Modular design of a novel wireless sensor node for

Modular design of a novel wireless sensor node for smart environments Massimo Grisostomi, Lucio Ciabattoni, Mariorosario Prist, Luca Romeo, Gianluca Ippoliti, Sauro Longhi Dipartimento di Ingegneria dell’Informazione, Universit`a Politecnica delle Marche Via Brecce Bianche 12, 60131 Ancona, Italy Email: {m.grisostomi, l.ciabattoni, m.prist, l.romeo, g.ippoliti, s.longhi}@univpm.it

Abstract—Most of the existing commercial node architectures provide little flexibility and configurability. This limitation constrains the usability of the same node across various applications, including the ambient intelligence issue. In this paper a novel architecture for the design of a modular wireless sensor node is proposed, dividing the connection and sensing functions in two separate boards. The division of the wireless transducer interface module (WTIM) in two independent boards allows to perform in a separate way the connection and sensor interfacing function of the WTIM always respecting IEEE 1451 standards. The versatility of the novel architecture has been tested in two different application scenarios. In the first application the modular node has been used in a factory to monitor the efficiency and reliability of the production line. The designed node has been experimentally tested and results shown. Concerning the second application, a smart home approach is proposed. Using different sensing boards, an architecture to monitor in a non-invasive way several home parameters has been presented. Index Terms—Wireless Sensors Network, Modular Wireless Node, IEEE1451 Standard, Ambient Intelligence.

I. N OTES The following version of the paper has been personally revised by the authors. For further information (e.g. details on the hardware designed) do not hesitate to send an email. Sincerely Lucio Ciabattoni II. I NTRODUCTION The topic of smart environments, also called ambient intelligence, is nowadays one of the hottest topics in media and research centers. According to [1], smart means having the capability to autonomously collect and apply knowledge while environment is made up of our surroundings. In an engineering perspective the so called ambient intelligence can be obtained embedding sensors and actuators in an environment to automatically react to users, devices and machines [2]–[4]. The recent interest in this topic can be attributed to several factors. 1) The growing availability on the market of small and inexpensive sensors and devices easily embeddable. 2) The worldwide diffusion of networking technologies, such as Wi-Fi, Ethernet and Bluetooth that makes easier the communication between devices. 3) The presence of small computing devices (such as smartphones, tablets and netbooks) in almost every dwelling. The first step to achieve the ambient intelligence is the installation in any environment of several devices to detect its

state and provide information to automated control systems or human supervisors. In many cases the adoption of standard wired solutions to supply power and acquire sensors data could suffer of various problems thus making harder the realization of a sensor network (e.g. the need of expensive and often complex installation and the lack of flexibility in placing sensors). In this scenario a wireless solution adopted both for data transfer and power supply could clearly be a solution but, on the same time, still poses many challenges. Wireless Sensors Networks (WSN) technology has been widely studied in many universities and research centers in recent years [5]– [7]. WSN are composed by sensor nodes that autonomously operate gathering sensors information and combining both communication and computation capabilities in a small form factor. These nodes, establishing a wireless link, collaborate with each other to execute application tasks. The main obstacles to the spread diffusion of this technology are mainly represented by communication issues (in terms of reliability and latency), power supply issues (nodes battery powered need the lowest power consumption possible) and flexibility [8], [9]. While on one hand this technology offer to users the dream of a high flexibility level sensor network, in the practice there are various constraints that move the dream far away from reality. Most of the existing commercial node architectures indeed provide little flexibility, configurability and the absence of interoperability between them. Daughter boards provide sensing capabilities but the processing and communication modules are fixed and cannot be often extended. These limitations constrains the cross-usability of the same node in different applications and the use of different branded nodes in the same application. Due to these problems the automation of a building can be extremely expensive through the installation of a WSN. A set of solutions for design variation and miniaturization in each of the functional circuit blocks [10]–[12] has been proposed in literature together with some initiatives to address the interoperability problem in the home automation such as UPnP (Universal Plug &Play) [13] or Jini [14]. In this paper we face the flexibility and customization problem presenting a novel architecture for the design of a modular wireless sensor node dividing the connection and sensing functions in two separate boards. The new architecture, providing a higher level of customization for the whole WSN, makes possible the implementation of different features using the same communication structure following the IEEE 1451

standard. Two applications of the node with different plug and play sensing modules have been proposed. The considered applications concern cost effective solutions for the monitoring of a line production system and a smart house. The paper is organized as follows. The proposed innovative design of the node and introduction to the IEEE standards are discussed in Section II, the hardware chosen for the prototype is reported in Section III. The proposed applications are described in Section IV where the modular node has been used in a factory to monitor the efficiency and reliability of the production line and, using different sensing boards, to monitor in a noninvasive way home parameters. III. W IRELESS S ENSOR N ODE D ESIGN Nowadays in all the hottest markets the compatibility between systems plays a crucial role for their success and for the development of every involved technologies. In this context the standardization for wireless nodes has a great impact on WSN market success. The standardization helps to decrease the cost of the system deployment and industrialization reducing on the same time the cycle of development. Among the existing and emerging standards for WSN used for factory automation, IEEE 1451 has been used for the design of proposed WSN. Furthermore, the developed prototype system, named ArgosD, facilitates the flexible connection of different sensing devices as shown in section IV.

