Integration of a Wireless Sensor-actuator Network and an FPGA

for Intelligent Inhabited Environments

Javier Echanobe, Estibaliz Asua and Inés del Campo

Department of Electricity and Electronics, University of the Basque Country, 48940, Leioa, Spain

Keywords: Wireless Sensor Network, FPGA, Intelligent Environments, Neurofuzzy Systems.

Abstract: Wireless Sensor and Actuator Networks together with processing elements named intelligent agents are

achieving great importance in environmental control. The trend in this field points to implement small, low

power, low cost and fast systems, which is in general, hard to achieve. In this paper, an electronic system

that consists of several sensors and actuators and a FPGA endowed with Neurofuzzy based intelligent

algorithms is presented. The purpose of this work is to demonstrate the effectiveness of the FPGA to

provide intelligence to a Wireless Sensor and Actuator Networks. As example of application, a system

which acts over a floor lamp intensity and an opening of a window in an autonomous way is presented. This

autonomous action is calculated by the FPGA based on several parameters provided by the network

(temperature, humidity and luminosity). By integrating the low power WSAN and the FPGA-based

Intelligent Agent, a small, low power, low cost high-performance intelligent environment system is

achieved.

1 INTRODUCCTION

In the last decade, the research area known as

Wireless Sensor and Actuator Networks (WSAN) has

rapidly grown mainly due to several technological

advances such as the hardware miniaturization, the

maturity of the wireless technologies and protocols,

and also the sensor integration. The area is at present

quite mature for small networks and hence, the

number of application fields where WSAN can be

found is very large (Dargie, 2010): Environmental

Control (Ambient Intelligence, Home Automation,

Intelligent Environments) (Cook, 2009), Body Area

Networks (health care, telemedicine, elder care,

remote patient monitoring) (Acampora, 2014),

Machine-to-Machine Communications, Internet of

Things (IoT), surveillance, manufacturing, etc.

Together with the sensor network, a processing

counterpart to handle the amount of information

gathered by the sensors is often required. In many of

cases, these processing elements must provide, in

autonomous scenarios, a response which is sent back

to the actuators in the network. In addition, many

applications demand those elements to be small, low

power, low cost but fast enough to provide real-time

response, which is in general hard to achieve: i.e.,

intelligence demands high computational power and

therefore large size elements with high power

consumption.

In order to face those requirements, in Inhabited

Intelligent Environments (also called Ambient

Intelligence Environments), where a number of

sensors and intelligent elements have to be deployed

throughout the environment without the user being

aware of its presence, Intelligent Agents are

proposed (Jang, 1997). Intelligent Agents are

autonomous units of intelligence whose actions are

driven by a goal; they are able to take decisions

based on their internal state and information

collected from the environment. In this sense, soft

computing approaches (Doctor, 2005), mainly fuzzy

systems and neural networks are commonly used.

In this paper we propose an electronic system to

control ambient parameters in an intelligent

inhabited environment. The system is based on a

WSAN, which receives/sends data from/to the

environment and on a FPGA which addresses the

Intelligent Agent. The Intelligent Agent

implemented on the FPGA is, in turn, based on

several Neurofuzzy systems whose ability to learn

and model the dynamics of smart environments has

been demonstrated by the authors in recent works

(Del Campo, 2012).

331

Echanobe J., Asua E. and del Campo I..

Integration of a Wireless Sensor-actuator Network and an FPGA for Intelligent Inhabited Environments.

DOI: 10.5220/0004680703310336

In Proceedings of the 3rd International Conference on Sensor Networks (SENSORNETS-2014), pages 331-336

ISBN: 978-989-758-001-7

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

As will be explained throughout this article, the

proposed system meets the requirements above

mentioned related to the trade-off between high

performance on the one hand and low power, low

cost and small size on the other. In addition, it has

some other important advantages like flexibility and

scalability.

