Wireless sensor network (WSN) consists of a large amount of limited battery-powered sensor nodes. In general, energy consumption will be a significant concern for WSN owing to irreplaceable battery constraints of sensor nodes. The zone formation approach could be an adequate data aggregation technique which efficiently minimizes the energy consumption by categorizing sensor nodes into zones. However, the main constraints like zone head (ZH) selection, frequent change of ZH, and multi-hop communication from ZH to the sink have a direct impact on the network consistency of WSN. In this paper, a novel efficient intra- and inter-zone routing scheme has been proposed in order to prolong the network consistency of WSN. In the proposed scheme, the hybrid algorithm is established in which harmony search algorithm incorporates with modified moth flame optimization algorithm. This hybrid algorithm provides the appropriate ZH selection for intra-zone routing that reduces the frequent change of ZH in the network. Furthermore, the path balancing in inter-zone routing is acquired through multi-criteria-based optimal path routing algorithm. The performance results confirm that the proposed scheme enhances the network consistency compared with an existing scheme.
Object tracking is a noteworthy application in the field of wireless sensor networks that has attracted major Research attention recently. Most object tracking schemes uses prediction scheme to minimize the energy consumption and to maintain low missing rate in a sensor network. However objects need to be localize, when object was found missing during tracking process. In this article, we proposed a swarm intelligence mechanism, such as particle swarm optimization (PSO) to accurately estimate the location of the missing object, using updated object position and velocity and the extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm against the centroid and weighted centroid methods to evaluate its performance in terms of localization error.
Purpose The purpose of this paper is to enhance the network lifetime of WSN. In wireless sensor network (WSN), the sensor nodes are widely deployed in a terrestrial environment to sense and evaluate the physical circumstances. The sensor node near to the sink will deplete more energy faster than other nodes; hence, there arises an energy hole and network partitioning problem in stationary sink-based WSN. Even though many mobile sink-based WSN is formulated to mitigate energy hole, inappropriate placement of sink leads to packet drop and affect the network lifetime of WSN. Therefore, it is necessary to have an efficient sink mobility approach to prevent an aforesaid problem. Design/methodology/approach In this paper, zone-based sink mobility (ZBSM) approach is proposed in which the zone formation along with controlled sink mobility is preferred for energy hole mitigation and optimal sink node placement. In ZBSM, the sink decides to move toward strongly loaded zone (SLZ) for avoiding network partitioning problems where the selection of SLZ can be carried out by using Fuzzy Logic. Findings The performance results confirm that the proposed scheme reduces energy consumption as well as enhances the network lifetime compared with an existing scheme. Originality/value A new optimal sink node placement is proposed to enhance the network lifetime and packet delivery ratio of WSN.
Purpose Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper. Design/methodology/approach Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively. Findings Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved. Originality/value This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.
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