The IoT wave is on rise and it is considered as the biggest world changing computing ecosystem after the invention of Internet where the meaning of lifestyle is expected to be changed. IoT is now diffusing pervasively in most areas of life like smart home, smart cities, smart irrigation, smart healthcare etc. The concerned industry is trying to reap maximum benefits from this regime without putting extra efforts or investing much to make the related infrastructure secure and trustworthy. IoT end device, a.k.a smart object, is one component of this ecosystem, responsible to interact with the physical environment and gather the data, along with communication technologies, processing capabilities like fog or cloud computing and applications to interact with the device (s). It is possibility that such devices can be faulty, compromised or misbehaving because of internal or external factors like hardware malfunctioning or cyber-attacks. In this situation the data gathered and transferred by such devices can be disaster and challenging in decision making specifically in an area where the human life is involved like IoT healthcare. We have proposed a mathematical model to estimate the trust of such devices. Trust on IoT devices and gathered data from such trusted devices will boost the confidence of end users on this new computing regime; especially in healthcare environment. The estimated trust status (trusted, uncertain, and untrustworthy) will be saved in a database or CSV file with a timestamp to be used as reputation by healthcare applications. Patients are assigned their SOI based on their specific diagnoses and procedures performed during their medical encounter. Similarly, for a patient with heart diseases or having hypertension can be considered in extreme category with a value of γ = 1 if there is some deviation of readings. 

Biometrics is being commonly used nowadays for the identification and verification of humans everywhere in the world. In biometrics humans unique characteristics like palm, fingerprints, iris etc. are being used. Pattern Recognition and image processing are the major areas where research on signature verification is carried out. Hand written Signature of an individual is also unique and for identification of humans are being used and accepted specially in the banking and other financial transactions. The hand written signatures due to its importance are at target of fraudulence. In this paper we have surveyed different papers on techniques that are currently used for the identification and verification of Offline signatures.