<p>Today Internet of things (IoT) interconnects any object possessing sensing and computing capabilities to the internet. In this era, increasing number of electronic devices and applications in Internet of Things (IoT) requires secured communication with low power consumption capabilities. As security is a major challenge in internet of things, it is important to design a key management solution that considers resource constrained nodes and hence key management in public key cryptography is a crucial issue. In this paper, a novel key exchange algorithm was developed and implemented on a low powered “Raspberry pi machine” to realize the overall impact it creates on the device. The performance of the proposed algorithm had shown a great improvement over the popular Diffie Hellman key exchange algorithm and a two-level security for data exchange between the parties is implemented.</p>
Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM 2.5. The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Patterngrowth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database. INDEX TERMS Data mining, weighted frequent itemset, pattern-growth technique, spatiotemporal database.
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