Cat Ari Nucci 2014

Integration of UHF RFID and WSN Technologies in Healthcare Systems

L. Catarinucci, D. De Donno, L. Mainetti, L. Palano, L. Patrono, M. L. Stefanizzi, and L. Tarricone University of Salento, Innovation Engineering Department

Via per Monteroni, 73100, Lecce, Italy E-mail:{firstname.lastname}@unisalento.it

Abstract—This work describes a Smart Hospital System (SHS) based on the integration of Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) and IEEE 802.15.4 Wireless Sensor Network (WSN) technologies. SHS is able to provide patient localization, tracking, and monitoring services within hospitals or nursing institutes through the deployment of a heterogeneous network of RFID-WSN nodes relaying data to a central server. A set of software applications based on RESTful Java and database Push Notification (PN) technologies has been designed, implemented, and installed on the central in order to manage alert events (e.g. patient falls) and promptly inform the nursing staff through an iOS mobile app which has been designed ad hoc for the smart hospital scenario.

Keywords— RFID; WSN; integration; healthcare; E-health

I. INTRODUCTION Improving the efficiency of health care infrastructures and

biomedical systems is one of the most challenging goals of modern-day society. In fact, the need of delivering quality care to patients while reducing the healthcare costs and, at the same time, tackling the nursing staff shortage problem is a primary issue.

As highlighted in [1], current procedures for patient monitoring, care, management, and supervision are often manually executed by nursing staff, which represents de facto an efficiency bottleneck. The recent achievements in the Internet of Things (IoT) technologies [2] open up opportunities to develop innovative, smart systems supporting the improvement of healthcare and biomedical-related processes, e.g. the automatic identification, monitoring, and tracking of both patients and employees in hospitals and nursing institutes. Such smart systems could spread the application of the well-known “five-rights” method, as required by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO). According to such method, the right patient should be treated with the right drug in the right dose in the right way at the right time [3], [4].

Among the IoT technologies enabling smart, pervasive, and ubiquitous healthcare systems, the Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSNs) represent two of the most promising solutions. RFID is a low-cost, low-power technology consisting of passive and/or battery-assisted passive (BAP) devices, named tags, which are able to transmit data when powered by the electromagnetic

field generated by an interrogator, named reader. Since passive RFID tags do not need a source of energy to operate, their lifetime can be measured in decades, thus making the RFID technology well suited for many scenarios, including the healthcare one [5]-[7]. The main drawback of RFID tags stems from the fact that they can operate solely under the reader coverage region, thus making the use of RFID technology limited to object identification in small areas. The integration of RFID and WSN technologies could be a well-suited approach to overcome this limit and enable the development of complex, next-generation applications. Basically, WSNs consist of a large number of low-cost, low-power embedded devices, named sensor nodes, which are able to self-configure and self-organize. These characteristics make them suitable to be deployed even in harsh environments in order to detect important parameters (e.g. temperature, light, humidity, etc.) without human intervention. The collected data are delivered, in a multi-hop mode, to a central point (sink) for proper utilization [8]. One of the main issues to be addressed in the development of complex WSN applications is the power consumption of sensor nodes. In fact, they are usually battery-powered and deployed in large areas where changing or replacing batteries is impractical or completely unfeasible [9]-[12].

Although RFID and WSN were originally designed with different objectives, the benefits provided by both technologies suggest the development of an integrated solution, able to combine identification (RFID) and advanced communication (WSN) capabilities [13] in order to improve the effectiveness of both technologies and give new perspectives to a broad range of novel applications, e.g. in the healthcare domain.

A lot of initiatives within the field of E-Health have been launched worldwide in the last few years by hospitals, pharmaceutical companies, public institutions, and governments [14]. Moreover, the European Commission is continuously stimulating research activities in the field by promoting the development of sustainable and personalized healthcare systems (e.g. the ICT Challenge 5, Framework Programme 7). Many interesting solutions have been already presented in the literature, however only few of them have been implemented on real devices. In [15], a wireless localization network able to track the location of patients in an indoor environment and also to monitor their physical status is

2014 IEEE RFID Technology and Applications Conference (RFID-TA)

978-1-4799-4680-8/14/$31.00 ©2014 IEEE 289

TABLE I. TYPES OF NODES MAKING UP THE SHS SYSTEM

Node typology

R Router Node

C Coordinator Node

RR Router Reader Node

EDT End Device Tag Node

Fig. 1. The proposed architecture of the Smart Hospital System (SHS) based on the integration of UHF RFID and WSN technologies.

presented. A location-aware WSN to track patients using an algorithm called REMA (Ranging using Environment and Mobility Adaptive) filter is proposed in [16].

