A Bayesian Packet Sharing Approach for Noisy IoT Scenarios

Vegni, Anna Maria; Loscrì, Valeria; Neri, Alessandro; Leo, Marco

doi:10.1109/iotdi.2015.28

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…As far as we know, there exist only three works that take in account this heterogeneous aspect of IoT [3], [8] and [11]. In [3], the authors presented a cloudbased Adaptive Sensing Belief Propagation protocol with energy-efficient data fusion for IoT applications.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Bayesian Communication Approach for Smart Agriculture Applications

Razafimandimby

Loscrì

Vegni

et al. 2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

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Abstract-To meet the food demand of the future, farmers are turning to the Internet of Things (IoT) for advanced analytics. In this case, data generated by sensor nodes and collected by farmers on the field provide a wealth of information about soil, seeds, crops, plant diseases, etc. Therefore, the use of high tech farming techniques and IoT technology offer insights on how to optimize and increase yield. However, one major challenge that should be addressed is the huge amount of data generated by the sensing devices, which make the control of sending useless data very important.To face this challenge, we present a Bayesian Inference Approach (BIA), which allows avoiding the transmission of high spatio-temporal correlated data. In this paper, BIA is based on the PEACH project, which aims to predict frost events in peach orchards by means of dense monitoring using low-power wireless mesh networking technology. Belief Propagation algorithm has been chosen for performing an approximate inference on our model in order to reconstruct the missing sensing data. According to different scenarios, BIA is evaluated based on the data collected from real sensors deployed on the peach orchard. The results show that our proposed approach reduces drastically the number of transmitted data and the energy consumption, while maintaining an acceptable level of data prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Finally, the authors of [11] presented a theoretical bayesian based approach for data sharing in the IoT context, while in [8] we go a step further by proposing an extensive experimentation scenario for indoor environments. In this paper, we will present additional experiments and discussions in a realistic outdoor environment.…”

Section: Related Workmentioning

confidence: 99%

Efficient Bayesian Communication Approach for Smart Agriculture Applications

Razafimandimby

Loscrì

Vegni

et al. 2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

Self Cite

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“…As far as we know, there exists only two works that take in account this heterogeneous aspect of IoT. The first work was proposed in [4], and the other was proposed in [17].…”

Section: B Iot Contextmentioning

confidence: 99%

“…Then, the authors of [17] in turn presented a theoretical bayesian based approach for data sharing in the IoT context. In this paper, we go a step further than [17] by proposing an extensive experimentation scenario and smart gateway based communications.…”

Section: B Iot Contextmentioning

confidence: 99%

“…Models which are comprised of directed edges are known as Bayesian networks, whilst models that are composed of undirected edges are known as Markov random fields (MRF) [18]. As in [17], we present an inference approach under the hypothesis of MRF, modeled by means of Factor Graphs. It follows that our goal is to estimate the state X of the sensed environment starting from the sets of data collected by each sensor node.…”

Section: A Modelmentioning

confidence: 99%

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A Bayesian and smart gateway based communication for noisy IoT scenario

Razafimandimby

Loscrì

Vegni³

et al. 2017

2017 International Conference on Computing, Networking and Communications (ICNC)

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Bayesian reasoning machine on a magneto-tunneling junction network

Nasrin¹,

Drobitch²,

Shukla³

et al. 2020

Nanotechnology

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The recent trend in adapting ultra-energy-efficient (but error-prone) nanomagnetic devices to non-Boolean computing and information processing (e.g. stochastic/probabilistic computing, neuromorphic, belief networks, etc) has resulted in rapid strides in new computing modalities. Of particular interest are Bayesian networks (BN) which may see revolutionary advances when adapted to a specific type of nanomagnetic devices. Here, we develop a novel nanomagnet-based computing substrate for BN that allows high-speed sampling from an arbitrary Bayesian graph. We show that magneto-tunneling junctions (MTJs) can be used for electrically programmable ‘sub-nanosecond’ probability sample generation by co-optimizing voltage-controlled magnetic anisotropy and spin transfer torque. We also discuss that just by engineering local magnetostriction in the soft layers of MTJs, one can stochastically couple them for programmable conditional sample generation as well. This obviates the need for extensive energy-inefficient hardware like OP-AMPS, gates, shift-registers, etc to generate the correlations. Based on the above findings, we present an architectural design and computation flow of the MTJ network to map an arbitrary Bayesian graph where we develop circuits to program and induce switching and interactions among MTJs. Our discussed framework can lead to a new generation of stochastic computing hardware for various other computing models, such as stochastic programming and Bayesian deep learning. This can spawn a novel genre of ultra-energy-efficient, extremely powerful computing paradigms, which is a transformational advance.

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A Bayesian Packet Sharing Approach for Noisy IoT Scenarios

Cited by 8 publications

References 12 publications

Efficient Bayesian Communication Approach for Smart Agriculture Applications

Efficient Bayesian Communication Approach for Smart Agriculture Applications

A Bayesian and smart gateway based communication for noisy IoT scenario

Bayesian reasoning machine on a magneto-tunneling junction network

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