Multipoint Synchronization for Fog-Controlled Internet of Things

Olaniyan, Richard; Maheswaran, Muthucumaru

doi:10.1109/jiot.2019.2930367

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…6 can be considered a hierarchical model which is suitable for achieving synchronization in IoT. More, synchronization, as defined in [ 42 , 43 ], is the coordination of a group of devices to harmonize the execution of a task by time alignment. So, related to our topology, we can find in the lower level the IoT devices which send a request to the OVS (second level) to forward a packet.…”

Section: Test Scenario and Resultsmentioning

confidence: 99%

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

2022

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Internet of Things (IoT), an emerging technology, connects billions of smart devices to the Internet through heterogeneous communication technologies enabling novel services and applications. This complex and variable environment encounters several challenges such as managing and controlling the network and IoT applications, programming IoT devices, responding to some specific Quality of Service requirements of certain applications. The current IoT network based on the complex, static, and closed traditional architecture represents a serious concern, especially with the amounts of data generated from the huge and heterogeneous IoT devices. In this paper, we present a new model for IoT architecture that brings together the benefits of Software Defined Networking (SDN) architecture and containerization (i.e., Docker) to address most IoT challenges and abstract the underlying communication and hardware subsystem. The SDN concept decoupled the control plane from data plane which implies a centralized global network view and provides flexibility in network configuration and resource management. Moreover, containerization technique through the docker in the device brings the portability and scalability of the applications. First, we propose, an SDN-based architecture for IoT. Then, with lightweight virtualization of IoT device supplied by Docker, we empower the functionalities and effectiveness of the SDN based architecture solution to challenge the IoT heterogeneity. Furthermore, we use the smart supermarket as a use case to validate our SDN-Docker based architecture in a real IoT scenario. The experimental results validate the architecture implementation.

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Section: Test Scenario and Resultsmentioning

confidence: 99%

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

2022

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“…Some studies, for example, believe that Blockchain should be used to foster fog computing paradigms [191], while others believe that fog computing should simply act as a middleware-type framework for otherwise traditional cloud computing methods [192]. The exact architecture is also highly debated between studies [190,192,193]; some focus on optimized architecture for real time performance [190,192], while others are focused more on synchronization between nodes [193]. Others acknowledge the need for both synchronization and real-time efforts, but instead focus on adjacent implementations, such as sensor virtualizations [190].…”

Section: Open Research Ideasmentioning

confidence: 99%

A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures

et al. 2020

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The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.

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“…The synchronization schemes proposed in [22] and [23] focused mainly on achieving synchronization without much consideration on the message overhead caused by the constant communication between worker nodes and the controller. The algorithms were based on the task attributes, time and component redundancy as well as localization of worker nodes.…”

Section: B Synchronization In Real-time Systemsmentioning

confidence: 99%

“…Unlike DSSP, where workers are expected to have the same or very similar runtimes per iteration, we assume a highly heterogeneous system where runtimes may vary from one iteration to another. The synchronization schemes in [22] and [23] incur extra communication overhead from heavy involvement of the controller. This work achieves fast synchronization by reducing the number of messages sent through clustering and by minimizing the involvement of the controller in making synchronization decisions.…”

Section: Comparison Of This Work and Related Workmentioning

confidence: 99%

A Fast Edge-Based Synchronizer for Tasks in Real-Time Artificial Intelligence Applications

Olaniyan¹,

Maheswaran²

2020

Preprint

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Real-time artificial intelligence (AI) applications mapped onto edge computing need to perform data capture, process data, and device actuation within given bounds while using the available devices. Task synchronization across the devices is an important problem that affects the timely progress of an AI application by determining the quality of the captured data, time to process the data, and the quality of actuation. In this paper, we develop a fast edge-based synchronization scheme that can time align the execution of input-output tasks as well compute tasks. The primary idea of the fast synchronizer is to cluster the devices into groups that are highly synchronized in their task executions and statically determine few synchronization points using a game-theoretic solver. The cluster of devices use a late notification protocol to select the best point among the pre-computed synchronization points to reach a time aligned task execution as quickly as possible. We evaluate the performance of our synchronization scheme using trace-driven simulations and we compare the performance with existing distributed synchronization schemes for real-time AI application tasks. We implement our synchronization scheme and compare its training accuracy and training time with other parameter server synchronization frameworks.

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Multipoint Synchronization for Fog-Controlled Internet of Things

Cited by 7 publications

References 25 publications

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

Empowering SDN-Docker Based Architecture for Internet of Things Heterogeneity

A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures

A Fast Edge-Based Synchronizer for Tasks in Real-Time Artificial Intelligence Applications

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