In addition to seamless connectivity and smartness, the objects in the Internet of Things (IoT) are expected to have the social capabilities -these objects are termed as "social objects". In this paper, an intuitive paradigm of social interactions between these objects are argued and modeled.The impact of social behavior on the interaction pattern of social objects is studied taking Peerto-Peer (P2P) resource sharing as an example application. The model proposed in this paper studies the implications of competitive vs. cooperative social paradigm, while peers attempt to attain the shared resources / services. The simulation results divulge that the social capabilities of the peers impart a significant increase in the quality of interactions between social objects.Through an agent-based simulation study, it is proved that cooperative strategy is more efficient than competitive strategy. Moreover, cooperation with an underpinning on real-life networking structure and mobility does not negatively impact the efficiency of the system at all; rather it helps.

With new developments and upcoming technologies, new sensing techniques are becoming available. Unfortunately, none of these techniques provides output interpreted the way human perception works. An inability to improve the effectiveness of these technologies limits their use in dedicated applications and increases their complexity. The growing adoption of this technology makes it critical to create a system capable of handling e-nose challenging issues such as noise, drift, imbalanced data, dynamic environment, and high uncertainties. Without appropriate pattern recognition methods that allow inferences to be derived based on patterns observed within these data sets, it will not be possible to improve the performance of current e-nose systems. In this chapter, e-nose drift issue is introduced and the available drift counteraction methods is discussed.

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Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (>40), and GDD values exceeded (0.92). Key space is determined by u 2 s i z e A 0 where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases.

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