Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Mehrjoo, Saeed; Khunjush, Farshad

doi:10.1111/coin.12132

Cited by 11 publications

(13 citation statements)

References 25 publications

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“…There are some works that take the benefits of both clusterbased and the tree-based techniques such as [10], [16], [17] and [18]. According to the particularity of the data in Internet of things, In [10], the author proposed a data fusion clustertree construction algorithm based on event-driven (DFCTA) and the data fusion architecture of fusion tree composed of fusion nodes.…”

Section: B Tree Structure-based Data Fusion Algorithmmentioning

confidence: 99%

“…In [17], the author proposed a new Cluster-Tree routing scheme for gathering data (CTRS-DG), the proposed scheme outperforms existing baseline algorithms in terms of stability period and network lifetime. In [18], the sensor nodes are organized in a tree structure, and the data aggregation are done in intermediate nodes at the junction of tree branches. One of the main characteristics of tree protocols is reduction of energy consumption through optimizing the structure of a data aggregation tree.…”

Section: B Tree Structure-based Data Fusion Algorithmmentioning

confidence: 99%

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A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

et al. 2020

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In order to effectively reduce the redundant information transmission in the network, a data fusion algorithm based on extreme learning machine optimized by bat algorithm for mobile heterogeneous wireless sensor networks is proposed. In this paper, the data fusion process of mobile heterogeneous wireless sensor networks is mainly studied, and regards the nodes of wireless sensor networks as neurons in the neural network of extreme learning machines. The neural network of the extreme learning machine extracts the sensory data collected by mobile heterogeneous wireless sensor network and combines the collected sensor data with the clustering route to greatly reduce the amount of network data sent to the sink node. Aiming at the problem that the extreme learning machine randomly generates the input layer weight and the hidden layer threshold before training, the output result is unstable, affecting the data fusion efficiency and the long delay, a new method of data fusion for mobile heterogeneous wireless sensor networks based on extreme learning machine optimized by bat algorithm is proposed. Simulation experiments are carried out from two aspects: mobile heterogeneous wireless sensor networks and heterogeneous mobile heterogeneous wireless sensor networks. The simulation results show that compared with the traditional SEP algorithm, BP neural network algorithm and ELM algorithm, the proposed BAT-ELM-based data fusion algorithm can effectively reduce network traffic, save network energy, improve network work efficiency, and significantly prolong network's lifetime. INDEX TERMS Mobile heterogeneous wireless sensor networks, data fusion, extreme learning machine, bat algorithm, energy efficient, reliability.

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Section: B Tree Structure-based Data Fusion Algorithmmentioning

confidence: 99%

Section: B Tree Structure-based Data Fusion Algorithmmentioning

confidence: 99%

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

et al. 2020

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“…Another approach based on the RFD metaheuristic is presented in Mehrjoo and Khunjush. 14 The algorithm Figure 1. General scenario where the sensor nodes are connected to the sink which computes the routing tree and serves as a gateway to the application.…”

Section: Related Workmentioning

confidence: 99%

“…Another approach based on the RFD metaheuristic is presented in Mehrjoo and Khunjush. 14 The algorithm starts by broadcasting messages that let the nodes know its distance in hops to the sink. The authors consider the remaining battery in the algorithm design, so each time a packet is processed, nodes with more energy available have a higher probability of routing packets.…”

Section: Related Workmentioning

confidence: 99%

Improved continuous enhancement routing solution for energy-aware data aggregation in wireless sensor networks

Ticona-Zegarra

Schouery

Villas

et al. 2018

International Journal of Distributed Sensor Networks

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Wireless sensor networks consist of hundreds or thousands of nodes with limited energy resources, and thus, efficient use of energy is necessary for these networks. Given that transmissions are the most energy-demanding operation, routing algorithms should consider efficient use of transmissions in their designs in order to extend the network lifetime. To tackle these challenges, a centralized algorithm is proposed, called improved continuous enhancement routing (ICER), for computing routing trees of refined quality, based on data aggregation while being aware of the battery energy state. Comparisons between ICER and other known solutions in the literature are performed. Our experiments show that ICER is able to ensure, on average, the survival of 99.6% and the connectivity of 99.3% of the network nodes compared to 90.2% and 72.4% in relation to the best-compared algorithm. The obtained results show that ICER significantly extends the network lifetime while maintaining the quality of the routing tree.

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“…We have considered multiple‐source, single‐sink (MSSS) systems with data aggregation techniques for our analysis [15]. Research on developing an optimal data aggregation tree based on intelligent water drops (IWD) algorithm [16] and swarm intelligent algorithm [17] has been carried out. However, we focus on implementing simple data aggregation strategies like min–max and averaging to reduce computational overheads.…”

Section: Introductionmentioning

confidence: 99%

Optimal hops for minimal route power under SINR constraints in wireless sensor networks

Lambor¹,

Joshi²

2018

IET wirel. sens. syst.

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This study attempts to deduce the optimal hops in a multi-hop wireless sensor network (WSN) ensuring powerefficient communication under signal-to-interference noise ratio (SINR) constraints. The authors model the total route power consumption under wireless channel quality constraints. WSNs usually employ a multiple-source, single-sink structure for information transfer, necessitating data aggregation. The authors calculate optimal hops for three different data aggregation schemes. They further propose a robust hybrid data aggregation strategy that demonstrates an improved performance with respect to minimal power consumption when compared with employing individual data aggregation schemes. The data aggregation scheme employing optimal hops has been implemented and results have been verified for an illustrative application in water supply and drainage monitoring system. The authors have formulated the problem as a standard convex optimisation problem. They employ Karush-Kuhn-Tucker optimality conditions to derive the analytical expressions of the globally optimal hops, which incorporate the influence of route power, receiver noise, and SINR thresholds. The transmit power and hop count are varied simultaneously until an optimised hop count is achieved. The relation between the node transmit power and the number of hops in the route is derived and the condition for feasible optimal hops is presented.

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Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Cited by 11 publications

References 25 publications

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks

Improved continuous enhancement routing solution for energy-aware data aggregation in wireless sensor networks

Optimal hops for minimal route power under SINR constraints in wireless sensor networks

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