Fig. 1: New network architecture and IEEE 1451 standard division.

A. IEEE 1451 Standards

B. Modularity

IEEE 1451 is family of standards introduced to add plug and play capabilities to smart transducers. It has been developed by the Institute of Electrical and Electronics Engineers (IEEE) Instrumentation and Measurement Society’s Sensor Technology Technical Committee. As transducers, used for industrial control and process monitoring, are a crucial part of the WSN technology a coherent and open standard for these sensor interfaces is the key for the market successes [15]. The integration, interoperability and scalability with the existing wired system are the main aims of the standard [16], [17]. Different versions of the standard have been proposed since 1997: • In 1997 IEEE 1451.2 [18] specified the key definition of data formats and communication protocols for Transducer Electronic Data Sheet (TEDS). • In 1999 IEEE 1451.1 [19] settled a smart transducer object model in frame of network-capable application processors (NCAPs) to support multiple control networks. • In 2003 IEEE 1451.3 [20] offered technical solutions for interfacing multiple and physically separated transducers extending the point-to-point configuration to distributed multidrop systems. • The IEEE 1451.4 in 2004 [21] introduced the mixedmode interface (MMI) to connect transducer modules in a plug and play mode to instruments, computers and NCAPs. • IEEE 1451.5 [22] in 2007 defined the wireless communication and TEDS formats and specified sensor-to-

As defined in IEEE standard 1451.5 [22] a wireless transducer interface module (WTIM) is a device connected to transducers and, via Dot5AR protocol, to the NCAPs. A WTIM differs from the standard TIM, as defined in IEEE Std 1451.0 − 2007, only for the wireless communication to the NCAP and provides two different functions. On one side it allows the connection with the NCAP node while on the other makes possible the sensors interfacing. The main design novelty presented in this paper is the division of the WTIM in two independent boards to perform in a separate way the connection and sensor interfacing function of the WTIM always respecting IEEE 1451 standards, as shown in Fig. 1. The connection board (represented in Fig. 2) performs only actions involved in the wireless connection process with the NCAP node: it maintains in memory only the wireless related PHY TEDS and communication module commands. For what regards transducers related commands it acts as a gateway for the sensor board. The sensor board (a sample of a comfort sensing board is represented in Fig. 3) has another micro controller to perform the remaining functions: transducers interfacing, signal acquisition and conditioning. In this board TEDS are stored and all the information coming from the network (through the communication board) processed. Since the IEEE 1451 standards do not provide a specific hardware communication protocol between the two boards, it has been adopted a UART protocol with a 3.3 V line.

• •

NCAP connection for Wi-Fi, ZigBee, ultra wide band and Bluetooth. The IEEE 1451.6 has been proposed as a draft to interface the TEDS using the high-speed CANopen network [23]. The last member of the family, the IEEE 1451.7 was introduced in 2010 [24] to facilitate the communication between smart RFID systems and integral transducers.

TABLE I: Radio transmitter CC2520 key features. Data Type

Value

Adjacent channel rejection

49 dB

Alternate channel rejection

54 dB

Temperature range

-40 to +125◦ C

Supply range

1.8 V - 3.8 V

RX (receiving frame, -50 dBm) consumption

18.5 mA

TX (@ 0 dBm) consumption

28.5 mA

Power Down consumption

< 1 µA

Fig. 2: Connection board architecture. V. M ODULAR N ODE A PPLICATION

Fig. 3: Comfort sensor board architecture.

IV. WSN H ARDWARE 1) Core Micro MSP430F1611: The core micro chosen for the sensor node is the ultra low power micro controller of the Texas Instruments M SP 430 family [25]. The architecture is combined with five low power modes and optimized to achieve extended battery life in portable measurement applications. 2) Core Radio CC 2520: The CC2520 is a ZigBee (IEEE 802.15.4) transceiver for the 2.4 GHz unlicensed band. This chip enables industrial grade applications by offering several features as reported in table I.

Fig. 4: Sensor board (on the left) and connection node (on the right) real dimensions compared with a 2 ecoin.

