HIERARCHICAL AGENT MONITORING DESIGN PLATFORM

Towards Self-aware and Adaptive Embedded Systems

Liang Guang, Bo Yang, Juha Plosila, Jouni Isoaho and Hannu Tenhunen

Department of Information Technology, University of Turku, Turku, Finland

Keywords:

Adaptiveness, Design approach, Embedded systems, Hierarchical agent monitoring, Self-awareness.

Abstract:

Hierarchical agent monitoring design platform (HAM) is presented as a generic design approach for the emerg-

ing self-aware and adaptive embedded systems. Such systems, with various existing proposals for different

advanced features, call for a concrete, practical and portable design approach. HAM addresses this neces-

sity by providing a scalable and generically applicable design platform. This paper elaborately describes the

hierarchical agent monitoring architecture, with extensive reference to the state-of-the-art technology in em-

bedded systems. Two case studies are exempliﬁed to demonstrate the design process and beneﬁts of HAM

design platform. One is about hierarchical agent monitored Network-on-Chip with quantitative experiments

of hierarchical energy management. The other one is a projectional study of applying HAM on smart house

systems, focusing on the design for enhanced dependability.

1 INTRODUCTION

The research on embedded computing has entered the

era of massively parallel and distributed systems. For

one thing, the number of components integrated on

a single chip has been constantly increasing due to

the continuous scaling of transistor sizes. For in-

stance, an 80-tile 1.28TFLOPS single-chip proces-

sor is fabricated with 100-million transistors (Vangal

et al., 2007). For another, more physically scattered

entities are being integrated into distributed embed-

ded systems, as indicated by the emerging platform

of Cyber-Physical Systems (CPS) (Lee, 2008). As the

complexity of embedded systems is steadily increas-

ing, the concept of autonomic computing gets widely

recognized, which refers to computing systems that

can manage themselves given high-level objectives

from administrators (Kephart and Chess, 2003). Since

the release of IBM autonomic computing manifesto

(Horn, 2001), many works have been developing the

concept and its implementation, for instance the pro-

posal of organic computing (W¨urtz, 2008).

Despite the diversity of works to promote the

adaptiveness and self-management of computing sys-

tems, there remain major challenges to be tack-

led. In particular, generic and scalable design ap-

proaches are needed to reduce the design complex-

ity. Previous works have proposed a list of ap-

pealing self-aware and adaptive functions, including

self-conﬁguration, self-optimization, self-healing and

self-protection (Kephart and Chess, 2003). For in-

stance, (Al Faruque et al., 2008) presents algorithms

for run-time mapping on Network-on-Chip (NoC)

with optimized computational effort. However, there

are few works about generic design approaches to re-

alize these functions. Considering the complexity and

diversityof emerging parallel and distributed systems,

designing adaptivefeatures on these systems in an ad-

hoc manner is very time-consuming and non-scalable.

This paper presents a generic design platform,

hierarchical agent monitoring (HAM), for designing

embedded system with self-aware and adaptive fea-

tures. Our previous work (Guang et al., 2010) pre-

sented the formal speciﬁcation of the design platform,

while this work focused on the design theory and sys-

tem architecture with studies on two speciﬁc architec-

tures, NoC and smart houses. We will ﬁrstly present

our conceptual deﬁnition of self-aware and adaptive

systems (Section 2). Then we will extensively present

the HAM platform (Section 3), including the beneﬁts

of platform-based design (Section 3.1) and the hier-

archical agent monitoring architecture (Section 3.2).

The design example of hierarchical agent monitored

NoC is given in Section 4. The agent functional par-

tition and architectural design are presented for hier-

archical energy management. Section 5 projects the

use of HAM design platform for distributed embed-

ded systems, exemplifying the smart house system.

573

Guang L., Yang B., Plosila J., Isoaho J. and Tenhunen H..

HIERARCHICAL AGENT MONITORING DESIGN PLATFORM - Towards Self-aware and Adaptive Embedded Systems.

DOI: 10.5220/0003396805730581

In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems (SAAES-2011), pages

573-581

ISBN: 978-989-8425-48-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The study will focus on the application of HAM to

generalize the design process for any potential func-

tions and enhanced dependability. The paper is con-

cluded in Section 6 with discussions of future works.

