Fuzzy Service Aware Adaptive Cooperative Spectrum Sensing Algorithm

Based on the service characteristics and the sensing ability for secondary users, a joint optimization scheme of spectrum detection and allocation is investigated to expand the available sensing region and allocate the Qo S-specified channels. On the aspect of spectrum detection, due to the available detection index with the global detection metrics, cooperation thresholds are adaptively adjusted to select the cooperative model for maximizing the available sensing region. On the aspect of spectrum allocation, for different service category, the idle channels are efficiently allocated that depend on their stability and available bandwidth. Meanwhile, based on the requested rates defined by fuzzy theory, the secondary users can be divided into two categories, i.e.,delay sensitive service and reliability sensitive service. Finally, the Qo S-specified channels from the targeted spectrum subset are allocated to secondary users. Simulation results show that our proposed algorithm can not only expand the available sensing region,but also decrease the outage probability of delay sensitive services. Additionally, it enables stable power consumption in the time-variation channel.

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data centre.Currently,web service composition gains maximum attention among researchers due to its significance in real-time applications.Quality of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS.But these models have failed to handle the uncertainties of QoS.The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users.On the other hand,trip planning is an essential technique in supporting digital map services.It aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the query.But the available web service composition solutions do not consider the complicated spatio-temporal features.For resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location awareness.The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,etc.At the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition plans.Followed by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite plans.Besides,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user rating.An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

Hierarchy Bayesian model based services awareness of high-speed optical access networks

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.