22,710 research outputs found
On improving the performance of optimistic distributed simulations
This report investigates means of improving the performance of optimistic distributed simulations
without affecting the simulation accuracy. We argue that existing clustering algorithms
are not adequate for application in distributed simulations, and outline some characteristics
of an ideal algorithm that could be applied in this field. This report is structured as follows.
We start by introducing the area of distributed simulation. Following a comparison of the
dominant protocols used in distributed simulation, we elaborate on the current approaches
of improving the simulation performance, using computation efficient techniques, exploiting
the hardware configuration of processors, optimizations that can be derived from the
simulation scenario, etc. We introduce the core characteristics of clustering approaches and
argue that these cannot be applied in real-life distributed simulation problems. We present
a typical distributed simulation setting and elaborate on the reasons that existing clustering
approaches are not expected to improve the performance of a distributed simulation. We
introduce a prototype distributed simulation platform that has been developed in the scope
of this research, focusing on the area of emergency response and specifically building evacuation.
We continue by outlining our current work on this issue, and finally, we end this
report by outlining next actions which could be made in this field
Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking
P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols
A Scale-Free Topology Construction Model for Wireless Sensor Networks
A local-area and energy-efficient (LAEE) evolution model for wireless sensor
networks is proposed. The process of topology evolution is divided into two
phases. In the first phase, nodes are distributed randomly in a fixed region.
In the second phase, according to the spatial structure of wireless sensor
networks, topology evolution starts from the sink, grows with an
energy-efficient preferential attachment rule in the new node's local-area, and
stops until all nodes are connected into network. Both analysis and simulation
results show that the degree distribution of LAEE follows the power law. This
topology construction model has better tolerance against energy depletion or
random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
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