The purpose of this paper is to investigate the performance of UDP over various routing protocols in ad hoc networks. For this investigation we have chosen three routing schemes, DSDV, DSR and AODV and four network scenarios of 4, 8, 16 and 32 nodes of various node mobility speeds. Results are produced by evaluating throughput and end to end packet delay over the UDP connection through simulation experiments.
The revolution in technology for storing and processing big data leads to data intensive computing as a new paradigm. To find the valuable and precise big data knowledge, efficient and scalable data mining techniques are required. In data mining, different techniques are applied depending on the kind of knowledge to be mined. Association rules are generated from the frequent itemsets computed by frequent itemset mining (FIM) algorithms. The problem of designing scalable and efficient frequent itemset mining algorithms on the Spark RDD framework. The research done in this thesis aims to improve the performance (in terms of execution time) of the existing Spark-based frequent itemset mining algorithms and efficiently re-design other frequent itemset mining algorithms on Spark. The particular problem of interest is re-designing the Eclat algorithm in the distributed computing environment of the Spark. The paper proposes and implements a parallel Eclat algorithm using the Spark RDD architecture, dubbed RDD-Eclat. EclatV1 is the earliest version, followed by EclatV2, EclatV3, EclatV4, and EclatV5. Each version is the consequence of a different technique and heuristic being applied to the preceding variant. Following EclatV1, the filtered transaction technique is used, followed by heuristics for equivalence class partitioning in EclatV4 and EclatV5. EclatV2 and EclatV3 are slightly different algorithmically, as are EclatV4 and EclatV5. Experiments on synthetic and real-world datasets.
This paper provides an effective Wireless Sensor Network(WSN) routing solution for Internet of Things(IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.
This work presents a Secure and Energy Efficient TDMA based MAC Protocol in Wireless Sensor Networks. The presented technique is handled in the following stages. In the initial phase, adaptable step size grey wolf inspired (ASGWI) clustering methodology is presented for producing viable cluster trees by optimal selection of cluster heads. The ASGWI clustering decreases the expense of finding the ideal situation for the head hubs in a cluster. In the second stage, reliable routing is provided by the adaptive quantum logic (AQL) coding to advance the system security in WSN. At last, the energy effective secure information correspondence approach is proposed inside the cluster instead of the base station for TDMA scheduling. Here, the determination models of the objective function are created dependent on the remaining energy, Headcount, intra-cluster distance, and node degree. The presented TDMA scheduling for Cluster-tree topology in WSNs meets the practicality and the energy demands. The exploratory outcomes show the predominance of the introduced approach contrasting and the current strategies regarding network throughput, end-to-end delay, packet delivery ratio, and the remaining energy level of the nodes
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.
Heterogeneous wireless sensor networks (HWSNs) satisfy researchers' requirements for developing real-world solutions that handle unattended challenges. However, the primary constraint of researchers is the privacy of the sensor nodes. It safeguards the sensor nodes and extensions in the HWSNs. Therefore, it is necessary to develop secure operational systems. Multicast scaling with security and time efficiency is described in heterogeneous wireless sensor networks to maximize network performance while also successfully protecting network privacy. This study evaluates the initial security and time efficiency measures, such as execution time, transmission delay, processing delay, congestion level, and trust measure. Subsequently, the optimal location of the heterogeneous nodes is determined using sigmoid-based fuzzy c-means clustering. Finally, successful cluster routing was achieved via support-value-based particle swarm optimization. The experimental results indicate that the proposed strategy surpasses existing strategies in terms of network delivery ratio, end-to-end delay, throughput, packet delivery, and node remaining energy level.
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