In this digital age, privacy preservation has attracted much attention, as a huge amount of data are generated from multiple sources and transmitted across the Internet. Several perturbation algorithms have emerged to keep sensitive data hidden behind additive noises. In this paper, a novel un-realization algorithm is developed based on a classification and regression tree (CART). First, the sample dataset was distorted, and the duplicate elements were removed, creating a perturbed dataset and an un-realized dataset. Then, a decision tree was set up by the modified CART algorithm and another by the traditional CART based on the un-realized dataset. Finally, the Gini values of the two trees were compared. If the results are the same, then the privacy of the data is preserved. The proposed algorithm was compared with several traditional un-realization algorithms through experiments. The results show that our algorithm achieved excellent results in Gini value, time complexity and output accuracy.
The process of routing in MANET (Mobile ad hoc network) requires a trust based environment and therefore security is one of the major concern. A backbone network in a MANET is difficult when it is implemented for a specific application. A security based environment is one of the most critical issues in a MANET because it is mostly involved with sensitive and secret information. This work deals with a specific type of denial-of-service (DOS) attack called node isolation attack and thus analyze the vulnerabilities of a pro-active routing protocol called optimized link state routing (OLSR). Based on this analysis, this work proposes a mechanism called enhanced OLSR (EOLSR) protocol which is a trust based technique to secure the OLSR nodes against the attack. According to the proposed technique, isolation can be detected by the hello packets it sends. Verification is done through this, whether a node is advertising correct topology information or not thus leading to detection of the isolation node that perform the DOS attack. Enhanced OLSR is further improved using the trust based system called Trustbased OLSR (TOLSR). Once the node is detected as attacker using EOLSR, its trust value is reduced to half of its initial value. Hence in future, selection of attacker as MPR node is prevented since all the nodes will select only high trust node as MPR node. The concept of ensuring security to the network does not involve much computational complexity and therefore, the proposed scheme is a light weight technique.
In this paper, using support value-based adaptive fuzzy c-means clustering and krill herd optimization, we demonstrate how to effectively localise energy harvesting enabled underwater wireless sensor networks. Replacement or recharge of a sensor node's battery is challenging in an aquatic environment. As a result, building an energy harvester that is both efficient and dependable is essential to ensure the continued operation of an underwater wireless sensor network (UWSN). We presented a technique that is capable of harvesting energy from a variety of sources and distributing it to the sensor nodes. The proposed work gathers energy from sensor nodes with insufficient batteries and begins communicating once they have sufficient energy storage. The RSS (received signal strength) and TOA (time of arrival) of active nodes are used to determine the network's location. This is based on the characteristics of the channels used in underwater optical communication. Following that, the RSS and TOA measures' support values are determined. Then, using support value-based adaptive fuzzy c-means clustering, support kernel matrices are created. The proposed support kernel matrices significantly reduce path error during data transmission. To increase sensor node localisation, the obtained support kernel matrices are further improved using a krill herd optimization approach. The proposed method outperforms existing techniques in the laboratory.
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