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Vlachou, E.; Karras, A.; Karras, C.; Theodorakopoulos, L.; Halkiopoulos, C.; Sioutas, S. Distributed Bayesian Inference for Large-Scale IoT Systems. Encyclopedia. Available online: https://encyclopedia.pub/entry/53212 (accessed on 15 June 2024).
Vlachou E, Karras A, Karras C, Theodorakopoulos L, Halkiopoulos C, Sioutas S. Distributed Bayesian Inference for Large-Scale IoT Systems. Encyclopedia. Available at: https://encyclopedia.pub/entry/53212. Accessed June 15, 2024.
Vlachou, Eleni, Aristeidis Karras, Christos Karras, Leonidas Theodorakopoulos, Constantinos Halkiopoulos, Spyros Sioutas. "Distributed Bayesian Inference for Large-Scale IoT Systems" Encyclopedia, https://encyclopedia.pub/entry/53212 (accessed June 15, 2024).
Vlachou, E., Karras, A., Karras, C., Theodorakopoulos, L., Halkiopoulos, C., & Sioutas, S. (2023, December 28). Distributed Bayesian Inference for Large-Scale IoT Systems. In Encyclopedia. https://encyclopedia.pub/entry/53212
Vlachou, Eleni, et al. "Distributed Bayesian Inference for Large-Scale IoT Systems." Encyclopedia. Web. 28 December, 2023.
Distributed Bayesian Inference for Large-Scale IoT Systems
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The Internet of Things (IoT) has emerged as a transformative force in contemporary society, substantially impacting various facets of daily life. Nevertheless, the IoT ecosystem’s rapid expansion is accompanied by a significant increase in data generation, known as Big Data. This expansion presents a complex challenge, necessitating advanced, scalable, and efficient data processing techniques. Given the complex nature of large-scale data analysis in IoT systems, distributed Bayesian inference arises as a practical and efficient solution in this domain. Bayesian methods, which are influential in deriving informed conclusions and predictions from complex datasets, are widely recognized for their probabilistic underpinnings.

IoT big data distributed Bayesian inference wireless sensor networks iot systems

1. Introduction

The emergence of the Internet of Things (IoT) has marked the beginning of a significant era where the digital and physical worlds merge, leading to an extraordinary increase in both the amount and speed of data produced by interconnected devices. The Internet of Things (IoT) has emerged as a transformative force in contemporary society, substantially impacting various facets of daily life. This technology extends its influence across numerous sectors, significantly enhancing healthcare delivery, streamlining transportation systems, and facilitating the evolution of smarter urban environments. Nevertheless, the IoT ecosystem’s rapid expansion is accompanied by a significant increase in data generation, known as Big Data. This expansion presents a complex challenge, necessitating advanced, scalable, and efficient data processing techniques.
Current traditional methodologies often struggle to manage the enormity and real-time processing demands of data originating from IoT sources. This shortfall can lead to less effective decision-making processes and undermine the overall performance of IoT systems. The consequences of such inefficiencies in data management within IoT applications are extensive. They not only impede progress in critical infrastructure sectors but also constrain the broader scope of innovation, thus obstructing the full realization and benefits that IoT technology promises to deliver. Given the complex nature of large-scale data analysis in IoT systems, distributed Bayesian inference arises as a practical and efficient solution in this domain. Bayesian methods, which are influential in deriving informed conclusions and predictions from complex datasets, are widely recognized for their probabilistic underpinnings. An examination of these methodologies within a distributed computation framework tailored for the massive data systems of the Internet of Things is essential to this field of study.
The distributed implementation of Bayesian inference simplifies immense datasets into more manageable elements through the use of a systematic approach. The methodology functions within a decentralized framework, enabling the analysis of these segments simultaneously. The workflow commences with the dataset being segmented, subsequently undergoing parallel analysis on each segment. To achieve this goal, numerous algorithms have been developed, such as variational Bayesian (VB) and Markov chain Monte Carlo (MCMC) methods, which are specifically designed for distributed applications. These techniques, along with neural networks, Gaussian mixture models (GMMs), and generalized linear models (GLMs), have been applied to a wide variety of modelling scenarios. On the basis of theoretical and empirical research, the effectiveness of distributed Bayesian inference algorithms may be comparable to that of conventional, centralized methodologies. As supported by the results of several research studies in the respective domain [1][2][3][4], the algorithms under consideration demonstrate remarkable effectiveness in terms of both computational speed and statistical accuracy. These methodologies illustrate the efficacy of modern computational techniques in handling substantial amounts of data, producing accurate results that are computationally viable.

