A Functional Taxonomy of Wireless Sensor Network Devices - CiteSeerX

A Functional Taxonomy of Wireless Sensor Network Devices Sivaram Cheekiralla

Daniel W. Engels

Auto ID Laboratory Massachusetts Institute of Technology Cambridge, Massachusetts 02139 Email: [email protected]

Healthcare Research Initiative Massachusetts Institute of Technology Cambridge, Massachusetts 02139 Email: [email protected]

Abstract— With the availability of cheap computing technology and the standardization of different wireless communication paradigms, wireless sensor networks (WSNs) are becoming a reality. This is visible in the increasing amount of research being done in the WSN area, and the growing number of companies offering commercial WSN solutions. In this regard, we realize that there is a need for an unambiguous classification and definition of WSN devices. Such a classification scheme would benefit the WSN research and industrial community. We consider the spectrum of wireless devices ranging from passive RF devices to gateway devices for the classification. The classification scheme is based on functionality of the devices. This scheme is analogous to an object-oriented classification, where an object is classified on the basis of its functionality and is described by its attributes. Attributes of the wireless devices are grouped into five broad categories: communication, sensing, power, memory, and “other features”. Each of these are further classified to provide sufficient details that are required for a typical WSN application.

I. I NTRODUCTION A wireless sensor network (WSN) is a network of wireless sensing devices. A WSN is a distributed system with a tiered architecture. WSN devices are typically low-powered devices having sensing and communication capabilities. The application determines what sensors are used. Communication is usually RF-based and in some cases free-space optical (FSO) communication is used [1]. WSN devices collaborate to form a wireless ad hoc network that is used for communicating with other wireless sensing devices or with a gateway device. These devices either run on batteries or use energyharvesting schemes. As these devices are expected to operate with minimum or no human intervention; to conserve power and hence prolong their lifetime, these usually operate in low duty cycles [2]. With the recent technological developments, low-power and cheap computing power is available more readily. The breakthrough with micro electro-mechanical systems (MEMS) technology has made it possible to have cheap, compact and low-powered sensing devices. The development of standards for wireless personal area networks (WPAN), wireless industrial automation and control has increased the usage of wireless devices. All these factors have spurred research in different aspects of WSNs and an increasing use of WSNs for different applications. Common WSN applications include

civil infrastructure monitoring, wild-life habitat monitoring, and environmental monitoring. The term WSN is often assumed to consist of homogeneous devices akin to motes. New broad-band technologies like ultra wide band (UWB) [3] are being used for WSN applications. Radio frequency identification (RFID) technology, widely adopted in the supply chain management, is also being used for WSN applications. Apart from these diverse technologies, devices like laptops and personal digital assistants (PDAs) are commonly used in WSN applications. We expect the convergence of cellular telephony, WiMax, Wi-Fi, ZigBee and RFID towards a single network that includes low-bandwidth communication devices such as RFID, ad hoc networking devices such as ZigBee, broadband communications such as 4G cellular telephone networks. It is appropriate to name such heterogeneous networks as wireless communication networks (WCNs) instead of WSNs. In this paper, we refer to WSN as a homogeneous network of motelike devices or when referring to any previous work; and WCN as a heterogeneous network of wireless communication devices. A WSN is a subset of a WCN and in this paper we target the WSN component of WCN. The bigger goal of the taxonomy is to extend the classification scheme to WCN devices. WCNs present new challenges in developing routing protocols, and algorithms for optimizing the lifetime of the network. In such a scenario, a classification scheme will give a better perspective of the role of different WCN devices. Our classification scheme provides a way to distinguish WCN devices based on their functionality. Once classified, these devices are characterized on the basis of their attributes. Such a classification scheme is scalable and gives a functional perspective of WCN devices. There is no comprehensive body of work that address the definition and classification of WSN devices. Hill et al [4] in their paper give a tiered view of WSN devices and classify them on a hierarchical basis. The drawback of their approach is the lack of a functional classification of WSN devices. Hellerstein et al [5] describe a few WSN devices ranging from RFID tags to remote sensing. Their approach is oriented towards database research. Tilak et al [6] describe WSN from a networking point of view, classifying microsensor networks on the basis of communication and network