To well illustrate the proposed system, a

particular implementation has been carried out. This

implementation addresses the control of the intensity

of a floor lamp and the opening of a window in an

intelligent environment, taking into account the

values of temperature, humidity and luminosity

inside the room and also the value of outside

temperature.

The rest of the article is organised as follows:

Section 2 describes the Neurofuzzy algorithms that

are used in the system as Intelligent Agents. Section

3 presents the global architecture of the proposed

system. In Section 4 an implementation example is

presented. We explain all the particular details

related to the hardware and software subsystems.

Finally, Section 5 presents the main conclusions of

the work.

2 NEURO-FUZZY SYSTEMS FOR

MODELLING INHABITED

ENVIRONMENTS

NeuroFuzzy Systems (NFS) are intelligent

algorithms that combine the learning capabilities of

Neural Networks and the interpretability of the

knowledge of Fuzzy Systems. NFS have been used

in different fields because of their modelling abilities

when dealing with real-live scenarios. In particular,

the authors have proved (Del Campo, 2012) how

NFS can act as suitable Intelligent Agents to control

ambient parameters in inhabited environments. In

particular, the authors proposed a PWM-ANFIS

system which is a low computational-cost version of

the well known ANFIS system (Jang, 1993).

Basically, ANFIS is a Fuzzy Inference System

that can be represented as a Neural Network and

hence, it can be endowed with typical Neural

Networks training algorithms. By applying some

particular restrictions on the membership functions,

a computationally-efficient algorithm is obtained

with little loss of modelling performance as was

shown by the authors in works (Echanobe, 2008).

These restrictions are: i) the membership functions

are overlapped by pairs, ii) they are triangular

shaped, and iii) they are normalized in each input

dimension. As a result of this, the output of the

system has piece-wise multilinear (PWM) behaviour

and hence the authors have referred to it as PWM-

ANFIS.

The PWM-ANFIS system is able to learn from a

set of sample data, and, once it has learned, given

other real data, it will respond according to the

learning. In order to provide real-time response of

PWM-ANFIS algorithms, the authors proposed in

previous works an FPGA-based efficient hardware

architecture (Echanobe, 2008). This implementation

was carried out taking into account the great amount

of parallel resources available in FPGAs. This

PWM-ANFIS efficient hardware architecture is used

in this work.

3 SYSTEM ARCHITECTURE

The proposed complete system is based on the

architecture depicted in figure 1. It comprises two

main parts: The Wireless Network and the FPGA-

based Intelligent Agent. Using the sensors included

in the Wireless Network, several environmental data

are measured. Those measurements are sent by the

WiFi module to the FPGA, where the signals to

drive the actuators (also presented in the Wireless

Network) are calculated. This calculus is performed

by a PWM-ANFIS algorithm, which has been

previously trained to simulate a user behaviour

facing those conditions. So, the complete system

will act as the user would do it.

Figure 1: Architecture of the proposed system.

In the following paragraphs we analyze the

architecture in detail.

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3.1 WaspMote: A Sensor and Actuator

Wireless Network

The wireless network is based on the Waspmote

Platform (Libelium Comunicaciones Distribuídas,

2013). This is a modular and flexible hardware

platform to easily implement and deploy a WSAN.

The main element of this platform is the Waspmote

board which is based on the ATmega 1281

microcontroller. The board contains several

connectors or expansion ports that permits to attach

(in an stacked way) a sensor board which integrates

sensors or actuators and a WiFi communication

module (called XBee) to handle the wireless

communication. Figure 2 shows these three elements

assembled. We will refer to this 3-part body as

Waspmote-Sensor module. Up to eight different

wireless protocols are supported by the WiFi

module: 802.15.4, 802.15.4-Pro, ZigBee, ZigBee-

Pro, 868 SMA, 900 SMA, XSC SMA and Bluetooth.

The signal range is up to 12 Km. depending on the

protocols and the antennas used. For an inhabited

environment (as it is our case) it is enough a 50-

meter range and accordingly the 802.15.4 protocol is

used, which is the one that less power requires.