2 SELF-AWARE AND ADAPTIVE

SYSTEMS

Self-aware and adaptive systems originate from the

classic autonomic computing concept, while focus-

ing on the two distinctive aspects of system behav-

iors, sensing (for awareness) and reconﬁguration (for

adaptation).

We deﬁne a self-aware and adaptive embedded

system as one that is monitoring its own state and the

environment in order to achieve the expected perfor-

mance under potential environmental changes. The

performance refers to both functional (e.g. execution

time) and non-functional metrics (e.g. energy efﬁ-

ciency), either as hard constraints (e.g. power bud-

get) or soft requirements (e.g. with as low power as

possible).

A self-aware and adaptive system can be ab-

stracted as in Fig. 1. In terms of awareness, the

system needs to be aware of its objectives, such as

the power budget and dependability requirements,

and the run-time status including its own status and

the surrounding environment. In terms of adapta-

tion, the system processes the gathered information

based on intrinsic cost functions, and decides on the

proper reconﬁguration to achieve the objectives. Cost

functions are pre-conﬁgured or dynamically reconﬁg-

urable models relating the status parameters to perfor-

mance metrics. For instance, the energy consumption

can be modeled as a function of input parameters in-

cluding system activity ratio and supply voltage.

Self-Aware and Adaptive System

Reconfiguration

Logic reconfiguration

Physical

reconfiguration

Processing

Cost functions

Awareness

Adaptation

Run-time Status

System Status

Environment

Objectives

Power budget

Dependability

Figure 1: Concept of Self-Aware and Adaptive Systems.

Compared to the classic concept of autonomic

computing with the four aspects of self-management

(self-conﬁguration, self-optimization, self-healing

and self-protection (Kephart and Chess, 2003)), the

notion of self-aware and adaptiveness focuses on

the two distinctive phases, awareness and adaptation,

which are universal for any type of self-management.

As illustrated in Fig 2., awareness is the prerequi-

site for effective reconﬁguration. A platform with

a generic support for adaptation triggered by self-

awareness is able to provide different types of auto-

nomic operations, given proper conﬁguration of the

algorithms and microarchitectures.

The major obstacle of autonomic computing is the

complexity (Horn, 2001). The development and inno-

vation of design approaches have always been an es-

sential enabler to effectivelyreducing the design com-

plexity and time-to-market (Keutzer et al., 2000). For

self-aware and adaptive systems, in order to make the

design process generic and reusable for any type of

adaptive functions, it is important to innovate on the

design approach.

3 HIERARCHICAL AGENT

MONITORING DESIGN

PLATFORM

3.1 Platform-based Design

Platform-based design (Sangiovanni-Vincentelli and

Martin, 2001; Keutzer et al., 2000) is an important de-

sign methodology addressing design productivity and

reusability. Platform-based design abstracts the sys-

tems as platforms, which can be modeled at different

levels of elaboration. With platform-based design, in-

stead of building each component from scratch, the

designers can reuse a proper platform as a founda-

tion, and modify the components as needed. A con-

crete example applying the platform-based design is

Network-on-Chip (NoC) (Rabaey, 2004) (Fig. 3),

which provides a reusable platform for a large diver-

sity of parallel applications.

Hierarchical agent monitoring design platform

follows the platform-based design methodology by

adding a new design dimension for monitoring and di-

agnostic services. As the unpredictableness and varia-

tions of system and circuit increase, it becomes more

necessary to utilize various types of monitoring ser-

vices, for instance power monitoring (Shang et al.,

2003) and thermal monitoring (Shang et al., 2004).

HAM design platform provides a design layer dedi-

cated to such monitoring services, which integrates a

generic monitoring architecture (Fig. 3). The mon-

itoring architecture is built upon existing platforms

with modularized computation and communication

elements.

The term agent originally comes from artiﬁcial

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Awareness

Self-configuration

Self-optimization

Self-healing

Self-protection

objectives

Run-time status

Run-time errors

Future status (predicted

based on current status)

Automatic configuration

based on high-level policies

Constantly seek possibilities to

improve run-time performance

Automatically detect and repair

software and hardware errors

Automatically protect itself

from potential failures

Adaptation

the four ”Self-”

Figure 2: Self-Aware and Adaptive Systems: A Special Perspective on Autonomic Computing.