parameters. Vieira et al [7] describe important features of the motes. These features are power consumption, computational and communication resources. A generic survey on WSNs is given by Akylidiz [8], where they cover the architecture, communication protocols, and algorithms for different aspects of WSNs. We realize that most of the work done on classifying WSN devices is either based on hardware or from a networking point of view. The network is also assumed to be homogeneous in nature. Functionality is not addressed in detail in any of the previous work and we like to address this aspect in our paper. Th goal of this paper is to classify the spectrum of WCN devices on the basis of their functionality. We include a broad range of devices for our classification scheme. A commonly used communication model for WSNs is in which the deployed devices send their data to a base-station. This model is applicable to RFID devices with the RFID reader playing the role of the base-station. Hence, we include RFID tags in the spectrum of devices. As far as we know, there isn’t a single body of work combining RFID tags and WSNs. The communication model also allows us to include devices like cell phones as a part of the spectrum of WCN devices for the classification. We also intend to stimulate discussion on unambiguous definitions of WCN devices as this increasingly complex field moves forward. For our classification scheme, we target devices that use RF-based digital communications and devices that could potentially be used in WCN applications. We do not address physical mobility issues of devices as encountered in robotics in this paper. Physical mobility is not an attribute of a WCN device taxonomy, while communication mobility is. However, we envision that in the near future a robotic cell phone like device would come into existence. Such a device would be small enough to use and is mobile from physical as well as communication point of view. We mention exceptions to our classification scheme whenever and wherever necessary. This scheme is an attempt to look at the spectrum of WCN devices from a functionality point of view. We believe that such an organized view of these devices will give a better perspective and help researchers to develop more appropriate algorithms/protocols. In Section II, we describe the functionality based classification of WCN devices. In Section III, we state the different attributes and use these attributes to characterize WCN devices. In Section IV, we summarize the paper by giving a few example representations of these devices using our taxonomic approach. Finally we conclude with some comments about our classification scheme. II. F UNCTIONAL C LASSIFICATION In this section, we describe the functionality-based classification of WCN devices. We classify devices into the following (Fig. 1): 1) Passive RF devices 2) Active RF devices 3) Ad hoc networking devices 4) Gateway devices

WCN devices

passive RF devices

Fig. 1.

active RF devices

ad−hoc networking devices

gateway devices

Tree view of the functional classification

The above order of listing the categories has an inherent hierarchy with passive RF devices having the least communication and computing capability and gateway devices having the most. Functionality is defined as “performing or able to perform a regular function”[http://www.m-w.com]. In the context of WCNs, functionality is defined in terms of communication, computing capabilities and power requirements. We realize that these factors are relevant for describing functionality of WCN devices. Passive RF devices Passive RF devices have a passive communication module and a minimal amount of memory. They operate only in the presence of a reader. A reader emits electromagnetic energy and reads the reflected energy from the passive RF devices to communicate with them. Since these devices operate only in the presence of a reader, they cannot communicate with other passive RF devices. A specific example of this type are passive RFID tags. Engels and Sarma [9] define a classification scheme for RFID tags that uses an encapsulation scheme for classifying different kinds of tags based on their functionality. The minimum functionality of a passive RF device is to respond back with data when queried by a reader. Active RF devices Active RF devices have an active RF transmitter and hence they have an on-device power source. They may also have memory to store data. Thus, the minimum functionality of an active RF device is the ability to communicate actively. Specific examples of this type are active RFID tags and radio ICs. Ad hoc networking devices The minimum functionality of ad hoc networking devices is their ability to communicate in an ad hoc manner. They usually have: • A controlling unit or intelligence unit that controls the communication and sensor modules. • A dedicated communication unit usually in the form of radio ICs. • A power source in the form of a battery and in some cases additionally energy harvesting schemes are used. • One or more sensors depending on the application. A specific example of ad hoc networking devices are the motes. “Motes are tiny, self-contained, battery-powered computers with radio links, which enable them to communicate and exchange data with one another, and to self-organize into ad hoc networks. Motes form the building blocks of wireless sensor networks.” [10].

