A Grid-Based Clustering Algorithm via Load Analysis for Industrial Internet of Things

Zhang, Jing; Feng, Xia‐Ting; Zhuang, Lei

doi:10.1109/access.2018.2797885

Cited by 47 publications

(22 citation statements)

References 38 publications

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“…In [13], the optimal unequal cluster size is obtained at different layers with the symmetrical and unsymmetrical deployment of nodes. In [15], the network is partitioned into unequal grids according to the optimal cluster size and all the nodes of a grid are formed into a cluster.…”

Section: Clustering Technology and Related Work 21 Clustering Methodsmentioning

confidence: 99%

Interference-free Clustering Protocol for Large-Scale and Dense Wireless Sensor Networks

Chen¹,

Lin²,

Wang³

et al. 2019

KSII TIIS

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Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference-Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.

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Section: Clustering Technology and Related Work 21 Clustering Methodsmentioning

confidence: 99%

Interference-free Clustering Protocol for Large-Scale and Dense Wireless Sensor Networks

Chen¹,

Lin²,

Wang³

et al. 2019

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Meanwhile, how to organize sensor nodes into a cluster has a great impact on the performance of WSN. Numerous researches have been conducted based on traffic load analysis [20], network stability [21] and compressed data gathering [22], etc.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Optimization of Data Access and Hybrid Transmission in Wireless Sensor Network

et al. 2020

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In recent years, the rapid advancements in Internet of Things boost the utilization of Wireless Sensor Network (WSN). Through WSN, a huge amount of various kinds of data can be acquired from different environments, which further facilitate us to harness the world we live. However, the error-prone and unpredictable natures of wireless links significantly impair the quality of data transmission and the utility of acquired data in WSN. Therefore, to solve this crucial problem, this paper focuses on data access control and hybrid transmission control of WSN. Based on the mathematical models of the two control operations, an intractable optimization problem is first formulated with numerous considerations, including data utility, energy consumption, network stability and data loss rate. Because of the complexity and intractability of the originally formulated problem, a Lyapunov function-based network optimization theory is utilized to transform and decompose it into three relatively simple subproblems. Stochastic Data Scheduling Mechanism (SDSM) is designed based on the solutions to the three subproblmes. The optimality and implementation of SDSM are also analyzed. Finally, the performance of SDSM is demonstrated through extensive evaluations. INDEX TERMS Wireless sensor network, data access, hybrid transmission, stochastic scheduling.

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“…The issue of load balancing in WSN clustering also becomes critical that leads to increasing the WSN energy consumption and is addressed by some authors [22,23] that consider the hop distances for the clusters. A scheme named energy-delay index for trade-off (EDIT) in [24] is proposed to select the CH for the optimization of both delay and energy.…”

Section: Related Workmentioning

confidence: 99%

Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network

Younus

Islam²,

Kim

2019

Sensors

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A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.

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A Grid-Based Clustering Algorithm via Load Analysis for Industrial Internet of Things

Cited by 47 publications

References 38 publications

Interference-free Clustering Protocol for Large-Scale and Dense Wireless Sensor Networks

Interference-free Clustering Protocol for Large-Scale and Dense Wireless Sensor Networks

Stochastic Optimization of Data Access and Hybrid Transmission in Wireless Sensor Network

Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network

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