A Programming Language and System for Heterogeneous Cloud of Things

Wenger, Robert; Zhu, Xiru; Krishnamurthy, Jayanth; Maheswaran, Muthucumaru

doi:10.1109/cic.2016.033

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Future work will address the remaining design goals and challenges and develop a full-featured edge twin to implement autodrive assist. The edge twin framework we proposed in this paper will be built using JAMScript -a programming language for edgeoriented mobile IoT that we have already developed [36,37]. Several design goals of the edge twin align nicely with those of JAMScript making it a natural host for the edge twin.…”

Section: Discussionmentioning

confidence: 99%

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges

Maheswaran¹,

Yang²,

Memon³

2019

Preprint

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Autonomous driving is expected to provide a range of far-reaching economic, environmental and safety benefits. In this study, we propose a fog computing based framework to assist autonomous driving. Our framework relies on overhead views from cameras and data streams from vehicle sensors to create a network of distributed digital twins, called an edge twin, on fog machines. The edge twin will be continuously updated with the locations of both autonomous and human-piloted vehicles on the road segments. The vehicle locations will be harvested from overhead cameras as well as location feeds from the vehicles themselves. Although the edge twin can make fair road space allocations from a global viewpoint, there is a communication cost (delay) in reaching it from the cameras and vehicular sensors. To address this, we introduce a machine learning forecaster as a part of the edge twin which is responsible for predicting the future location of vehicles. Lastly, we introduce a box algorithm that will use the forecasted values to create a hazard map for the road segment which would be used by the framework to suggest safe manoeuvres for the autonomous vehicles such as lane changes and accelerations. We present the complete fog computing framework for autonomous driving assist and evaluate key portions of the proposed framework using simulations based on a real-world dataset of vehicle position traces on a highway.

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Section: Discussionmentioning

confidence: 99%

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges

Maheswaran¹,

Yang²,

Memon³

2019

Preprint

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“…The machine learning based handover optimization system presented here is envisioned as a part of JAMScript [25]. The JAMScript project is a polyglot programming language for IoT computing that caters to the hierarchical and distributed architecture of edge computing.…”

Section: System Architecturementioning

confidence: 99%

“…To accomplish this, we can use a variety of strategies including sending a duplicate request to the fog close to the switchover point and duplicating the data to the next fog. A lot of the enabling mechanisms for proactively using the fogs are supported by JAMScript, a programming language, and middleware for edge-oriented IoT systems [25].…”

Section: Introductionmentioning

confidence: 99%

Using machine learning for handover optimization in vehicular fog computing

Memon

Maheswaran

2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

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Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long shortterm memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set.

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“…3) The practicability of the synchronous scheduling scheme is shown by implementing the time-based redundancy algorithm in JAMScript -"a programming language and framework developed for Cloud of Things [6]". Hereafter, IoT devices are referred to as workers and sync is used to refer to "synchronization" or "synchronous".…”

Section: Introductionmentioning

confidence: 99%

Multipoint Synchronization for Fog-Controlled Internet of Things

Olaniyan

Maheswaran

2019

IEEE Internet Things J.

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This paper presents a fog-resident controller architecture for synchronizing the operations of large collections of Internet of Things (IoT) such as drones, Internet of Vehicles, etc. Synchronization in IoT is grouped into different classes, use cases identified and multi-point synchronous scheduling algorithms are developed to schedule tasks with varying timing requirements; strict (synchronous) and relaxed (asynchronous and local) onto a bunch of worker nodes that are coordinated by a fog resident controller in the presence of disconnections and worker failures. The algorithms use time-based or component-based redundancy to cope with failures and embed a publish-subscribe message update scheme to reduce the message overhead at the controller as the number of workers increase. The performance of the algorithms are evaluated using tracedriven experiments and practicability is shown by implementing the time-based redundancy synchronous scheduling algorithm in JAMScript -a polyglot programming platform for Cloud of Things and report initial findings.

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A Programming Language and System for Heterogeneous Cloud of Things

Cited by 10 publications

References 6 publications

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges

Using machine learning for handover optimization in vehicular fog computing

Multipoint Synchronization for Fog-Controlled Internet of Things

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