Wireless Sensor Network (WSN) is a collection of tiny sensing devices capable of aggregating and transmitting environmental information to common sink nodes. Due to limited battery capacity, the nodes cover a small region wherein multi-hop communications are adapted for long-distance relaying. Early energy drain and node replacement results in the coverage-hole problem, resulting in information loss. This paper introduces a Distance-to-Coverage Transformed Relaying (DCTR) method to lessen information loss due to connectivity issues. The proposed method relies upon a conclusive machine learning paradigm for analyzing the residual energy-to-path existence feature of a neighboring node. Based on the residual energy, the distance or coverage metric is satisfied for non-intervening transmissions. The learning outcome provides definite transmission intervals preventing additional delay due to re-discovery. The transmissions are confined in the provided interval recommended, based on connectivity and coverage. The performance of the proposed method is analyzed using coverage rate, data loss ratio, and energy consumption. The proposed method improves the coverage factor by 7.93%, reduces data loss by 9.64%, and energy consumed by 8.49% for different unconnected nodes.
Loss of sensitive data can be stopped employing Data Leak Prevention (DLP). Most of such tools can be quite effective while protecting private information known already. At the same time, plenty of private information has not been recognized until it has been disclosed to various unknown users or other competition enterprises. Clustering refers to a data mining technique that can classify a certain set of instances into different clustersusing a measure of similarity. One of the most common algorithms based on partitioning is the K-Means. However, it has many drawbacks like it can generate local optimal solutions that are based on initial centroids that are chosen randomly. The Tabu Search (TS) Tranquility Search and the Stochastic Diffusion Search (SDS) have been proposed in this work. In one of the most recent algorithms called Tranquility Search, optimal global solutions are obtained by exploring through the entire solution space. Some studies show hybrid algorithms that are a combination of two different ideas producing better solutions. In this work, a new approach is presented which is a combination of two different ideas producing better solutions. The Improved Tranquility Search technique and the K-means algorithm are combined. For this, a hybrid Tranquility-TABU-SDS algorithm is applied in the social network for the DLP. The results of the experiment have proved that the method proposed performs better in comparison to other methods.
VANET is the standout amongst the most rising Technology in current situation. Step by step street mishaps are rising. So as to keep mind these mishaps knowledge VANET came into picture. Yet, still greater upgrade is required to stop mishaps for that discovering area of Vehicle is vital thing. To discover the area of a vehicle in VANET we take the assistance of cell organizes. Still with 1G, 2G, 3G, 4G there are a few issues like blurring, Handoff, and so forth.., .To stay away from mishaps in VANET, finding careful area of vehicle and getting speedy reaction is imperative. This can be conceivable with development innovation 5G. Thus, to locate the correct area of vehicle in VANET, we executed a novel strategy utilizing AOA-MUSIC (Angle of Arrival – for Multiple Signal Classification), Godara, and LMS (Least Mean Square) Algorithms and this is an ideal system among straightforward and complex methods. We have planned and executed the proposed technique utilizing MATLAB reproduction and results demonstrate that our strategy gives progressively exact estimations of area of versatile vehicles, and exact scratch-off of obstruction signals contrasted with other existing strategies.
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