2. Distributed Bayesian Inference in the IoT

Distributed Bayesian inference is becoming increasingly well-known within the domain of the Internet of Things (IoT), mainly because of its wide-ranging applications in object classification, target monitoring, and medical diagnosis, among others. Since the majority of IoT systems are distributed and operate with constrained resources, critical information is likely to be stored on a limited number of nodes at any given time. Therefore, for effective inference, the notion of Information-Driven Distributed Sensing (IDDS) assumes paramount importance, as it directs the allocation of resources toward the detection and transmission of valuable data [5].
There exists a multitude of studies dedicated to distributed Bayesian inference in IoT ecosystems [5]. An example of such research is the development of a centralized (C-IDDS) and distributed (D-IDDS) algorithm that utilizes exponential family distributions to facilitate efficient Bayesian inference. Both are online algorithms, characterized by their adaptability to stochastic system conditions without foreknowledge. The researchers demonstrated, through a detailed theoretical evaluation, that these proposed algorithms deliver an asymptotically optimal system-wide utility. This theoretical proposition was further substantiated by real-world testing on an established testbed.
Another study delves into the opportunities and challenges associated with approximate Bayesian deep learning for smart IoT frameworks [6]. The authors propose potential solutions to mitigate model storage requirements and enhance computational scalability, such as model pruning and distillation methods. Additionally, the study underscored the significance of Bayesian inference as a theoretical base for developing uncertainty-aware, robust deep learning-centric intelligent IoT systems.
A different study presents a pragmatic approach that identifies removable connections in ResNet without considerably affecting the model’s efficacy, facilitating distribution in scenarios with resource constraints. This outcome forms the basis for formulating a multi-objective optimization problem focused on latency minimization and accuracy maximization considering the available resources [7]. The experimental results indicate that an adaptable ResNet architecture can diminish shared data, energy consumption, and latency during distribution while preserving high accuracy.
Lastly, a research paper proposes ApDeepSense, an efficient and effective method for deep learning uncertainty estimation suitable for resource-limited IoT devices [8]. ApDeepSense estimates output uncertainty by utilizing an implicit Bayesian approximation that correlates neural networks with deep Gaussian processes. It was shown that the implementation of an innovative layer-wise approximation approach, as opposed to traditional sampling-based methods that require significant computational resources for uncertainty estimation, can significantly decrease the execution time and energy usage associated with uncertainty estimation.
The utilisation of distributed Bayesian inference is crucial for enabling intelligent inference in Internet of Things (IoT) systems. In a variety of scholarly articles, algorithms and methodologies to enhance the scalability and effectiveness of Bayesian inference in IoT systems have been proposed. These encompass methodologies such as approximate Bayesian deep learning, adaptive ResNet architecture, and deep learning uncertainty estimation [9].
The field of the Internet of Things (IoT) has experienced a notable transition in recent times towards the implementation of distributed Bayesian inference to improve the scalability of systems, as evidenced by numerous research initiatives. Ullah et al. emphasize the notion of context-aware Bayesian inference in an innovative manner. By integrating multi-sensor data, the aforementioned approach generates a reliable and precise inference model, thereby streamlining the integration of vast quantities of data from various origins [10]. Meanwhile, the work in [11] presents a novel methodology for implementing decentralized data flows in the Internet of Things (IoT) systems through the utilization of DX-MAN semantics. Through the reduction of intricate data transmission control among numerous coordinating entities, this approach substantially enhances the overall efficiency of the system.
In order to address the complexities associated with distributed co-simulation in cloud environments particularly for IoT systems, [12], employs domain-specific languages and CoHLA. This methodology enhances the management of vast amounts of IoT data and streamlines cloud-based simulations through adherence to HLA and FMI standards [12]. Quasi-Deterministic Transmission Policy (QDTP) is suggested by [13] as a potential resolution to the Massive Access Problem (MAP) associated with the Internet of Things. By utilizing QDTP, a methodology based on diffusion analysis, the likelihood of missing crucial data deadlines is effectively diminished, thereby enhancing the dependability and efficacy of Internet of Things systems. Based on the results of previous research, Internet of Things systems may be made much more functional and scalable by using strategic techniques, such as distributed Bayesian inference.
A detailed analysis of the difficulties of applying distributed Bayesian inference to large-scale Internet of Things systems as in [14] reveals several important variables. The study highlights that the variety of IoT data sources is a key obstacle. A suggested analytical framework with two tiers is designed to handle data obtained from the Internet of Things, considering the challenges discussed in [15]. This strategy effectively reduces the existing uncertainty by incorporating detailed events into Bayesian networks. In another interesting work related to the security in distributed Internet of Things (IoT) systems, the authors provide a novel perspective on this domain and countermeasures as they developed and introduced a set of methodologies to guarantee secure distributed inference [16]. Ultimately, ref. [17] presents an alternative method that employs a trust model grounded on Bayesian decision theory.

