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A. Christy Jeba Malar

Bio: A. Christy Jeba Malar is an academic researcher from College of Information Technology. The author has contributed to research in topics: Network packet & Mobile ad hoc network. The author has an hindex of 4, co-authored 25 publications receiving 73 citations.

Papers
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Journal ArticleDOI
TL;DR: From the performance evaluation carried out in this research work, it is proved that the proposed MCER-ACO approach is providing optimal energy efficient routing while comparing with few other existing methods.
Abstract: A mobile ad-hoc network (MANET) is a group of advanced mobile devices which are capable of self-organization. Due to the diverse nature of mobile devices and wireless connectivity, MANET faces several issues like topology management, energy management due to battery power limits, data communication issues etc. The utilization rate of battery powered energy and QoS properties are significant in MANET. In order to address these issues, we propose a new ant colony inspired technique for energy efficient routing in MANET. The proposed technique is a multi-objective constraints applied energy efficient routing technique based on ant colony optimization in mobile adhoc networks (MCER-ACO). The proposed MCER-ACO technique selects the next hop node centered on the constraints, residual energy of mobile node, no of packets in path and dynamic movement of topology. By applying ant colony technique on objectives and constraints, probability of choosing next hop node as forwarding node is determined. From the performance evaluation carried out in this research work, it is proved that the proposed MCER-ACO approach is providing optimal energy efficient routing while comparing with few other existing methods.

55 citations

Book ChapterDOI
01 Jan 2020
TL;DR: This chapter presents an IoT-based environment monitoring framework utilizing the WSN innovation to give environmental parameters at remote areas utilizing the web using wireless sensors associated with the Internet.
Abstract: Life becomes exceptionally less difficult in all stages with the improvement of automation innovation. Today, programmed techniques are being picked over manual strategies. The Web has turned into a part of life, and the Internet of things (IoT) is the cutting edge technology developing web expertise in accordance with the rapid increase in the quantity of web clients. From mechanical apparatus to client, the IoT is creating a system of regular articles that can share information and complete obligations when you are occupied with different happenings. In this chapter, we present an IoT-based environment monitoring framework utilizing the WSN innovation. The primary goal of the framework is to give environmental parameters at remote areas utilizing the web. This proposed framework speaks to the environmental parameter monitor utilizing wireless sensors associated with the Internet. There are two distinctive sensor nodes in the framework. Clients can observe the information by means of the web application from anyplace on the Internet. In the event that the sensor node information surpasses the designed range in the web application, a notice message is sent to clients to improve environmental conditions.

24 citations

DOI
TL;DR: A hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-based Optimal Cluster Routing (MPO-IPSO-OCR) is proposed for ensuring both efficient CH selection and data transmission and a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.
Abstract: Wireless Sensor Networks (WSNs) play an indispensable role in the lives of human beings in the fields of environment monitoring, manufacturing, education, agriculture etc., However, the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence. In this context, several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time. However, there still exists a room for improvement in Cluster Head (CH) selection based on the integration of critical parameters. The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant. In this paper, a hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-based Optimal Cluster Routing (MPO-IPSO-OCR) is proposed for ensuring both efficient CH selection and data transmission. The robust characteristic of MPOA is used in optimized CH selection, while improved PSO is used for determining the optimized route to ensure sink mobility. In specific, a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA. The high-speed ratio, unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point. The simulation investigation and statistical results confirm that the proposed MPO-IPSO-OCR is capable of improving the energy stability by 21.28%, prolonging network lifetime by 18.62% and offering maximum throughput by 16.79% when compared to the benchmarked cluster-based routing schemes.

14 citations

Journal ArticleDOI
TL;DR: In this article, a hybrid Stochastic Ranking and Opposite Differential Evolution enhanced Firefly Algorithm (HSRODE-FFA)-based clustering protocol is proposed for handling the issues of location-based CH selection approaches that select duplicate nodes with increased computation and poor selection accuracy.
Abstract: Ensuring stability and extending network lifetime in Wireless Sensor Networks (WSNs) achieved through significantly reduced energy consumption is considered as a potential challenge. The selection of Cluster Head (CH) during the process of clustering is determined to be highly complicated in spite of its role in facilitating efficient and balanced energy consumption in the network. In this paper, Hybrid Stochastic Ranking and Opposite Differential Evolution enhanced Firefly Algorithm (HSRODE-FFA)-based clustering protocol is proposed for handling the issues of location-based CH selection approaches that select duplicate nodes with increased computation and poor selection accuracy. This HSRODE-FFA clustering scheme includes the process of sampling for selecting the CHs from among the sensor nodes that exist in the sample population and address the problems introduced by different locations of nodes and CHs. It is proposed as an attempt to improve stability and lifetime of WSNs based on the merits of Stochastic Firefly Ranking (SFR) that enhances the exploration capability of Firefly Algorithm (FFA). The hybridization of the enhanced FFA with Opposition Differential Evolution (ODE) aids in speeding and ensuring optimal exploitation in the selection of CHs. The proposed HSRODE-FFA thereby maintains a balance between the rate of exploitation and exploration for deriving mutual benefit of rapid and potential selection of CHs from the sampling population. The experimental results of the proposed HSRODE-FFA scheme confirm an enhanced stability period and network lifetime of 16.21% and 13.86% respectively in contrast to the benchmarked Harmony Search and Firefly Algorithm-based Cluster Head Selection (HSFFA-CHS), Krill Herd Optimization and Genetic Algorithm-based Cluster Head Selection (KHOGA-CHS), Particle Swarm Optimization with Energy Centers Searching-based Cluster Head Selection (PSO-ECS-CHS) and Spider Monkey Optimization-based Cluster Head Selection (SMO-CHS) schemes.

