N. Lavanya

Bio: N. Lavanya is an academic researcher. The author has contributed to research in topics: Wireless sensor network & Node (networking). The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Hybrid based Energy Efficient Cluster Head Selection using Camel Series Elephant Herding Optimization Algorithm in WSN

Abstract: The rapid growth in wireless technology is enabling a variety of advances in wireless sensor networks (WSNs). By providing the sensing capabilities and efficient wireless communication, WSNs are becoming important factor in day to day life. WSNs have many commercial, industrial and telecommunication applications. Maximizing network lifespan is a primary objective in wireless sensor networks as the sensor nodes are powered by a non-rechargeable battery. The main challenges in wireless sensor networks (WSNs) are area of coverage, network’s lifetime and aggregating. Balanced node establishment (clustering) is the foremost strategy for extending the entire network's lifetime by aggregating the sensed information at the head of the cluster. The recent research trend suggests Meta-heuristic algorithms for the intelligent selection of ideal Cluster Heads (CHs). The existing Cluster Head Selection (CHS) algorithm suffers from the inconsistent trade-offs between exploration – exploitation and global best examine constraints. In this research, a novel Camel series Elephant Herding Optimization (CSEHO) algorithm is proposed to enhance the random occurrences of Camel algorithm by the Elephant Herding Optimization algorithm for optimal CHS. The Camel algorithm imitates the itinerant actions of a camel in the desert for the scavenging procedure. The visibility monitoring condition of the camel algorithm improves the efficiency of exploitation, whereas the exploration inefficiency of a Camel algorithm is compensated optimally by the Elephant Herding Optimization operator (Clan and separator). The superior performance of the proposed CSEHO algorithm is validated by comparing its performance with various other existing CHS algorithms. The overall attainment of the offered CSEHO algorithm is 21.01%, 31.21%, 44.08%, 67.51%, and 85.66%, better than that of EHO, CA, PSO, LEACH, and DT, respectively.

An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for Wireless Sensor Networks

Abstract: In wireless sensor networks (WSNs), energy efficiency is a significant design challenge that can be resolved by clustering and routing approaches. They are considered as Non-deterministic Polynomial (NP)-hard optimization problems, and the optimal or near-optimal solutions can be determined by using Swarm-Intelligence (SI) based algorithms. With this inspiration, this study focuses on two approaches, viz. the Hybrid Butterfly and Ant Colony optimization algorithm along with Static sink node (HBACS) and HBAC along with Mobile sink node (HBACM), which is a hybridization of Butterfly Optimization (BOA) and Ant Colony Optimization (ACO) algorithm. BOA determines the optimal cluster head, and ACO performs energy-efficient routing, thereby minimizing the energy consumption and maximizing the network’s lifetime. Furthermore, in this study, mobility of the sink node is used to eliminate the multi-hop communication between cluster heads and sink nodes, hence addressing the hot-spot issue and further extending the network lifetime. The proposed HBACS and HBACM approaches are implemented in the NS2 simulator. The simulation findings reveal that the HBACS shows percentage improvement regarding residual energy by 24.23%, 41.98%, and 66.67%; an improved number of alive nodes by 28.19%, 37.81%, and 53.12%; and improved throughput by 8.11%, 14.29%, and 17.65% over CRWO, ERP, and IHSBEER algorithms respectively. Moreover, the HBACM approach performs better in LDN by 18.76%, 63.66%, and 66.28%; HDN by 8.35%, 56.26%, and 58.15%, and FDN by 7.77%, 52.76%, and 74.29% over HGWSFO, SFO, and GWO based approaches, respectively.

A Hybrid Artificial Bee Colony and Harmony Search Algorithm-Based Metahueristic Approach for Efficient Routing in WSNs

Abstract: The replacement of sensor node battery in wireless sensor network (WSN) is considered to be the highly crucial task in hostile environments. The process of partitioning the region of sensing in WSNs into clusters is determined to be the ultimate solution for attaining maximized network lifetime and gaining high energy efficiency. However, proper selection of CHs plays an anchor role in enhancing the network lifetime, since they need to utilize addition energy to handle the task of data gathering and data aggregation from the sensor member nodes of clusters and finally disseminate them to the base station.In this paper, a Hybrid Artificial Bee Colony (ABC) and Harmony Search Algorithm-based Metaheuristic Approach (HABC-HSA-MA) is proposed for attaining efficient CH selection with the view to sustain stable energy utilization with enhanced network lifetime. This HABC-HSA-MA includes the global optimization potential of Harmony Search Algorithm (HSA) and local exploitation potential of the classical ABC algorithm for achieving significant CH selection for stabilizing energy and extending network lifetime. It also incorporated the benefits of harmony adjusting factor for inducing the process of improving the dynamic search efficiency during the process of CH selection in order to prevent worst candidates from being selected as CHs. The simulation results of the proposed HABC-HSA-MA confirms an enhancement in mean network lifetime of 23.64% and balanced average energy consumption rate of 20.28% compared to the benchmarked hybrid meta-heuristic CH selection schemes.

Red Deer and Simulation Annealing Optimization Algorithm-Based Energy Efficient Clustering Protocol for Improved Lifetime Expectancy in Wireless Sensor Networks

Abstract: Energy stability is considered as the core challenge of Wireless Sensor Networks (WSNs) as it is the main factor that contributes towards improved network lifetime expectancy. Clustering with maximized energy efficiency is determined to be the well documented NP hard optimization solution that has the possibility of resulting in prolonged network lifetime. Diversified number of computational approaches that includes nature inspired metaheuristic algorithms, reinforcement schemes and evolutionary algorithms were used for attaining efficient clustering process in WSNs. In this paper, Red Deer and Simulation Annealing Optimization Algorithm-based Energy Efficient Clustering Protocol (RDSAOA-EECP) is proposed for improving lifetime expectancy and energy stability in WSNs. This clustering protocol is proposed with the global optimization capability of the red deer optimization algorithm and local optimization potential of simulation annealing. In specific, RDA is used for diversification and Simulated Annealing (SA) for intensification in order to establish balance between them during the clustering process in order to sustain energy stability. This RDSAOA-EECP protocol is proposed with SA as the search tool that prevents the phases of fighting and roaring phases of RDA involved in the intensification process. Moreover, this clustering protocol inherits the potentialities of the proposed metaheuristic properties in order to achieve optimal cluster heads together with the optimal base station location for the objective of enhancing energy efficiency. The simulation results of the proposed RDSAOA-EECP protocol guaranteed its potentiality over the baseline clustering approaches in improving network lifetime, throughput, packet deliver rate with minimized network latency and energy consumptions independent to the homogeneous and heterogeneous sensor network setup considered for implementation.

A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks

Abstract: In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network’s lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.