An Energy Efficient Virtual-MIMO Communication for Cluster Based Cooperative Wireless Sensor Network

Sachan, Akash; Nigam, Shivangi; Bajpai, Abhishek

doi:10.1109/icccnt.2018.8493985

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…This issues also has been mention in section 2. All these also can control the packet lost performance as in [18] said in their paper. Both of these issues need to be studied indeeply to resolve all possible problems.…”

Section: Load Balancingmentioning

confidence: 95%

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Survey on Optimization of The Energy Consumption and Load Balancing in Wireless Sensor Network

A¹

2019

IJATCSE

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In era Internet of Thing (IoT), Wireless Sensor Network (WSN) is the most important device involved in communication networks between two or more nodes [1]. The growth of communication in the network may cause the performance of delivering packets to become slower or it could be loss packets during transmission time. There are many factors need to be considered in order to decrease the packet loss such as energy consumption, memory space, load balancing, network lifetime, etc. The main focus in this paper is to identify the most problematic issues that happen in the Wireless Sensor Network which is focus on balance heavy traffic load and the energy consumption on the network system. Some comparison will be recognized based on several reviewed research paper and will show the strength and limitation on Wireless Sensor Network communication.

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Section: Load Balancingmentioning

confidence: 95%

“…Y. Cheng, D. Yang, and H. Zhou, 2018 [18] • Two metric such as miss packed deadline and ratio packet loss need to be considered to remove from AP's. • Make sure that station can be selected by optimal AP's.…”

Section: Smentioning

confidence: 99%

Survey on Optimization of The Energy Consumption and Load Balancing in Wireless Sensor Network

A¹

2019

IJATCSE

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“…Sensor nodes will collaborate to form communication paths, thereby extending the data transmission range and preventing data loss due to disruptions [14]- [16]. By implementing a cooperative communication system, energy usage in the sensor nodes can be conserved, resulting in an extended battery life [17]- [21].…”

Section: Introductionmentioning

confidence: 99%

Shrimp Pond Monitoring System using Cooperative Wireless Sensor Network Multi-Hop Technique based on Internet of Things

Zickri,

Novandri,

Adriman

et al. 2023

JNTE

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Water quality is a crucial factor in maintaining the survival and growth of shrimp. Manual water quality monitoring in shrimp ponds is no longer effective due to the need for periodic monitoring to maintain stable water quality. Therefore, online monitoring using various sensors installed in each pond is necessary. However, there are several challenges to overcome, such as the large expanse of the shrimp ponds, which may lead to data loss due to signal disruptions, and limited energy to power the sensors. To address these issues, this paper proposes the cooperative Wireless Sensor Network (WSN) technique with a multi-hop method for communication in the monitoring process. The system consists of five sensor nodes: temperature sensor, pH sensor, water level sensor, intake water flow sensor, and drain water flow sensor. The cooperative WSN multi-hop technique helps reduce energy consumption in the sensor nodes during measurement and data transmission, while also preventing data packet loss. This is achieved through the use of relay nodes that strengthen signals and forward data to the sink node. As a result, the battery life is extended, and energy usage in the monitoring process can be optimized. The system enables real-time online monitoring and can be accessed through a smartphone application. The results of this study show that the total energy consumption for data transmission in the sensor nodes is 9.64 J, while the total energy consumption for data forwarding in the relay nodes is 9.15 J. The total energy consumption in the transmit and receive processes is 18.79 J or 5.2 mWh. Therefore, it can be concluded that the energy savings of the proposed system is 4.3 mWh or approximately 45%, and is more efficient than the previous system.

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“…Remote gadget hubs are input gadgets that detect different physical conditions such as temperature, pressure, sound, wetness, and so forth. WSNs are widely utilized in numerous natural applications, military applications, and canny vehicle systems [2]. The liberated hub sends the apparent information to the peak design descended gadget for investigation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Bali

Gill²,

Choudhary³

et al. 2022

Energies

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In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

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An Energy Efficient Virtual-MIMO Communication for Cluster Based Cooperative Wireless Sensor Network

Cited by 15 publications

References 12 publications

Survey on Optimization of The Energy Consumption and Load Balancing in Wireless Sensor Network

Survey on Optimization of The Energy Consumption and Load Balancing in Wireless Sensor Network

Shrimp Pond Monitoring System using Cooperative Wireless Sensor Network Multi-Hop Technique based on Internet of Things

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

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