11,909 research outputs found
Constrained time-critical routing for multiple mobile agents
In
the
Information
Era,
integrating
technology
with
the
real-Ââworld
environment
is
a
trending
paradigm
that
attracts
researchers
in
many
fields.
For
example,
Smart
Citiesâ
applications
integrate
information
technology
with
existing
infrastructures
to
optimize
many
aspects,
such
as
time,
energy,
and
cost.
However,
many
difficulties
show
up,
including
a
time
constraint
in
some
of
the
applications
when
it
is
implemented
in
the
real
world.
One
of
these
applications
is
smart
transportation.
This
thesis
explores
Vehicle
Routing
Problem
(VRP)
and
introduces
a
variant
of
VRP
that
relates
to
time
constraints
called
VRP
with
Time
Window
(VRPTW).
Firstly,
the
problem
is
formulated
into
a
linear
mathematic
program
with
the
objective
of
minimizing
the
number
of
agents
used
in
routing
and
minimizing
the
time
spent
in
agentsâ
routing.
A
heuristic
approach
solves
this
problem
by
using
a
combined
of
A*
Search
and
Ruin
and
Recreate
algorithms
to
find
the
shortest
path
for
agents.
Additionally,
the
Local
Search
Algorithm
is
used
to
minimize
the
number
of
agents
used
in
routing.
Two
case
studies
test
this
heuristic
approach:
a
case
study
in
changing
number
of
nodes,
and
a
case
study
in
changing
nodesâ
duration.
The
results
are
represented
in
numbers
to
show
the
reduced
number
of
agents
and
time
cost,
while
graph
plots
show
the
agentsâ
routings.Department of Computer ScienceBackground -- Methodologies and design -- Hueristic approach -- Simulation results.Thesis (M.S.
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications
Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability
Routing Using Safe Reinforcement Learning
The ever increasing number of connected devices has lead to a metoric rise in the amount data to be processed. This has caused computation to be moved to the edge of the cloud increasing the importance of efficiency in the whole of cloud. The use of this fog computing for time-critical control applications is on the rise and requires robust guarantees on transmission times of the packets in the network while reducing total transmission times of the various packets.
We consider networks in which the transmission times that may vary due to mobility of devices, congestion and similar artifacts. We assume knowledge of the worst case tranmssion times over each link and evaluate the typical tranmssion times through exploration. We present the use of reinforcement learning to find optimal paths through the network while never violating preset deadlines. We show that with appropriate domain knowledge, using popular reinforcement learning techniques is a promising prospect even in time-critical applications
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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