a framework for reducing the modeling and simulation ... · in miniaturization, speed, power, and...

A Framework for Reducing the Modeling andSimulation Complexity of Cyberphysical Systems

Nikolaos Zompakis and Kostas SioziosSchool of Electrical and Computer Engineering, National Technical University of Athens, Greece

Email: {nzompakis, ksiop}@microlab.ntua.gr

Abstract—As systems continue to evolve they rely less onhuman decision-making and more on computational intelligence.This trend in conjunction to the available technologies forproviding advanced sensing, measurement, process control, andcommunication leads us towards the new field of Cyber-PhysicalSystem (CPS). Although these systems exhibit remarkable char-acteristics, the increased complexity imposed by numerous com-ponents and services makes their design extremely difficult. Thispaper proposes a software-supported framework for reducingthe design complexity regarding the modeling, as well as thesimulation of CPS. For this purpose, a novel technique basedon system scenarios is applied. Evaluation results prove theeffectiveness of introduced framework, as we achieve to reducementionable the modeling and simulation complexity with acontrollable overhead in accuracy.

I. INTRODUCTION

The wide reach of the Internet along with rapid advances

in miniaturization, speed, power, and mobility have led to

the pervasive use of networking and information technologies

across all economic sectors. Increasingly, these technologies

are combined with elements of the physical world (e.g.,

machines, devices, structures) to create smart systems. The

challenge of integrating embedded computing and physical

processes with feedback loops, where physical processes affect

computations and vice versa has been recognized for some

time, since it allows physical devices to operate in changing

environments [1].

Tightly coupled cyber and physical systems that exhibit

this level of integrated intelligence are referred to as Cyber-

Physical System (CPS). With their unique functionalities,

CPS has the potential to change every aspect of life. Un-

paralleled analytical capabilities, real-time networked infor-

mation, and pervasive sensing, actuating, and computation

are creating powerful opportunities for systems integration,

enabling among others CPS to execute extraordinary tasks that

are barely imagined otherwise. Today, CPS can be found in

such diverse domains such as automotive, energy, healthcare,

manufacturing, communications, etc.

Analyzing the main components of a CPS system we

distinguish three basic implementation layers. With a top-down

approach the first layer provides all the necessary mechanisms

for supporting the make decision, the second one refers to the

connectivity (network) among the CPS components, whereas

the last layer deals with the tasks of monitoring and actua-

tor. From functionality perspective, the make-decision layer

realizes the central control, the monitor and actuator layer

implements the system observation and defines the system

actions and reactions, while the network is the interface mean

between the previous two.

In order to take advantage of the competitive features of

CPS, it is necessary to incorporate all the necessary high-

confidence mechanisms that are able to interact with humans

and the physical world in dynamic environments and under

unforeseen conditions. Key role to these techniques are given

to the system’s modeling and co-simulation, which typically

integrates various models of computation and communication.

A methodology that integrates both discrete and continu-

ous time models at different layers of abstraction (SystemC

and Simulink) are discussed in [2]. A similar framework is

discussed in [3], where the co-simulation framework inte-

grates VDM++ and 20-sim. Although these approaches exhibit

remarkable results, they are not directly applicable to the

design of CPS. In [4], a methodology of virtual prototyping

is proposed which combines SystemC, QEMU, and Open

Dynamics Engine to achieve a holistic design view. The True-

Time toolbox, which considers timing aspects introduced by

computation and communication, has been proposed and used

in MATLAB/Simulink environment to enable CPS simulation

[5]. However, this toolbox cannot either integrate hardware

models, or support different abstraction levels. A co-simulation

framework that deals with the joint design of software in C,

hardware in HDL and mechanical components in MATLAB

is discussed in [6]. Although this framework tackles the three

main aspects of a CPS, it exhibits a limitation regarding the

efficient design of cyber part of the system. Finally, Hardware-

in-the-Loop (HIL) simulators are also proposed [7], which

enable co-simulation between software and hardware modules.

The previously mentioned analysis indicates the plenty of

simulators and design environments at different layers of

abstraction. However, none of them is directly applicable

to CPS, as they cannot support cross-domain concepts for

architecture, communication and compatibility. Specifically,

existing approaches can be classified at two complementary

directions: (i) cyberizing the physical systems means to endow

physical subsystems with cyber-like abstractions and interfaces

(wrap software abstractions around physical subsystems); and

(ii) physicalizing the cyber means to endow software and

network components with abstractions and interfaces that

represent their dynamics in time.