Another interesting aspect concerns the movements classification. Several studies have been focused on this topic in the past [17]. Most of the proposed solutions in the literature rely on low-cost, small size multi-axial accelerometers in order to monitor postural orientation of patients as well as their body movements [18]-[22]. In such works, the classification of movements is often done using fixed thresholds, pattern-recognition, and fuzzy logic. In [1], a smart WSN providing patient localization, tracking, and monitoring services within nursing institutes is presented. The localization and tracking engine rely on the Received Signal Strength Indicator (RSSI) and particle filters while bi-axial accelerometers are used to classify the movements of patients.

To the best of authors’ knowledge, only few attempts have been done to leverage the combined use of UHF RFID and WSN technologies in healthcare scenarios. In [23], RFID, WSN, and GSM are exploited together to track patients in hospitals and monitor their physiological parameters while a smart system using active UHF RFID, WSN, and GSM for real-time supervision of patients is presented and discussed in [24].

This paper describes a smart system based on UHF RFID and WSN technologies for the automatic monitoring, localization, and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. In particular, the system augments standard WSN functionalities through the deployment of advanced network nodes featuring both WSN communication/sensing and UHF RFID identification capabilities. The system is able to collect, in real time, both patient’s physiological parameters and environmental conditions, and, in case of emergency, to promptly inform the nursing staff via a customized software application specifically designed for mobile devices such as smartphones, tablets, etc.

The remainder of the paper is organized as follows. Section II outlines the architecture of the proposed smart hospital system along with involved hardware and software components. Section III describes and experimentally validates a proof-of-concept implementation of the system. Finally, concluding remarks are given in Section IV.

II. SMART HOSPITAL SYSTEM The proposed integration of UHF RFID and WSN

technologies is exploited in this work to implement a Smart Hospital System (SHS) whose architecture is outlined in Fig 1. Basically, SHS provides innovative services for the automatic supervision of both patients and personnel within hospitals or nursing institutes through the deployment of an IEEE 802.15.4-based WSN able to collect and deliver data to a Central Server. The controller unit analyses the received information and sends an alert message in case of emergency.

According to the ZigBee WSN paradigm, the main components of such a network are Router (R) and Coordinator (C) nodes. The R node provides forwarding and routing

capabilities, while the C node collects the received data and forwards them to the server machine. Referring to the proposed RFID-WSN integrated system, in which both RFID readers and RFID tags are physically interfaced with WSN nodes, two new typologies of nodes can be introduced in the network (Table I):

• the Router Reader (RR), which is a R node interfaced with an UHF RFID reader;

• the End Device Tag (EDT), which is a typical ZigBee End Device (ED) node (i.e. a WSN node without routing and forwarding capabilities) interfaced with an UHF RFID tag.

As depicted in Fig. 1, the SHS consists of a ZigBee WSN with several R and RR nodes scattered in the hospital to monitor environmental parameters, e.g. temperature and ambient light conditions. Such information is delivered, in a multi-hop manner, to the C node, which, in turn, stores the collected data in a database located in the Central Server. The main function of RR nodes is to track patients, nursing staff, and biomedical devices labeled with UHF RFID tags. As for patients, they wear an EDT node capable to detect the patient’s status and relay the information to the C node through the WSN. More specifically, the EDT node periodically logs on the RFID tag’s user memory patient’s physiological parameters, such as heartbeat, body temperature, breath, movement characteristics. The stored data are retrieved by RR nodes deployed in the environment and delivered to the Central Server, or directly to the nursing personnel equipped with a


290

Fig. 3. The RR node embedding the UHF RFID reader.

Fig. 2. The EDT node physically interfaced with the UHF RFID tag.

portable UHF RFID reader. In case of medical emergency or critical situations, such as patient’s falls or heartbeat irregularities, the EDT node activates its IEEE 802.15.4 radio transceiver to send an alert message to the control center. In such a way, EDT nodes keep their radio off for most of the time, thus maximizing battery lifetime. At the Central Server, a patient monitoring application exploiting database Push Notifications (PN) is used to inform the nursing staff.

Details about the hardware and software components involved in the SHS are provided in the following sub-sections.

