A two‐vector<scp>data‐prediction</scp>model for<scp>energy‐efficient data‐aggregation</scp>in wireless sensor network

Jain, Khushboo; Singh, Akansha

doi:10.1002/cpe.6898

Cited by 13 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the previous, using TDMA method will reduce nodes energy loss, thus will increase nodes lifetime [26]. In addition, using the TDMA method will reduce packets loss caused by packets collisions, by scheduling the process of sending nodes to the CH, which will save more energy in the nodes [27]. To save more nodes energy, and allow non-uniform nodes to a certain cluster, the proposed algorithm used the following mechanism when sending data:…”

Section: Data Packets Transfer Phasementioning

confidence: 99%

Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

Salman

Mohammed

Mutar

2023

JCSANDM

View full text Add to dashboard Cite

Wireless Sensor Network (WSN) is a group of small, intelligent sensors with limited resources. WSN has limited energy restrictions, so, the network lifetime is the major challenge that directly affect the efficiency of the network. This work presents an energy-saving clustering hierarchical algorithm for WSNs; it is an improvement of Low-Energy adaptive Clustering Hierarchy (LEACH) algorithm. The aim of this algorithm is to minimize power consumption by the appropriate election of new cluster heads in every data transfer round and avoid network collisions. This goal achieved by using an efficient function to select the best cluster heads nodes in each round, which takes into account the current energy in the sensors. The proposed algorithm improves the cluster formation process by relying on the shorter distance to the base station. The Time Division Multiple Access (TDMA) mechanism also utilized to schedule the transmission of data packets to cluster heads nodes and to avoid data packet collisions at the base station. Experiments conducted in MATLAB R (2020a) software showed that the suggested algorithm extended the network lifetime by 14.5%, and improved the network throughput by 16.8% compared to the LEACH algorithm. That means, the proposed energy-saving clustering hierarchy algorithm has improved the performance of the LEACH algorithm in term of enhancing network lifetime and increasing network throughput.

show abstract

Section: Data Packets Transfer Phasementioning

confidence: 99%

Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

Salman

Mohammed

Mutar

2023

JCSANDM

View full text Add to dashboard Cite

show abstract

Hierarchical Taylor quantized kernel least mean square filter for data aggregation in wireless sensor network

Ilango,

Ravichandran,

Sivarajan

et al. 2024

Int J Communication

View full text Add to dashboard Cite

SummaryThe advanced technology in recent years that has achieved more attention among researchers and the social community is the wireless sensor network (WSN) that includes a number of nodes that are commonly distributed in remote zones. While deploying the WSN in huge areas, WSNs produce a massive amount of data. Thus, there is a significant need to process the data through efficient models. The data aggregation technique is the common solution widely employed to obstruct congestion on large‐scale WSNs. However, the demanding part of the data aggregation scheme is to mitigate the network overhead without affecting the system efficiency. Most of the data transmitted by sensor nodes are repetitious and thus result in high power consumption. Therefore, sensor nodes should utilize an efficient data aggregation model for data transmission that minimizes duplicate data. In order to maintain such complications, this article proposes a hierarchical Taylor quantized kernel least mean square (HTQKLMS) filter for aggregating data in WSN. For this purpose, WSN is initially simulated, and then data aggregation is accomplished using developed HTQKLMS filter. Additionally, the HTQKLMS is derived by amalgamating the hierarchical fractional quantized kernel least mean square (HFQKLMS) filter with the Taylor series. Here, the data prediction mechanism is done by employing HFQKLMS model that is an integration of quantized kernel least mean square (QKLMS) and hierarchical fractional bidirectional least mean square (HFBLMS). Apart from this, data redundancy is achieved by broadcasting needed data utilizing data detected at the destination. Furthermore, HTQKLMS approach has delivered a minimum energy consumption of 0.0333 J and less prediction error of 0.0326.

show abstract

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is "energy consumption." However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy-efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

show abstract

A two‐vectordata‐predictionmodel forenergy‐efficient data‐aggregationin wireless sensor network

Cited by 13 publications

References 26 publications

Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

Hierarchical Taylor quantized kernel least mean square filter for data aggregation in wireless sensor network

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Contact Info

Product

Resources

About