1,075 research outputs found
A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches
Multipath TCP (MPTCP) has been widely used as an efficient way for
communication in many applications. Data centers, smartphones, and network
operators use MPTCP to balance the traffic in a network efficiently. MPTCP is
an extension of TCP (Transmission Control Protocol), which provides multiple
paths, leading to higher throughput and low latency. Although MPTCP has shown
better performance than TCP in many applications, it has its own challenges.
The network can become congested due to heavy traffic in the multiple paths
(subflows) if the subflow rates are not determined correctly. Moreover,
communication latency can occur if the packets are not scheduled correctly
between the subflows. This paper reviews techniques to solve the
above-mentioned problems based on two main approaches; non data-driven
(classical) and data-driven (Machine Learning) approaches. This paper compares
these two approaches and highlights their strengths and weaknesses with a view
to motivating future researchers in this exciting area of machine learning for
communications. This paper also provides details on the simulation of MPTCP and
its implementations in real environments.Comment: 13 pages, 7 figure
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
Use of Clustering-based Routing Protocols in Low Power and Lossy Networks � A Survey
Internet of Things (IoT) is the one of the emerging field today, which consists of various resource-constrained devices that are limited in resources and work in the lossy wireless network. Therefore, IoT requires efficient routing protocol so that devices can communicate fast and power efficiently. Among different protocols available for wireless networks, Routing Protocol for Low Power and Lossy Networks (RPL) is a protocol specially standardized by IETF for efficient communication between IoT devices. Routing technique is one of the important factors of a routing protocol, which affects the performance of a protocol. In recent years, researchers contributed to improving RPL performance by providing various solutions and clustering is one of those ways to improve RPL performance by using Cluster- parent based Destination Oriented Directed Acyclic Graph (DODAG). In this paper, we discuss the various clustering-based routing protocols in a Low power and lossy networks (LLNs) and concludes that this survey might be helpful for future researchers
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