Wireless Sensor Networks and Satellite Simulation

Lucas, Peter; Long, Nguyen Huu Van; Truong, Tuyen Phong; Pottier, Bernard

doi:10.1007/978-3-319-25479-1_14

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…This framework is used to solve the k-coverage problem, the target coverage problem and the area coverage and within this framework local solutions are used to obtain a global one. In [19] The authors have proposed a method to produce automatically the representation of the topologies of the complex sensor networks that have several distant WSN joined with a getaway which is controlled by mobile sensors. For synchronization the authors have used concurrent process architectures and graphic accelerators.…”

Section: Related Workmentioning

confidence: 99%

Maximum Lifetime Target Coverage in Wireless Sensor Networks

Saadi

Bounceur

Euler

et al. 2019

Wireless Pers Commun

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The challenge in the deployment of wireless sensor networks is to ensure the coverage of targets with high energy efficiency, particularly when coverage constraint and energy constraint must be taken into account. Many algorithms have been suggested over the years to enhance network lifetime as sensor nodes are powered by batteries. These algorithms divide a set of sensor nodes into a number of subsets which can monitor all targets. The subsets are either joint or disjoint. In this paper, we propose an efficient method to compute the maximum lifetime target coverage. Our proposed method is centered around a target that is covered by a minimum number of sensor nodes. Based on this approach, we also propose an algorithm to expand the network lifetime which guarantees the monitoring of all targets by forming subsets of sensor nodes. Our obtained results are compared with the work reported in [1]. They are also compared with a method of computing the maximum lifetime target coverage (MLTC). The results obtained by simulation show that the proposed algorithm enhances the network lifetime, which illustrates the efficiency of this algorithm.

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Section: Related Workmentioning

confidence: 99%

Maximum Lifetime Target Coverage in Wireless Sensor Networks

Saadi

Bounceur

Euler

et al. 2019

Wireless Pers Commun

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“…The browser Quickmap [ 33 ] supports standard map tiles, including OpenStreetMap, and a variety of other items, either for maps or aerial images. This tool also allows for describing sensor systems, communication links, and mobile trajectories, for example, Low Earth Orbit (LEO) satellites [ 34 ].…”

Section: Mapping Geographic Space Into Cell Systemsmentioning

confidence: 99%

Cellular Simulation for Distributed Sensing over Complex Terrains

Truong

Pottier

Huynh

2018

Sensors

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Long-range radio transmissions open new sensor application fields, in particular for environment monitoring. For example, the LoRa radio protocol enables connecting remote sensors at a distance as long as ten kilometers in a line-of-sight. However, the large area covered also brings several difficulties, such as the placement of sensing devices in regards to topology in geography, or the variability of communication latency. Sensing the environment also carries constraints related to the interest of sensing points in relation to a physical phenomenon. Thus, criteria for designs are evolving a lot from the existing methods, especially in complex terrains. This article describes simulation techniques based on geography analysis to compute long-range radio coverages and radio characteristics in these situations. As radio propagation is just a particular case of physical phenomena, it is shown how a unified approach also allows for characterizing the behavior of potential physical risks. The case of heavy rainfall and flooding is investigated. Geography analysis is achieved using segmentation tools to produce cellular systems which are in turn translated into code for high-performance computations. The paper provides results from practical complex terrain experiments using LoRa, which confirm the accuracy of the simulation, and scheduling characteristics for sample networks. Performance tables are produced for these simulations on current Graphics Processing Units (GPUs).

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“…However, the direct radio links between sensor fields and LEO satellites are not considered in literature. In recent years, it emerges as an attractive topic because of the current innovation solutions such as LoRa Semtech and solutions from vendors QB50 [11].…”

Section: Related Workmentioning

confidence: 99%

Modeling the Connections of Dynamic Sensor Fields Based on BT-Graph

Truong¹,

Luong²,

Huynh³

et al. 2016

EAI Endorsed Transactions on Context-aware Systems and Applicat

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International audienceIn this article, we propose a new approach to model and optimize the dynamic sensor field for both internal network connections and LEO satellite connection based on BT Graph. Due to the shift of LEO satellite’s orbit at each revolution, a dynamic sensor field (DSF), which is able to redetermine its gateways, is suitable to improve successful data communications. It is convenient to present a DSF as a BT-Graph that aims to utilize optimization algorithms. Parallel search algorithms are also deployed for more efficient execution time when DSFs consist of a significant large number of nodes. The simulation experiments are performed on an abstract forest fire surveillance network to validate our proposed approach

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Wireless Sensor Networks and Satellite Simulation

Cited by 5 publications

References 3 publications

Maximum Lifetime Target Coverage in Wireless Sensor Networks

Maximum Lifetime Target Coverage in Wireless Sensor Networks

Cellular Simulation for Distributed Sensing over Complex Terrains

Modeling the Connections of Dynamic Sensor Fields Based on BT-Graph

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