Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

The Internet of Things (IoT) is enhancing our lives in a variety of structures, which consists of smarter cities, agribusiness, and e-healthcare, among others. Even though the Internet of Things has many features with the consumer Internet of Things, the open nature of smart devices and their worldwide connection make IoT networks vulnerable to a variety of assaults. Several approaches focused on attack detection in Internet of Things devices, which has the longest calculation times and the lowest accuracy issues. It is proposed in this paper that an attack detection framework for Internet of Things devices, based on the DWU-ODBN method, be developed to alleviate the existing problems. At the end of the process, the proposed method is used to identify the source of the assault. It comprises steps such as preprocessing, feature extraction, feature selection, and classification to identify the source of the attack. A random oversampler is used to preprocess the input data by dealing with NaN values, categorical features, missing values, and unbalanced datasets before being used to deal with the imbalanced dataset. When the data has been preprocessed, it is then sent to the MAD Median-KS test method, which is used to extract features from the dataset. To categorize the data into attack and nonattack categories, the features are classified using the dual weight updation-based optimal deep belief network (DWU-ODBN) classification technique, which is explained in more detail below. According to the results of the experimental assessment, the proposed approach outperforms existing methods in terms of detecting intrusions and assaults. The proposed work achieves 77 seconds to achieve the attack detection with an accuracy rate of 98.1%.

In healthcare industry, Neural Network has attained a milestone in solving many real-life classification problems varies from very simple to complex and from linear to non-linear. To improve the training process by reducing the training time, Adaptive Skipping Training algorithm named as Half of Threshold (HOT) has been proposed. To perform the fast classification and also to improve the computational efficiency such as accuracy, error rate, etc., the highlighted characteristics of proposed HOT algorithm has been integrated with Strassen's matrix multiplication algorithm and derived a novel, hybrid and computationally efficient algorithm for training and validating the neural network named as Strassen's Half of Threshold (SHoT) Training Algorithm. The experimental outcome based on the simulation demonstrated that the proposed SHOT algorithm outperforms both BPN and HOT algorithm in terms of training time and its efficiency on various dataset such as such as Hepatitis, SPeCT, Heart, Liver Disorders, Breast Cancer Wisconsin (Diagnostic), Drug Consumption, Cardiotocography, Splice-junction Gene Sequences and Thyroid Disease dataset that are extracted from Machine Learning Dataset Repository of UCI. It can be integrated with any type of supervised training algorithm.

In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. However, more focus needs to be given to the accuracy aspect. In addition, for grouping similar antipatterns, a clustering process was performed to eradicate the design errors. To address the above-said issues and further enhance the antipattern detection accuracy with minimum time and false positive rate, in this work, Random Forest Bagging X-means SQL Query Clustering (RFBXSQLQC) technique is proposed. Different patterns or queries are initially gathered from the input SQL query log, and bootstrap samples are created. Then, for each pattern, various weak clusters are constructed via X-means clustering and are utilized as the weak learner (clusters). During this process, the input patterns are categorized into different clusters. Using the Bayesian information criterion, the similarity measure is employed to evaluate the similarity between the patterns and cluster weight. Based on the similarity value, patterns are assigned to either relevant or irrelevant groups. The weak learner results are aggregated to form strong clusters, and, with the aid of voting, a majority vote is considered for designing strong clusters with minimum time. Experiments are conducted to evaluate the performance of the RFBXSQLQC technique using the IIT Bombay dataset using the metrics like antipattern detection accuracy, time complexity, false-positive rate, and computational overhead with respect to the differing number of queries. The results revealed that the RFBXSQLQC technique outperforms the existing algorithms by 19% with pattern detection accuracy, 34% minimized time complexity, 64% false-positive rate, and 31% in terms of computational overhead.

In today’s world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (Ec), Duration of BH attack detection (Attduration), Packet delivery ratio (Pdr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%.