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Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other's network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.

Sign Language is the language of deaf. There are different types of sign languages spread all over the world. American Sign Language (ASL) is one of the sign languages. ASL is used by deaf Americans. We had created a system that translates sign language videos to simple sentence in English. This system is taking a lot of time for mapping the sign language videos to its corresponding sign writing images. This paper discusses an approach for minimizing the processing time taken for mapping the sign language videos to its corresponding sign writing images.

Cognitive Wireless Sensor Networks follow IEEE 802.22 standard which is based on the concept of cognitive radio. In this paper we have studied the Denial of Service (DOS) attack. Spectrum Sensing Data Falsification (SSDF) attack is one such type of DOS attack. In this attack the attackers modify the sensing report in order to compel the Secondary User (SU) to take a wrong decision regarding the vacant spectrum band in other's network. In this paper we have proposed a similaritybased clustering of sensing data to counter the above attack.