TABLE I T YPICAL DATA RATES AND FREQUENCY OF OPERATION FOR DIFFERENT STANDARDS [11]–[16]

WCN device

Communication

Power

Fig. 2.

Memory

Sensors

Other Features

Communication

beacon nodes

ad−hoc network

Device talk first

proprietary /others

ISO

Mobility

human controlled devices

communication modules

Modes

EPC

UWB

802.11

ZigBee

Bluetooth

Standards

data rate

Standard

Typical Data rate

Bluetooth 2.0 ZigBee

2.1 Mb/s 250/40/20 kb/s

UWB IEEE 802.11a IEEE 802.11b IEEE 802.11g RFM TR 1000 (proprietary) Chipcon CC 1000 (proprietary)

up to 1 Gb/s 54 Mb/s 11 Mb/s 54 Mb/s 19.2 kb/s 100 kb/s

Tree view of attributes

Frequency of Operation 2.45 GHz 2.4 GHz or 915 MHz or 868 MHz 3.1-10.6 GHz 5 GHz 2.4 GHz 2.4 GHz 916.5 MHz 433 MHz

frequency Tag

Fig. 3.

Reader

Tree view of communication attribute

Gateway devices Gateway devices either collect data from a cluster of WCN devices or connect a cluster of WCN devices to another cluster of WCN devices. A specific example of a gateway device is a laptop which collects data from motes. The basic categories of WCN devices have been defined on the basis of their functionality. These are characterized further on the basis of attributes, which are described in the next section. III. ATTRIBUTES OF WCN DEVICES In this section, we describe the attributes for characterizing WCN devices. The attributes (Fig. 2) are classified into the following groups: 1) Communication 2) Power 3) Memory 4) Sensors 5) Other features Each of these attributes is broken down and the subattributes are broken down further depending on the level of detail that is required for a typical WCN application. For some sub-attributes, a representative value is given. A. Communication The communication attribute is broken down into the following sub-attributes (Fig. 3): • Communication protocols/standards • Communication modes • Communication modules • Mobility 1) Standards: Standards are important for characterizing WCN devices as they specify the maximum data rate, the frequency of operation, and a portion of the communication stack. We realize that it is not possible to discuss all the standards available for wireless devices. We classify the standards

as shown in Fig. 3. It should be noted that some standards are classified as proprietary/others. This is true specifically for radio ICs, where many details of the physical layer are hidden from the user. Table I summarizes the maximum available data rates and the frequency of operation for different standards. In this regard, some important standards are briefly discussed here. 1) Bluetooth is an industrial consortium formed to develop a standard for wireless personal area networks (WPAN). A WPAN is a communication network of personal devices like computers, printers, cell phones, etc. Bluetooth is a spread spectrum technology operating in the 2.45 GHz Industrial, Scientific and Medical (ISM) band. The latest version (version 2.0) of this standard specifies the maximum data rate as 2.1 Mb/s. Bluetooth enabled devices typically have a communication range in the order of 10 m [15]. Leopold et al [17] have done a study about using a Bluetooth radio for WSN applications. Their results show that Bluetooth is not a good option for scatternet kind of networks, and maintaining networks even in low duty cycles is power expensive. A scatternet is a set of piconets connected through sharing devices and a piconet is an ad hoc network of Bluetooth devices. One of the limitations of using Bluetooth radio for WSN applications is that applications cannot access low level relevant information, which is needed for synchronization and device discovery. 2) ZigBee is an industrial consortium that has developed a new standard for low-cost, low-powered wireless monitoring and control systems [13]. ZigBee uses direct spread spectrum technology with a specified maximum data rate of 250 kb/s. The networking and application layer of ZigBee are implemented over the IEEE 802.15.4 standard for WPAN [18]. Potential applications of the ZigBee compatible devices include industrial control and automation, home automation, etc. 3) IEEE 802.11 is the standard for wireless local area networks (WLAN). The three important versions of this standard are 802.11a, 801.11b and 802.11g [16]. The 802.11b version is commonly used for wireless routers.

