7,045 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Performance comparison of dynamic vehicle routing methods for minimizing the global dwell time in upcoming smart cities
Traffic jams in urban scenarios are often caused by bottlenecks related to
the street topology and road infrastructure, e.g. traffic lights and merging of
lanes. Instead of addressing traffic flow optimization in a static way by
extending the road capacity through constructing additional streets, upcoming
smart cities will exploit the availability of modern communication technologies
to dynamically change the mobility behavior of individual vehicles. The
underlying overall goal is to minimize the total dwell time of the vehicles
within the road network. In this paper, different bottleneck-aware methods for
dynamic vehicle routing are compared in comprehensive simulations. As a
realistic evaluation scenario, the inner city of Dusseldorf is modeled and the
mobility behavior of the cars is represented based on real-world traffic flow
data. The simulation results show, that the consideration of bottlenecks in a
routing method decreased the average travel time by around 23%. Based on these
results a new routing method is created which further reduces the average
travel time by around 10%. The simulations further show, that the
implementation of dynamic lanes in inner cities most of the time only shift
traffic congestion to following bottlenecks without reducing the travel times
Architectures for Wireless Sensor Networks
Various architectures have been developed for wireless sensor networks. Many of them leave to the programmer important concepts as the way in which the inter-task communication and dynamic reconfigurations are addressed. In this paper we describe the characteristics of a new architecture we proposed - the data-centric architecture. This architecture offers an easy way of structuring the applications designed for wireless sensor nodes that confers them superior performances
Computational Challenges in Cooperative Intelligent Urban Transport
This report documents the talks and group work of Dagstuhl Seminar 16091
“Computational Challenges in Cooperative Intelligent Urban Transport”. This
interdisciplinary seminar brought researchers together from many fields
including computer science, transportation, operations research, mathematics,
machine learning and artificial intelligence. The seminar included two formats
of talks: several minute research statements and longer overview talks. The
talks given are documented here with abstracts. Furthermore, this seminar
consisted of significant amounts of group work that is also documented with
short abstracts detailing group discussions and planned outcomes
Will SDN be part of 5G?
For many, this is no longer a valid question and the case is considered
settled with SDN/NFV (Software Defined Networking/Network Function
Virtualization) providing the inevitable innovation enablers solving many
outstanding management issues regarding 5G. However, given the monumental task
of softwarization of radio access network (RAN) while 5G is just around the
corner and some companies have started unveiling their 5G equipment already,
the concern is very realistic that we may only see some point solutions
involving SDN technology instead of a fully SDN-enabled RAN. This survey paper
identifies all important obstacles in the way and looks at the state of the art
of the relevant solutions. This survey is different from the previous surveys
on SDN-based RAN as it focuses on the salient problems and discusses solutions
proposed within and outside SDN literature. Our main focus is on fronthaul,
backward compatibility, supposedly disruptive nature of SDN deployment,
business cases and monetization of SDN related upgrades, latency of general
purpose processors (GPP), and additional security vulnerabilities,
softwarization brings along to the RAN. We have also provided a summary of the
architectural developments in SDN-based RAN landscape as not all work can be
covered under the focused issues. This paper provides a comprehensive survey on
the state of the art of SDN-based RAN and clearly points out the gaps in the
technology.Comment: 33 pages, 10 figure
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