The present part of the work aims to provide practical solutions to the ambient intelligence problem using different sensor boards connected to the proposed modular node. The considered environments have been divided into two main categories: industrial and domestic ones. For what concerns the industrial scenario the results of a real WSN installed in a factory will be reported, while in the domestic one a cost effective WSN architecture will be proposed for energy and comfort monitoring. In both proposed scenarios, modular nodes (shown in Figure 4) and a custom programmer are used to create Low Power Area Networks (LowPans), as described in [22], adopting a Contiki operative system. In these project 2 main hardware categories are considered: 1) wireless sensor nodes: acquire analog and digital inputs (depending on the application) and send measured values to the edge router every second using the 6LowP an standard 2) wireless edge router: opens the virtual channel to send data from WSN to the server, provide the communication between LowPans and Internet, implementing all the required features A. Industrial Ambient Intelligence Application The focus of the first part of the section is the integration of wireless sensor network (WSN) technologies in industrial applications as described in [26]. In particular we considered a packing factory for the analysis and experimental tests, where raw materials and semi-finished products (SF) are refined through sequential operations to produce the final product (a packaging box). The sensing board for the node was composed by two types of digital sensors. The first one is the roller limit switch, a contact sensor with two boolean states used to monitor the setup time of each section of the line production. The other sensor is a photoelectric one with an infrared light transmitter and a photoelectric receiver, mainly used as an items counter. The developed architecture, shown in Fig. 5 is tested in a typical industrial scenario, where WSNs have to be configured to provide remote monitoring service for the line production status. We analyzed the overall equipment effectiveness (OEE) and the total effective equipment productivity (TEEP) of the cell, as defined in [27]–[29]. These indexes represent a measure of the value added to production through equipment, which

Fig. 5: WSN and communication architecture designed for the line production monitoring . TABLE II: June-July 2013. Effectiveness indexes computed for the four different product lines. Fig. 6: Nodes placement in a typical house. Data Type

Line A

Line B

Line C

Line D

TEEP

74, 2%

43, 1%

69, 9%

65, 1%

OEE

69, 0%

28, 3%

65, 7%

58, 4%

is a function of machine availability, performance efficiency and the rate of quality. Results of this application have been reported in [26], where historical and real time performance indexes have been computed. Tables II and III show a sample of the efficiency and reliability indexes computed for different product lines and machines. In particular the analysis results showed a dramatic loss of efficiency for the higher customization level products (Line B). B. Smart House Architecture Lot of research focuses on non-invasive wireless sensors used in existing home environment to transform it into a smart home. In this work we propose an application of the presented modular wireless sensor node to create a smart environment. By placing sensor nodes everywhere in the house, the temperature, brightness, noise level, humidity as well as power absorbed by appliances can be collected and analyzed. Two main sensor boards for our modular node ArgosD have been designed and realized to be integrated in a house, as Fig. 6 shows. 1) Comfort Sensing Board: The predicted mean vote, one of the most important indexes for the estimation of the indoor comfort [30], [31], is determined by several parameters. In TABLE III: June-July 2013. Reliability indexes computed for the single machines of the manufacturing cell. Machines

MTTR (min)

MTBF (min)

Loader

15.3

436.0

Printer

8.6

541.3

Die Cutter

8.6

501.0

Sticker

9.3

287.1

Bender

9.4

313.9

Labeler

6.9

162.4

Unloader

6.3

53.2

TABLE IV: Temperature/Humidity sensor HIH6130 features. Characteristic

Humidity

Temperature

Resolution

0.04 %

0.025 C

◦

◦

Accuracy

4%

1 C

Response Time

6-8s

5 - 30 s

Operating Range

0 - 100 %

−25 - 85 C

◦

the design of our comfort board, as shown in Figs. 3 and 4, we had to consider all the parameters necessary for the PMV computation: the indoor air temperature, air humidity, air flow rate, noise and brightness. The sensors we used are: • Humidity and Temperature Sensor model HIH6130: the main features of this sensor are temperature compensation, a digital I 2 C or SPI output, energy efficiency (required for a WSN application) and, obviously, small package. The voltage supply can operate down to 2.3 Vdc, allowing its use without compromising battery life. Another built in feature of the sensor is its capability to go into the so called deep sleep mode when not taking a measurement within the application, consuming only 1 µA of power versus 650 µA in full operation. The sleep mode helps to maximize battery life thus reducing is power supply size and the application’s overall consumptions. • Acceleration sensor model M M A7455L: a digital output (I 2 C and SPI) capacitive accelerometer. The main features are: built in signal conditioning with a low pass filter, temperature compensation, self-test, capability to detect 0g. The power consumption, one of the most important features of the sensor, is 400 µA during the operation mode and 10 µA in standby mode. • Brightness sensor BH1751F V I: a digital ambient light sensor for I 2 C bus interface with a supply current of 120 µA and a standby current of 0.85 µA • Noise sensor CM C − 5042P F − AC: is an omnidirectional noise sensor with a sensitivity of −42 dB. 2) Energy Sensing Board: The active power absorbed by some appliances and the overall household consumption can be measured with a microchip M CP 3905A, providing three

measures: • • •

the instant active power measured with a frequency of 20 − 80 Hz the energy counter with a frequency < 1 Hz the voltage - current phase VI. C ONCLUSION

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