Analysis of the applicability of Wireless Sensor Networks attacks to Body Area Networks

Segovia, Mariana; Grampín, Eduardo; Baliosian, Javier

doi:10.4108/icst.bodynets.2013.253720

Cited by 4 publications

(13 citation statements)

References 14 publications

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“…This is validated manually by looking at Table 9. The combinations that gives L min = 5 are [ (1,2,5), (1,3,4), (1,3,5), the best combination is (1,3,5) where it has the least number of sensors and best information quality among the combinations that place three sensors out of five. Moreover, the optimal solutions are checked using another method that guarantee global optimization called branch and bound 11 using a solver called the BARON.…”

Section: Results Of Implementing Brute Force On a Five-sensors Story mentioning

confidence: 99%

“…, in 10 different runs, we get the same minimized number of sensors (4). On the other hand, two different node placements appear among 10 different runs, which are the combinations [ (1,2,3,5) and (1,3,4,5)] as shown in Table 10. In some other cases, we always get the same optimal solution of the same sensor locations as in =80.…”

Section: Results Of Implementing Ga On a Five-sensors Story Buildingmentioning

confidence: 99%

“…Nowadays, many applications use wireless sensor networks (WSN), such as wireless body area networks (WBAN) [1], surveillance systems [2], and structural health monitoring (SHM). Structural health monitoring is the science of applying damage detection to structures.…”

Section: Overviewmentioning

confidence: 99%

“…For instance, in Table 10, when = 20, the best solution is placing three sensors out of five. And from the different runs, there are two ways that satisfy this: using the combination (1,3,5) or the combination (2,3,5). If you look at Table 9, you can find that the combination (1,3,5) has quality information=33.28, and the combination (2,3,5) has quality information=22.991.…”

Section: Validationmentioning

confidence: 99%

“…where you will not find any other solution that meet the constraints and the objective function of the problem other than (1,3,5) and (2,3,5).…”

Section: Validationmentioning

confidence: 99%

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Minimising number of sensors in wireless sensor networks for structure health monitoring systems

El-Qawasma

Elfouly

Ahmed

2019

IET wirel. sens. syst.

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Nowadays, wireless sensor networks (WSNs) are considered an essential candidate to apply structural health monitoring (SHM). An important problem in this area is sensor placement optimization. In many research works, solving this problem focuses only on the network properties and requirements such as energy consumption, network coverage, …etc., without considering the civil engineering requirements. However, there are other research works that consider network and civil requirements while optimizing the sensor placement. Unfortunately, although minimizing the number of sensors is important, it has never been addressed. This could be noticed from the limited literature found that addresses this problem while considering both the civil and the network requirements. As a result, in this thesis we study the problem of minimizing the number of sensors for SHM in WSNs. The idea behind this research is to reduce the network size, which can solve some problems such as the scalability, installation time and cost. Our contribution in this work is not limited to the mathematical model of the mentioned problem, but will extend to solve the problem using different methods: the exhaustive search, genetic algorithm (GA), and a heuristic algorithm that applies the binary search. The problem is then solved for different number of sensors as well as different placements in many conducted experiments. Finally, the time complexity is evaluated to compare between all the applied methods. The obtained results showed that minimizing the number of sensors becomes more significant with big structures. Furthermore, the binary search algorithm is the best to use to solve the problem for small buildings. But, For larger buildings, there is a trade-off between the performance, and time complexity, where

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