A. Hardware components The EDT node consists of an UHF RFID tag physically

interfaced to the microcontroller unit (MCU) of a WSN node via the I2C bus (see Fig. 2). The printed circuit board (PCB) RFID tag has been prototyped in our labs by using a photolithography process on FR4 substrate and handy soldering off-the-shelf discrete components (specifically an UHF RFID chip and a decoupling ceramic capacitor). The developed board is equipped with a battery holder (not exploited in the proposed RFID-WSN integration since the required power is fed directly from the 3-V battery of the WSN node) enabling a stand-alone mode of operation as a battery-assisted passive (BAP) RFID tag [25]. The designed dipole-like UHF RFID antenna is patterned directly on the PCB. Note that, depending on the application, a directive patch antenna could be used to achieve higher performance (e.g. a longer read range) and platform tolerance [26]. The input impedance of the antenna has been tuned to achieve a complex conjugate impedance matching with an Impinj Monza X-2K RFID chip [27] (Zchip=Rchip+jXchip = 20.83-j181.39 Ω) at 866.5 MHz, i.e. the central frequency of the European UHF RFID band. The Monza X-2K is a new-generation UHF RFID integrated circuit (IC), compliant with the EPCglobal Class-1 Generation-2 (Gen2) standard, with

2176 bits of non-volatile memory (NVM) and an I2C interface. In our prior works, this RFID chip has been used to develop computational RFID tags [28]-[30] (i.e. augmented tags with sensing and computing capabilities) specifically designed for heterogeneous RFID-based WSNs. As an I2C device, Monza X-2K operates as a standard EEPROM whose contents can also be accessed via the Gen2 air interface. In the fabricated prototype, the small female header exposing the I2C bus is used to interface the RFID chip with a MB851 board developed by ST Microelectronics [31]. This board is equipped with a 32-bit ARM Cortex-M3 MCU operating up to 24 MHz and embedding 16-KB RAM and 256-KB eFlash ROM. It integrates also a 2.4 GHz wireless transceiver compliant with the IEEE 802.15.4 standard. The featured MCU is optimized to provide high performance at very low power consumption. The WSN board is also equipped with 24 highly configurable GPIOs with Schmitt trigger inputs. Among the exposed GPIOs, the SDA and SCL lines, the ground pin (GND), and an output pin (DCI) have been used to implement the I2C communication with the RFID component and provide power to the RFID chip. Finally, the MB851 board is equipped with a temperature sensor and a 3-axis micro-electromechanical system (MEMS) acceleration sensor which can be exploited for the development of several smart applications. Details about the power consumption of the EDT node are reported in our earlier work [32].

The other key component of the SHS is the RR node (see Fig. 3). It consists of an UHF Gen2 RFID reader interfaced with the XM1000 sensor board from Advanticsys [33] via the UART communication bus. The board is equipped with a 16-bit ultra-low-power TI MSP430F2618 MCU and embeds 8 Kbytes of RAM and 116 Kbytes of Flash memory. Wireless communication capabilities are provided by the TI CC2420 transceiver with transmission frequency of 2.4 GHz. In addition, the board integrates temperature, humidity, and ambient light sensors. The selected reader is the Sensor ID


291

Fig. 4. The overall software architecture.

Discovery Gate UHF [34] which can be easily configured and controlled by the XM1000 board via the UART interface. The reader supports standard Read/Write Gen2 commands for reading/writing data from/to the tag user memory up to approximately 8 m of distance.

B. Software components The operating system (OS) chosen to develop the firmware

for the WSN node is the Contiki OS [35]. It is a popular open-source operating system targeted to small microcontroller architectures and developed by the Swedish Institute of Computer Science. It provides a full IP network stack, with standard IP protocols such as UDP, TCP, and HTTP, in addition to the new low-power standards. Furthermore, Contiki is highly memory efficient and provides a set of useful mechanisms for memory allocation. These features make Contiki the ideal choice for the development of innovative, smart applications, capable to exploit the new possibilities offered by the integration of RFID and WSN technologies.

In the proposed SHS, the software aspects concerning the interfacing between WSN nodes and RFID components have been implemented in Contiki OS as system drivers. Specifically, as for the RR node, several functions to set hardware parameters, configure the UART communication interface, and manage the user memory of singulated tags have been developed. As for the EDT node, the implemented driver includes all the functions needed to manage the Monza X-2K RFID memory banks via the I2C communication bus.

Furthermore, to store and analyze the patients’ data transmitted through the deployed WSN, two different applications have been implemented and installed on the Central Server:

• Standalone Java Application (SJA), which collects the data received from the C node by using the jNetPcap SDK [36], parses and sends them to the RJA application through RESTful requests. Note that jNetPcap SDK is one of the most advanced open source tool for packets capturing at the time of implementation.