Throughout this paper we introduce a framework to pro-

vide a holistic solution to the modeling and simulation of

978-1-4673-7311-1/15/$31.00 ©2015 IEEE

3rd Workshop on Virtual Prototyping of Parallel and Embedded Systems (ViPES 2015)

360

CPS. The competitive advantage of this approach relies to

the considerable reduced complexity as compared to existing

approaches, with almost similar accuracy. Additionally, the

proposed framework employees a virtualization methodology

for enabling part(s) of the physical components to be replaced

with their equivalent software models without distributing

the overall system’s functionality. Although this approach

is relevant to the HiL technique discussed previously, the

introduced solution is also applicable for system debugging,

since it can isolate specific parts of the CPS.

For demonstration purposes, the introduced framework is

applied to model a large scale WiMAX network. Such a

network can be though as a CPS, since it includes sensors (e.g.

for detecting signal power, noise, etc), actuators (e.g. apply

different coding scheme depending on the desired Quality-of-

Service), and computation mechanisms (e.g. for determining

the optimum coding scheme). Experimental results prove the

effectiveness of introduced solution, as we achieve to reduce

the complexity of system’s modeling with a controllable

overhead in accuracy, as compared to the static and full-custom

implementations.

The rest of the paper is organized as follows: Section II

describes the proposed framework, while the technique for

reducing the complexity of system’s modeling and simulation

is discussed in Section III. Experimental results that evaluate

the efficiency of introduced solution, as compared to an

existing approaches are depicted at Section IV. Finally, Section

V concludes the paper.

II. PROPOSED FRAMEWORK

This section introduces the proposed methodology (depicted

schematically at Fig. 1) for supporting the efficient modeling

and simulation of cyberphysical systems. Additionally, the

introduced methodology, which is a complete in-the-loop flow,

consists of several chronological steps and supports system

testing both at component, as well as system level. As it is

depicted at Fig. 1, on the left side of the V cycle, initially we

perform a Model-in-the-Loop (MiL) simulation, followed by

a Software-in-the-Loop (SiL) simulation. Then, on the right

side of the V cycle, Hardware-in-the-Loop (HiL) simulation

is applied.

Specifically, the MiL simulation consists of mixing the

physical plant model (including mechanical, electrical, ther-

mal, etc., effects) with the control strategy at the algorithmic

level. This creates a complete CPS model that can be simulated

together to find out whether the behavior of the CPS is correct.

Once the MIL simulation verifies the accuracy of our model,

we proceed with its implementation in software level. The

realization of control model can be done either manually (i.e.

by writing C code), or automatically through code generation

tools. Initial tests can be created at this level, which should be

reusable for the next subsequent phases. Next step involves the

validation of our implementation with the SiL simulations. For

this purpose, the source code is simulated with the same physi-

cal plant model used in the MiL phase. Although different tests

can be applied, it is usual to reuse the same test for assuring

Requirements

System Design

Component Design Integration

CalibrationSystem Test

Component Test

HiL Simulation

SiLSimulation

MiL Simulation

Software Design

Fig. 1. The proposed methodology.

the equivalence of results (back-to-back testing). Finally, the

last phase involves the HiL simulation, where the real plant is

added in the loop (instead of conventional simulation, where

only models are employed). Such a simulation is applicable

both at the development and testing of CPS. More specifically,

with HIL simulation the physical part of a machine (or system)

is replaced by a simulation model.

This is a valuable instrument for system designers, as it

enables among others the incremental system development

and debugging. Next subsection describes in more detail the

employed software tools that instantiate the proposed method-

ology.

A. Software Support

Even though there are plenty of simulation and design

tools that tackle software (SW) and hardware (HW) problems

individually, there are only a few approaches that leverage

problems arising in systems that tightly integrate SW and

custom HW. This mainly occurs due to the challenges related

to system integration that have to be addressed. Even limited,

there are approaches which promise to alleviate the integra-

tion problem in RTL simulation, emulation and prototyping

environments. However, these solutions are often too complex,

slow and expensive. Usually, it is the communication link

between the host computer and prototyping HW that is mostly

constrained.

In order to overcome this limitation, this section describes

the introduced framework that automates the methodology

discussed previously. This framework, depicted schematically

at Fig. 2, incorporates all the necessary toolflows to support

the Mil, SiL and HiL simulations for a CPS. Specifically, the

MiL and SiL simulations are performed based on Simulink [8]

and OVP [9] suites, respectively. However, we have to notice

that apart from these tools, any other flow with similar features

can be employed for the scopes of these simulations.

Such a modularity is highly desirable because it enables

easier framework’s upgrade for supporting additional features.

