A hierarchical approach for accelerating IoT data management process based on SDN principles

Eghbali, Zahra; Lighvan, ‪Mina Zolfy

doi:10.1016/j.jnca.2021.103027

Cited by 24 publications

(4 citation statements)

References 11 publications

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“…For instance, networks formed by wearable IoT devices in battlefields or remote hospitals. 2) Edge/Fog-Assisted IoT networks: these are the hierarchical networks where the data from IoT devices is collected and processed by the edge/fog gateways [6]. These gateways then form a mesh network where DFL must be implemented.…”

Section: A Motivationmentioning

confidence: 99%

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

et al. 2023

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Federated Learning (FL) presents a mechanism to allow decentralized training for machine learning (ML) models inherently enabling privacy preservation. The classical FL is implemented as a client-server system, which is known as Centralised Federated Learning (CFL). There are challenges inherent in CFL since all participants need to interact with a central server resulting in a potential communication bottleneck and a single point of failure. In addition, it is difficult to have a central server in some scenarios due to the implementation cost and complexity. This study aims to use Decentralised Federated learning (DFL) without a central server through one-hop neighbors. Such collaboration depends on the dynamics of communication networks, e.g., the topology of the network, the MAC protocol, and both large-scale and small-scale fading on links. In this paper, we employ stochastic geometry to model these dynamics explicitly, allowing us to quantify the performance of the DFL. The core objective is to achieve better classification without sacrificing privacy while accommodating for networking dynamics. In this paper, we are interested in how such topologies impact the performance of ML when deployed in practice. The proposed system is trained on a well-known MINST dataset for benchmarking, which contains labelled data samples of 60K images each with a size 28×28 pixels, and 1000 random samples of this MNIST dataset are assigned for each participant' device. The participants' devices implement a CNN model as a classifier model. To evaluate the performance of the model, a number of participants are randomly selected from the network. Due to randomness in the communication process, these participants interact with the random number of nodes in the neighborhood to exchange model parameters which are subsequently used to update the participants' individual models. These participants connected successfully with a varying number of neighbours to exchange parameters and update their global models. The results show that the classification prediction system can achieve 95% accuracy using the three different model optimizers in the training settings (i.e., SGD, ADAM, and RMSprop optimizers). Consequently, the DFL over mesh networking shows more flexibility in IoT systems, which reduces the communication cost and increases the convergence speed which can outperform CFL.INDEX TERMS Simplicity, privacy, federated learning, decentralization learning. I. INTRODUCTIONI N recent years, the number of Internet of Things (IoT) and smart wearable devices have witnessed an increased proliferation in several vertical domains (e.g., wearables, home automation systems, smart glasses, health monitors, health-fitness trackers, smart grids, etc). These devices use a variety of wireless technologies and thus manifest different topological properties. For instance, LoRa deployment support one hop connection to the gateway with a distancedependent spreading factor manifesting star topology. In

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Section: A Motivationmentioning

confidence: 99%

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking

et al. 2023

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“…e application presentation layer is divided into two parts: the WeChat applet side and the global wide web (Web) server side. e framework for the applet side utilizes its own Multipurpose Infrastructure for Network Applications (MINA) framework [8]. e applet side is displayed on the WeChat platform and is linked to the network layer by the socket WebSocket protocol [9].…”

Section: Application Presentation Layer and Network Layermentioning

confidence: 99%

Research on College English Teaching Evaluation System Based on Mobile Terminal

2022

Scientific Programming

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The 21st century is the era of information technology, and the rapid development of information technology has had a huge impact on educational technology. With the popularity of mobile terminals in recent years, the development of mobile learning will promote changes in the way teaching, learning, and assessment are conducted. Traditional teaching evaluation methods focus on summative evaluation, although process evaluation is also involved, although it is limited by time and assessment recording means and does not achieve the purpose of evaluation well. In this study, we conduct a research on the evaluation system of secondary school chemistry classroom teaching based on mobile terminals and construct an evaluation index system of English teaching in colleges and universities based on mobile terminals, hoping to have some practical significance to the reform of teaching evaluation nowadays. The system uses the WeChat applet framework, the WeChat Web developer tool to output and display the front-end interface, and the Model-View-Controller framework development model to complete the invocation of the backend data. The results show that both students and teachers can access the assessment system through the WeChat applet and complete the teaching assessment and view the assessment results.

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“…However, centralized management of SDN poses significant challenges to the implementation of SDIC-IoT. Therefore, network management requirements ask for efficient management of SDIC-IoT using efficient strategies that have been tested on large scale networks [63], [64]. Such as the strategy mentioned in [63] prevents the controller from becoming a single point of failure using different management and load balancing strategies.…”

Section: A Requirements Of Heterogeneous Network Managementmentioning

confidence: 99%

“…Therefore, network management requirements ask for efficient management of SDIC-IoT using efficient strategies that have been tested on large scale networks [63], [64]. Such as the strategy mentioned in [63] prevents the controller from becoming a single point of failure using different management and load balancing strategies. The authors in [64] proposed a framework for topology and device management to fulfil application-specific requirements of IoT.…”

Section: A Requirements Of Heterogeneous Network Managementmentioning

confidence: 99%