A simple dynamic clustering approach to achieve energy efficiency for wireless sensor networks

Lee, Gil Jae; Kwon, Yoonjoo; Seok, Woojin; Lee, Minsun

doi:10.1108/17427370810890373

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The feasibility of the system plays major role in system process and operations. Recently wireless communication has been developed which is more energy efficient and operated for longer duration (Lee et al, 2008). Where wireless data transmission has 2 consents that is low speed data service over geographical areas and high-speed data service for local areas Jean, L. ( 2001).…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Key Enablers of Sensors and Sensing Systems

Patil,

Banbheru,

Karanje

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Sensors and Sensing Systems (SASS) are important components of technology that collect data from any chemical sector, physical quantity and biological quantity. It can be found in a wide range of applications. As a result, it must be stable and properly applied to its application, as well as undergo continual system improvement. This research focuses on identifying sensors and sensing system enablers that may be used to improve and quality findings. The main enablers for SASS are determined using a Pythagorean fuzzy Analytic Hierarchy Process (AHP) technique. The identified enablers can be implemented successfully to enhance the sensing process.

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Section: Literature Reviewmentioning

confidence: 99%

Identification of Key Enablers of Sensors and Sensing Systems

Patil,

Banbheru,

Karanje

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…They optimized the upper bound on the lifetime of a sensor network by exploring the limits of data‐collection networks and characterizing the behavior of such networks. Lee et al (2008) proposed an energy efficient dynamic clustering approach to construct clusters using the knowledge of remaining residual energy and the active sensors. The authors mainly concentrated to increase the lifespan of the sensor network.…”

Section: Related Workmentioning

confidence: 99%

Restoring coverage area for WSN through simulated annealing

Habib

Marimuthu²

2011

International Journal of Pervasive Computing and Communications

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PurposeContinuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.Design/methodology/approachThe authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.FindingsThe experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.Practical implicationsThe proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.Originality/valueThe paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.

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“…The BS needs to ensure that the energy load is evenly distributed among the all the nodes (Vinh Tran Quang & Takumi Miyoshi, 2008). Another approach is the BS selects cluster head nodes depending on the number of clusters alive in the network (Giljae Lee et al, 2008). Secondly, LEACH does not guarantee the number of cluster head nodes and their distribution because the cluster head nodes are selected stochastically by the value of probability.…”

Section: Introductionmentioning

confidence: 99%