Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks

Hua, Cunqing; Yum, T.-S.P.

doi:10.1109/tnet.2007.901082

Cited by 145 publications

(15 citation statements)

References 29 publications

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“…Network lifetime is the number of cycles the data transmissions are performed till the first sensor is drained out of its energy [8]. The main aim is to implement data aggregation such that there is even energy consumption in the network.…”

Section: Network Lifetimementioning

confidence: 99%

A study on data aggregation scheme over wireless sensor network

Geethapriya¹,

Kala²,

Karthik³

2016

2016 10th International Conference on Intelligent Systems and Control (ISCO)

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Nowadays, wireless sensor networks (WSNs) seen as an emerging technology due to its multi serviceable, low cost and low power sensor nodes, that are deployed randomly and densely over a network to collect a useful information from there. Since the nodes are deployed densely, makes it hard to recharge or replace their batteries. Basically, the node senses the data and transmit it to a base station. The node, which is nearer to the base station may deplete their energy quickly than other nodes due to more traffic relaying on it. Furthermore the nodes may sense and transmit the redundant information to other nodes, which leads to energy wastage. Due to these types of issues, it is difficult to design a data aggregation scheme with optimal energy consumption across the sensing area to enhance the network lifetime. In this paper, a detailed study has been made about the available data aggregation techniques in the form of taxonomy.

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Section: Network Lifetimementioning

confidence: 99%

A study on data aggregation scheme over wireless sensor network

Geethapriya¹,

Kala²,

Karthik³

2016

2016 10th International Conference on Intelligent Systems and Control (ISCO)

View full text Add to dashboard Cite

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“…Further, the large quantity of unwanted and useless data can lead to high computation costs and the overutilization of resources in a constrained IoT sensor network. The most common data processing techniques are data denoising, data imputation, data outlier detection, and data aggregation [10].…”

Section: Introductionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

200

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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“…Energy is therefore of utmost importance in power‐constrained data gathering sensor networks. Energy consumption should be well‐managed to maximize the network lifetime . The challenge in designing a sensor network is that sensors must be organized into a robust multi‐hop wireless network that should be able to function properly for a long period.…”

Section: Introductionmentioning

confidence: 99%

“…Wireless sensor networks (WSNs) will enable a wide variety of applications, including environmental monitoring, medical treatment, emergency response, outer space exploration, and so forth. In a sensor network, a large number of sensors is deployed in a large area with each sensor capable of monitoring or collecting data from its surrounding environment and pass the information to the sink for remote user access through various communication technologies .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy chessboard clustering and artificial bee colony routing method for energy‐efficient heterogeneous wireless sensor networks

Alshawi

Yan

Pan

et al. 2013

Int J Communication

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SUMMARY Energy is an extremely critical resource for battery‐powered wireless sensor networks (WSNs), thus making energy‐efficient protocol design a key challenging problem. However, uneven energy consumption is an inherent problem in WSNs caused by multi‐hop routing and many‐to‐one traffic pattern among sensors. In this paper, we therefore propose a new clustering method called fuzzy chessboard clustering (FFC), which is capable to overcome the bottleneck problem and addressing the uneven energy consumption problem in heterogeneous WSNs. We also propose an energy‐efficient routing method called artificial bee colony routing method (ABCRM) to find the optimal routing path for the heterogeneous WSNs. ABCRM seeks to investigate the problems of balancing energy consumption and maximization of network lifetime. To demonstrate the effectiveness of FCC‐ABCRM in terms of lessening end‐to‐end delay, balancing energy consumption, and maximization of heterogeneous network lifetime, we compare our method with three approaches namely, chessboard clustering approach, PEGASIS, and LEACH. Simulation results show that the network lifetime achieved by FCC‐ABCRM could be increased by nearly 25%, 45%, and 60% more than that obtained by chessboard clustering, PEGASIS, and LEACH, respectively. Copyright © 2013 John Wiley & Sons, Ltd.

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Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks

Cited by 145 publications

References 29 publications

A study on data aggregation scheme over wireless sensor network

A study on data aggregation scheme over wireless sensor network

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Fuzzy chessboard clustering and artificial bee colony routing method for energy‐efficient heterogeneous wireless sensor networks

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