20-03-2014, 04:12 PM
[u]Adaptive Forwarding Delay Control for VANET Data Aggregation[/u]
Adaptive Forwarding Delay.pdf (Size: 727.38 KB / Downloads: 14)
Abstract
In-network data aggregation is a useful technique to reduce redundant data and to improve communication efficiency.
Traditional data aggregation schemes for wireless sensor networks usually rely on a fixed routing structure to ensure data can be
aggregated at certain sensor nodes. However, they cannot be applied in highly mobile vehicular environments. In this paper, we
propose an adaptive forwarding delay control scheme, namely Catch-Up, which dynamically changes the forwarding speed of nearby
reports so that they have a better chance to meet each other and be aggregated together. The Catch-Up scheme is designed based on
a distributed learning algorithm. Each vehicle learns from local observations and chooses a delay based on learning results. The
simulation results demonstrate that our scheme can efficiently reduce the number of redundant reports and achieve a good trade-off
between delay and communication overhead.
INTRODUCTION
VEHICULAR Ad hoc Networks (VANETs) have been
regarded as an emerging and promising field in both
industry and academia. It has potential to improve the
efficiency and safety of future highway systems. One good
example is traffic congestion detection. Once a vehicle
detects the number of its neighboring vehicles exceeds a
certain limit, it will broadcast a warning to the vehicles
following behind. This warning could travel a rather long
distance so that vehicles, possibly several kilometers away,
can have enough time to choose an alternate route. Since each
vehicle in VANETs is able to detect traffic conditions and
generate traffic reports distributedly and independently,
redundant reports can be generated and forwarded in the
network, consuming a considerable amount of bandwidth.
Bandwidth utilization is an important issue for VANET
communications. For example, in Dedicated Short Range
Communications [1] (DSRC), the communication range can
be up to 1,000 m, while the data rate is only 6 to 27 Mbps.
Imagine the rush hour traffic, it is very likely that there are
more than a hundred vehicles within the range of 1,000 m,
all of which share a quite limited bandwidth.
RELATED WORK
In mobile ad hoc networks, data aggregation is studied in
two main aspects: report routing and data management.
Report routing focuses on routing problems such as when
and where two (or more) reports can meet each other and
be aggregated together. Data management studies data
representation, data processing, and data merging calcula-
tion. In this paper, we focus on the routing-related issues in
VANET data aggregation.
In the report routing aspect, great efforts have been made
in sensor networks in the past few years. Since sensor nodes
are usually static, it is feasible to establish a routing
structure to facilitate data aggregation. In [6] and [8], a
fixed forwarding tree is established in advance, and sensor
nodes periodically measure the environments and generate
reports, which, later, are aggregated at tree forks. In [10], a
semistructure approach was introduced to deal with event-
based applications. However, in VANETs, it’s not feasible
to maintain a structure or semistructure due to the highly
mobile nature of VANETs. Fan et al. [9] proposed a
structure-free data aggregation protocol. This is a probabil-
istic approach in which each node applies a randomized
delay before forwarding the report to the next hop. Due to
the differences in propagation speed, a report has a chance
to be aggregated with other reports. This approach helps to
increase the opportunity of report encounters, but there is
no guarantee on when or where reports from a single event
source can be aggregated together.
SYSTEM MODEL
In this section, we introduce the system model which is
applicable to our aggregation scheme.
Basic assumptions. We assume each vehicle is equipped
with an on-board computing device, a wireless radio, a GPS
device, and digital maps. Time in different vehicles can be
synchronized by GPS technologies. For the purpose of
neighbor discovery, each vehicle periodically broadcasts
beacons so that neighboring vehicles are aware of its
identity, speed, and GPS location.
Applications. Our work focuses on providing generic data
aggregation service for various applications, such as traffic
congestion detection, road condition (i.e., icy road surface)
detection, pothole detection. A modern vehicle is usually
equipped with a number of sensors, including speedometer,
camera, stability control sensor, suspension system sensor,
etc. The sensor readings from a single vehicle may be
inaccurate or even erroneous. For example, when a vehicle
runs over a pothole, the on-board computer may obtain a
80 percent probability that there is a pothole. Therefore, it is
desired that information from different vehicles can be
merged to increase its accuracy. From the perspective of
applications, it is important to efficiently aggregate raw
data to improve the accuracy of observation.
CONCLUSION
In this paper, we have presented a data aggregation scheme
for VANETs based on distributed learning. Essentially, the
difference in propagation speed helps reports encounter
each other, and we formulate this issue as a distributed
learning problem where vehicles adaptively choose for-
warding delays to make nearby reports have a better chance
to meet each other. In order to avoid introducing extra
communication overhead, we propose a new paradigm of
distributed learning—“Learning-From-Others.” We design
a Q-learning-based algorithm to implement this new
paradigm, and our simulation results demonstrate the
effectiveness of our scheme.