3. Bayesian Inference in Wireless Sensor Networks

Bayesian inference-based wireless sensor networks (WSNs) help people make decisions faster and get around problems. Studies show that WSN is used to find outliers, find and fix faults, find the cause of a problem, and figure out how much trust to put in something. A study in [18] shows that Bayesian reasoning could improve the performance of wireless sensor networks by finding and solving problems. When sensor readings are checked and fixed, WSN data are more accurate and reliable. Using the Bayesian method, new sensor data can be used to test theories and figure out how unclear something is. This makes sure that mistakes are found and fixed correctly, which protects network data.
In another study [19], it was discovered that Bayesian reasoning is needed to find outliers in Wireless Sensor Networks (WSNs). Bayesian inference networks find parts of sensor data that depend on each other in certain situations. Bayesian networks that show how sensor measures are connected make it possible for technology to find data points that do not fit with expected trends. Bayesian inference uses statistical reasoning and the network of sensor characteristics that are linked to figure out how likely it is that an observation is not normal. This system needs to work well to find problems that could be caused by monitors that are not working correctly or strange conditions in the surroundings. Also, ref. [20] discovered that Bayesian reasoning is very important for making network nodes believe each other in Wireless Sensor Networks (WSN). Bayesian fusion lets us figure out how reliable a network node is by combining different trust factors. The program analyses and includes trust data, including uncertainty from various trust characteristics. Sensor data, previous knowledge, and statistical models are used in Bayesian inference to synthesize information. This association helps provide accurate evaluations.
The results of the investigation strongly demonstrate the feasibility of using Bayesian inference in large-scale Internet of Things infrastructures. An effective method to improve the efficiency of implementing the Bayesian inference process is by using PySpark, a Python package particularly built for distributed computing. PySpark can handle large amounts of data produced by IoT-connected devices. Moreover, in addition to the aforementioned capabilities, it facilitates system optimization, anomaly detection, real-time decision-making, and defect tolerance. Through the utilization of PySpark’s scalable and distributed architecture, it becomes viable to perform Bayesian inference on computing clusters, concurrently analyze data produced by the Internet of Things (IoT), and effectively manage the substantial resources necessary for the deployment of large-scale IoT systems.

