Node ranking in wireless sensor networks with linear topology

Komguem, Rodrigue Domga; Stanica, Razvan; Tchuenté, Maurice; Valois, Fabrice

doi:10.1109/wd.2017.7918127

Cited by 9 publications

(2 citation statements)

References 16 publications

(25 reference statements)

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“…Work on LWSNs focused on partitioning, deployment, and clustering techniques to improve certain performance metrics. Indeed, in Komguem et al, 21 the authors propose a technique for deploying nodes based on an anchor node. This allows the other nodes of the network to synchronize and calculate their coordinates by using many iterations.…”

Section: Related Workmentioning

confidence: 99%

Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol

Ndoye

Diallo

Hakem

et al. 2020

Int J Communication

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Summary Wireless sensor networks (WSNs) have received a lot of attention from both academia and industry due to the increasing need for ubiquitous computing for monitoring applications, the continuous advances in miniaturization of electronic devices, and the ultra‐low‐power wireless technologies. These innovations in technology have driven the curiosity to use sensor networks in a new kind of applications such as road track or railway monitoring, border monitoring, oil and gas, or even water pipeline monitoring. Due to the underlying linear topology of these applications, a new type of network, called a linear sensor network (LSN), has emerged. Because of the specific characteristics of this application and the resource constraints of sensors, some of the major challenges faced in LSNs are to reduce end‐to‐end delays, to maximize the packet delivery ratio to a sink, and an even distribution of the load between nodes. To achieve these objectives, it is necessary to control node‐to‐node packet traffic conditions and to manage radio interference created by simultaneously active nodes. This paper addresses these challenges and proposes a new method of clustering LSNs that reduces or controls radio interference risks in order to satisfy these objectives, application needs, and the resource limitations of sensor nodes in the best possible way. This method is applied for LSNs using a token‐passing mechanism to access the medium. The performance evaluation is conducted by using a realistic propagation model in the analytical evaluation and also a NS‐2 simulation process.

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Section: Related Workmentioning

confidence: 99%

Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol

Ndoye

Diallo

Hakem

et al. 2020

Int J Communication

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show abstract

“…In the calculation of a large number of communication information, the received signal strength indication value (RSSI) generated by the communication among nodes has the characteristics of computing nodes and the minimum hardware equipment, which is suitable for the positioning of large-scale WSN wireless sensor nodes. However, the current accuracy of the positioning results based on the RSSI calculation is low, and the error increases with the increase of the radius in the positioning of the WSN in a wide range [10].…”

Section: Introductionmentioning

confidence: 99%

Research on WSN Node Localization Algorithm Based on RSSI Iterative Centroid Estimation

Liu

2020

Teh. vjesn.

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For the traditional RSSI-based sensor nodes the positioning accuracy is low and sensitive to noise, which can not be applied to the rapid positioning of large-scale WSN wireless sensor nodes. Based on the traditional localization algorithm, this paper proposes a WSN node localization algorithm based on RSSI iterative centroid estimation. The algorithm determines the convergence condition by the positional relationship between the node to be located and the existing beacon node, and uses the RSSI value instead of the traditional distance centroid estimation. The experiment is carried out in a random node distribution simulation environment of 100 × 100 m. The effects of communication distance variation and beacon node ratio on the algorithm are verified, and the influence of distance calculation error on the algorithm is verified. Because the signal strength difference of the main beacon node is used in the localization algorithm, and the beacon node corresponding to the maximum signal strength value is selected as the main beacon node, the error caused by the conversion of the signal strength value into the distance is successfully suppressed. The influence of obstacle interference on the positioning of the node reduces the positioning error and achieves better positioning accuracy. The simulation results show that the proposed algorithm has better positioning accuracy and robustness to noise, and is suitable for large-scale WSN wireless sensor node location.

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Pipeline surveillance along the international border using hybrid optimization algorithm

Varshney

Kumar

Swaroop

2021

J Ambient Intell Human Comput

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Node ranking in wireless sensor networks with linear topology

Cited by 9 publications

References 16 publications

Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol

Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol

Research on WSN Node Localization Algorithm Based on RSSI Iterative Centroid Estimation

Pipeline surveillance along the international border using hybrid optimization algorithm

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