sarangsukumar A

Abstract – Sensing optimal idle channel for future
transmission is the hot research topic in Cognitive radio (CR)
network environment. CR Network performance greatly
depends upon the waiting time of the Cognitive User (CU) for
getting free channel from licensed band. Probability of
available free channel will be less when arrival rate of
primary user to their allocated licensed band is high. So the
effective channel selection and switching strategy needed to
overcome this issue and increasing throughput of the data
transmission over cognitive radio network. In this paper
focusing prediction on channel selection scheme pro-actively
and comparing the performance with existing reactive
prediction polices.

Abstract - As the use of the web is greatly increased so as
the need for effective web service selection and retrieval
techniques. So many good algorithms have been
developed for web service selection problem. The need
for effective web service clustering technique is also
came forward, which will help the users to search
within their needed clusters, that will reflect different
tradeoffs such as price , quality etc. Recent works on
web service selection and retrieval techniques has found
out that for getting greater accuracy and stability, all
the algorithms for web service selection and clustering
must satisfy the following two criteria. First, it should
consider all the parameter matches. Second, multiple
criteria should be taken into consideration while
performing the parameter matching. In this paper we
are proposing an efficient web service clustering
technique based on multi criteria service dominance
relationship, which satisfies the above criteria. Recent
work on the same area proposed the use of Hilbert
space filling curve, and then applying a simple
algorithm for forming the clusters. Space filling curve is
a way of mapping multi-dimensional space into the one
dimensional space. So the effectiveness of the formed
clusters are having a direct relationship with how
effective the used space filling curve is. In this paper we
propose the use of Peano Space filling curve for the
multidimensional reduction, which tends to show less
irregularity, more fairness and more scalability than
Hilbert space filling curve.