An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

Wang, Jin; Gao, Yu; Liu, Wei; Sangaiah, Arun Kumar; Kim, Hye-Jin

doi:10.1177/1550147719839581

Cited by 167 publications

(109 citation statements)

References 32 publications

(38 reference statements)

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“…The authors proposed a supervised learning method to do the classification that is very interesting and can be applied to our method [43,44]. The authors in [45][46][47][48][49][50][51][52][53] gave a novel mobile sink-based method to optimize system performance, which can be referred to.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

2019

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This paper deals with the leader-follower formation control of underactuated autonomous surface vehicles in the presence of model uncertainties and input constraints. In a leader-follower formation, an autonomous surface vehicle (ASV) called leader tracks a pre-described trajectory and other ASVs called followers that are controlled to follow the leader with a desired distance and desired relative bearing. To this end, some adaptive robust techniques are adopted to guarantee the robustness of the closed-loop system against model uncertainties, external disturbances, and input saturation constraints. Based on the Lyapunov synthesis, it is proven that with the developed formation controllers, the closed-loop system is stable and all the formation errors converge to a small neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

2019

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“…In addition to the works discussed above, there is also a large body of research investigating the use of mobile sink in the network 11–16 . Tirani and Avokh 11 propose two data aggregation methods named “Energy‐aware CS‐based Data Aggregation (ECDA)” and “Energy‐balanced High level Data aggregation Tree (EHDT).” These methods not only balance the energy consumption among different sensors but also increase the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

“…Since the ultimate goal is to deliver data to the sink node, the sink location also plays an important role in the network lifetime. Using mobile sink is one of the most efficient methods for reducing the energy consumption 11–16 . In this method, a mobile sink starts a tour to periodically traverse the network and collect data of sensors.…”

Section: Introductionmentioning

confidence: 99%

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Sadeghi

Avokh

2020

Int J Communication

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This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster-based scheme named "Cuckoo-search Clustering with Two-hop Routing Tree (CC-TRT)" to develop a two-hop load-balanced data aggregation routing tree in the network. CC-TRT uses a modified energyaware cuckoo-search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo-search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC-TRT scheme to present two methods for constructing multi-hop data aggregation routing trees, named "Cuckoo-search Clustering with Multi-Hop Routing Tree (CC-MRT)" and "Cuckoo-search Clustering with Weighted Multi-hop Routing Tree (CC-WMRT)." Both CC-MRT and CC-WMRT rely on a two-level structure; they not only use an energy-aware cuckoo-search algorithm to fairly select the best CHs but also adopt a load-balanced high-level routing tree to route the aggregated data of CHs to the sink node. However, CC-WMRT slightly has a better performance thanks to its low-level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC-TRT, CC-MRT, and CC-WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.

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“…Besides the lifetime optimization algorithms, various mechanisms are presented in literature to filter out any redundant data and outliers using different data fusion or aggregation approaches. 38 Collaborative signal processing in node environment (C-SPINE) is presented where multisensor data fusion technique, particularly among collaborative body area networks, is used to create a hybrid data analysis tool, ie, classification, filtering, and a timely dependent integration of data. 15 A network's status-based clustering and fusion mechanism was presented in Jung et al 39 It is based on an unrealistic assumption that the sensor nodes always generate true values.…”

Section: Literature Reviewmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

Cited by 167 publications

References 32 publications

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

Adaptive Leader-Follower Formation Control of Under-actuated Surface Vessels with Model Uncertainties and Input Constraints

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

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