Recent advancements in wireless technology have created an exponential rise in the number of connected devices leading to the internet of things (IoT) revolution. Large amounts of data are captured, processed and transmitted through the network by these embedded devices. Security of the transmitted data is a major area of concern in IoT networks. Numerous encryption algorithms have been proposed in these years to ensure security of transmitted data through the IoT network. Tiny encryption algorithm (TEA) is the most attractive among all, with its lower memory utilization and ease of implementation on both hardware and software scales. But one of the major issues of TEA and its numerous developed versions is the usage of the same key through all rounds of encryption, which yields a reduced security evident from the avalanche effect of the algorithm. Also, the encryption and decryption time for text is high, leading to lower efficiency in IoT networks with embedded devices. This paper proposes a novel tiny symmetric encryption algorithm (NTSA) which provides enhanced security for the transfer of text files through the IoT network by introducing additional key confusions dynamically for each round of encryption. Experiments are carried out to analyze the avalanche effect, encryption and decryption time of NTSA in an IoT network including embedded devices. The results show that the proposed NTSA algorithm is much more secure and efficient compared to state-of-the-art existing encryption algorithms.

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Traditional decision tree algorithms face the problem of having sharp decision boundaries which are hardly found in any real-life classification problems. A fuzzy supervised learning in Quest (SLIQ) decision tree (FS-DT) algorithm is proposed in this paper. It is aimed at constructing a fuzzy decision boundary instead of a crisp decision boundary. Size of the decision tree constructed is another very important parameter in decision tree algorithms. Large and deeper decision tree results in incomprehensible induction rules. The proposed FS-DT algorithm modifies the SLIQ decision tree algorithm to construct a fuzzy binary decision tree of significantly reduced size. The performance of the FS-DT algorithm is compared with SLIQ using several real-life datasets taken from the UCI Machine Learning Repository. The FS-DT algorithm outperforms its crisp counterpart in terms of classification accuracy. FS-DT also results in more than 70% reduction in size of the decision tree compared to SLIQ.