This is an upmost important objective especially for the CPS

domain due to the continues demand for newer and more flexi-

ble design suites. In addition to that, the introduced framework

provides a PC-based co-simulation technique, which trade-offs


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HotTalk API(Device driver, SW stack)

Co-DebuggingCo-Simulation

@ RUN-TIME!

ConventionalPrototyping

ProposedSiL & HiL simulation

Early Prototyping

device-in-the-loop

CPS Functionality Control Mechanisms(e.g. software executed onto ARM)

System model

Execution flow for HiL simulationExecution flow for SiL simulation

Fig. 2. Proposed rapid virtual prototyping for designing CPSs control.

between speed (functional simulation) and accuracy (cycle-

accurate simulation), depending on designer requirements.

The increased simulation speed provided by OVPSim ensures

that complex systems can be modeled in reasonable amount

of time (hundreds of millions of simulated instructions per

second). As the OVP models are pre-built, they support fully

functional simulation of a complete embedded system. Also,

since these models are binary-compatible with the simulated

HW, the developed software can be executed onto the target

(final) system without any modifications, resulting to faster

software development. Another interesting feature provided

by proposed framework is the close interaction between SW

and HW teams during the CPS development phases, which is

provided by adopting TLM-SystemC models. Among others

such a feature enables as long as new IPs are developed,

the HW design team is able to incrementally test these IPs

by replacing a functionality of the employed SystemC/TLM

model with the equivalent HDL prototype mapped onto FPGA

boards. The connection between virtual platform (OVP) and

FPGA is established with HotTalk API [7].

III. BALANCE SYSTEM’S COMPLEXITY WITH ACCURACY

This section describes the proposed instrument for balancing

the system’s complexity with the accuracy of derived solutions.

This is especially crucial for CPS platforms because these

systems usually impose the integration of numerous compo-

nents, such as sensors, actuators, control mechanisms, etc.

The main idea of our technique relies to cluster a number of

system’s operations at representative cases, called scenarios.

Thus, instead of modeling and simulating each operation

individually, we deal only with a few scenarios. This leads to

significant smaller complexity, which in turn improves deign

time and cost. Next, we describe in more detail these steps.

A. System Variability Analysis

An exploration of the system variability initializes the

context of the analysis. The sources of variation can be

adjustable or non-adjustable. Such a distinguish leads to a

premier classification of the system’s operations into run-

time situations (RTSs) and configurations. RTSs represent the

individual uncontrollable operation situations while configu-

rations represent the driven system reactions. In order this

declaration to be appropriately defined, it is presupposed the

deep knowledge of the targeted system’s characteristics.

B. Cost Optimality Exploration

The optimal cost dimensioning of the previous derived

system’s operation situations and configurations is the aim

of the existing step. For this purpose, a number of well-

defined cost metrics are employed. Since we focus on a

multi-objective problem, pareto optimality [10] exploration is

useful for handing the cost validation, giving a set of optimal

design trade-offs. This leads to a Pareto surface of potential

exploitation control points in the multi-dimensional cost space.

C. Classification

The efficient classification of system’s operation space is

the next key issue. Towards this direction, a number of opera-

tion scenarios are defined, which cover the whole system’s

functionality [11] [12]. By grouping individual operations

based on the cost similarity, it is feasible to balance the

number of scenarios with the respecting modeling accuracy.

From modeling perspective the same scenario operations are

represented by the same cost dimensioning and the same

system’s configuration. This leads to a mentionable decrease of

the modeling complexity with an parallel increase of overesti-

mated modeling evaluations due to function fluctuations on the

same scenario. In other words, a fundamental tradeoff exists

between modeling accuracy that is inversely proportional to

the clustering overhead and the modeling complexity that is

proportional to the required modeled operation cases.

In order to clarify this, Fig. 3 plots three individual run-

time operation situations (RTSs) with corresponding Pareto

curves (RTS1, RTS2, RTS3) in a 2 dimensional cost space. The

outcome of the classification step is the cost representation of

the whole scenario by the worst Pareto curve (RTS1). The cost

characterization of the included RTSs by the worst estimation

case inevitably introduces a clustering overhead (light gray and

brown area at Fig. 3). This overhead manifests every time that

these RTSs occur at modeling time. Thus, the total modeling

accuracy is also inversely proportional to the frequency of the

RTS appearance.

D. Control Scheduler

The run-time instantiation of a scheduling mechanism,

which detects the scenarios and triggers the suitable modeling

scheme is the final step. The detection is achieved throught

monitoring the system’s changes. The detection implementa-

tion cost is proportional to the mapping complexity of these

changes into scenarios. So heuristic ways, which keep the

detection overhead in reasonable levels, have to be explored.