References

  1. Guhaniyogi, R.; Li, C.; Savitsky, T.; Srivastava, S. Distributed Bayesian inference in massive spatial data. Stat. Sci. 2023, 38, 262–284.
  2. Srivastava, S.; Xu, Y. Distributed Bayesian inference in linear mixed-effects models. J. Comput. Graph. Stat. 2021, 30, 594–611.
  3. Ye, B.; Qin, J.; Fu, W.; Zhu, Y.; Wang, Y.; Kang, Y. Distributed Bayesian inference over sensor networks. IEEE Trans. Cybern. 2021, 53, 1587–1597.
  4. Yu, Z.; Chen, F.; Liu, J.K. Sampling-Tree Model: Efficient Implementation of Distributed Bayesian Inference in Neural Networks. IEEE Trans. Cogn. Dev. Syst. 2020, 12, 497–510.
  5. Zhou, C.; Li, Q.; Tham, C.K. Information-Driven Distributed Sensing for Efficient Bayesian Inference in Internet of Things Systems. In Proceedings of the 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, China, 11–13 June 2018; pp. 1–9.
  6. Vadera, M.P.; Marlin, B.M. Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems. In Proceedings of the 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), Virtual, 13–15 December 2021; pp. 252–261.
  7. Khan, F.M.; Baccour, E.; Erbad, A.; Hamdi, M. Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems. In Proceedings of the 2023 International Wireless Communications and Mobile Computing (IWCMC), Dubrovnik, Croatia, 30 May–3 June 2023; pp. 1543–1549.
  8. Yao, S.; Zhao, Y.; Shao, H.; Zhang, C.; Zhang, A.; Liu, D.; Liu, S.; Su, L.; Abdelzaher, T. ApDeepSense: Deep Learning Uncertainty Estimation without the Pain for IoT Applications. In Proceedings of the 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria, 2–6 July 2018; pp. 334–343.
  9. Baccour, E.; Erbad, A.; Mohamed, A.; Hamdi, M.; Guizani, M. RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for Low Latency IoT Systems. IEEE Trans. Netw. Sci. Eng. 2022, 9, 2066–2083.
  10. Ullah, I.; Kim, J.B.; Han, Y.H. Compound Context-Aware Bayesian Inference Scheme for Smart IoT Environment. Sensors 2022, 22, 3022.
  11. Arellanes, D.; Lau, K.K. Decentralized data flows in algebraic service compositions for the scalability of IoT systems. In Proceedings of the 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15–18 April 2019; pp. 668–673.
  12. Nägele, T.; Hooman, J. Scalability analysis of cloud-based distributed simulations of IoT systems using HLA. In Proceedings of the 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), Singapore, 11–13 December 2018; pp. 1075–1080.
  13. Gelenbe, E.; Nakıp, M.; Marek, D.; Czachorski, T. Diffusion analysis improves scalability of IoT networks to mitigate the massive access problem. In Proceedings of the 2021 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Virtual, 3–5 November 2021; pp. 1–8.
  14. Raut, A.; Kumar, D.; Chaurasiya, V.K.; Kumar, M. Distributed Decision Fusion for Large Scale IoT- Ecosystem. In Proceedings of the 2022 IEEE 15th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC), Penang, Malaysia, 19–22 December 2022; pp. 112–119.
  15. Akbar, A.; Kousiouris, G.; Pervaiz, H.; Sancho, J.; Ta-Shma, P.; Carrez, F.; Moessner, K. Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications. IEEE Access 2018, 6, 10015–10027.
  16. Chen, Y.; Kar, S.; Moura, J.M. The Internet of Things: Secure Distributed Inference. IEEE Signal Process. Mag. 2018, 35, 64–75.
  17. Kurniawan, A.; Kyas, M. A trust model-based Bayesian decision theory in large scale Internet of Things. In Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 7–9 April 2015; pp. 1–5.
  18. Krishnamachari, B.; Iyengar, S. Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans. Comput. 2004, 53, 241–250.
  19. Janakiram, D.; Kumar, A.; Reddy V., A.M. Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks. In Proceedings of the 2006 1st International Conference on Communication Systems Software & Middleware, New Delhi, India, 8–12 January 2006; pp. 1–6.
  20. Momani, M.; Challa, S.; Al-Hmouz, R. Bayesian Fusion Algorithm for Inferring Trust in Wireless Sensor Networks. J. Netw. 2010, 5, 815–822.
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