Figure 2: Waspmote-Sensor module. Three components

are stacked: Waspmote board, XBee wireless module and

sensor board. The blue element is a battery.

Waspmote hardware architecture has been

specially designed to achieve extremely low

consumption. Digital switches allow to turn on and

off any of the sensor interfaces as well as the radio

modules whenever they are not used. Also sleeping

modes are available. For autonomous operation the

board can be powered by rechargeable batteries (like

in picture 2) or by solar panels, both provided by the

company.

The sensor boards to be attached are designed

depending on the area of operation: Agriculture

board, general Events, Gases, Smart Metering, etc.,

and each board supports various sensors, such as

temperature, luminosity, weight, bending, movement

by infrareds, position based on hall effect, water

presence, etc. The ATmega microcontroller governs

the operation of the board -in an autonomous way-

by executing a program which is developed by the

user.

3.2 FPGA System

The second part of the proposed system addresses

the Intelligent Agent. It is based on i) a FPGA in

which the Neurofuzzy algorithms are implemented,

ii) a JTAG port to program and debug the FPGA,

and iii) a Serial Port (RS232) to communicate with

the Wireless Network. Let us see in detail these

three parts.

The FPGA (figure 3) is configured with a

HW/SW architecture. It consists of a microprocessor

(hardcore or soft-core), a RAM block to store the

program to be executed by the microprocessor, one

or several PWM-ANFIS hardware blocks and a

RS232 peripheral controller. The number of FPGA

resources required by this HW design depends

basically on the amount of Neurofuzzy blocks to be

implemented. For a small number (about 2 or 3) a

low cost FPGA is enough.

The global operation of the FPGA system is as

follows: the microprocessor reads periodically the

RS232 port to collect data from the sensor network.

These data are sent to the Neurofuzzy hardware

blocks which calculate the various outputs. These

outputs are then sent back to the microprocessor

which in turn sends them to the actuators via RS232.

4 IMPLEMENTATION AND

DISCUSSION

In order to test and verify the complete system we

have implemented an Ambient Intelligent example.

It consists of a system that controls the intensity of a

floor lamp and the opening of a window in an

inhabited environment, (particularly a room). The

system takes as inputs the internal temperature,

luminosity and humidity of the room and external

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Figure 3: Scheme of the HW implemented on the FPGA.

temperature (all provided by the WSAN), and

calculates -via the intelligent agent- the intensity

level of the floor lamp and the position of the

window: i.e. three positions: closed, semi-opened

and full opened. For this purpouse, 2 Waspmote-

Sensor modules are respectively placed inside and

outside the room. Another Waspmote board to

address the actuators (the floor lamp and the

window) is also used.

4.1 Sensors and Actuators

One of the Waspmote-Sensor modules contains the

sensors of temperature (MPC9700A termistor),

humidity (808H5V5 capacitive-based sensor) and

the luminosity (PDV-P9203 LDR). This module is

placed inside the room. The other one, placed

outside, contains an external temperature sensor

(MPC9700A termistor).

The actuators are driving by another Waspmote

board. In this case, no sensor board is attached;

instead, we use the set of digital outputs that

contains the board. Some of these digital outputs are

connected to a D/A converter to provide an analog

signal to the floor lamp regulator. In particular, the

converter used is the DAC8228 from Analog

Devices. Two other digital outputs are used to

control the three window states: closed, semi-opened

and full opened.

4.2 PWM-ANFIS Modelling

The system contains two PWM-ANFIS cores, one

for the light-level response and another one for the

window opening. The first one depends on the four

input variables above mentioned, while in the

second one the luminosity is not taken into account.

The intelligent agent is built up from linguistic

information provided by a user, in the form of fuzzy

IF-THEN rules. Hence, the user can express its

habits and preferences in a very flexible (i.e. without

restrictions) and natural way. Some of the rules

formulated are for example the following:

IF tmp_int IS high AND tmp_ext IS normal

AND humidity IS high THEN window is full-opened

IF tmp_int IS very high AND tmp_ext IS very

high AND luminosity IS high THEN light-level is

very low.