A typical implementation relies on decision trees, which are

graphs with nodes and edges that correspond the system’s

variable values to scenarios. Each path of the graph concludes

to a scenario detection.


362

Fig. 3. Example of clustering overhead.

IV. EVALUATION RESULTS

This section quantifies the efficiency of introduced frame-

work to reduce the modeling and simulation complexity of

CPS through the previously mentioned scenario-based ap-

proach. For this purpose, we present the way that our method-

ology deals with these issues comparing the results with two

border solutions: (i) a full-custom, and (ii) a static system

implementation.

A. Description of Usecase

Our case study occupies with a wireless CPS based on

IEEE 802.16 (WiMAX) communication protocol [13], which

covers a wide range of high and low data rate networks. Fig.

4 outlines the topology of underline CPS. Each distributed

network has an access point base station where the terminals

connect. The base stations are connected each others through

a central station using wires. We consider that the control

of the transmission power is implemented on the distributed

networks. The access point stations collect the local network

data (e.g. noise, available bandwidth etc) and act individually.

The central station controls the whole system applying service

policies. Our objective is to model (MiL) and simulate (SiL)

the transmission signal power between access points and

terminals under different operation situations. More precisely,

our case study handles the signal power transmissions under

different channel noise distributions. Such an approach is

possible to replace any real WiMAX link, or to create potential

links in the context of testing the scaling capabilities of the

whole CPS, through the HiL simulation. Then, it would be

feasible to provide trade-offs between modeling accuracy and

simulation complexity.

B. System Modeling and Simulation

The modeling process starts with the exploration of the

system variability. The modeled components of our targeted

CPS are the transmitter, the receiver and the air channel.

Fig. 5 outlines the 16 function blocks instantiations of these

components according to the WiMAX protocol specification

[13]. More precisely, we focus on the error correction coding,

the signal modulation (iQ mapper), the OFDM modulation

Fig. 4. The employed WiMAX usecase.

Antenna

A/D Converter Simulatedprocess

D/A Converter

I/O device

Control signal(analog)

Measurement signal(analog)

Control signal(digital)

Simulink on a PC

Simulated processmeasurement (digital)

Simulatedprocess

disturbance

Simulatedmeasurement

noise

Fig. 5. Modeling and analysis of a WiMAX link.

and the antenna functionality, since they present the main

functional variation during a transmission. The model rep-

resents the correlation connections among these blocks. In

this direction the Shannon and Harley theorem provides a

theoretical fundamental basis. The theorem bounds the signal

to noise power ratio (SNR) in respect with transmission data

rate, bandwidth and channel capacity [14].

The theoretical minimum SNR (SNRmin) for an error-free

transmission is impossible to reach in practice. The modulation

schemes define how close to this theoretical SNRmin the

transmission can reach. Every modulation scheme is charac-

terized by a SNRmin that allows the demodulation of the

transmitted symbols without errors. Knowing this SNRmin

per modulation scheme (MS), we can define the minimum

signal power per MS for specific levels of noise. Equation 1


363

Fig. 6. Symbol error probability for different MCs at WiMAX.

Fig. 7. RTS Characterization.

provides the symbol error probability (Ps) for each MS [14].

Ps,M =

(

M − 1

M

)

× erfc×(

3

M2 − 1×Es × average

N0

)12

(1)

Channel coding improves the SNRmin by a factor R [14].

So the derived curves (Equation 1) can be normalized for equal

energy per information bit (pre-coding) bearing in mind that

the energy per transmitted bit is less than the energy per infor-

mation bit by a factor equal to the code rate R. The graphical

expression of the symbol error probability for the modulation

and coding schemes (MCs) of the WiMAX is presented in Fig.

6. Every transmission situation is characterized by two cost

dimensions: the total signal power and the bit error rate (BER).

The signal power is inversely proportional to the symbol error

probability. Finally, each situation is characterized by a curve

in the two-dimensional space of total signal power and BER.

For demonstration purposes, Fig. 7 presents 3 representative

examples of these curves. Note that the complete analysis for

Fig. 7 imposes 594 distinct curves.

