A novel energy-balanced routing algorithm in energy harvesting sensor networks

Li, Feng; Xiong, Ming; Wang, Li; Hong, Peilin; Hua, Jingyu; Liu, Xin

doi:10.1016/j.phycom.2018.02.010

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…ere is no master node in the whole clustering process, and each node is based on the algorithm, independently decides, and joins the corresponding cluster [22]. At the beginning of the cluster establishment, all effector nodes in the Internet will randomly generate a random number with a range of [0, 1], and then, the random number is compared with the threshold T(n); if it is less than the threshold T(n), then the effector node corresponding to the random number will be elected as a cluster head of the round, and then, a broadcast message is sent to inform other effector nodes that if the random number is greater than the threshold T(n) [23,24], then it will not be elected as the cluster head [25,26]. If the selected cluster head node has been elected as a cluster head, then the value of T(n) is changed to 0, so as to avoid the same node continuously acting as the cluster head, resulting in excessive energy consumption of the node [27].…”

Section: Cluster Head Selectionmentioning

confidence: 99%

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Zhou

2021

Mathematical Problems in Engineering

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With the rapid development and maturity of unconnected communication technology, effector technology, embedded computing technology, and distributed information processing technology, as well as the rapid advancement of digital processing and computing capabilities, unconnected effector Internet has received more and more attention. It is a new type of self-organizing unconnected multihop Internet. With the development and progress of technology and society, the development of unconnected effector Internet is also advancing by leaps and bounds, and it has a wide range of applications in many fields such as military, civil, environmental, medical, and industrial. At present, the research on unconnected effector Internet mainly focuses on the communication protocol, but there is almost no research on input review of unconnected effector Internet. Due to the limited energy of the effector and the limitation of the transmission signal bandwidth, the use of limited resources for input review research in effector Internet is of great significance to the development of unconnected effector Internet. Therefore, this paper studies the input review of unconnected effector Internet based on the L-A model on the on-chip embedded tune system. Because the classic low-power adaptive cluster layering protocol (LEACH) has the problems of unbalanced energy consumption and short node life cycle, this paper uses the embedded tune technology based on the L-A model and analyzes the remaining energy and location of the unconnected effector Internet node. Parameters are tested and studied. Through the research on the input review of unconnected effector Internet, the simulation results obtained show that the research in this paper is feasible and reasonable.

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Section: Cluster Head Selectionmentioning

confidence: 99%

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Zhou

2021

Mathematical Problems in Engineering

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“…Therefore, reducing the number of intersatellite links between different orbital surfaces on the routing path can increase the continuity of the routing. The calculation formula for the weight of the intersatellite link between satellite node m and satellite node n is [18]…”

Section: Methods For Setting Routing Link Weight Of Low-orbitmentioning

confidence: 99%

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Liu

Sun

Zhang

2021

Wireless Communications and Mobile Computing

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Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

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“…Li et al 58 have presented energy‐balanced routing model in energy harvesting sensor networks. The aim of the presented method was to attain a balanced‐based routing approach with enhanced transmission performance.…”

Section: Literature Surveymentioning

confidence: 99%

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

Wilson

Radhamani²

2021

Softw Pract Exp

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In this article, energy efficient ensemble clustering method (EECM) with black widow optimization (EECM‐BWO) algorithm is proposed for effective data transmission with the help of real time flood disaster monitoring wireless sensor network (WSN). Initially, unified scalable ensemble clustering algorithm based on ensemble generation and consensus function is proposed for selecting the optimal routing path among the node using BWO algorithm. Then, biologically inspired routing black widow spiders optimization algorithm is proposed to trade off the nodes energy level, self‐organization, and self‐configuration in the WSN. The simulation is performed using NS2 simulator for validating the performance of the proposed EECM‐BWO method. Here, in node, low delay achieves 24.07%, 72.58%, 51.36%, 81.75%, 77.74%, high packet delivery ratio achieves 70.83%, 53.93%, 90.23%, 43.58%, 24.58%, low packet drop attains 77.93%, 72.76%, 61.56%, 51.87%, 34.35%, low energy consumption attains 75.9%, 52.94%, 65.81%, 58%, 41.2% compared with existing energy‐efficient clustering approach consolidated game theory as well as dual‐cluster‐head mode for WSNs energy‐aware clustering by cuckoo optimization approach (EECM‐COA), energy‐aware clustering‐based routing using multi‐path reliable transmission with routing and control board (EECM‐RCB‐MRT), adaptive repair algorithm with temporally ordered routing algorithms for flood control strategy (EECM‐AR‐TORA‐FCS), passive multi‐hop clustering algorithm (EECM‐PMC), dynamic source routing protocol based on genetic algorithm‐bacterial foraging optimization (DSR‐GA‐BFO).

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A novel energy-balanced routing algorithm in energy harvesting sensor networks

Cited by 18 publications

References 13 publications

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Parameter Detection of an On-Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Real time flood disaster monitoring based on energy efficient ensemble clustering mechanism in wireless sensor network

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