Weight Based Approach for Optimal Position of Base Station in Wireless Sensor Network

Shah, Indra Kumar; Maity, Tanmoy; Dohare, Yogendra Singh

doi:10.1109/icict48043.2020.9112581

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…The work presented in [17] mainly focused on reducing the message overhead and enhancing the cluster lifetime. Different types of clustering algorithms have been extensively review and discussed in [18][19][20][21].…”

Section: Literature Reviewmentioning

confidence: 99%

New Hybrid Clustering and Routing Algorithm for Energy-Harvesting Wireless Sensor Network

Angeeta

Nitin

Ravi

2023

Preprint

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Wireless Sensor Networks (WSNs) are made up of a large number of randomly distributed sensor nodes that are monitor and store the physical information from their surroundings and send the collected datato base station through wireless medium. However, replacement of battery in sensor node is almost impossible once it drain out completely in hostile environment. Thereby, maintain the network survivability or network lifetime and reliable communication between nodes in such a harsh environment is the difficult task for the researchers. The goal of this research is to enhance the network survivability and Network Throughput (NTh) of the WSN. This study uses the simplified energy harvesting model to improve the network survivability. This paper proposes a new hybrid Cluster Head (CH) selection and routing scheme for energy harvesting WSN called Modified Leach Ant Colony Optimization (MLACO). The proposed scheme not only improves NTh but also improve the network survivability. Furthermore, when compared to MLEACH and FEEDC clustering schemes, the proposed MLACO strategy performs better than both the existing schemes. MLACO’s average network throughput is 31.5% and 26.6% higher than the existing MLEACH and FEEDC CH selection strategies, respectively. Meanwhile, the proposed scheme’s network survivability approaches that of existing schemes and even outperforms them between 900 and 1300 rounds.

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Section: Literature Reviewmentioning

confidence: 99%

New Hybrid Clustering and Routing Algorithm for Energy-Harvesting Wireless Sensor Network

Angeeta

Nitin

Ravi

2023

Preprint

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“…Based on the traditional model, two types of major problems were considered, and then their structure was implemented and evaluated in realistic wireless network sensor topologies. Shah et al [37] proposed an algorithm for the optimum position of the base station in the cluster WSN. Bogdanov et al [38] selected the base station's location to maximize the WSN's energy consumption, and the simulation results demonstrated that data rates can be increased with different algorithms to enhance the configuration of the base station position.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network

2021

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Wireless sensor networks have attracted worldwide attention in recent years. The failure of the nodes is caused by unequal energy dissipation. The reasons that cause unequal energy dissipation are, first and foremost, the distance between the nodes and the base station, and secondly, the distance between the nodes themselves. In wireless sensor networks, the location of the base station has a substantial impact on the network’s lifetime effectiveness. An improved genetic algorithm based on the crossover elitist conservation genetic algorithm (CECGA) is proposed to optimize the base station location, while for clustering, the K-medoids clustering (KMC) algorithm is used to determine optimal medoids among sensor nodes for choosing the appropriate cluster head. The idea is to decrease the communication distance between nodes and the cluster heads as well as the distance among nodes. For data routing, a multi-hop technique is used to transmit data from the nodes to the cluster head. Implementing an evolutionary algorithm for this optimization problem simplifies the problem with improved computational efficiency. The simulation results prove that the proposed algorithm performed better than compared algorithms by reducing the energy use of the network, which results in increasing the lifetime of the nodes, thereby improving the whole network.

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“…However, it considers relay selection and power control simultaneously to address the residual energy balancing problem of sensor nodes. In [27–31] the authors investigated a new relay selection scheme for randomly distributed WSNs. These schemes are useful for large scale wireless networks where nodes are randomly distributed across the network.…”

Section: Related Workmentioning

confidence: 99%

Neuro‐fuzzy and fuzzy schemes for cooperative communication in wireless sensor network: a military battlefield scenario

2020

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In the military battlefield, maintaining high survivability of energy constraint wireless sensor network (WSN) when some nodes are destroyed by the foe troops is one of the prominent issues for the researchers. This study proposes the fuzzy and neuro‐fuzzy based relay selection schemes for cooperative WSN which reduce the bit error rate (BER) while improving the network lifetime. Meanwhile, the proposed model maintains the uninterrupted communication between military nodes in a warfare situation. The proposed proposed strategies are compared with the maximum residual energy‐based relay selection (MRERS), minimum energy consumption‐based relay selection (MECRS), and random relay selection (RRS) strategies. The performance of the proposed and existing strategies is evaluated on the basis of BER, network lifetime, number of dead nodes, and average energy of nodes in the network. The simulation results demonstrate that proposed schemes has 13.19–19.28% improvement in BER than the existing strategies. Moreover, 76.00–90.88% network lifetime improves when compared with the MECRS and RRS strategies. However, 6–7.76% is less than MRERS strategy. Furthermore, the results show that network survivability and average network energy is also better when the number of dead nodes in the network increases.

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Weight Based Approach for Optimal Position of Base Station in Wireless Sensor Network

Cited by 6 publications

References 22 publications

New Hybrid Clustering and Routing Algorithm for Energy-Harvesting Wireless Sensor Network

New Hybrid Clustering and Routing Algorithm for Energy-Harvesting Wireless Sensor Network

Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network

Neuro‐fuzzy and fuzzy schemes for cooperative communication in wireless sensor network: a military battlefield scenario

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