OGWO-CH: Hybrid Opposition-Based Learning with Gray Wolf Optimization Based Clustering Technique in Wireless Sensor Networks

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

The emergence of wireless sensor network (WSN) makes the communication between people and things more extensive, and provides a new way for people to obtain information and more accurate real‐time communication. However, the regional distance in WSN is still a big problem. This method can not only optimize the cluster structure, but also reduce the number of member nodes in the cluster, the cluster head receiving energy, and reduce the data transmission distance between nodes. At the same time, the number of packets received by the base station increases, so that more sensing information can be obtained. The simulation results showed that compared with the smart Ethernet protection (SEP) protocol and the low energy adaptive clustering hierarchy (LEACH) protocol, the number of first node deaths (FND) of the grey wolf optimizer (GWO) algorithm was 153 and 233 times different from those of the SEP and LEACH protocols respectively at 100m*100m. In the case of 200m*200m, compared with that of SEP and LEACH, the number of FND of GWO was 152 and 200 times different. Finally, in the case of 300m*300m, compared with that of SEP and LEACH, the number of FND of GWO was 142 and 158 times different. The GWO algorithm has a higher node ARE ratio in a wider region. In the same 1000 rounds, the node ARE of GWO was 61%, while the node ARE of SEP and LEACH protocols were 7% and 3%, respectively. From the above results, it can be found that the GWO algorithm has been greatly improved in terms of network energy efficiency, network lifetime, etc., and can still work normally under larger scale monitoring. The innovation of this article lies in the localization problem of fixed node mobile sensor networks, proposing a mobile sensor network localization method based on mobile error nodes. This can optimize the distance between nodes and improve the positioning accuracy of the algorithm in both distributed and centralized situations. This article is protected by copyright. All rights reserved.

Wireless Sensor Network (WSN) is a promising approach that is developed for a wide range of applications due to its low installation cost. However, the nodes in the WSN are susceptible to different security threats, because these nodes are located in hostile or harsh environments. Moreover, an inappropriate selection of routing path affects the data delivery of the WSN. The important goal of this paper is to obtain secure data transmission while minimizing energy consumption. In this paper, Trust Centric - Multiobjective Black Widow Optimization (TC-MBWO) is proposed for selection of Secure Cluster Head (SCH) from the large-scale WSN. Moreover, the secure routing path is generated by using the TC-MBWO, in which the factors considered for the cost function are: residual energy, distance, trust and node degree. Therefore, the secured clustering and routing achieved by using TC-MBWO, provides the resistance against malicious nodes and simultaneously the energy consumption is also minimized by identifying the shortest path. The proposed TC-MBWO method is analyzed in terms of alive nodes, dead nodes, energy consumption, throughput, and network lifetime. Here, the TC-MBWO method is compared with different existing methods such as Low Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm Optimization - Grey Wolf Optimizer (PSO-GWO), Particle-Water Wave Optimization (P-WWO) and Particle-based Spider Monkey Optimization (P-SMO). The alive nodes of the TC-MBWO are 70 for 2800 rounds which are higher in number when compared to the PSO-GWO, P-WWO and P-SMO.

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).

Internet of Things (IoT) is an important technique in the modern wireless telecommunications field. It is based on a collection of sensor nodes connected through wireless sensor networks (WSNs). The lifetime of this network is affected by the battery power of the connected sensor nodes. Network clustering techniques are used to improve energy consumption and extend the lifetime of the WSN. These techniques divide the sensor nodes into clusters and every cluster has a unique cluster head (CH) node. Recently, clustering-based metaheuristic techniques are used to solve this problem and find the optimal CH nodes under certain considerations such as less energy consumption and high reliability. This paper proposes a new clustering scheme for heterogeneous WSN using Coyote Optimization based on a Fuzzy Logic (COFL) algorithm. It uses the coyote optimization algorithm (COA) in conjunction with fuzzy logic (FL) system to reinforce and balance the clustering process for increasing the wireless network lifetime and reducing energy consumption. FL based clustering is adapted to determine a tentative set of CHs. The output of the FL is added as a solution within the initial solutions of the COA. Furthermore, a new fitness function has been adapted to minimize the total intra-cluster distance between each CH node and its cluster members and minimize the inter-cluster distance between the CHs nodes and the base station. An extensive simulation with three different scenarios is performed. The performance of the proposed COFL algorithm is compared with the well-known algorithms; namely low-energy adaptive clustering hierarchy protocol (LEACH) and stable election protocol (SEP) as traditional protocols and also coyote optimization algorithm (COA), grey wolf optimization (GWO), and particle swarm optimization (PSO). The COFL algorithm outperforms other algorithms in terms of alive node analysis, energy consumption, throughput, and central tendency measurements for alive nodes and normalized energy.

