74,422 research outputs found
Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studies—often clustered under the collective term of
geocomputation, as they apply to geography—are ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale
Reliability of Mobile Agents for Reliable Service Discovery Protocol in MANET
Recently mobile agents are used to discover services in mobile ad-hoc network
(MANET) where agents travel through the network, collecting and sometimes
spreading the dynamically changing service information. But it is important to
investigate how reliable the agents are for this application as the
dependability issues(reliability and availability) of MANET are highly affected
by its dynamic nature.The complexity of underlying MANET makes it hard to
obtain the route reliability of the mobile agent systems (MAS); instead we
estimate it using Monte Carlo simulation. Thus an algorithm for estimating the
task route reliability of MAS (deployed for discovering services) is proposed,
that takes into account the effect of node mobility in MANET. That mobility
pattern of the nodes affects the MAS performance is also shown by considering
different mobility models. Multipath propagation effect of radio signal is
considered to decide link existence. Transient link errors are also considered.
Finally we propose a metric to calculate the reliability of service discovery
protocol and see how MAS performance affects the protocol reliability. The
experimental results show the robustness of the proposed algorithm. Here the
optimum value of network bandwidth (needed to support the agents) is calculated
for our application. However the reliability of MAS is highly dependent on link
failure probability
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