A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

A framework for modelling mobile radio access networks for intelligent fault management

Postprin

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

Analysis of Energy Consumption Performance towards Optimal Radioplanning of Wireless Sensor Networks in Heterogeneous Indoor Environments

In this paper the impact of complex indoor environment in the deployment and energy consumption of a wireless sensor network infrastructure is analyzed. The variable nature of the radio channel is analyzed by means of deterministic in-house 3D ray launching simulation of an indoor scenario, in which wireless sensors, based on an in-house CyFi implementation, typically used for environmental monitoring, are located. Received signal power and current consumption measurement results of the in-house designed wireless motes have been obtained, stating that adequate consideration of the network topology and morphology lead to optimal performance and power consumption reduction. The use of radioplanning techniques therefore aid in the deployment of more energy efficient elements, optimizing the overall performance of the variety of deployed wireless systems within the indoor scenario

Automatic map-based FTTx access network design

Several mature and standardized optical access network technologies are available for network operators providing broadband services, being now in deployment phase; therefore cost estimation, business analysis, efficient deployment strategies, network and topology design issues for FTTx access networks play an increasingly important role regarding profitability and market success. In a competitive environment, techno-economic evaluation supports the optimal choice among available technologies. Even the tradeoff between future proof technical superiority and short term investment minimization requires a farseeing decision. In our point of view, cost estimation and techno-economic evaluation is strongly related to strategic network design: among others the uneven population density, irregular street system or infrastructure have significant impact on the network topology, thus the deployment costs as well. In order to deal with these aspects, a high-level, strategic network design is necessary that adapts to geospatial characteristics of the services area, providing accurate and detailed network information for the techno-economic evaluation [1]. We have developed a topology designer methodology that supprts the above requirements, providing (near) optimal topology of the fully or partially optical access network, based on the geospatial information about the service area: digital maps, existing infrastructure and subscriber database. Automatic topology design for large-scale service areas, with 10.000s of subsribers is a highly complex mathematical problem. The tough algorithms for a near optimal, yet efficient solution. The developed algorithms were evaluated regarding their speed and accuracy. Based on topology design results, a detailed and flexible techno-economic comparison is carried out, since the framework handles various broadband access network technologies, as presented in a case study. --Topology design,Strategic Design,Network planning,GIS,Map,Techno-economic,Cost estimation