B. Power WCN devices need power to sense, compute and communicate. The attribute “Power” is further classified into three categories: “Storage”, “Energy harvesting mechanisms” and

TABLE II E NERGY CONSUMPTION PER BIT FOR VARIOUS STANDARDS [3] Standard Bluetooth 2.0 ZigBee UWB RFM TR 1000

Energy consumption per bit 2.5 µJ 0.5 µJ 0.01 µJ 2.5 µJ

Power

Fig. 4.

backscatter

capacitive coupling

Transfer

inductive coupling

Capacitor

thermal gradient

Batteries

vibrational

Energy Harvesting Mechanisms

Storage

photo−voltaics

4) UWB technology refers to a modulation technique based on transmitting in very short pulses [14]. Work done by Oppermann et al [3] show significant promise for lowpower, low-cost wide-deployment of sensor networks. The signal being noise like in nature, is resistant to multipath effects and jamming. Sensor networks based on UWB radios provide better localization and better battery lives. The IEEE standard for UWB, 802.15.3.a is under development. 5) ISO standards, published by the International Organization for Standardization specify world-wide industrial and commercial standards [19]. The ISO 8000 series specifies standards for the air-interface communication parameters for different kinds of RFID tags [20]. 6) Electronic Product Code (EPC) is the other important standard for RFID tags. EPCGlobal Inc. specifies the different versions of the EPC, and protocols for the airinterface communication [21]. 7) Proprietary/other standards are those standards that do not follow in any of the above categories. This includes proprietary standards that are commonly used for radio ICs. 2) Communication modes: Different kinds of communication modes exist for RFID devices [9]. These modes are generalized to WCN devices. Some of the WCN devices may have more than one communication mode. 1) “Device talk first” is specifically relevant to RFID devices. This attribute is further classified into • “Tag talk first” devices are those that respond first in the presence of the reader’s electromagnetic energy. • “Reader talk first” devices are those that wait for an instruction from the reader. 2) “Beacon” devices are those that periodically announce their identification and other relevant data. 3) “Ad hoc” devices are those that have ad hoc communication capability. 4) “Human controlled” devices are those that are controlled by humans. 3) Communication modules: This attribute refers to the number of RF communication modules a WCN device has. In many cases, WCN devices have one communication module, but some gateway devices have more than one communication module. For example, some cell phones in addition to the main communication module have a Bluetooth communication module. 4) Mobility: Mobility is the ability to handle communication capabilities when the device is in motion. Cell phones have mobile capabilities. Mobility is difficult to characterize and in the example classifications in Section IV, we give the level of mobility achievable.

Tree view of the power attributes

“transfer”. Power consumption of WCN devices determine the lifetime of the network. Since many of these devices are expected to operate with minimum human intervention, optimizing power consumption is an important research area. Most of the work done in this area involves developing lowpower communication algorithms and protocols for routing, resource discovery, etc. The trade off is usually seen between communication and computation, and sensing is often assumed to be a lower-power consuming task than the other two. This assumption in general, is not a valid one as some applications require sensors that consume more power than communication and computation tasks. We give typical values for the power consumption of WCN devices for sensing, communication and computation tasks. These values are representative and may vary widely depending on the application. Table II shows the values for energy (energy = power * time) consumption of devices based on various standards and different radio ICs. The energy consumption in Table II refers to the energy consumed per bit, which is defined as Eb = Etx + Erx + Edec /η; where Eb is the energy consumed per bit, Etx and Erx are the transmitter and receiver power consumption per bit, respectively, Edec is the energy required for decoding a packet, and η is the payload length in bits [3]. Warneke et al [22] give typical energy values required for computation. For example, a typical instruction for microprocessors consume energy in the order of pico Joules. Table III gives the typical power consumption for different sensors. Since the power consumption of a sensor depends on its principle of operation and the underlying physics, the values here are only representative. These values also vary for devices depending on the way they are manufactured. The signal conditioning hardware needs additional power to operate.