• RESTful Java Application (RJA), which analyses the received information and stores it on a MySQL database. When the RJA identifies an alert message from an EDT node, it sends a Push Notification (PN) on the mobile device of the nearest doctor in the hospital or nursing institute by using the Apple Push Notification service. It is a RESTful Java Web Application running on Apache Tomcat application server and developed by using the Jersey Framework.

The presented mobile application has been implemented on the iOS operating system; however, we are currently developing it on different OSs (i.e., Android and Windows Phone). It has been implemented by using the Model View Controller (MVC) design pattern with a Controller module and two Views: one for the patients’ list and another for the patient details.

More in detail, the Controller module is responsible for:

• managing the interactions between the App and the RJA by using the Cocoa Touch Framework APIs;

• parsing the received information.

The use of the Apple Push Notification Mechanism (APNM) instead of other technologies (e.g. GSM) allows us to directly interface with the mobile App and, therefore, to access all the information about the patient stored in the database.

The overall software architecture of the proposed system is shown in Fig. 4.

III. EXPERIMENTAL VALIDATION In this section, a prototype implementation of the proposed

SHS is described and validated. A simple proof of concept has been developed in order to demonstrate that the proposed solution can meet the real requirements of a smart hospital environment.

The 3-axis MEMS accelerometer of the MB851 board has been used to evaluate a patient fall and generate an alert. Especially for hospitalized elder, these accidents could give rise to serious consequences if aid is not given in time. Although many solutions in the literature propose sophisticated mechanisms to detect and prevent falls, a simple threshold-based approach has been considered in this work. The thresholds have been set based on empirical data obtained through several tests carried out in our laboratory. It is worth noting that the aim of this paper is to demonstrate the feasibility of just one of the several possible use-case scenarios of the proposed SHS. Therefore, the definition of specific, optimum algorithms to detect patient falls is outside the scope of this work.

In Fig. 5, a simple validation scenario is reported. In our implementation, the RFID tag embedded into the EDT node of a patient contains, in addition to physiological information periodically saved by the sensor node, the Electronic Product Code (EPC), which is used to univocally identify the patient in the hospital. An RFID tag is also used to identify the nursing staff. Furthermore, in our scenario, the database stores information about the location of each RR node in the hospital (e.g. the RR #1 is placed in Room #21) and important data on the employed doctors. An Apple iPhone 4S has been used to validate the system. The mobile App is installed on this device


292

Fig. 5. Validation scenario.

(a) (b) (c)

Fig. 6. Some screenshots of the prototype SHS: (a) the doctor receives the PN on the mobile phone; (b) the doctor visualizes details about the emergency situation; (c) the doctor visualizes the list of patients.

and uses the Wi-Fi or 3G Internet access. When an alert event occurs (e.g. patient fall), the application receives the PN sent by the RJA and notifies the doctor with a sound. Moreover, the mobile App allows the doctor to retrieve all information related to the event and the patient from the Central Server.

The first part of the considered use case is related to the patients monitoring system which relies on the following operations:

1. the RR node reads the EPC and all the information about the patient’s health status stored in the User Memory of the RFID tag embedded into the EDT node;

2. the RR node reads the EPC stored into the RFID tag of the doctor;

3. the RR node sends the retrieved data, along with its identification number, to the C node for a proper utilization;

4. the SJA and the RJA applications running on the Central Server analyze the received data and store them in the database.

The second part of the use case, instead, concerns the management of emergency situations according to the following procedure:

5. the EDT node detects the patient fall, activates its radio transceiver, and sends an alert message to the C node. Such a message contains the patient’s EPC and the last physiological information sensed by the node;

6. the RJA retrieves from the database information about the nearest doctor (e.g. the mobile phone number) and sends him/her a PN. The doctor receives on the App installed on his/her mobile phone the emergency notification (Fig. 6a) and visualizes location and health status of the patient (Fig. 6b). Note that the location refers to the last position where the RFID tag has been read.

Finally, as shown in Fig. 6c, the developed App is also able to retrieve all the information stored in the database concerning hospitalized patients.