Restricted battery life is a crucial constraint in wireless sensor networks (WSNs). Many routing protocols have been proposed to reduce the energy consumption by extending the network lifetime. So, a new design of energy efficient routing using clustering is proposed where we use heterogeneous nodes for data transmission through the optimal path between cluster heads (CHs) and the base station (BS). This paper discusses the algorithm called Advanced-Multi-hop Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with heterogeneous nodes. Multi-hop LEACH protocol changes the mode of communication to prolong the network lifetime from single hop to multi-hop between the CH and BS by reducing energy consumption. The paper tries to evaluate the performance of the network in terms of lifetime and stability by introducing the concept of optimum number of clusters and also considers the optimum number of hops in the network with heterogeneous nodes. This paper also provides the algorithm for the avoidance of the overlapping of the clusters and collisions in the network. For the evaluation and comparison of the performance of advanced single-hop and multi-hop energy efficient LEACH protocols with heterogeneous nodes, MATLAB simulations are carried out. Results show that advanced-multi-hop LEACH protocol outperforms the advanced single-hop LEACH protocol regarding energy efficiency, stability and network lifetime.

Routing Algorithm determines the optimal route to transmit data packet from source to destination. The nodes in wireless sensor networks (WSN) are devoid of abundant energy and hence energy efficiency is the vital benchmarks for data transmission in WSN. The basic importance of wireless sensor networks (WSN) in present scenario is to reduce the node energy consumption such that it improves the data transmission efficiency and reliability to extend entire network life time. In these respect, we need to search the optimal route for data transmission in WSN. Here an Improved Ant Colony Optimization (IACO) based energy efficient routing algorithm is proposed in WSN to solve the issue. The function of improved Low Energy Adaptive Clustering Hierarchy (LEACH) routing algorithm is to select cluster heads (CH) by considering residual energy and the distance in between the nodes. To balance the energy consumption between the nodes, residual energy is used as an energy control factor, which affects the network life time and enhances the data transmission efficiency. An ACO algorithm is applied to search the optimal path from the various available routes in between CH and sink for data transmission. Simulation results show that the proposed method Improved Ant Colony Optimization based Low Energy Adaptive Clustering Hierarchy (IACOLEACH) is found to yield better results over other conventional algorithms.

Wireless Sensor Networks (WSNs) allow a large area to be monitored with various sensors. WSNs, although useful, are subject to Black Hole attacks. Defenses against attacks typically require more energy which reduces WSN lifetime. This thesis proposes a novel approach to detect and recover from attacks using an improved version of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is an energy efficient protocol for groups of battery-operated computers with sensor components in WSNs. This protocol works by having a node become a cluster head responsible for sending data to a sink. The sink can be either an access point or base station. After a set time the cluster heads go back to being regular nodes and a completely new set of nodes become cluster heads. There are secure versions of LEACH such as S-LEACH and MS-LEACH that provide encryption, but LEACH and its encrypted variants do not secure against Black Hole attacks. Our proposed approach, referred to as Anomaly Report Cycling - Low Energy Adaptive Clustering Hierarchy (ARC-LEACH), aims to provide defense from Black Hole attacks while also providing low energy cost. ARC-LEACH does this by absorbing an attack. When an attack occurs, it rotates cluster heads to reestablish communication, then sending a message from the sink to coordinate all nodes against the malicious nodes. ARC-LEACH actively blocks malicious nodes while leveraging the resilience of LEACH by building up stronger resistance to attacks. ARC-LEACH provides more defense capability when under attack from multiple malicious nodes that would otherwise be defenseless by LEACH, with only a limited increase in power consumption. The prototype for ARC-LEACH is implemented to show feasibility of the proposed approach.

