A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks

Liu, Tong; Gu, Tao; Jin, Ning; Zhu, Yanmin

doi:10.1109/twc.2016.2642098

Cited by 21 publications

(7 citation statements)

References 29 publications

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“…In summary, the clustering methods such as distributed clustering of DCEM and EEUC are comparatively more advantageous, because of the small amount of computation in the energy-efficient clustering and multi-hop routing algorithms. Nonuniform clustering methods such as UCFIA and UDEB requiring globally centralized calculations are effective, but the amount of calculation is large [23][24][25]. The selection of inter-cluster multi-hop routing adopted by DCEM is based on minimizing energy consumption, which can lead to uneven energy consumption of nodes in the network [26][27][28].…”

Section: Non-uniform Clustering Algorithmmentioning

confidence: 99%

Simplified clustering and improved intercluster cooperation approach for wireless sensor network energy balanced routing

Yao

Chen

Guo

et al. 2020

J Wireless Com Network

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The equilibrium use of energy is very important for wireless sensor networks (WSN) with limited energy in order to avoid premature network collapse. The existing methods either need too complex calculations for precise clustering, or are too simple to overburden a few cluster heads. In order to solve these problems, we proposed energy balanced clustering routing (EBCR) in this paper. It could maximize the WSN life in energy non-harvesting scenario or improve energy utilization efficiency in energy harvesting scenario without increasing the amount of calculations. It gives a complete solution to the process of cluster head election, clustering, and intercluster routing algorithm. Firstly, a light weight cluster head election and a distributed cluttering method are proposed by introducing dynamic cluster radius and intersection region node division schemes with new principles. Thus, lightweight distributed clustering achieves the advantages of balancing the burden of cluster heads and alleviating hot zone problem. Then we optimized the cluster cooperative routing algorithm by analyzing cooperation and competition among cluster heads. The intercluster cooperative routing algorithm greatly improves the transmission efficiency between cluster heads. Moreover, this paper analyzes the reasons why the algorithm achieves more balanced energy usage, higher energy efficiency, and fewer calculations compared to the existing mainstream algorithms. At last, simulation results show that EBCR algorithm has advantages in terms of network energy consumption, number of surviving nodes in energy non-harvesting scenario compared with the delay-constrained energy-efficient cluster-based multi-hop routing (DCEM) method. Simulation also gives EBCR algorithm performance under various energy harvesting scenarios, which is quite satisfactory in energy utilization efficiency comparing with DCEM method. EBCR algorithm has superior performance in terms of balanced energy usage, low computation complexity, and high energy efficiency.

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Section: Non-uniform Clustering Algorithmmentioning

confidence: 99%

Simplified clustering and improved intercluster cooperation approach for wireless sensor network energy balanced routing

Yao

Chen

Guo

et al. 2020

J Wireless Com Network

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“…Therefore the nodes are having the communication and sensing capabilities [2]. The sensor nodes are designed with an even smaller size thus providing the returns of resource usage and hardware in a limited manner and are supplied with battery resources [3].…”

Section: Introductionmentioning

confidence: 99%

WOAPR: an affinity propagation based clustering and optimal path selection for time‐critical wireless sensor networks

et al. 2019

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Wireless sensor networks (WSNs) spread their returns to every field having the requirement of continuous monitoring. Clustering is an important mechanism in WSN for minimum energy consumption and better network performance. In these networks, optimal paths need to be determined for efficient flow of data. The power efficiency and quality of service are the two most essential aspects looked-for to realise the network. However, this hypothesis has been a changing characteristic based on the innovative applications. Consequently, the quality-of-service aspect is gaining much significance than the efficient power design of the sensor network. This study proposes a clustering-based routing protocol for time-critical WSN named as whale optimised affinity propagation-based routing protocol. This protocol reduces energy consumption and improves energy efficiency through clustering and routing algorithms. The performance of the proposed protocol is evaluated using various metrics and the proposed approach shows significant improvement over the existing approaches.

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“…The existing energy-saving research on WSN data aggregation mainly focuses on energy-aware routing scheduling and data compression. In energy-aware routing scheduling [17], [23], [24], existing research still has limitations as follows. Most of them are to delay the death of the first node as the optimization goal, and pursuing the uniform energy consumption of the node is the method.…”

Section: Introductionmentioning

confidence: 99%

“…Most of them are to delay the death of the first node as the optimization goal, and pursuing the uniform energy consumption of the node is the method. However, some studies have realized that the cost of pursuing uniform energy consumption is great [18], [23], [24]. The reasons are as follows: there are at least 15 definitions of lifetime for different scenarios in the WSN studies [6].…”

Section: Introductionmentioning

confidence: 99%

“…In real-world applications, some nodes are allowed to die in many scenarios, unless the criticality and importance of each node are equal and any death cannot be tolerated. Some existing researches have sacrificed the energy consumption of the whole network in pursuit of uniform energy consumption [23], [24]. For the common needs in practical applications (allowing a certain percentage of deaths), it is negative optimization [18].…”

Section: Introductionmentioning

confidence: 99%

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A Lossless Convergence Method for Reducing Data Fragments on WSN

Xiao

et al. 2019

IEEE Access

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This article focuses on the most common application scenarios for data collection and uploading in WSN (Wireless Sensor Networks). First, we measure the energy consumption of widely used hardware. According to the characteristics of transmission energy consumption, a MIP (mixed integer programming) model called FAT-WSN (fragmentation aggregation transmission WSN) is proposed to minimize the number of data fragments. Moreover, we propose an iterative solution for this MIP problem with elasticity and low complexity. The main optimization method for this model is to adjust topology and traffic distribution. It focuses on optimizing the number of data transfers without modifying any data and without introducing a compression calculation burden. Finally, simulation and small-scale real node verifications are performed for the FAT-WSN scheme. The experimental results show that FAT-WSN can effectively reduce the number of data transmission and reception, thereby reducing energy consumption and improving network life. Compared with the MinST model, JGDC (Jointly Gaussian Distributed Compress) model and AMREST (Approximately Maximum min-Residual Energy Steiner Tree) model, the network life can be increased by 10%-30% without extending the calculation time.INDEX TERMS Wireless sensor network, data convergence, maximum lifetime, energy efficient routing protocol.

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A Mixed Transmission Strategy to Achieve Energy Balancing in Wireless Sensor Networks

Cited by 21 publications

References 29 publications

Simplified clustering and improved intercluster cooperation approach for wireless sensor network energy balanced routing

Simplified clustering and improved intercluster cooperation approach for wireless sensor network energy balanced routing

WOAPR: an affinity propagation based clustering and optimal path selection for time‐critical wireless sensor networks

A Lossless Convergence Method for Reducing Data Fragments on WSN

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