Distributed Genetic Algorithms on Portable Devices for Smart Cities

Morell, José Ángel; Alba, Enrique

doi:10.1007/978-3-319-59513-9_6

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…For this purpose, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices solving a set of well-known optimization problems using Genetic Algorithms (GAs). This research is an extension of a previous work [4].…”

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confidence: 70%

Running Genetic Algorithms in the Edge: A First Analysis

Morell¹,

Alba²

2018

Advances in Artificial Intelligence

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Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.

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confidence: 70%

Running Genetic Algorithms in the Edge: A First Analysis

Morell¹,

Alba²

2018

Advances in Artificial Intelligence

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“…where H sel-cross represents the number of bytes transferred during the selection and crossover, including commands and acknowledge messages to collect fitness values, and commands and acknowledge messages to collect and send individuals; and H col represents the number of bytes transferred during the collection of the best individuals, which includes the commands and acknowledge messages to collect one individual from each node. The total number of bytes transferred is the sum of Equations (12) and (13). Using this result, the equation to calculate the total overhead in seconds is…”

Section: Communication Between Microcontrollersmentioning

confidence: 99%

Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

Medeiros

Fernandes

2020

Electronics

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This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy focuses on making a distributed version of GA feasible to run as a low cost and a low power consumption embedded system utilizing devices such as 8-bit microcontrollers (µCs) and Serial Peripheral Interface (SPI) for data transmission between those devices. Details about how the distributed GA was designed from a previous standalone implementation made by the authors and how the project is structured are presented. Furthermore, this work investigates the implementation limitations and shows results about its proper operation, most of them collected with the Hardware-In-Loop (HIL) technique, and resource consumption such as memory and processing time. Finally, some scenarios are analyzed to identify where this distributed version can be utilized and how it is compared to the single-node standalone implementation in terms of performance and energy consumption.

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“…WebSocket (https://www.w3.org/TR/websockets/) provides full duplex communication channels over a single TCP connection. This technology is more suitable than HTTP in situations where low latency is required [32]. WebRTC [33], [34] is a browserbased real-time peer-to-peer communication without plugins.…”

Section: Bbvc 3rd Generationmentioning

confidence: 99%

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

2019

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In 2019, around 57% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a half hours online every day. But in spite of being a great amount of time, those resources are idle most of the day. Therefore, taking advantage of them presents an interesting opportunity. In this study, we introduce JSDoop, a prototype implementation to profit from this opportunity. In particular, we propose a volunteer web browser-based high-performance computing library. JSdoop divides a problem into tasks and uses different queues to distribute the computation. Then, volunteers access the web page of the problem and start processing the tasks in their web browsers. We conducted a proof-of-concept using our proposal and TensorFlow.js to train a recurrent neural network that predicts text. We tested it in a computer cluster and with up to 32 volunteers. The experimental results show that training a neural network in distributed web browsers is feasible and accurate, has a high scalability, and it is an interesting area for research.

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Distributed Genetic Algorithms on Portable Devices for Smart Cities

Cited by 5 publications

References 11 publications

Running Genetic Algorithms in the Edge: A First Analysis

Running Genetic Algorithms in the Edge: A First Analysis

Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

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