to extend the coverage of terrestrial networks using fourth-generation (4G), fifth-generation (5G), and beyond 5G services. Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs. Despite of all these approaches, there are still open issues for an efficient maritime communication network (MCN). For example, due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications, conventional communication and networking theories and methods should be tailored for maritime scenarios. Towards this end, we provide a survey on the demand for maritime communications, the state-of-the-art MCNs, and key technologies for enhancing transmission efficiency, extending network coverage, and provisioning maritime-specific services. Future challenges in developing an environment-aware, service-driven, and integrated satellite-air-ground MCN to be smart enough to utilize external auxiliary information, e.g., sea state and atmosphere conditions, are also discussed

Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems

Cooperative Cyber-Physical Systems (Co-CPSs) can be enabled using wireless communication technologies, which in principle should address reliability and safety challenges. Safety for Co-CPS enabled by wireless communication technologies is a crucial aspect and requires new dedicated design approaches. In this paper, we provide an overview of five Co-CPS use cases, as introduced in our SafeCOP EU project, and analyze their safety design requirements. Next, we provide a comprehensive analysis of the main existing wireless communication technologies giving details about the protocols developed within particular standardization bodies. We also investigate to what extent they address the non-functional requirements in terms of safety, security and real time, in the different application domains of each use case. Finally, we discuss general recommendations about the use of different wireless communication technologies showing their potentials in the selected real-world use cases. The discussion is provided under consideration in the 5G standardization process within 3GPP, whose current efforts are inline to current gaps in wireless communications protocols for Co-CPSs including many future use casesinfo:eu-repo/semantics/publishedVersio

Overhead in available bandwidth estimation tools: Evaluation and analysis

Current Available Bandwidth Estimation Tools (ABET) insert into the network probing packets to perform a single estimation. The utilization of these packets makes ABET intrusive and prone to errors since they consume part of the available bandwidth they are measuring. This paper presents a comparative of Overhead Estimation Tools (OET) analysis of representative ABET: Abing, Diettopp, Pathload, PathChirp, Traceband, IGI, PTR, Assolo, and Wbest. By using Internet traffic, the study shows that the insertion of probing packets is a factor that affects two metrics associated to the estimation. First, it is shown that the accuracy is affected proportionally to the amount of probing traffic. Secondly, the Estimation Time (ET) is increased in high congested end-to-end links when auto-induced congestion tools are use

Overhead in Available Bandwidth Estimation Tools: Evaluation and Analysis

The current Available Bandwidth Estimation Tools (ABET's) to perform an estimation, using probes packets are inserted into the network. The utilization These packages, makes ABET's are intrusive and consumes part of which is measuring bandwidth to noise known as "Overhead Estimation Tools" (OET); it’s can produce negative effects on measurements performed by the ABET. This paper presents a complete and comparative analysis of behavior of Available Bandwidth (av_bw), of the ABET's most representative, as well as: Abing, Diettopp, Pathload, PathChirp, Traceband, IGI, PTR, Assolo and Wbest. The study with real Internet traffic, shows the percentage of test that is a factor packets affecting two main aspects of the estimation. The first, the accuracy, and increased indicating that EOT is directly proportional to the percentage of RE, reaching up to 70% in the tool evaluated with most of 30% of Cross-Traffic (CT). And second, the techniques used to send probes packets highly influences the Estimation Time (ET), where some tools that use slops spend up to 240s to converge when there is 60% CT in the network, ensuring that the estimate this technique av_bw highly congested channel, OET as much is used, resulting in inaccuracies in measurement

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control