Energy Optimization in WSN Using Evolutionary Bacteria Foraging Optimization Method

Dhondiyal, Shiv Ashish; Aeri, Manisha; Gulati, Paras; Rana, Deepak Singh; Singh, Sourabh

doi:10.1007/978-981-15-8443-5_41

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Here, either fuzzy logic or BFO will be worked for CH selection that leads to high computation time and there is no other options/conditions used that which algorithm must be worked first for selection process. [20] presents evolutionary bacteria foraging optimization method, named as EBFO, which uses a fitness function to measure the combined energy of the all sensor nodes in the cluster to make sure overall energy of cluster lesser energy than combined energy. It provides better field placement and cluster head selection.…”

Section: Related Workmentioning

confidence: 99%

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

Saritha

2023

IJRITCC

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As the amount of low-cost and low-power sensor nodes increases, so does the size of a wireless sensor network (WSN). Using self-organization, the sensor nodes all connect to one another to form a wireless network. Sensor gadgets are thought to be extremely difficult to recharge in unfavourable conditions. Moreover, network longevity, coverage area, scheduling, and data aggregation are the major issues of WSNs. Furthermore, the ability to extend the life of the network, as well as the dependability and scalability of sensor nodes' data transmissions, demonstrate the success of data aggregation. As a result, clustering methods are thought to be ideal for making the most efficient use of resources while also requiring less energy. All sensor nodes in a cluster communicate with each other via a cluster head (CH) node. Any clustering algorithm's primary responsibility in these situations is to select the ideal CH for solving the variety of limitations, such as minimising energy consumption and delay. Kinetic Gas Molecule Optimization (KGMO) is used in this paper to create a new model for selecting CH to improve network lifetime and energy. Gas molecule agents move through a search space in pursuit of an optimal solution while considering characteristics like energy, distance, and delay as objective functions. On average, the KGMO algorithm results in a 20% increase in network life expectancy and a 19.84% increase in energy stability compared to the traditional technique Bacterial Foraging Optimization Algorithm (BFO).

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Section: Related Workmentioning

confidence: 99%

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

Saritha

2023

IJRITCC

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“…Cheng et al [15] suggested an event-oriented IoT-based collaboration framework for SOA to deal with interoperability problems between large numbers of physical and heterogeneous IoT services. The author proposes a user-centered IoT Service-Oriented architecture that incorporates services that use IoT resources in an urban computing context.…”

Section: Related Workmentioning

confidence: 99%

Increased Energy Conservation in Internet of Things (IoT) Related Wireless Networking

Sripriya

Mallikeswari²,

Reshma³

2021

Advances in Parallel Computing Technologies and Applications

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The key emergent technology named Internet of Things (IoT) is used in various industries and daily activities of individuals. The IoT sensors are a battery operated one that is connected with internet over various services than ranges between healthcare and industries. Energy conservation or energy efficiency of these sensors is an utmost concern since most of the IoT devices powered with battery may often die while transmitting the data between the other devices. Various researches are conducted to conserve the energy via energy conserving mechanism. In this paper, we develop a multi-objective optimization model to resolve the higher energy consumption by the IoT devices under different diverse environment. These IoT devices communicates wirelessly with other devices in terms of WiFi connectivity and hence the study analyses the efficacy of the model. The experimental results show that the multi-objective modelling of energy conservation shows a reduced consumption of battery power by the IoT devices than conventional mechanisms.

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Energy Optimization in WSN Using Evolutionary Bacteria Foraging Optimization Method

Cited by 2 publications

References 9 publications

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

Increased Energy Conservation in Internet of Things (IoT) Related Wireless Networking

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