CONCLUSION We have presented a Smart Hospital System (SHS) based

on a complex hardware/software architecture integrating UHF RFID and WSN technologies for patient monitoring, localization, and tracking. The system exploits a heterogeneous network of hybrid UHF RFID and IEEE 802.15.4-based WSN devices which can be rapidly deployed in any hospital or nursing institute. A central server implementing advanced database management techniques and running RESTful Java software applications constantly monitors the patients’ status and, in case of emergency, promptly sends Push Notifications (PN) to a smart, lightweight iOS App installed on the nursing staff mobile phone.

REFERENCES [1] A. Redondi, M. Chirico, L. Borsani, M. Cesana, and M. Tagliasacchi,

“An integrated system based on wireless sensor networks for patient monitoring, localization, and tracking,” Ad Hoc Networks, vol. 11, pp. 39-53, 2013.

[2] L. Mainetti, L. Patrono, and A. Vilei, “Evolution of wireless sensor networks towards the Internet of Things: A survey,” 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2011), pp. 1-6, Sept. 2011.

[3] P. Peris-Lopez, A. Orfila, A. Mitrokotsa, and J.C.A. van der Lubbe, “A comprehensive RFID solution to enhance inpatient medication safety”, International Journal of Medical Informatics, vol. 80, no. 1, pp.13–24, 2011.

[4] A. L. Guido, L. Mainetti, and L. Patrono, “Evaluating potential benefits of the use of RFID, EPCglobal, and ebXML in the pharmaceutical supply chain,” International Journal of Healthcare Technology and Management, vol. 13, no. 4, pp. 198-222, 2012.

[5] G. Calcagnini, F. Censi, M. Maffia, L. Mainetti, E. Mattei, L. Patrono, and E. Urso, “Evaluation of thermal and nonthermal effects of UHF RFID exposure on biological drugs”, IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 6, pp. 1051-1057, 2012.

[6] L. Catarinucci, R. Colella, M. De Blasi, V. Mighali, L. Patrono, and L. Tarricone, “High performance RFID tags for item-level tracing systems”, International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2010), pp. 21-26, Article number 5623656, 2010.

[7] M. Maffia, L., Mainetti, L. Patrono, and E. Urso, “Evaluation of potential effects of RFID-based item-level tracing systems on the integrity of biological pharmaceutical products”, International Journal of RF Technologies: Research and Applications, vol. 3, no. 2, pp. 101-118, 2012.


293

[8] A. Capone, M. Cesana, D. De Donno, and I. Filippini, “Optimal placement of multiple interconnected gateways in heterogeneous wireless sensor networks,” NETWORKING 2009, Lecture Notes in Computer Science (LNCS), vol. 5550, pp. 442-455, 2009.

[9] D. Alessandrelli, L. Patrono, G. Pellerano, M. Petracca, and M. L. Stefanizzi, “Implementation and validation of an energy-efficient MAC scheduler for WSNs by a test bed approach,” International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2012), Article number 6347615, 2012.

[10] D. Alessandrelli, L. Mainetti, L. Patrono, G. Pellerano, M. Petracca, and M. L. Stefanizzi, “Performance evaluation of an energy-efficient MAC scheduler by using a test bed approach,” Journal of Communication Software and Systems, vol. 9, no. 1, pp. 84–96, 2013.

[11] L. Catarinucci, S. Guglielmi, L. Mainetti, V. Mighali, L. Patrono, and M. L. Stefanizzi, “An energy-efficient MAC scheduler based on a switched-beam antenna for wireless sensor networks”, Journal of Communication Software and Systems, vol. 9, no. 2, pp. 117–127, 2013.

[12] L. Catarinucci, R. Colella, G. Del Fiore, L. Mainetti, V. Mighali, L. Patrono, and M.L. Stefanizzi, “A cross-layer approach to minimize the energy consumption in wireless sensor networks”, International Journal of Distributed Sensor Networks, Vol. 2014, Article number 268284, 2014.

[13] M. Petracca, S. Bocchino, A. Azzarà, R. Pelliccia, M. Ghibaudi, and P. Pagano, “WSN and RFID integration in the IoT scenario: An advanced safety system for industrial plants,” Journal of Communications Software and Systems, vol. 9, no. 1, pp. 104-113, Mar. 2013.

[14] J. Ko, T. Gao, R. Rothman, and A. Terzis, “Wireless sensing systems in clinical environments: Improving the efficiency of the patient monitoring process,” IEEE Magazione on Engineering in Medicine and Biology, vol. 29, no. 2, pp. 103-109, 2010.

[15] M. D’Souza, T. Wark, and M. Ros, “Wireless localisation network for patient tracking,” 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2008), pp. 79-84, Dec. 2008.