For the needs of our analysis, we consider four different

White Gaussian Noise (WGN) distributions. The average

values (μ) and standard deviations (σ) of these distributions

are summarized as follows:

• Channel 1: μ=120mWatt, σ=25mWatt




WGN is suitable metric for testing because it represents

equal energy at all frequencies in a spectrum area and mimics

the effect of random processes that occur in nature. The noise

distribution defines the appearance probability of the running

situations, while a different noise distribution creates an alter

probability density for these situations. Regarding our study,

the probability density of the operation situations, with a noise

range between (μ, μ+σ) and (μ, μ-σ), is 34.13% for a Gaussian

distribution. Correspondingly, the situations probability for (μ,

μ± 2σ), (μ, μ± 3σ) and (μ, μ± 4σ) are estimated. Thus, we

have in total 9 bounding levels of noise, which in combination

with the 66 potential blocks instantiations (depicted at Fig.

5) creates an exploration space of 594 (66×9) simulation

situations per channel distribution, similar to those depicted

at Fig. 7.Simulating each individual case for each channel can creates

huge complexity issues. For example during a transmission any

channel variation leads to a different modulation and coding

scheme. A full-custom approach requires a detail simulation of

each functionality variation. Thus, for each fluctuation an extra

modeling instantiation is implemented. On the other hand, a

static approach that implements only the worst case leads to

remarkable modeling and simulation overestimation.Our methodology overcomes these limitations by clustering

the 594 RTSs (similar to Fig.7) of each channel into scenar-

ios. In this direction, we balance the number of operation

scenarios taking into consideration the modeling accuracy

and the simulation complexity. The noise distribution defines

the appearance probability of the running situations of the

scenarios. Thus, we have a different optimal set of m scenarios

for every channel distribution. We consider a minimum BER,

for a guarantee Quality of Services (QoS), equals to 10−8.

Also, similar to relevant approaches [15], we assume that the

run-time reconfiguration cost of the transmission corresponds

to 10% of the maximum signal power consumption. Without

affecting the generality of this use case, our analysis concen-

trates on the four aforementioned channels providing optimal

modeling scenario sets.Table I outlines the results of the signal power modeling for

the four channels in comparison with an accurate full-custom

and a static model. The first column presents the number of

the modeled instantiations. The full-custom approach takes

into consideration the full 66 combination of the function

blocks (depicted in Fig. 5) for each channel. Respectively,

the scenario approach has a fluctuated number of considered

instantiations (ranging between 10 and 22) depending on the

channel’s characteristics (noise distribution) and the desired

accuracy. Error-correction, M-QAM, OFDM and BW columns

present the required implemented blocks for each system’s

implementation. Finally, the last two columns present the total

function blocks number and the respective achieved modeling

accuracy.


364

TABLE IOUTCOME FROM THE PROPOSED MODELING AND SIMULATION ANALYSIS.

1/2 2/3 3/4 5/6 2 4 16 64 128 512 1024 2048 1.25 3.5 5 10Full-Custom Approach 66 16 100%

Scenario Approach 10 8 71%

Static Approach 1 4 7%

Full-Custom Approach 66 16 100%









ModelingBlocks(UL/DL)

ModelingAccuracyM-QAM OFDM mode Bandwidth (BW)

Function BlocksModelingInstantiationsModeling Approach

Cha

nnel

1C

hann

el2

Cha

nnel

3C

hann

el4

Error Corretion

A number of conclusions might be derived based on this

analysis. Among others, the full-custom approach in Channel

1 provides maximum accuracy by instantiating 16 function

blocks, while the scenario approach for the same channel

provides a slightly reduced accuracy (71%) but with the half

number of blocks. Correspondingly, for the rest three channels

we have respecting trade-offs between the system’s accuracy

and the number of modeled blocks. Hence, system designer

can select the appropriate number of scenarios depending on

the desired modeling requirements. Summarized an extended

number of scenarios rise the probability for additional imple-

mented blocks but at the same time increases the modeling

representativeness. Finally, we have to notice that although the

modeling and simulation complexity differs among function

blocks (i.e., error correction, M-QAM, OFDM mode, BW), the

alternative instances per function block exhibits comparable

complexity.

V. CONCLUSIONS

A software-supported framework for enabling efficient mod-

eling and simulation of CPS, was introduced. In contrast

to relevant approaches, the introduced framework exhibits

considerable reduced complexity with a controllable penalty

in system’s accuracy. Additionally, the infrastructure for sup-

porting this feature, called scenario, is applicable to a wide

range of complex systems. For demonstrations purposes, we

proven that it can achieve to balance the number of blocks

that need to be modeled and simulated into a CPS (WiMAX

network), with the accuracy of derived solution, as compared

to static and full-custom approaches.

ACKNOWLEDGMENT

The work presented in this paper is partially supported by

the FP7-2013612069-2013-HARPA EU project.

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