  We consider nonparametric or universal sequential hypothesis testing problem
when the distribution under the null hypothesis is fully known but the
alternate hypothesis corresponds to some other unknown distribution. These
algorithms are primarily motivated from spectrum sensing in Cognitive Radios
and intruder detection in wireless sensor networks. We use easily implementable
universal lossless source codes to propose simple algorithms for such a setup.
The algorithms are first proposed for discrete alphabet. Their performance and
asymptotic properties are studied theoretically. Later these are extended to
continuous alphabets. Their performance with two well known universal source
codes, Lempel-Ziv code and Krichevsky-Trofimov estimator with Arithmetic
Encoder are compared. These algorithms are also compared with the tests using
various other nonparametric estimators. Finally a decentralized version
utilizing spatial diversity is also proposed. Its performance is analysed and
asymptotic properties are proved.