Wireless sensor networks are battery-powered ad hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. Since these networks consist of sensors that are battery operated, care has to be taken so that these sensors use energy efficiently. This paper proposes an optimized hierarchical routing technique which aims to reduce the energy consumption and prolong network lifetime. In this technique, the selection of optimal cluster head (CHs) locations is based on artificial fish swarm algorithm that applies various behaviors such as preying, swarming, and following to the formulated clusters and then uses a fitness function to compare the outputs of these behaviors to select the best CHs locations. To prove the efficiency of the proposed technique, its performance is analyzed and compared to two other well-known energy efficient routing techniques: low-energy adaptive clustering hierarchy (LEACH) technique and particle swarm optimized (PSO) routing technique. Simulation results show the stability and efficiency of the proposed technique. Simulation results show that the proposed method outperforms both LEACH and PSO in terms of energy consumption, number of alive nodes, first node die, network lifetime, and total data packets received by the base station. This may be due to considering residual energies of nodes and their distance from base station , and alternating the CH role among cluster's members. Alternating the CH role balances energy consumption and saves more energy in nodes.

Energy efficient routing is the paramount point of significance in any wireless sensor network (WSN) so that the network sustains without any human intervention. Various energy efficient protocols have been proposed, multi-level Low Energy Adaptive Clustering Hierarchy (LEACH) being one among these. The multi-level LEACH protocol involves election of cluster heads at various hierarchical levels. If the area of deployment is large, the farther cluster heads cannot reach the base station directly. In such cases, it is needed to make use of an energy efficient routing technique for communication among cluster heads and forward the data towards the base station. Energy consumption is reduced by adopting routing mechanisms based on multi-hop and inverted tree in this multi-level LEACH protocol. This work deals with multi-level LEACH comprising of 1L-LEACH and 2L-LEACH. Directed diffusion carried out in 1L-LEACH and flat topology are compared with flooding. In directed diffusion, routing is done between a source and a sink optimizing the path taken. NS-3 simulation platform has been used and the results are presented.

Wireless Sensor Network (WSN) is a self-configured network of light-weight nodes, that are used in many applications by sending their data through this network to a Base Station (BS), which in turn, delivers the data to its final destination. In WSN, the sensor node’s energy is usually limited, and thus, the overall power consumption of the network is a real challenge. Numerous methods have been proposed to overcome this challenge by improving the overall power consumption and thus, prolonging the lifetime of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) protocol has been considered as a leading routing protocol in WSN, due to its low power consumption resultant from its cluster-based behavior. However, for dense networks, LEACH suffers from large burden over cluster head (CH), which might result in packet loss due to the induced congestion in the CH. In this paper, an improved algorithm based on LEACH is proposed to maximize the lifetime of WSN, by using another node to relieve CH’s burden. The routing path selection process is refined through selecting a new node that has the highest residual energy in each cluster to be an alternative gateway for the cluster head. Then, the routing path will be decided based on distance. This modified algorithm outperforms the original LEACH by 4.35% increase in the residual energy, which prolongs the network lifetime. However, there is a slight increase in the overhead ratio.

