14,618 research outputs found
Integrating static and dynamic information for routing traffic
The efficiency of traffic routing on complex networks can be reflected by two
key measurements i.e. the system capacity and the average data packets travel
time. In this paper, we propose a mixing routing strategy by integrating local
static and dynamic information for enhancing the efficiency of traffic on
scale-free networks. The strategy is governed by a single parameter. Simulation
results show that there exists a optimal parameter value by considering both
maximizing the network capacity and reducing the packet travel time. Comparing
with the strategy by adopting exclusive local static information, the new
strategy shows its advantages in improving the efficiency of the system. The
detailed analysis of the mixing strategy is provided. This work suggests that
how to effectively utilize the larger degree nodes plays the key role in the
scale-free traffic systems.Comment: 5 pages, 5 figure
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data
Accurately modeling traffic speeds is a fundamental part of efficient
intelligent transportation systems. Nowadays, with the widespread deployment of
GPS-enabled devices, it has become possible to crowdsource the collection of
speed information to road users (e.g. through mobile applications or dedicated
in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced
speed data also brings very important challenges, such as the highly variable
measurement noise in the data due to a variety of driving behaviors and sample
sizes. When not properly accounted for, this noise can severely compromise any
application that relies on accurate traffic data. In this article, we propose
the use of heteroscedastic Gaussian processes (HGP) to model the time-varying
uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a
HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of
sample size information (probe vehicles per minute) as well as previous
observed speeds, in order to more accurately model the uncertainty in observed
speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we
empirically show that the proposed heteroscedastic models produce significantly
better predictive distributions when compared to current state-of-the-art
methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies
(Elsevier
A Trust Based Congestion Aware Hybrid Ant Colony Optimization Algorithm for Energy Efficient Routing in Wireless Sensor Networks (TC-ACO)
Congestion is a problem of paramount importance in resource constrained
Wireless Sensor Networks, especially for large networks, where the traffic
loads exceed the available capacity of the resources. Sensor nodes are prone to
failure and the misbehavior of these faulty nodes creates further congestion.
The resulting effect is a degradation in network performance, additional
computation and increased energy consumption, which in turn decreases network
lifetime. Hence, the data packet routing algorithm should consider congestion
as one of the parameters, in addition to the role of the faulty nodes and not
merely energy efficient protocols. Unfortunately most of the researchers have
tried to make the routing schemes energy efficient without considering
congestion factor and the effect of the faulty nodes. In this paper we have
proposed a congestion aware, energy efficient, routing approach that utilizes
Ant Colony Optimization algorithm, in which faulty nodes are isolated by means
of the concept of trust. The merits of the proposed scheme are verified through
simulations where they are compared with other protocols.Comment: 6 pages, 5 figures and 2 tables (Conference Paper
- …