CEAG

CLAG

Cell

NINI

CEAG

CEAG CEAG

CEAG

Cluster

Cell

Cell Cell

Cell

CLAG

PLAG

AAG

AAG: application agent

PLAG: platform agent

CLAG: cluster agent

CEAG:cell agent

NI NI

A Generic NoC:

A Platform with Computation and Communication Units

Hierarchical Agent Monitored NoC:

A Platform with Computation, Communication and Monitoring Componenents

PE: processing elements

R: Router

NI: Network interface

Figure 3: The Contrast between Conventional NoC Plat-

form and Hierarchical Agent Monitored NoC Platform.

intelligence and software engineering, referring to a

large diversity of intelligent components which are

sensing, monitoring and reconﬁguring the systems.

From the design method’s perspective, agent becomes

a design abstraction, which may have different imple-

mentations, software, hardware or hybrid.

3.2 Hierarchical Agent Monitoring

Architecture

A generic monitoring architecture, hierarchical agent

structure, is provided by the HAM design platform

(Fig. 4). Agents at different levels make a joint ef-

fort to offer self-aware and adaptive system features

Cell

Agent

Cell

Agent

. . .

Cluster

Agent

. . .

Platform Agent

Cell

Agent

Cell

Agent

. . .

Cluster

Agent

Application

Agent

Requirements

(timing, dependability, etc.)

Figure 4: Hierarchical Agent Monitoring Architecture.

(Table 1).

The application agent is a module capturing the

run-time requirements of the application, for instance

timing constraints (e.g. soft or hard deadlines),

and dependability requirements (e.g. mean-time-to-

failure). In other words, the application agent enables

the system to be aware of its objectives.

The platform agent is aware of the overall system

performance, and makes major adaptation that affect

the whole platform. For example, it is responsible for

resource allocation, including mapping processors to

application tasks, and network conﬁguration. At run-

time, it traces the overall system performance, for

example the total power consumption or the general

speed. When necessary the platform agent may re-

conﬁgure system settings to be applied to the whole

platform, for instance the network topology.

Each cluster agent is responsible for a particular

region (a cluster) in the platform. It follows the gen-

eral settings as decided by the platform agent, while

being able to conﬁgure its own cluster. Dynamic volt-

age and frequency scaling, for instance, can be ap-

plied to different voltage islands on a many-core plat-

form (Ogras et al., 2009). Such regional services be-

long to the responsibilities of cluster agents.

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Table 1: Hierarchical Agent Monitoring for Self-Aware and Adaptive Systems.

Agent Level Main Features Awareness Adaptation

Application platform independent system objectives modify the system

Agent objectives

Platform coarse granularity, overall system performance, conﬁgure general system

Agent platform-wide inﬂuence, cluster agents status, setting, perform major system

infrequent lower level components reconﬁguration,

(when cluster agents fail) monitor low level components

(when cluster agents fail)

Cluster medium granularity, cluster performance, conﬁgure cluster setting,

Agent only inﬂuence one cluster cell agents, re-conﬁgure clusters,

cells monitor cells

(when cell agents fail) (when cell agents fail)

Cell Agent ﬁne granularity, cell performance, reconﬁgure cell

quick response, often issued circuit state

Cell agents work on a ﬁne granularity. Each cell

agent tightly monitors the local hardware with very

quick response. (Truong et al., 2009) presents a

many-core platform where each core can adjust its

own voltage and frequency quickly. In this case, the

cell agent shall be responsible for voltage and fre-

quency switching in each core.

As agents may also have errors themselves, each

agent is also responsible for monitoring its lower-

level agents. In case one agent fails, its supervising

agent shall take over its resources until the failure is

ﬁxed.

4 HIERARCHICAL AGENT

MONITORED

NETWORK-ON-CHIP

NoC is a widely adopted scalable architecture for

massively on-chip parallelism (Jantsch and Tenhunen,

2003). Due to the intensity and diversity of paral-

lel computing on NoC, various run-time monitoring

and optimization techniques have been proposed (Ta-

ble 2). These monitoring services provide the NoC

system with a diversityof self-aware and adaptivefea-

tures. HAM platform can be conveniently applied to

many-core NoC systems for the design of these mon-

itoring services.

4.1 Exempliﬁed Monitoring Operations

for Energy Efﬁcient

Communication

Here we exemplify a set of monitoring operations on

NoC to perform energy-efﬁcient communication.

This set of operations provide the awareness and

adaptation at four agent levels (Table 3), in order

to achieve the minimal communication energy while

meeting the performance objective.

