Reliability of Wireless Sensor Network: Hotspot and critical challenges

Yao, Nan; Wang, Shaoping; Shang, Yi; Shi, Jian

doi:10.1109/indin.2012.6301383

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Reliability of a system is a probability that it operates correctly [4]. In WSN context, every component in a WSN has a reliable probability [23]. The failure (unreliability) probability of a component is defined as the subtraction of 1 and its reliable probability.…”

Section: Probabilistic Wireless Sensor Networkmentioning

confidence: 99%

“…Most papers focus on using Markov model to express reliability of WSNs, for instance in [20,23]. However, this paper uses Discrete Time Stochastic Coloured Petri Net (DTSCPN), an extension with colour of Discrete Time Stochastic Petri Net, to model WSN with the reliable probabilities due to two reasons.…”

Section: Discrete Time Stochastic Coloured Petri Net Model Of Wsnmentioning

confidence: 99%

“…First, Discrete Time Stochastic Petri Net (DTSPN) is a powerful technique to analyse stochastic aspects [11]. DTSPN is more observable, scalable, and portable than Markov model [23]. Second, DTSPN allows that more than one transition (binding element) may fire (occur) at a time step.…”

Section: Discrete Time Stochastic Coloured Petri Net Model Of Wsnmentioning

confidence: 99%

“…Thus, attaching reliable probability on devices increases advantage when system verification. There are currently plenty of works on WSN reliability modelling and reliability analysis [23,20]. Most of such works is applied to the developing countries with low infrastructure or in extremely harsh environment and hostile region [14,13].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Congestion of Wireless Sensor Networks: a Coloured Petri Net based Approach

Le¹,

Cao²,

Phuc³

et al. 2017

CAE

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Analysing probability properties on Coloured Petri Nets (CPNs) model is one of a favorite topic on system verification recently. This paper focuses on verifying congestion probability on Wireless Sensor Networks (WSNs) which is modelled by CPN. Actually, WSNs are the collection of sensors. A WSN topology is formed by the interaction among sensors via Wi-Fi connections. However, sensors can be consider as unsteady devices when working in the harsh environment due to limited processing capacity, non-replacement battery, etc. Hence, each sensor needs to attach a reliable probability so that users can know the probability of reaching the sink of data. Such probabilities are added into the transitions in our CPN probability model before checking congestion. Whole verifying process introduces also in order to emphasize the purpose of this paper via a straight example.

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Section: Probabilistic Wireless Sensor Networkmentioning

confidence: 99%

Section: Discrete Time Stochastic Coloured Petri Net Model Of Wsnmentioning

confidence: 99%

Section: Discrete Time Stochastic Coloured Petri Net Model Of Wsnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Congestion of Wireless Sensor Networks: a Coloured Petri Net based Approach

Le¹,

Cao²,

Phuc³

et al. 2017

CAE

View full text Add to dashboard Cite

show abstract

“…Wireless sensor networks (WSNs) are an important technology for large-scale monitoring, which provides sensor measurements at high temporal and spatial resolution [1][2][3][4]. A typical WSN is composed of different sensor nodes distributed in a region or environment to be monitored.…”

Section: Introductionmentioning

confidence: 99%

On Harvesting Energy from Tree Trunks for Environmental Monitoring

Souza

Carvalho

Silva

et al. 2016

International Journal of Distributed Sensor Networks

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This work describes an experimental study on the possibilities of harvesting energy from tree trunks in order to power sensor nodes for environmental monitoring, particularly in wild forests. As the trunk of a living tree can be divided into isothermal subvolumes, which are generally referred to as annual rings, and the trunk is a good heat storage material, depending on the tree dimensions and its species, it can potentially offer different temperature gradients according to the tree trunk depths. The hypothesis is to consider the application of this temperature gradient on the faces of a Peltier cell to obtain electrical energy. In order to evaluate this hypothesis, a wireless sensor network was developed for measuring internal temperature of trunks from different trees. The experimental results show that it is possible to obtain a sufficient temperature gradient to harvest energy from tree trunks. Additionally, it is also shown that it is possible to harvest thermal energy during the day and during the night while photovoltaic cell only works under sunlight.

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Probabilistic modelling for congestion detection on wireless sensor networks

Trinh

Bui

et al. 2017

2017 4th International Conference on Control, Decision and Information Technologies (CoDIT)

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Reliability of Wireless Sensor Network: Hotspot and critical challenges

Cited by 6 publications

References 6 publications

Probabilistic Congestion of Wireless Sensor Networks: a Coloured Petri Net based Approach

Probabilistic Congestion of Wireless Sensor Networks: a Coloured Petri Net based Approach

On Harvesting Energy from Tree Trunks for Environmental Monitoring

Probabilistic modelling for congestion detection on wireless sensor networks

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