Typical Power consumption for sensing passive i.e. 0 1.8 mW 60 mW 50 mW

C. Memory Memory is used for storing data, which is either userdefined or application-related. For WCN applications, memory is needed for storing application related data and performing computations on this data. Having more memory implies more computational resources but usually with an increase in the power consumption of the device. We classify memory into two kinds: one depending on the purpose of storage and the other on the accessibility to memory. Based on purpose of storage, memory is divided into the two categories. These are:

Typical Memory O(100 b) O(1 kB) O(100 kB) O(100 MB)

Memory

Fig. 5.

Read−Write

Accessibility

Write once

Purpose

Read−only

1) Storage: Storage refers to the way WCN devices store power for their operation. Storage is done either by using batteries or by using capacitors. Batteries are used when a longer life is required and capacitors are used in applications that require bursty powers for very short durations. Batteries are classified into different types on the basis of their operation. Vieira et al [7] compare some of the battery technologies available. Capacitors can also be classified into different types, but the operating principle of a capacitor is the same irrespective of its type. 2) Energy harvesting mechanisms: Some WCN devices have energy harvesting mechanisms by which they harvest power from the ambient environment. Some of these mechanisms are: 1) Harvesting light energy using photo-voltaic (PV) materials. Light that falls on the PV materials gets converted into electrical energy because of photo-electric effect. The pico radio project at Berkeley has actually developed a radio that uses ambient light energy for communication [24]. 2) Ambient vibrational energy can be harvested and Meninger et al [25] discuss methods to convert vibrational energy into electrical energy. 3) Energy harvesting using thermal gradients. For example, it is possible to use body heat to store energy and use this energy for powering medical sensors [26]. 3) Transfer: Energy transfer is the way by which passive RF devices are powered. The energy transfer mechanisms are inductive coupling, capacitive coupling, and passive backscattering. Inductive coupling is the transfer of energy between two circuits due to the the mutual inductance between the two circuits. Similarly, capacitive coupling is the transfer of energy between two circuits due to the mutual capacitance between the two circuits. Passive backscattering is a way of reflecting back the energy from one circuit to another [20].

Devices Passive tags Active tags Motes Gateway devices

user memory

Types of sensors Temperature Accelerometer Pressure Humidity

TABLE IV T YPICAL VALUES OF TOTAL AVAILABLE MEMORY FOR DIFFERENT CATEGORIES OF WCN DEVICES [20]

program memory

TABLE III T YPICAL POWER CONSUMPTION VALUES OF DIFFERENT SENSORS [23]

Tree view of memory

1) User memory used for storing application-related or personal data. 2) Program memory used for programming the device. This memory also contains identification data if the device has any. Table IV shows the total amount of memory present for different WCN devices. Memory is classified into three categories on the basis of its accessibility. 1) Read-only memory allows the memory to be read and is not accessible for writing. The contents of the memory are usually written during the manufacturing stage. 2) Write-once memory can be accessed only once for writing data and once written, the memory is not accessible for further writes. 3) Read and write memory allows multiple reads and writes. D. Sensors Many WCN devices consist of sensors. Commonly used sensors for WCN applications include temperature, pressure, humidity sensors, etc. In intrusion detection applications, video cameras are usually used. WCN devices like cell phones have cameras and in Section IV we give an example representation of a cell phone along with other devices using our classification scheme. E. Other features In this section, we consider two important features that are not categorized into any of the attributes. These are:

