GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network

Sahoo, Biswa Mohan; Pandey, Hari Mohan; Amgoth, Tarachand

doi:10.1016/j.swevo.2020.100772

Cited by 131 publications

(88 citation statements)

References 41 publications

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“…CH selection using an efficient algorithm by including some critical parameters was performed in [33]. Routing via the selected CH will be efficient for performance enhancement.…”

Section: A Few Recent Work Related To Our Proposal Is Given Belowmentioning

confidence: 99%

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Arya

Bagwari²,

Chauhan

2022

IEEE Access

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For the past few years, huge interest and dramatic development have been shown for the Internet of Things (IoT) based constrained Wireless sensor network (WSN) to achieve efficient resource utilization and better service delivery. IoT requires a better communication network for data transmission between heterogeneous devices and an optimally deployed energy-efficient WSN. The clustering technique applied for WSN node deployment needs to be efficient; therefore, the entire architecture can obtain a better network lifetime. The entire network is partitioned into various clusters. Moreover, the cluster head (CH) selection process also needs proper attention to achieve efficient data communication towards the sink node via selected CH and increase the node reachability within the Cluster. An energy-efficient deep belief network (DBN) based routing protocol is developed in this proposed framework, which achieves better data transmission through the selected path. Due to this, the packet delivery ratio (PDR) gets improved. In this framework, the nodes in the whole network are initially grouped as clusters using a reinforcement learning (RL) algorithm, which assigns a reward for the nodes belonging to the particular Cluster. Then, the CH required for efficient data communication is selected using a Mantaray Foraging Optimization (MRFO) algorithm. The data is transmitted to the sink node via the selected CH using an efficient deep learning approach. Finally, the performance of the proposed deep network-based routing protocol is evaluated using different evaluation metrics: network lifetime, energy consumption, number of alive nodes, and packet delivery rate. Finally, the evaluated results are compared with a few existing algorithms. The proposed DBN routing protocol has achieved a better network lifetime among all these algorithms.

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“…CH selection using an efficient algorithm by including some critical parameters was performed in [33]. Routing via the selected CH will be efficient for performance enhancement.…”

Section: A Few Recent Work Related To Our Proposal Is Given Belowmentioning

confidence: 99%

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Arya

Bagwari²,

Chauhan

2022

IEEE Access

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“…Sahoo et al [22] designed and also created a hierarchical hybrid method for distributed clustering utilizing PSO and GA algorithm in wide-level WSNs. These are two levels of clustering exactly where GA is used for the cluster, which should be at ground level, and for increased level clustering, GA is used to provide much better convergence.…”

Section: Related Workmentioning

confidence: 99%

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Sahoo

Amgoth

2021

SN COMPUT. SCI.

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Clustering is one of the imperative solutions to the problem of energy unbalancing prevailing in the network. The major issues of wireless sensor network with unequal clustering and multi-hop communication are the energy stability and network longevity. In this paper, we consider the improved Bat optimization algorithm to improve the unequal clustering techniques by forming groups before selecting Cluster Heads. In terms of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. Bat algorithm (BA), optimization of particle swarm, differential evolution, and whale optimization algorithm is now becoming efficient clustering methods as per the metaheuristic approach. Evaluate the life span of the entire network; this paper proposes an improved BA. The BA's primary goal of unequal clustering is to reduce energy consumption and extend the lives of the WSNs. An objective function has been formulated to reduce energy consumption and increase the network's lifespan to achieve these goals. Compared with three existing optimization methods, the experimental results showed that the proposed improved BA achieved better efficiency in reducing total power consumption: whale optimization for topology control, and improved particle swarm optimization algorithm.

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“…By WSNs, different data such as temperature, pressure, motion, humidity, and pollution are collected from different environments. Having this information without knowing the location of sensor nodes is incomprehensible and useless [3]. One of the most challenging issues on WSNs technology is the issue of sensor nodes localization [4].…”

Section: Introductionmentioning

confidence: 99%

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Kamfar¹,

Zandhessami²,

Alborzi³

2021

Preprint

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Nowadays, Wireless Sensor Networks (WSNs) are significantly applied in engineering and scientific research. WSNs consist of a group of distributed space sensors that track the environment's physical conditions and control the collected data at one central location. Examples of these sensors' applications are smart cities, transport, volcano surveillance and environmental activity, earthquake monitoring, medicine, post-disaster response, and military control. Wireless sensor networks have a lot of research issues like access to the media, implementation, time synchronization, network security and localization of the nodes. One of the most critical problems in this network research is the optimum position of the sensors to have access to maximum coverage and increase network life span to decrease maintenance costs, develop and manage the network. One of the main causes of the failure in these networks is running out of sensor battery and replacing them which impose high costs to maintenance and managing of the network. In order to solve the issues related to optimization and localization, researchers have focused on the algorithms like Swarm Intelligence (SI), because they enable us to solve complicated issues of optimization and NP-Hard issues to solve optimization. However, most of these algorithms are specialized for a purpose or a special program, and the majority of the solutions are not compatible with most of the wireless network sensors. The DV-Hop is one of the most popular node algorithms. But the main problem of the DV-Hop is the possibility of error in calculating the assessed distance between the unknown node and the nodes of anchor. Therefore, minimizing this error is the key to improve this algorithm. To reduce the problem of high localization error, two meta-heuristic algorithms have been proposed based on a combination. In this paper, a new optimization method based on a combination of Krill Herd Algorithm (KHA) and Particle Swarm Optimization (PSO) called KHAPSO is suggested to improve DV-Hop. Simulation results in MATLAB 2016 show that the KHAPSO model has a lower mean error compared to the DV-Hop, DV-Hop-KHA and DV-Hop-PSO models. Also, energy consumption in the KHAPSO model is less in comparison to the other models. The KHAPSO model with 400 unknown nodes and 30 anchor nodes was able to reduce energy consumption by about 35% and at the same time 27% reduction in Average Localization Error (ALE) compared to DV-Hop.

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GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network

Cited by 131 publications

References 41 publications

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

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