Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

Efficient Energy and Delay Reduction Model for Wireless Sensor Networks

In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.

Text Sentiment Analysis Using Frequency-Based Vigorous Features

Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.

An Efficient Connectivity Restoration Technique(ECRT)for Wireless Sensor Network

Node failure in Wireless Sensor Networks(WSNs)is a fundamental problem because WSNs operate in hostile environments.The failure of nodes leads to network partitioning that may compromise the basic operation of the sensor network.To deal with such situations,a rapid recovery mechanism is required for restoring inter-node connectivity.Due to the immense importance and need for a recovery mechanism,several different approaches are proposed in the literature.However,the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration.Moreover,most of these approaches rely on the excessive mobility of nodes for restoration connectivity that affects both coverage and energy consumption.This paper proposes a novel technique called ECRT(Efficient Connectivity Restoration Technique).This technique is capable of restoring connectivity due to single and multiple node failures.ECRT achieves energy efficiency by transmitting a minimal number of control packets.It is also coverage-aware as it relocates minimal nodes while trying to restore connectivity.With the help of extensive simulations,it is proven that ECRT is effective in connectivity restoration for single and multiple node failures.Results also show that ECRT exchanges a much smaller number of packets than other techniques.Moreover,it also yields the least reduction in field coverage,proving its versatility for connectivity restoration.