Foraging-based optimization of pervasive displays

Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

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“…In [39], the authors used the pervasive technique. Pervasive helps the compute provide all resources to the nodes to offload their task.…”

Section: Nonementioning

confidence: 99%

Secured Optimized Resource Allocation in Mobile Edge Computing

Bibi¹,

Majeed²,

Ali

et al. 2022

Mobile Information Systems

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“…In generative design, the goals are twofold: 1) to solve problems that are rendered hard by a large problem size or number of objectives and 2) construct designs algorithmically with high quality of the design for end users, encompassing goals such as usability, good ergonomics and user experience, and reliability and recovery from errors [62]. Demonstrated applications for GUIs include generation of graphical layouts [13], [30], [42], [46], [48], [60], [63], [64], creation of document layouts [65], multiplatform design [66]- [69], generation from program code [70], generation of forms from data descriptions [71], [72], retargeting of input spaces for ergonomics [73], retargeting of web pages [74], dialogue box layouts [75], distributed user interfaces [76], pervasive information displays [77], multiappliance services [78], and functionality design [39], as well as design for web pages [79], [80], menus [81], scatterplot visualizations [82], and widget layouts [83].…”

Section: Applications 1) Generative Designmentioning

confidence: 99%

“…Subjects were twice as fast with computationally generated designs, particularly when the approach accounted for expectations of element locations due to prior experience. In a field experiment on information displays [77], IP-generated information layouts, created with IFT for the objective function, were compared to regular slide-based rotating displays. The attention of passersby was measured by a computer vision sensor.…”

Section: G Empirical Evaluationmentioning

confidence: 99%

“…Also, materials and tasks can be "cherry-picked" to produce confirmatory results. Indeed, we could find only one paper on work wherein computer-generated designs were compared outside the laboratory [77]. Another challenge that remains is to assist designers, who may not have used optimization systems before, to control multiobjective optimization [76].…”

Section: C O N C L U S I O Nmentioning

confidence: 99%

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Combinatorial Optimization of Graphical User Interface Designs

et al. 2020

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Reading Articles Online

Karrenbauer¹,

Kovalevskaya²

2020

Combinatorial Optimization and Applications

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We study the online problem of reading articles that are listed in an aggregated form in a dynamic stream, e.g., in news feeds, as abbreviated social media posts, or in the daily update of new articles on arXiv. In such a context, the brief information on an article in the listing only hints at its content. We consider readers who want to maximize their information gain within a limited time budget, hence either discarding an article right away based on the hint or accessing it for reading. The reader can decide at any point whether to continue with the current article or skip the remaining part irrevocably. In this regard, Reading Articles Online, RAO, does differ substantially from the Online Knapsack Problem, but also has its similarities. Under mild assumptions, we show that any α-competitive algorithm for the Online Knapsack Problem in the random order model can be used as a black box to obtain an (e + α)C-competitive algorithm for RAO, where C measures the accuracy of the hints with respect to the information profiles of the articles. Specifically, with the current best algorithm for Online Knapsack, which is 6.65 < 2.45e-competitive, we obtain an upper bound of 3.45eC on the competitive ratio of RAO. Furthermore, we study a natural algorithm that decides whether or not to read an article based on a single threshold value, which can serve as a model of human readers. We show that this algorithmic technique is O(C)-competitive. Hence, our algorithms are constant-competitive whenever the accuracy C is a constant.

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Foraging-based optimization of pervasive displays

Cited by 6 publications

References 46 publications

Secured Optimized Resource Allocation in Mobile Edge Computing

Secured Optimized Resource Allocation in Mobile Edge Computing

Combinatorial Optimization of Graphical User Interface Designs

Reading Articles Online

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