[16] A.-K. Chandra-Sekaran, P. Dheenathayalan, P. Weisser, C. Kunze, and W. Stork, “Empirical analysis and ranging using environment and mobility adaptive rssi filter for patient localization during disaster management,” 2009 5th International Conference on Networking and Services (ICNS ’09), pp. 276–281, 2009.

[17] C. Occhiuzzi, and G. Marrocco, “The RFID Technology for Neurosciences: Feasibility of Limbs' Monitoring in Sleep Diseases”, IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 1, pp. 37 – 43, Jan. 2010.

[18] B. Steele, L. Holt, B. Belza, S. Ferris, S. Lakshminaryan, and D. Buchner, “Quantitating physical activity in COPD using a triaxial accelerometer,” Chest 117, May 2000.

[19] G. Currie, D. Rafferty, G. Duncan, E. Bell, and A. Evans, “Measurement of gait by accelerometer and walkway: a comparison study,” Medical and Biological Engineering and Computing, vol. 30, 1992.

[20] K. Kiani, C. Snijders, and E. Gelsema, “Computerized analysis of daily lifemotor activity for ambulatory monitoring,” Technology and Health Care 5, 1997.

[21] F. Foerster and J. Fahrenberg, “Motion pattern and posture: correctly assessed by calibrated accelerometers,” Behavior Research Methods, Instrumentation and Computers, vol. 32, 2000.

[22] K. Aminian, P. Robert, E. Buchser, B. Rutschmann, D. Hayoz, and M. Depairon, “Physical activity monitoring based on accelerometry: validation and comparison with video observation,” Medical and Biological Engineering and Computing, vol. 37, pp. 304–308, 1999.

[23] N. Renuka, N. C. Nan, and W. Ismail, “Embedded RFID tracking system for hospital application using WSN platform,” 2013 IEEE International Conference on RFID Technologies and Applications (RFID-TA 2013), Sept. 2013.

[24] S. M. Rajesh, “Integration of active RFID and WSN for real time low-cost data monitoring of patients in hospitals,” 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE 2013), pp. 1-6, Dec. 2013.

[25] D. De Donno, L. Catarinucci, and L. Tarricone, “A battery-assisted sensor-enhanced RFID tag enabling heterogeneous wireless sensor networks,” IEEE Sensors Journal, vol. 14, no. 4, pp. 1048-1055, Apr. 2014.

[26] H. Lee, S. Kim, D. De Donno, and M. M. Tentzeris, “A novel universal inkjet-printed EBG-backed flexible RFID for rugged on-body and metal mounted applications,” 2012 IEEE MTT-S International Microwave Symposium Digest, pp. 1–4, June 2012.

[27] Monza X-2K RFID IC [Online]. Available: http://www.impinj.com/. [28] D. De Donno, L. Catarinucci, and L. Tarricone, “An UHF RFID energy-

harvesting system enhanced by a DC-DC charge pump in silicon-on-insulator technology,” IEEE Microwave and Wireless Components Letters, vol. 23, no. 6, June 2013.

[29] D. De Donno, L. Catarinucci, and L. Tarricone, “Enabling self-powered autonomous wireless sensors with new-generation I2C RFID chips,” 2013 IEEE MTT-S International Microwave Symposium Digest (IMS), pp. 1-4, 2013.

[30] D. De Donno, L. Catarinucci, and L. Tarricone, “RAMSES: RFID augmented module for smart environmental sensing,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 7, pp. 1701-1708, July 2014.

[31] STMicroelectronics, MB851 user manual [Online]. Available: http://www.st.com/.

[32] D. De Donno, M. L. Stefanizzi, L. Catarinucci, L. Mainetti, L. Patrono, and L. Tarricone, “Integrating passive UHF RFID tags with WSN nodes: Challenges and opportunities,” Journal of Communication Software and Systems, vol. 10, no. 2, June 2014.

[33] Advanticsys, AS-XM1000 resource manual [Online]. Available: http://www.advanticsys.com/.

[34] Sensor ID, Discovery Gate UHF Datasheet [Online]. Available: http://www.sensorid.it/

[35] A. Dunkels, B. Gronvall, and T. Voigt, “ Contiki – a lightweight and flexible operating system for tiny networked sensors”, IEEE Workshop on Embedded Networked Sensors, Nov. 2004.

[36] JNetPcap OpenSource User Guide [Online]. Available: http://jnetpcap.com/userguide/.


294

Documents

Cat Ari Nucci 2014