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Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicles (UAV) or drones are widely employed for several application areas such as surveillance, disaster management, etc. Since UAVs are limited to energy, efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station (BS). Therefore, clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs. In this aspect, this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system (GTOADL-SCS) technique for UAV networks. The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification. At the initial stage, the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads (CHs) and organize clusters. Besides, the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs, average neighoring distance, and UAV degree. For classification process, the GTOADLSCS model applies pre-trained densely connected network (DenseNet201) feature extractor with gated recurrent unit (GRU) classifier. For ensuring the enhanced performance of the GTOADL-SCS model, a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio (PDR) of 92.60%. [ABSTRACT FROM AUTHOR]

The recent technological developments have revolutionized the functioning of Wireless Sensor Network (WSN)-based industries with the development of Internet of Things (IoT). Internet of Drones (IoD) is a division under IoT and is utilized for communication amongst drones. While drones are naturally mobile, it undergoes frequent topological changes. Such alterations in the topology cause route election, stability, and scalability problems in IoD. Encryption is considered as an effective method to transmit the images in IoD environment. The current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones (ASOCE-SIoD) environment. The key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by drones. The presented ASOCE-SIoD technique follows ASO-based Cluster Head (CH) and cluster construction technique. In addition, signcryption technique is also applied to effectually encrypt the images captured by drones in IoD environment. This process enables the secure transmission of images to the ground station. In order to validate the efficiency of the proposed ASOCE-SIoD technique, several experimental analyses were conducted and the outcomes were inspected under different aspects. The comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) interlink numerous Sensor Nodes (SN) to support Internet of Things (loT) services. But the data gathered from SNs can be divulged, tempered, and forged. Conventional WSN data processes manage the data in a centralized format at terminal gadgets. These devices are prone to attacks and the security of systems can get compromised. Blockchain is a distributed and decentralized technique that has the ability to handle security issues in WSN. The security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network system. This breaches the mutual trust and allows data immutability which in turn permits the network to go on. At some instances, few nodes die or get compromised due to heavy power utilization. The current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB technique. The primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain (BC) for inter-cluster communication in the network. To select ClusterHeads (CHs) and organize the clusters, the presented EACPIO-CB model designs a fitness function that involves distinct input parameters. Further, BC technology enables the communication between one CH and the other and with the Base Station (BS) in the network. The authors conducted comprehensive set of simulations and the outcomes were investigated under different aspects. The simulation results confirmed the better performance of EACPIO-CB method over recent methodologies. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a vital element in Internet of Things (IoT) as the former enables the collection of huge quantities of data in energy-constrained environment. WSN offers independent access to the target region and performs data collection in an effective manner. But energy constraints remain a challenging issue in WSN since it operates on in-built battery. The studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of WSN. Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms. Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN. In this background, the current study develops an Improved Duck and Traveller Optimization (IDTO)-enabled cluster-based Multi-Hop Routing (IDTOMHR) technique for WSN. Primarily, IDTO algorithm is exploited for the selection of Cluster Head (CH) and construction of clusters. Besides, Artificial Gorilla Troops Optimization (ATGO) technique is also used to derive an optimal set of routes to the destination. Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters. The proposed IDTOMHR model was experimentally validated for its performance under different aspects. The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches. [ABSTRACT FROM AUTHOR]

Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling. [ABSTRACT FROM AUTHOR]