10 citations

Book ChapterDOI
01 Jan 2020
TL;DR: A scheduling scheme to schedule Emergency packets (E-packets) and Regular packets (R-packs) and the next-hop nodes are chosen based on the trust value of nodes, which yields better results in terms of Packet Delivery Ratio (PDR), end-to-end delay, throughput and routing overhead.
Abstract: Internet of Things (IoT) based networks with sensors are energy and delay stringent. Efficient scheduling algorithms for IoT-based networks are the need of the hour. Nodes with selfish behavior degrade the performance of the network. Hence, a scheduling algorithm that schedules packets based on their emergencies and priorities yields better results. In this paper, M/M/1 and M/M/N scheduling scheme to schedule Emergency packets (E-packets) and Regular packets (R-packets) is proposed. The next-hop nodes are chosen based on the trust value of nodes. It is seen that the proposed scheme yields better results in terms of Packet Delivery Ratio (PDR), end-to-end delay, throughput and routing overhead.

9 citations


Cited by
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Journal ArticleDOI
TL;DR: An AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy and is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation.
Abstract: With the widely deployed wireless access points (APs) and the worldwide popularization of smartphones, WiFi-based indoor positioning has attracted great attention to both industry and academia. Locating and tracking objects within an indoor environment plays an important role in Internet of Things application and service. However, it is a challenging problem to achieve high accuracy using WiFi positioning technique due to the high instability in received signal strength from AP. Thus, it is desirable to select APs by considering both signal strength and connection quality. In this article, an AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy. The self-adaptive AP selection algorithm can be easily applied to various real scenarios and the performance of the new method is considerably better than classical algorithms. Learning algorithm is exploited to obtain the optimal solution for the self-adaptive AP selection algorithm. Experiments are conducted and the proposed algorithm is compared with classical algorithms. The experimental results demonstrate that the performance of the self-adaptive AP selection algorithm is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation. Meanwhile, the new method is robust to the random generation of initial particles and normalizing factor as their effect on the positional accuracy is less than 1 decimeter.

39 citations

Journal ArticleDOI
10 Mar 2020
TL;DR: The intelligent and an adaptive routing employing the bio inspired genetic bee colony algorithm and the ant colony optimization to have optimized energy utilization maximizing the longevity of the network is proposed.
Abstract: A WANET is a specific type of Adhoc network with a community of specialized, self-organizing mobile devices. Because of the complex existence of mobile devices and wireless networking, the adhoc network communicating in wireless manner faces many problems related to the topological organizations , battery capacity limitations that results in energy restrictions, transmission of information, etc. The significant attributes of an adhoc network communicating in wireless manner are the quality of service and the percentage of energy used. Nowadays as the bio-inspired computing plays a major role in the research field. Specifically bio-inspired algorithms motivated by nature's actions are used immensely to accomplish optimization in issues faced in communication. Massive works have been reported in the recent years to enhance the quality of service utilizing the Bio-inspired computing due to its intelligent and adaptive nature. The paper proposes the intelligent and an adaptive routing employing the bio inspired genetic bee colony algorithm and the ant colony optimization to have optimized energy utilization maximizing the longevity of the network. The proposed method is evaluated suing the network simulator-2 on the basis of the energy utilization, longevity and the service quality (Throughput) of the network, the results observed for the method put forth were better compared to the existing methods. Keyword: Adhoc Network, Wireless Adhoc Network (WANET), Bio Inspired Computing, Genetic Bee Colony Algorithm, Adaptive and Intelligent Routing

39 citations

Journal ArticleDOI
TL;DR: The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimize routing algorithm (ARA), and AntNet routing protocols.
Abstract: Vehicle ad-hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs). The VANETs communication framework is used to provide communication between moving vehicles in highway and urban road scenarios. Dynamic properties of VANETs, such as high dynamic topology, frequent route failure, high mobility of nodes, and bandwidth constraints, reduce the efficiency of routing. The long length route between source and destination affects the efficiency of the protocol in the form of high overhead, frequent disconnections, high packet loss rate, low packet delivery rate, and low throughput. In this paper, we propose an Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. The shortest path in the proposed protocol has low communication costs and the least number of hops between source and destination vehicles. The EHACORP has two phases. In phase 1, the EHACORP relies on a distance calculation method to compute the distance between vehicles. In phase 2, the source-based ant colony optimization is used to guide the ants to build a shorter path with the least number of hops to transmit data. The shortest path improves the efficiency of protocol in all aspects. The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimization routing algorithm (ARA), and AntNet routing protocols.

30 citations

Journal ArticleDOI
TL;DR: In this article , a deep learning algorithm called Graph Long Short-Term Memory (GLSTM) neural network was used to predict the air quality characteristics and the evolutionary algorithm called Dragon fly optimizer has been used to localize the node based on the prediction.

29 citations

Journal ArticleDOI
TL;DR: The proposed method to use decision trees and random forest algorithms in skin lesion image segmentation and classification is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images.
Abstract: Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images.

27 citations