Cyber-Physical Systems Design for Electric Vehicles

Lukasiewycz, Martin; Steinhorst, Sebastian; Sagstetter, Florian; Chang, Wanli; Waszecki, Peter; Kauer, Matthias; Chakraborty, Samarjit

doi:10.1109/dsd.2012.39

Cited by 21 publications

(14 citation statements)

References 28 publications

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“…The calculation only takes 100 ms when the taxi count is close to 10,000. The response time increases very slowly with the increase of taxi count n. This is because the calculation maps location to grid index, which has a constant complexity (1). Figure 8b shows how facility count affects response time.…”

Section: Facility Influence Computingmentioning

confidence: 94%

“…Cyber-physical vehicles [1,2] have emerged to focus on the integration of sensing, computing, and actuation technologies into everyday urban settings and lifestyles. In cyber-physical vehicle system, locations of vehicles are the key information which not only interact dynamics of connected vehicles but also provide people a new perspective to modern city [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influential spatial facility prediction over large scale cyber-physical vehicles in smart city

Wang

Feng

et al. 2016

J Wireless Com Network

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Facilities are critical infrastructures in smart city. Influence of facilities is affected by large-scale moving objects such as people and vehicles. Calculating the influence of facilities is an important task of urban computing, which adopts sensing technology to obtain objects' movement patterns in urban space and applies this information to discover many hidden issues cities face today. In this paper, we propose a computationally efficient grid partition method to compute the influence of facilities in real time, under trajectories of large-scale cyber-physical vehicles. We next predict the influence changes of facilities over dynamic vehicles using trajectory-based Markov model. We conduct evaluation using a real-world dataset, including 1-month taxi trajectories with 27,000 taxis and 1000 facilities. Experimental results shows that our solution is more efficient and achieves an accuracy of 85 %.

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Section: Facility Influence Computingmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Influential spatial facility prediction over large scale cyber-physical vehicles in smart city

Wang

Feng

et al. 2016

J Wireless Com Network

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“…Current practice in many safety-critical domains, such as electric vehicle [Lukasiewycz et al 2012] and avionics systems [Lin et al 2007], favors a time-triggered approach [Baruah and Fohler 2011]. In this paper, we consider a periodic time-triggered non-preemptive scheduling policy.…”

Section: Task Modelmentioning

confidence: 99%

“…With increasing requirement for high-performance, multi-core architectures such as MPSoCs (Multiprocessor System-om-Chip) are believed to be the major solution for future embedded systems, e.g., electronic vehicle [Lukasiewycz et al 2012]. Chip makers have released several MPSoCs, e.g., ARM Cortex-A15 MPCore [ARM 2012], Intel Atom processors [Intel 2009], and Marvell ARMADA MV78460 multi-core processors [Marvell 2012].…”

Section: Introductionmentioning

confidence: 99%

Energy optimization for real-time multiprocessor system-on-chip with optimal DVFS and DPM combination

Chen¹,

Huang²,

Knoll³

2014

ACM Trans. Embed. Comput. Syst.

114

125

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Energy optimization is a critical design concern for embedded systems. Combining DVFS+DPM is considered as one preferable technique to reduce energy consumption. There have been optimal DVFS+DPM algorithms for periodic independent tasks running on uni-processor in the literature. Optimal combination of DVFS and DPM for periodic dependent tasks on multi-core systems is however not yet reported. The challenge of this problem is that the idle intervals of cores are not easy to model. In this paper, a novel technique is proposed to directly model the idle intervals of individual cores such that both DVFS and DPM can be optimized at the same time. Based on this technique, the energy optimization problem is formulated by means of mixed integrated linear programming. We also present techniques to prune the exploration space of the formulation. Experimental results using real-world benchmarks demonstrate the effectiveness of our approach compared to existing approaches.

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“…While the distributed and heterogeneous nature of automotive E/E architectures makes them a perfect candidate for CPS-oriented design, it is only recently that some progress has been made in this direction [6], [7].…”

Section: Cps-oriented Designmentioning

confidence: 99%