Wireless Sensor Networks (WSNs) is the vast area and developing very speedily now-a-days. This paper gives some of the major applications and critical limitations of WSNs like power and lifespan, communication bandwidth, memory size. Sensor nodes are working by node energy so the energy consumption is the essential issue in WSN. Routing in WSN consumes more energy than any other phase. Various routing techniques in WSNs are discussed and different routing protocols are illustrated. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the popular and feasible hierarchical clustering routing protocols in WSN. Working of LEACH is explained in brief. Furthermore, the descendants of LEACH e.g. LEACH-C (Centralized LEACH), LEACH-B (Balanced LEACH), LEACH-F, Multihop LEACH, MS-LEACH (Multihop and Single hop LEACH), MH-LEACH etc. are discussed in terms of the energy utilization. By analyzing the variants of LEACH, it is observed that the energy requirement in the cluster set up phase or data transmission phase is minimized to minimize the total energy consumption of the WSN. To elect the proper cluster head node, centralized approach is followed. Afterwards, data transmission will use the multihop communication technique. This will help to save some energy when base station is located far away from the cluster head node. Proposed protocol aims to decrease the energy overhead of the sensor nodes. The new approach is proposed entitled PV-LEACH which will save the energy consumption in cluster setup phase and also in data transmission phase of the conventional LEACH protocol. So, this will enhance the performance ratio in terms of energy consumption and life time of the sensor nodes.

Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO) and modified bat algorithm (MBA). The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.

Leach (low energy adaptive clustering hierarchy) algorithm is a self-clustering topology algorithm. Its execution process is cyclical. Each cycle is divided into two phases: cluster building phase and stable data communication phase. In the stage of cluster building, the adjacent nodes cluster dynamically and randomly generate cluster heads. In the data communication phase, the nodes in the cluster send the data to the cluster head, and the cluster head performs data fusion and sends the results to the aggregation node. Because the cluster head needs to complete data fusion, communication with the convergence node and other works, the energy consumption is large. Leach algorithm can ensure that each node acts as cluster head with equal probability, so that the nodes in the network consume energy relatively evenly. The basic idea of Leach algorithm is to randomly select cluster head nodes in a circular way. It evenly distributes the energy load of the whole network to each sensor node in the network. It can reduce network energy consumption and improve network life cycle. Leach repeatedly performs cluster refactoring during its operation. This paper studies the parameter detection of wireless sensor network based on Leach algorithm on the on-chip embedded debugging system. Because the classical low-power adaptive clustering layered protocol (Leach) has the problem of energy imbalance and short node life cycle, this paper uses embedded debugging technology based on Leach algorithm and the residual energy and position of nodes in wireless sensor networks were tested for research. This Leach algorithm uses the concept of wheel. Each round consists of two phases: initialization and stabilization. In the initialization stage, each node generates a random number between 0 and 1. If the random number generated by a node is less than the set threshold T (n), the node publishes a message that it is a cluster head. Through the research on the parameter detection, the simulation results show that the research in this paper has good feasibility and rationality.

Background/Objectives: In the current work, a new zone based and energy-efficient cooperative routing protocol is presented by equally distributes the load of network among all nodes by establishing zones; separation of zones depends on the directions that address some of the requirements of Wireless Sensor Networks (WSNs). Methods/Statistical Analysis: Based on the analysis, cooperative communication improves the reliability and can be made the network topology more energy-efficient and stable. Reduction in the Transmission Time (TT) from source to destination by using Cluster Coordinates (CCOs) in inter-cluster and relay nodes in intra-cluster communication helps to prolong the network lifetime. The primary problem with existing solutions is the location of the Base Station (BS) increases transmission load upon Cluster Head (CH) which is nearby to the BS, so cluster depletes energy earlier as compared to others. Findings: The performance of proposed protocol is tested for dead nodes, alive nodes and packet send to BS. In homogeneous scenario, the results reveal that proposed Zone based Cooperative Routing Protocol (ZCRP) performs better in terms of energyefficiency and stability as compared to Chain based Cooperative Routing Protocol (CBCCP), Low Energy Adaptive Clustering Hierarchy (LEACH) and Stable Electron Protocol (SEP). Application/Improvements: Based on the performance of ZCRP, this can be beneficial for those real time applications where data delivered on time is essential like Border surveillance, healthcare systems and many others.