We consider a many-core NoC platform running

multiple sub-applications. Each sub-application will

run in a cluster, monitored by a cluster agent. The

application agent is aware of the performance re-

quirement of each sub-application. In this example,

we exemplify the average communication latency as

the performance requirement. The platform agent

ﬁrstly transforms the communication latency require-

ments to thresholds of network load. The network

load threshold is an indicator of network congestion,

which is directly related to the average communica-

tion latency (Guang and Jantsch, 2006). In a buffered

NoC architecture, the network load can be quantiﬁed

by the buffer load, which is the percentage of occu-

pied buffers out of the total number of buffers in the

network. Given the network topology and ﬂow con-

trol mechanism, we can simulate the proper buffer

load threshold to provide the required communica-

tion latency before execution time (Table 4). The

platform agent also performs energy-aware mapping

for each sub-application, which minimizes the overall

communication volumes between each two processes

multiplied by their distances after mapping. While

the design platform does not limit the choice of spe-

ciﬁc application mapping algorithm, here an in-house

tree-based mapping algorithm is utilized ((Yang et al.,

2010); Fig 5). Simply put, the network is abstracted

into a tree structure. The process with the highest

communication volume is mapped onto the root node

in the tree. Then the algorithm searches for the pro-

cess having the highest communication volume with

the mapped nodes, and maps it onto the highest avail-

able node. The iteration continues until all the pro-

cesses are mapped. The cluster level agent is per-

forming DVFS (dynamic voltage and frequency scal-

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Table 2: Representative Monitoring Operations on Many-core NoCs.

Monitoring Services Awareness Adaptation

Monitoring Performance communication speed improve communication speed

(Hu and Marculescu, 2005)

Fault Tolerance component (e.g. link) error ﬁx faults and errors

(Lehtonen et al., 2007)

power efﬁciency network load tradeoff communication performance

(Shang et al., 2003) with power efﬁciency

Thermal monitoring local temperature adjust temperature evenness

(Shang et al., 2004) and avoid hotspot

Table 3: Exempliﬁed Hierarchical Agent Monitoring on NoC for Energy Efﬁciency.

Level of Agent Awareness Adaptation

Application Agent communication requirements none

Platform Agent communication requirements, transform performance requirements to

network load threshold,

application communication graph energy-aware application mapping

Cluster Agent cluster network load intra-cluster DVFS

Cell Agent local buffer load report the load to the cluster agent

ing (Shang et al., 2003)) in each cluster, based on

the network threshold dictated by the platform agent.

The average network load in each cluster is calcu-

lated from the buffer load of each router, reported by

every cell agent. The cell agent is dedicated hard-

ware circuit inside each router, to calculate the buffer

load. Fig. 5 illustrates the exempliﬁed the hierarchi-

cal monitoring operations for energy efﬁciency.

4.2 Experimental Setting

Several synthetic and real traces were used to demon-

strate the operations (Table 4). A cycle-accurate

simulator is utilized to simulate an 8*8 mesh NoC,

assuming 65nm technology. Each channel on the

NoC is 1 mm long and 64-bit wide (32-bit per direc-

tion). For DVFS, two voltage and frequency levels,

(1.2V,2GHz) and (0.8V, 1GHz) are adopted. The en-

ergy consumption on routers and links is estimated

from Orion 2.0 (Kahng et al., 2009).

4.3 Quantitative Evaluation

The average per-ﬂit (a 32-bit word) energy consump-

tion for each trafﬁc trace, as well as the average per-

ﬂit latency, are reported in Table 5. We can observe

that the hierarchical monitoring operations effectively

achieve energy optimization under the performance

constraint. Compared to static high voltage and fre-

quency supply, platform-level application mapping

reduces the energy consumption for sub-applications

with trafﬁc spatial unevenness (H.264 and hotspot).

When application mapping does not provide signiﬁ-

cant energy reduction, cluster-level DVFS still offers

improved energy efﬁciency. When hierarchical moni-

toring operations are performed, we achieve consider-

able energy saving for all studied trafﬁc traces (19%-

64%). In all cases, the average communication la-

tency is well below the upper boundary set by the ap-

plication agent.

