Optimal leach protocol with improved bat algorithm in wireless sensor networks

© 2019, Korean Society for Internet Information. All rights reserved. A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT’s Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs)

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes

Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks

A group of small sensors can participate in the wireless network infrastructure and make appropriate transmission and communication sensor networks. There are numerous uses for drones, including military, medical, agricultural, and atmospheric monitoring. The power sources available to nodes in WSNs are restricted. Furthermore, because of this, a diverse method of energy availability is required, primarily for communication over a vast distance, for which Multi-Hop (MH) systems are used. Obtaining the optimum routing path between nodes is still a significant problem, even when multi-hop systems reduce the cost of energy needed by every node along the way. As a result, the number of transmissions must be kept to a minimum to provide effective routing and extend the system\u27s lifetime. To solve the energy problem in WSN, Taylor based Gravitational Search Algorithm (TBGSA) is proposed, which combines the Taylor series with a Gravitational search algorithm to discover the best hops for multi-hop routing. Initially, the sensor nodes are categorised as groups or clusters and the maximum capable node can access the cluster head the next action is switching between multiple nodes via a multi-hop manner. Initially, the best (CH) Cluster Head is chosen using the Artificial Bee Colony (ABC) algorithm, and then the data is transmitted utilizing multi-hop routing. The comparison result shows out the extension of networks longevity of the proposed method with the existing EBMRS, MOGA, and DMEERP methods. The network lifetime of the proposed method increased by 13.2%, 21.9% and 29.2% better than DMEERP, MOGA, and EBMRS respectively

A Co-evolutionary Algorithm-based Enhanced Grey Wolf Optimizer for the Routing of Wireless Sensor Networks

Wireless networks are frequently installed in arduous environments, heightening the importance of their consistent operation. To achieve this, effective strategies must be implemented to extend the lifespan of nodes. Energy-conserving routing protocols have emerged as the most prevalent methodology, as they strive to elongate the network\u27s lifetime while guaranteeing reliable data routing with minimal latency. In this paper, a plethora of studies have been done with the purpose of improving network routing, such as the integration of clustering techniques, heterogeneity, and swarm intelligence-inspired approaches. A comparative investigation was conducted on a variety of swarm-based protocols, including a new coevolutionary binary grey wolf optimizer (Co-BGWO), a BGWO, a binary whale optimization, and a binary Salp swarm algorithm. The objective was to optimize cluster heads (CHs) positions and their number during the initial stage of both two-level and three-level heterogeneous networks. The study concluded that these newly developed protocols are more reliable, stable, and energy-efficient than the standard SEP and EDEEC heterogeneous protocols. Specifically, in 150 m2 area of interest, the Co-BGWO and BGWO protocols of two levels were found the most efficient, with over than 33% increase in remaining energy percentage compared to SEP, and over 24% more than EDEEC in three-level networks

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Wireless Sensor Network (WSNs) can perform transmission within themselves and examination is performed based on their range of frequency. It is quite difficult to recharge devises under adverse conditions. The main limitations are area of coverage, network’s lifetime and aggregating and scheduling. If the lifetime of a network should be prolonged, then it can become a success along with reliability of the data transferred, conservation of sensor and scalability. Through many research works, this challenge can be overcome which are being proposed and the network’s lifespan improved which can preserve the sensor’s energy. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus increasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Challenges such as node deployment and energy-aware clustering can be considered as issues of optimization with regards to WSNs, along with data collection. An optimal solution can be gotten through evolutionary and SI algorithm, pertaining to Non-deterministic Polynomial (NP)-complete along with a number of techniques. In this work, Krill Herd Algorithm based clustering is proposed

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.publishedVersio

Optimal Coverage in Wireless Sensor Network using Augmented Nature-Inspired Algorithm

           One of the difficult problems that must be carefully considered before any network configuration is getting the best possible network coverage. The amount of redundant information that is sensed is decreased due to optimal network coverage, which also reduces the restricted energy consumption of battery-powered sensors. WSN sensors can sense, receive, and send data concurrently. Along with the energy limitation, accurate sensors and non-redundant data are a crucial challenge for WSNs. To maximize the ideal coverage and reduce the waste of the constrained sensor battery lifespan, all these actions must be accomplished. Augmented Nature-inspired algorithm is showing promise as a solution to the crucial problems in “Wireless Sensor Networks” (WSNs), particularly those related to the reduced sensor lifetime. For “Wireless Sensor Networks” (WSNs) to provide the best coverage, we focus on algorithms that are inspired by Augmented Nature in this research. In wireless sensor networks, the cluster head is chosen using the Diversity-Driven Multi-Parent Evolutionary Algorithm. For Data encryption Improved Identity Based Encryption (IIBE) is used.  For centralized optimization and reducing coverage gaps in WSNs Time variant Particle Swarm Optimization (PSO) is used. The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the reduced sensor lifetime and redundant information in Wireless Sensor Networks (WSNs) as well as will give real and effective optimum coverage to the Wireless Sensor Networks (WSNs)