A Wireless Cloud Network Platform for Industrial Process Automation: Critical Data Publishing and Distributed Sensing

Ascorti, Leonardo; Savazzi, Stefano; Soatti, Gloria; Nicoli, Monica; Sisinni, Emiliano; Galimberti, Stefano

doi:10.1109/tim.2016.2640579

Cited by 46 publications

(25 citation statements)

References 38 publications

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“…Cloud based solutions are paving the way to innovative services [4]. However, no matter the field, the main challenge to solve is the availability of lowpower and reliable wireless communications [5] [6].…”

Section: Introductionmentioning

confidence: 99%

On the evaluation of LoRaWAN virtual channels orthogonality for dense distributed systems

Ferrari

Flammini

Rizzi

et al. 2017

2017 IEEE International Workshop on Measurement and Networking (M&N)

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Abstract-Internet of Things (IoT) aims at collecting data from billions of devices connected altogether. Despite there is no one technology able to cope with all possible scenarios, LPWAN solutions are emerging as viable technologies for implementing private, low-cost cellular like wireless networks. Distributed systems could leverage this approach as a driving technology for services as smart environment sensing, pervasive sensing and so on. In the considered scenario, the capacity of the network is of main importance; even if communication is sporadic for most of the time, an event observed by many nodes results in a huge amount of simultaneous transmissions. Are the IoT technologies usable to this end? In this paper LoRaWAN technology is investigated, with the aim of evaluating the orthogonality of virtual channels permitted by the LoRa physical layer. In particular, measurements demonstrated that overlapping transmissions having the same power at the receiver can be correctly decoded if occurring with different spreading factors, whereas co-spread messages require at least 4ms spacing.

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Section: Introductionmentioning

confidence: 99%

On the evaluation of LoRaWAN virtual channels orthogonality for dense distributed systems

Ferrari

Flammini

Rizzi

et al. 2017

2017 IEEE International Workshop on Measurement and Networking (M&N)

View full text Add to dashboard Cite

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“…They are typically deployed in mission-critical control applications where edge nodes cannot rely on a remote unit for fast feedback and have to manage part of the computing tasks locally [12], cooperating with neighbors to self disclose the information. Typical examples are low-latency safety-related services for vehicular [13] or industrial [14] applications. Considering these trends, research activities are now focusing on fully decentralized (i.e., server-less) learning approaches.…”

Section: A Decentralized Fl and Related Workmentioning

confidence: 99%

“…The negotiation stage (13)-(14) is similar to the diffusion strategy proposed in [30] [31]. In particular, we aggregate the model first (8), then we run one gradient descent round using the received gradients (13), and finally, a number of SGD rounds (14) using local mini-batches. As 4 Sending multiple gradients (corresponding to mini-batches) is an alternative option, not considered here for bandwidth limitations.…”

Section: B Consensus Based Federated Averaging With Gradients Exchanmentioning

confidence: 99%

See 1 more Smart Citation

Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks

Savazzi¹,

Nicoli²,

Rampa³

2020

IEEE Internet Things J.

Self Cite

333

164

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Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. Rather than sharing, and disclosing, the training dataset with the server, the model parameters (e.g., neural networks weights and biases) are optimized collectively by large populations of interconnected devices, acting as local learners. FL can be applied to power-constrained IoT devices with slow and sporadic connections. In addition, it does not need data to be exported to third parties, preserving privacy. Despite these benefits, a main limit of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters; this has the drawback of a single point of failure and scaling issues for increasing network size. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network by iterating local computations and mutual interactions via consensus-based methods. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing, with intelligence distributed over the end-devices. The proposed methodology is verified by experimental datasets collected inside an industrial IoT environment.

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“…Sensing the phenomenon of interest with spatially distributed sensors and estimating the phenomenon with a centralized fusion center is a fundamental application of wireless sensor networks (WSNs). This scenario is referred to as distributed sensing and has wide applications in environment monitoring [1], industrial automation [2], target surveillance and tracking [3], and so on. In such sensing systems, one well known problem is whether the separate source-channel coding outperforms the joint source-channel coding or not.…”

Section: Introductionmentioning

confidence: 99%

Distributed Sensing With Orthogonal Multiple Access: To Code or not to Code?

Dong

2020

IEEE Trans. Signal Process.

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We consider the estimation distortion of a distributed sensing system with finite number of sensor nodes, in which the nodes observe a common phenomenon and transmit their observations to a fusion center over orthogonal channels. In particular, we investigate whether the coded scheme (separate source-channel coding) outperforms the uncoded scheme (joint source-channel coding) or not. To this end, we explicitly derive the estimation distortion of a coded heterogeneous sensing system with diverse node and channel configurations. Based on this result, we show that in a homogeneous sensing system with identical node and channel configurations, the coded scheme outperforms the uncoded scheme if the number of nodes is K = 1 or K = 2. For homogenous sensing systems with K ≥ 3 nodes and general heterogeneous sensing systems, we also present explicit conditions for the coded scheme to perform better than the uncoded scheme. Furthermore, we propose to minimize the estimation distortion of heterogeneous sensing systems with hybrid coding, i.e., some nodes use the coded scheme and other nodes use the uncoded scheme. To determine the optimal hybrid coding policy, we develop three greedy algorithms, in which the pure greedy algorithm minimizes distortion greedily, the group greedy algorithm improves performance by using a group of potential sub-polices, and the sorted greedy algorithm reduces computational complexity by using a pre-solved iteration order. Our numerical and Monte Carlo results show that the proposed algorithms closely approach the optimal policy in terms average estimation distortion.

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A Wireless Cloud Network Platform for Industrial Process Automation: Critical Data Publishing and Distributed Sensing

Cited by 46 publications

References 38 publications

On the evaluation of LoRaWAN virtual channels orthogonality for dense distributed systems

On the evaluation of LoRaWAN virtual channels orthogonality for dense distributed systems

Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks

Distributed Sensing With Orthogonal Multiple Access: To Code or not to Code?

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