6,434 research outputs found
Partner selection in indoor-to-outdoor cooperative networks: an experimental study
In this paper, we develop a partner selection protocol for enhancing the
network lifetime in cooperative wireless networks. The case-study is the
cooperative relayed transmission from fixed indoor nodes to a common outdoor
access point. A stochastic bivariate model for the spatial distribution of the
fading parameters that govern the link performance, namely the Rician K-factor
and the path-loss, is proposed and validated by means of real channel
measurements. The partner selection protocol is based on the real-time
estimation of a function of these fading parameters, i.e., the coding gain. To
reduce the complexity of the link quality assessment, a Bayesian approach is
proposed that uses the site-specific bivariate model as a-priori information
for the coding gain estimation. This link quality estimator allows network
lifetime gains almost as if all K-factor values were known. Furthermore, it
suits IEEE 802.15.4 compliant networks as it efficiently exploits the
information acquired from the receiver signal strength indicator. Extensive
numerical results highlight the trade-off between complexity, robustness to
model mismatches and network lifetime performance. We show for instance that
infrequent updates of the site-specific model through K-factor estimation over
a subset of links are sufficient to at least double the network lifetime with
respect to existing algorithms based on path loss information only.Comment: This work has been submitted to IEEE Journal on Selected Areas in
Communications in August 201
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
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