As a example, Figure 4 shows the surface generated

by the fuzzy rules for the window positioning output

with respect to the internal and external temperature.

Figure 4: Window position output provided by the user-

defined rule system.

However, a fuzzy rule inference system without

restrictions implies a very complex hardware

implementations which require large FPGA devices

and make more difficult to provide real-time

responses. To overcome this drawback we have used

the PWM-ANFIS system previously explained. This

system is trained from a set of samples provided by

the above fuzzy rules system. Figure 5 shows, as

example, the training error curve obtained during the

learning process. In particular, the mean squared

error (MSE) obtained between the system output and

the desired output vs. the number of iterations of the

algorithm is shown. As we can see, the values of the

obtained errors are very small. So the PWM-ANFIS

modellization is apropriate for this Ambient

Intelligent Scenario.

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Figure 5: MSE training curve for the Floor Lamp model.

4.3 FPGA

The FPGA used is the Virtex 5 -xc5vsx50t- from

Xilinx (Virtex 5, 2009). It features more than 8000

logic blocks, 4-Mbits RAM blocks and 288

Multipliers among other resources. Despite of its

many resources, it is a medium size and medium

cost device.

Inside this FPGA we have implemented the

HW/SW architecture described in section 2: a

Microblaze (soft-core from Xilinx), a RS232

controller to which a XBee is attached, a 64 Kbytes

RAM module and the two PWM-ANFIS cores. To

develop the system we have used the design tool

ISE-Suite from Xilinx. After implementation the

tool reports the following resource utilization: 40%

of the multipliers, 12% of the RAM memory, 9% of

logic elements and 7% of registers (i.e. Flip-Flops).

As we can see, less than the half of the FPGA is

used and therefore, more PWM-ANFIS could be

implemented if we want to control more

environmental parameters or devices. The frequency

operation of this hardware implementation is 100

MHz.

The software counterpart is the program stored in

the RAM module and executed by the Microblaze.

This program occupies only a few Kbytes because

the main operation of the Intelligent Agent is carried

out by the PWM-ANFIS hardware modules. It only

has to coordinate the data transfer between serial

port and the hardware modules according to the

wireless network protocol specifications. Also, it

performs some changes in the data format to adapt

these to the PWM-ANFIS algorithm.

5 CONCLUSIONS

In this paper, an embedded system for Intelligent

Inhabited Environments, based on a WSAN and on

an FPGA is presented. The WSAN has been carried

out by means of the Waspmote platform, which is a

modular, scalable and low power hardware platform

that allows deploying easily a wireless sensor

network.

In the system presented here, the WSAN takes

measures from environmental parameters like

temperature, humidity or luminosity and sends them

to the FPGA, which is integrated in the wireless

network thanks to a wireless communication module

attached to it. The FPGA addresses an Intelligent

Agent that processes the data of the sensors and

provides a real-time response that acts over the

environment via different actuators as an user would

do it. The intelligent agent is based on ANFIS-type

neurofuzzy systems for which a very efficient

hardware implementation has been designed.

As an example of the proposal, a system that

controls the intensity of a floor lamp and the opening

of a window in an inhabited environment has been

implemented. Aspects of this implementation are

described in detail: hardware elements, sensors used,

FPGA used, resources needed, execution time. This

example shows the feasibility and the simplicity of

the proposal and demonstrates that by integrating the

low power WSAN and the FPGA-based Intelligent

Agent, a small, low power, low cost high-

performance intelligent environment system is

achieved.

ACKNOWLEDGEMENTS

This work was supported in part by the Spanish

Ministry of Science and Innovation and European

FEDER funds under Grant TEC2010-15388 and by

the Basque Country Government under Grants

IT733-13, S-PC11UN012, and S-PC12UN016.

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