17,761 research outputs found
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
Analysing Fairness of Privacy-Utility Mobility Models
Preserving the individuals' privacy in sharing spatial-temporal datasets is
critical to prevent re-identification attacks based on unique trajectories.
Existing privacy techniques tend to propose ideal privacy-utility tradeoffs,
however, largely ignore the fairness implications of mobility models and
whether such techniques perform equally for different groups of users. The
quantification between fairness and privacy-aware models is still unclear and
there barely exists any defined sets of metrics for measuring fairness in the
spatial-temporal context. In this work, we define a set of fairness metrics
designed explicitly for human mobility, based on structural similarity and
entropy of the trajectories. Under these definitions, we examine the fairness
of two state-of-the-art privacy-preserving models that rely on GAN and
representation learning to reduce the re-identification rate of users for data
sharing. Our results show that while both models guarantee group fairness in
terms of demographic parity, they violate individual fairness criteria,
indicating that users with highly similar trajectories receive disparate
privacy gain. We conclude that the tension between the re-identification task
and individual fairness needs to be considered for future spatial-temporal data
analysis and modelling to achieve a privacy-preserving fairness-aware setting
Effective link operation duration: a new routing metric for mobile ad hoc networks
The dynamic topology of mobile ad hoc networks (MANETs) is caused by node mobility and fading of the wireless link. Link reliability is often measured by the estimated lifetime and the stability of a link. In this paper we propose that the stability of a link can be represented by the time duration in which the two nodes at each end of a link are within each other’s transmission range and the fading is above an acceptable threshold. A novel routing metric, called effective link operation duration (ELOD), is proposed and implemented into AODV (AODV-ELOD). Simulation results show that proposed AODVELOD outperforms both AODV and the Flow Oriented Routing Protocol (FORP)
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