•

•

Programmability allows reconfigurability of a device. For example, Mica motes are re-programmable, which allows them to do a multitude of things. On the other hand, RFID devices only allow a few operations to be performed. These operations are rewriting the memory, killing the tag, etc. Price of the device governs the usage of the device. To have a wider deployment, the price of the devices needs to be as low as possible. The price in turn is actually governed by the market demand for the applications. In this regard, passive RFID tags have very nominal prices (typically $.10, when bought in tens of thousands). This is due to the realization of the increasing need of RFID tags in the supply chain management. On the other hand, motes and gateway devices cost on the order of $100. Mica mote ad hoc networking device Communications Standards Proprietary/Others RFM Frequency [916.5 MHz] Data rate [19.2 kb/s] Modes ad hoc Communication modules [1] Mobility some extent Power Storage Battery Ni/Li AA cells Sensors Optional accelerometer, light sensor,etc. Memory Purpose User memory [256 kB] Program memory [10 kB] Accessibility Read-Write Other features Programmable yes Price [O($100)] Fig. 6.

Taxonomic classification of the Mica mote

IV. E XAMPLE CLASSIFICATIONS A few example representations of our classification scheme are given in this section. Representative attribute values are given in [ ]. The attribute values in some cases are typical values and they may vary depending on the particular device. 1) Mica mote: The Mica mote is an ad hoc networking device. The motes run on batteries, and have an optional set of sensors (Fig. 6). 2) High frequency RFID tag: The high frequency RFID tag is a passive RF device and doesn’t have any sensors (Fig. 7). The memory is usually used for storing identification data. High frequency RFID tag passive RF device Communications Standards EPC Frequency [13.56 MHz] Data rate [10 kb/s] Modes Device talk first Tag talk first Communication modules [1] Mobility none Power Transfer inductive coupling Sensors None Memory Purpose User memory [0] Program memory [128 b] Accessibility Write-once Other features Programmable not programmable Price [$0.10] Fig. 7.

Taxonomic classification of a high frequency RFID tag

3) Cell phone: The example representation of the cell phone here has a Bluetooth module and has a camera (Fig. 8). In this particular example, the cell phone is an active RF device and in some applications, the cell phone can be a gateway device.

Cell phone active RF device Communications Standards Proprietary Frequency [900 MHz] Data rate [100 kb/s] Modes Beacon Communication modules [2] Mobility good Power Battery Sensors Camera Memory Purpose User memory [O(10 MB)] Program memory [O(1 MB)] Accessibility Read-write Other features Programmable to some extent Price [O($100)] Fig. 8.

Taxonomic classification of a cell phone

Wireless router gateway device Communications Standards 802.11 b/g Frequency [2.4 GHz] Data rate [up to 54 Mb/s] Modes beacon Communication modules [1] Mobility to some extent Power Battery Sensors None Memory Purpose User memory [O(1 MB)] Program memory [O(1 MB)] Accessibility Read-write Other features Programmable to some extent Price [O($100)] Fig. 9.

Taxonomic classification of a wireless router

4) Wireless router: The particular wireless router works on the IEEE 802.11 1.b/g standards and is compatible with each of them (Fig. 9). Since wireless routers broadcast their presence, the communication mode is given as beacon. To summarize, we have classified the spectrum of WCN devices based on their functionality and have characterized them on the basis of their attributes. The examples given previously show how one can classify and characterize a WCN device.

of WCN devices refers to a heterogeneous network of devices and WSNs are a subculture of this larger taxonomy.

V. C ONCLUSIONS

[1] J. M. Kahn, R. H. Katz, and K. S. J. Pister, “Next century challenges: Mobile networking for ”smart dust”.” in MOBICOM, 1999, pp. 271–278. [2] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, “Instrumenting the world with wireless sensor networks,” in Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP ’01). 2001 IEEE International Conference on‘, vol. 4. ACM Press, 2001, pp. 2033–2036. [3] I. Oppermann, L. Stoica, A. Rabbachin, Z. Shelby, and J. Haapola, “Uwb wireless sensor networks: Uwen- a practical example,” IEEE Communications Magazine, vol. 42, no. 12, pp. 27–32, December 2004. [4] J. Hill, M. Horton, R. Kling, and L. Krishnamurthy, “The platforms enabling wireless sensor networks.” Commun. ACM, vol. 47, no. 6, pp. 41–46, 2004.