The platform agent is realized by software with

the application mapping algorithm written in C code

of around 600 lines. Cluster agents and cell agents

are realized as hardware circuits, with standard syn-

thesis ﬂow. Major accessory circuits to support dy-

namic voltage and frequency scaling are DC-DC con-

verters and PLLs (phase-locked loop). We estimated

that, on an 8*8 NoC platform, the hardware overhead

of hierarchical agents and accessory circuits is less

than 2mm

, which is very small compared to common

die sizes (for instance 275mm

as in (Vangal et al.,

2007)). The areas for DC converters and PLLs are

scaled from (Wibben and Harjani, 2007) and (Tierno

et al., 2008) respectively.

5 HIERARCHICAL AGENT

MONITORED SMART HOUSE:

A PROJECTION

Hierarchical agent monitoring design platform is

generic for any type of parallel and distributed em-

bedded system. The more widespread and heteroge-

neous the system is, the more effective HAM platform

is compared to ad-hoc designs. In this section, we an-

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Table 4: Setting of Trafﬁc Traces Mapped on an 8*8 NoC.

Trace Features Maximal Average Mapping Cluster Load

Latency threshold

linearly changing trafﬁc (I) trafﬁc injection 10ns 3*3 0.3

linearly increases mesh

hotspot trafﬁc (II) 4 processing elements 20ns 5*5 0.15

(Lu et al., 2008) receive 50% trafﬁc mesh

MP3 trace (III) audio decoder 15ns 5*3 0.18

(Truscan et al., 2008) mesh

H.264 trace (IV) video encoder 15ns 5*3 0.06

(Latif et al., 2008) mesh

Table 5: Experiment Results of Energy Efﬁciency and Communication Latency.

Trace Average energy Average Energy Average Energy Latency Average Latency

with high voltage with application with hierarchical Boundary with hierarchical

and frequency mapping operations operations

e-11J e-11J e-11J ns ns

I 4.17 4.17 3.37 10 9.78

II 6.12 4.91 4.19 20 15.20

III 4.01 4.2 2.22 15 7.15

IV 5.50 3.47 1.98 15 6.17

Platform-level

Energy-Aware

Application Mapping

P1 P2

C01

C05

C51

C12

C53

C23

Cluster-Level

DVFS

Cluster

Agent

Cluster network

load

Voltage,

frequency

Cluster1, for subapplication 1

Subapplication 1

Other Clusters

Cell

agent

Local buffer load

Cell-level

Local Buffer Load Tracing

Figure 5: Hierarchical Agent Monitoring Operations for Energy-Efﬁcient Communication.

alyze the application of HAM on smart houses, as an

example of distributed embedded systems.

5.1 Smart House: A Hierarchical View

The concept of autonomic computing is applied on

smart houses to facilitate human daily life. A diver-

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Copier state

sensor and

controller

Sensor/

Actuator

Layer

Service layer

Cell agent

Screen sensor

and controller

Video camera sensor

and controller

Cluster agent:

. . .

Platform Agent:

Application Agent

Application layer

Application managers to activate and

deactivate services, as well as necessary

interfaces with human

Managing full-scale services influencing the

whole house, for instance dynamic heating policy

Managing services for parts of the house,

for instance a video conferencing room

Service Service

Service

Managing a particular device or

location

Figure 6: Hierarchical View of Smart House (smart house hierarchy adapted from (Helal et al., 2005)).

sity of self-aware and adaptive functions have been

proposed (Helal et al., 2005; Stefanov et al., 2004), in

order to save domestic energy consumption, improve

dependabilityagainst variousfailures (e.g. power fail-

ures), or help patients and people with disabilities.

Existing works have focused on the proposal of

more functions, for instance run-time video confer-

encing to enable distant dining (Helal et al., 2005)

or robotic systems for moving assistance (Stefanov

et al., 2004). While these features seem to be technol-

ogy feasible, we still need a scalable design approach

so that new features can be efﬁciently added with an

increasing scale of distribution. (Helal et al., 2005)

proposes a hierarchical view for the large diversity of

control and monitoring components in a smart house,

which seamlessly ﬁts into the hierarchical agent mon-

itoring architecture (Fig. 6).

