40,629 research outputs found
Fuzzy based load and energy aware multipath routing for mobile ad hoc networks
Routing is a challenging task in Mobile Ad hoc Networks (MANET) due to their dynamic topology and lack of central administration. As a consequence of un-predictable topology changes of such networks, routing protocols employed need to accurately capture the delay, load, available bandwidth and residual node energy at various locations of the network for effective energy and load balancing. This paper presents a fuzzy logic based scheme that ensures delay, load and energy aware routing to avoid congestion and minimise end-to-end delay in MANETs. In the proposed approach, forwarding delay, average load, available bandwidth and residual battery energy at a mobile node are given as inputs to a fuzzy inference engine to determine the traffic distribution possibility from that node based on the given fuzzy rules. Based on the output from the fuzzy system, traffic is distributed over fail-safe multiple routes to reduce the load at a congested node. Through simulation results, we show that our approach reduces end-to-end delay, packet drop and average energy consumption and increases packet delivery ratio for constant bit rate (CBR) traffic when compared with the popular Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol
MAGDA: A Mobile Agent based Grid Architecture
Mobile agents mean both a technology
and a programming paradigm. They allow for a
flexible approach which can alleviate a number
of issues present in distributed and Grid-based
systems, by means of features such as migration,
cloning, messaging and other provided mechanisms.
In this paper we describe an architecture
(MAGDA – Mobile Agent based Grid Architecture)
we have designed and we are currently
developing to support programming and execution
of mobile agent based application upon Grid
systems
GCP: Gossip-based Code Propagation for Large-scale Mobile Wireless Sensor Networks
Wireless sensor networks (WSN) have recently received an increasing interest.
They are now expected to be deployed for long periods of time, thus requiring
software updates. Updating the software code automatically on a huge number of
sensors is a tremendous task, as ''by hand'' updates can obviously not be
considered, especially when all participating sensors are embedded on mobile
entities. In this paper, we investigate an approach to automatically update
software in mobile sensor-based application when no localization mechanism is
available. We leverage the peer-to-peer cooperation paradigm to achieve a good
trade-off between reliability and scalability of code propagation. More
specifically, we present the design and evaluation of GCP ({\emph Gossip-based
Code Propagation}), a distributed software update algorithm for mobile wireless
sensor networks. GCP relies on two different mechanisms (piggy-backing and
forwarding control) to improve significantly the load balance without
sacrificing on the propagation speed. We compare GCP against traditional
dissemination approaches. Simulation results based on both synthetic and
realistic workloads show that GCP achieves a good convergence speed while
balancing the load evenly between sensors
Collaborative Storage Management In Sensor Networks
In this paper, we consider a class of sensor networks where the data is not
required in real-time by an observer; for example, a sensor network monitoring
a scientific phenomenon for later play back and analysis. In such networks, the
data must be stored in the network. Thus, in addition to battery power, storage
is a primary resource: the useful lifetime of the network is constrained by its
ability to store the generated data samples. We explore the use of
collaborative storage technique to efficiently manage data in storage
constrained sensor networks. The proposed collaborative storage technique takes
advantage of spatial correlation among the data collected by nearby sensors to
significantly reduce the size of the data near the data sources. We show that
the proposed approach provides significant savings in the size of the stored
data vs. local buffering, allowing the network to run for a longer time without
running out of storage space and reducing the amount of data that will
eventually be relayed to the observer. In addition, collaborative storage
performs load balancing of the available storage space if data generation rates
are not uniform across sensors (as would be the case in an event driven sensor
network), or if the available storage varies across the network.Comment: 13 pages, 7 figure
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