Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network

Getahun, Masresha; Azath, M.; Sharma, Durga Prasad; Tuni, Amin; Adane, Abel

doi:10.1155/2022/4154742

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Some algorithms are suggested for Distributed Energy Saving Clustering (DEEC), the Development of Distributed Energy saving Clustering (DDEEC), Enhanced Distributed Energy Saving Clustering (EDEEC), and clustering in Threshold Distributed power saving (TDEEC) clusters. All of them aim to save power and increase the lifetime of WSNs [21].…”

Section: Energy Utilization Methods In Wsnmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

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In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

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Section: Energy Utilization Methods In Wsnmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

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“…The devices consume energy in sensing, processing, storing, transmission, and reception operations. The dominant consumer of energy is the network node's radio operations [52], [53]. Recent techniques to limit energy consumption in radio operations can be classified as: improved transceiver circuit design, transmission power control, cognitive radios and energy-efficient routing [54].…”

Section: Related Workmentioning

confidence: 99%

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Shafique,

Soliman,

Amjad

2024

IEEE Access

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Due to the technological advancements in wireless communication and their continuously increasing applications in collaborative and cooperative smart infrastructures, energy efficient data collection using wireless devices, has gained significant importance recently. Modern wireless sensor networks refer to network of low-powered and energy-constrained Internet of Things (IoT) devices. Although data collection using hierarchical routing with clustered network improves energy efficiency but introduces energy holes in the region closer to the data gathering center due to heavy relaying load on cluster heads. In this paper, first an improved data gathering center deployment technique for heterogeneous networks has been proposed. Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS), a multi-criteria decisionmaking technique, is used to determine the optimal location for the deployment of data gathering center. The proposed technique is adaptive to various shaped networks as required by IoT and increases energy efficiency. Secondly, an unequal clustering based on transmission distances has been proposed. Moreover, cubical, and spherical segmentation schemes for 3D heterogeneous networks have been proposed that assist the formation of unequal clusters. Finally, a shape independent data rate-based segmentation has been proposed that further extends the adaptability and scalability of the proposed unequal clustering. The results demonstrate that the proposed data traffic-based shape independent adaptive clustering scheme increases network lifetime up to 14.2% and 18.8% as compared to Fuzzy Logic based unequal clustering and IUCR respectively. It also reduces the overall network energy consumption up to 61.4% as compared to the state-of-the art unequal clustering methods.INDEX TERMS Balanced energy routing, Energy Holes, Internet of Things, Shape independent clustering, Scalable clustering protocol, Unequal clustering, 3D Wireless Sensor Network.

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“…Data fusion technology can merge the data information collected by multiple sensors and eliminate redundant and useless information [34,35]. (3) Self-contained energy replenishment device: e sensor node is equipped with the energy harvesting and conversion device to collect energy like solar energy [36][37][38]. However, renewable energy fluctuates with the environment and the energy density is low.…”

Section: Introductionmentioning

confidence: 99%

Two FCA-Based Methods for Reducing Energy Consumption of Sensor Nodes in Wireless Sensor Networks

Lei

Chen

et al. 2022

Scientific Programming

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In the design of wireless sensor networks (WSNs), it is important to reduce energy consumption and extend the service life of the sensors. Selecting one of the minimum sensor combinations (MSCs) that can monitor all areas, while the other MSCs are asleep, can effectively extend the lifetime of WSNs. This paper proposes two algorithms based on Formal Concept Analysis (FCA) for extracting some MSCs, to minimize the energy consumption and meet the coverage requirement. These two methods firstly extract the concept lattice from a monitor-areas context, and then it is simple to extract sensor nodes monitoring the overlapping area from the concept lattice. The algorithms consist of three steps as follows: the first step is to transform sensors and monitoring areas into a context, the second is to extract the concept lattice and implications among areas, and the third is to extract some different MSCs that can monitor all areas. Thus, some strategies are designed to awaken different MSCs to achieve the purpose of reducing energy consumption. Experimental results show that these methods have played a positive effect on extracting different MSCs and extending the lifetime of sensor networks.

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Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network

Cited by 9 publications

References 13 publications

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Two FCA-Based Methods for Reducing Energy Consumption of Sensor Nodes in Wireless Sensor Networks

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