The sensor and actuator layer is the one directly

interfacing with the physical world. For instance, on

video cameras, autonomous sensors can be installed

to identify any suspicious intruders. Cell agents shall

be responsible for this layer, since they are local mon-

itors based on the HAM design platform. The service

layer deﬁnes all types of basic or composite services

provided in the smart house. On HAM design plat-

form, these services shall be assigned to either the

platform agent or the cluster agent, depending the

scope of inﬂuences. Services which are managed cen-

trally with a system-wide inﬂuence, such as the uni-

versal heating policy, is the responsibility of the plat-

form agent. Other services which affect part of the

houses are managed by the cluster agent, for instance

the management of a video conferencing room. Some

services can be provided by either the platform or

cluster agent. For instance, if each room may adjust

its own temperature, then heating will be managed by

the cluster agent. The application layer includes the

application manager to activate or deactivate the ser-

vices and the interface to external monitors, for in-

stance designers. This layer shall be managed by the

application agent, which decides if certain services

are needed for the smart house.

5.2 HAM Design Platform for

Self-Aware and Adaptive Smart

Houses

HAM design platform, in addition to being a generic

platform for designing control and monitoring smart

houses, provides a monitoring architecture which of-

fers self-awareness and adaptivenesssystematically in

the smarthouse, as illustrated by Table 6.

One of the major beneﬁts of hierarchical agent

monitoring in providing self-aware and adaptiveness

is the enhanced dependability. Existing research is

mostly concerned with the fault or errors of original

devices, for instance the camera, copier or the heating

system. However, the added monitors, either hard-

ware or software, may have faults themselves. For

example, the adaptive sensor on the camera itself may

be broken, or the software managing the heating sys-

tem may have errors. Hierarchical agent monitoring

architecture relies on the hierarchical supervision of

agents to minimize the possibility of undetected mon-

itor errors, as illustrated in Table 7. Simply put, each

level of agent needs to regularly check the state of

the lower-level agents. At the highest level, the hu-

man may need to check if the platform agent is in a

healthy state, although such checking is very infre-

quent and only applied on the platform agent. One

convenient way to perform such state checking is to

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Table 6: Example Services in Self-Aware and Adaptive Smart Houses with Hierarchical Agent Monitoring .

Agent Level Awareness Adaptation

Application Required Services Activate or Deactivate Services

Agent

Platform the availability of power supply, switch to power shut-down mode,

Agent the average temperature in the house, adjust the house temperature,

any intrusion switch to guard mode

Cluster the temperature of the green house, adjust the temperature,

Agent the quality of the video conferencing adjust the general setting of

video conferencing (camera,

projector, loudspeaker, etc.)

Cell the state of the surveillance camera, adjust the setting of the camera

Agent and report intrusions to the platform agent,

the state of the copier machine adjust the setting of copier machines

Table 7: Enhanced Dependability in Hierarchical Agent Monitored Smart Houses.

Agent Level Monitoring Example

for dependability

Application Agent the state of platform agent the application agent tests if

all functions of the platform agent

work normally

Platform Agent the state of the platform, check the temperature and power supplies

of the house,

the state of cluster agent check if the cluster agent monitoring

the video conferencing room works normally

Cluster Agent the state of the cluster, check if the temperature of a particular room

remains normal,

the state of cell agents check if the cell agent monitoring

the surveillance camera works normally

Cell Agent the state of the cell check if the surveillance camera works normally

regularly send testing signals. A healthy agent should

be able to respond with the correct reply within an

expected time period.

6 CONCLUSIONS AND FUTURE

WORK

This paper presented hierarchical agent monitoring

(HAM) design platform for parallel and distributed

embedded systems. HAM approaches the goal of

autonomic computing by providing a scalable and

portable design framework, while focusing on the po-

tential integration of various self-aware and adaptive

features. The paper extensively presented the func-

tional partition among agents and their interactions to

fulﬁll the intended monitoring services. We demon-

strated the design platform on the many-core NoC

architecture, exemplifying the run-time energy man-

agement. With quantitative evaluations using real and

synthetic benchmarks, we observed the functional ef-

fectiveness and low physical overhead of HAM. A

projectional study on smart houses illustrates how

HAM can be applied to distributed embedded system,

for instance to provide enhanced dependability.

At current stage, the development of HAM prior-

itizes the study of generic design approach over spe-

ciﬁc algorithms and implementations. We will con-

tinue to research on more detailed architectures with

algorithm and implementation exploration. In partic-

ular, we are building elaborated simulation for a vir-

tual smart house model, using high-level tools for in-

stance Matlab. The study will focus on the system de-

pendability against potential errors, for instance pro-

cessor errors, power failure or heavy wireless channel

noise.

ACKNOWLEDGEMENTS

The work is supported by the Foundation of Nokia

Corporation.

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