In this paper, we have presented a novel and unique classification scheme of wireless communication devices. The classification scheme is analogous to an object-oriented classification and devices are classified on the basis of their functionality. The classification scheme presented here unambiguously defined WCN devices in terms of their functionality. The classification scheme considered a wide range of devices that exist today and that may exist in the future. The taxonomy

ACKNOWLEDGMENT The authors would like to thank the reviewers for their comments and suggestions. Sivaram Cheekiralla would like thank Hariharan Lakshmanan for his comments. R EFERENCES

[5] J. M. Hellerstein, W. Hong, and S. Madden, “The sensor spectrum: technology, trends, and requirements.” SIGMOD Record, vol. 32, no. 4, pp. 22–27, 2003. [6] S. Tilak, N. B. Abu-Ghazaleh, and W. Heinzelman, “A taxonomy of wireless micro-sensor network models,” SIGMOBILE Mob. Comput. Commun. Rev., vol. 6, no. 2, pp. 28–36, 2002. [7] M. A. M.Vieira, C. N. C. Jr, D. C. da Silva Junior, and J. M. Mata, “Survey on wireless sensor network devices,” in Emerging Technologies and Factory Automation(ETFA03). IEEE Conference, September 2003, pp. 16–19. [8] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Comput. Networks, vol. 38, no. 4, pp. 393– 422, 2002. [9] D. W. Engels, , and S. E. Sarma, “Standardization requirements within the rfid class structure framework,” January 2005. [10] Motes-definition, http://www.intel.com/research/ exploratory/motes.htm. [11] RFM products list, RF Monolithics, Inc., http://www.rfm.com/ products/spec.htm. [12] Chipcon products list, Chipcon AS, http://www.chipcon.com/ index.cfm?kat id=2. [13] ZigBee Website, ZigBee SIG, Inc., http://www.zigbee.org. [14] UWB-wikipedia, http://en.wikipedia.org/wiki/ Ultra wideband. [15] Bluetooth-wikipedia, http://en.wikipedia.org/wiki/ Bluetooth.

[16] 802.11-wikipedia, http://en.wikipedia.org/wiki/802.11. [17] M. Leopold, M. B. Dydensborg, and P. Bonnet, “Bluetooth and sensor networks: a reality check,” in SenSys ’03: Proceedings of the 1st international conference on Embedded networked sensor systems. ACM Press, 2003, pp. 103–113. [18] P. Cross, “Zeroing in on zigbee (part 1): Intorduction to the standard,” Circuit Cellar, pp. 16–23, February 2005. [19] ISO-wikipedia, http://en.wikipedia.org/wiki/ International Organization for Standardization. [20] K. Finkenzeller, RFID Handbook, 2nd ed. West Sussex, England: John Wiley and Sons Ltd., 2001. [21] EPC website, http://www.epcglobalinc.org. [22] B. Warneke, M. Last, B. Liebowitz, and K. S. J. Pister, “Smart dust: Communicating with a cubic-millimeter computer.” IEEE Computer, vol. 34, no. 1, pp. 44–51, 2001. [23] J. Fraden, Handbook of modern sensors :physics, designs, and applications, 2nd ed. Woodbury, N.Y: American Institute of Physics, 1997. [24] Pico Radio website, http://bwrc.eecs.berkeley.edu/ Research/Pico Radio/Default.htm. [25] S. Meninger, J. O. Mur-Miranda, R. Amirtharajah, A. Chandrakasan, and J. Lang, “Vibration-to-electric energy conversion,” in ISLPED ’99: Proceedings of the 1999 international symposium on Low power electronics and design. New York, NY, USA: ACM Press, 1999, pp. 48–53. [26] Thermal gradients based energy harvesting, http://www. devicelink.com/mddi/archive/02/11/012.html,.