Programming Languages for End-User Personalization of Cyber-Physical Systems

Srbljic, S.; Škvorc, Dejan; Popović, Maša

doi:10.7305/automatika.53-3.84

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…MCS services can produce detailed sensor readings and provide means to discover new phenomena in urban environments that otherwise cannot be measured by individuals, such as the occurrence of traffic congestion, or environmental noise pollution monitoring. Smart cities benefit from recent development in Internet of Things (IoT) [10][11][12][13][14]. Thus, this development provides added value to existing public services and improves the quality of citizens' lives.…”

Section: Introductionmentioning

confidence: 99%

Mobile crowdsensing accuracy for noise mapping in smart cities

et al. 2018

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This paper deals with the problem of traffic noise in urban areas in terms of noise mapping. It explains in detail the Mobile Crowdsensing (MCS) method and, furthermore, compares the results obtained with this method with the results gained from the standard method that uses a sound level metre. The research done in this paper shows that the MCS method can make noise mapping easier, cheaper and less time-consuming in terms of creating representative noise maps developed on measurements but also noise maps developed on calculations and simulations. The main idea is to show that accuracy and precision of measurements obtained by using calibrated smartphones are acceptable. The paper suggests that when using the smartphone measurement application, the calibration of the measurement chain can be done in free field with class 1 sound level metre, and noise map can be checked in a much larger number of points (in comparison with the standard measurement method) and therefore, smartphones can be used as instruments for creating or even checking final noise maps in urban environment. Another advantage of this method is that citizens can engage in noise monitoring in urban areas and become aware of the noise pollution in their cities. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Mobile crowdsensing accuracy for noise mapping in smart cities

et al. 2018

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“…The results of this paper can be exploited for implementing light-weight starvation-free semaphores, useful in general concurrent applications and cyber physical systems (e.g., [1]), and also in distributed shared memory systems where it is challenging to build a classical queuebased semaphore when the processes belong to distinct physical computation nodes (address spaces).…”

Section: Introductionmentioning

confidence: 99%

Modelling and Verification of Starvation-Free Mutual Exclusion Algorithms based on Weak Semaphores

Cicirelli

Nigro

2015

Annals of Computer Science and Information Systems

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Abstract-This paper proposes an original framework for modelling and verification (M&V) of starvation-free mutual exclusion algorithms based on weak semaphores, that are without a built-in waiting-process queue. The goal is to support the implementation of light-weight starvation-free semaphores useful in general concurrent systems including cyber physical systems. The M&V approach depends on UPPAAL. First known weak semaphores are modelled. Then they are exploited for model checking classic algorithms. Known properties are retrieved but subtle new ones are discovered. As part of the developed approach, a new algorithm is proposed which uses two semaphores of the weakest type, N bits (N being the number of processes) and a counter. This algorithm too is proved to be correct.

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