Feature

48 IEEE cIrcuIts and systEms magazInE 1531-636X/14/$31.00©2014IEEE sEcond QuartEr 2014

Digital Object Identifier 10.1109/MCAS.2014.2314265

Date of publication: 20 May 2014

Exploring Energy Efficient Architectures

in Passive Wireless Nodes for IoT Applications

Vyasa sai and marlin H. mickle

Abstractthe evolution of embedded intelligence in passively powered wireless nodes has led to the expansion of the applica-tion space for passive radio Frequency Identification (rFId) and Internet of things (Iot). this article presents recent advancements in energy efficient designs in wireless passive communica-tion nodes and its related applications.

I. INTRODUCTION

rapid progress towards ubiq-uitous networked communi-cation technologies and the

evolution of miniaturized wireless and mobile devices have led to the availability of various applications and services at any time and in any place. Internet of Things (IoT) is a rising technological phenomenon that enables the connections of the physical and the computing worlds forming the next link in the chain of ubiquitous communication. The human-human to human-thing to thing-thing (also known as (machine-to-machine) (M2M)) communication paradigms

is part of the IoT information revolution [1]. This new IoT network paradigm is an intelligent way of connecting existing devices with new devices through radio sens-ing or identifying technologies such as Radio Frequency Identification (RFID) or sensor networks [2], [3]. Each device node collects, transmits and processes sensor data and transmits back to the central node through various

© dIgItal VIsIon

sEcond QuartEr 2014 IEEE cIrcuIts and systEms magazInE 49

Vyasa Sai and Marlin H. Mickle are with the RFID Center of Excellence, Swanson School of Engineering, University of Pittsburgh, PA, 15261, USA (e-mail: vyasa.sai@ieee.org).

network interfaces. Fig. 1 shows a basic framework of the IoT [1]. This is an IoT architecture that interfaces the physical world with the Internet through associated communication networks and edge technologies such as RFID. The terminal nodes shown in Fig. 1 typically rep-resent sensors, RFID tags, RFID sensors, data processing and communication circuits. These terminal nodes com-municate the recorded data back to the requested gate-way device referred to as the RFID based interrogator. This data channel provides information to host system or any other information management system connected to the Internet for processing at any time and place. The RFID sensing enabled IoT expands the existing base of services and applications in the fields such as logistics/supply, manufacturing, agricultural management, health care, home automation, transportation, military, environ-mental monitoring and disaster warning [1].

Terminal nodes are an important part of the IoT net-work in providing efficient services. These nodes typically represent sensors. Deployment of wireless sensor based IoT networks for environmental monitoring is limited due to the active life span of the on-board non-rechargeable power source of the terminal node. The number of sensors required may be very large for such an application. The sen-sors are battery powered, and there is overhead involved for the periodic maintenance of the battery-assisted sen-sors. There has been much research into prolonging the limited lifetime of wireless sensor networks (WSNs) through efficient circuit, architecture and commu-nication techniques [4], [5]. In summary, the use of such an IoT network is strictly limited by the battery life of the terminal nodes. It is a major challenge to manage the maintenance cost of replacing batteries of such numerous nodes especially in hard-to-service areas. Hence the energy management of the terminal node of the IoT is an important factor in extending the lifetime of the sensors.

Passive RFID tags are battery-free nodes that use simple logic to respond with a unique code or data, when queried by an interrogator for the purpose of identifying objects [6]. The concept of remotely feeding a tag on the power from an external RF source has led to the emergence of the widely known passive RFID technology. Passive RFID technology is becoming increasingly common in different environments such as home, office, industry, hospitals, library, etc enabling quick and anytime access to real-time data on uniquely identifiable passive nodes throughout their entire lifetime [6]. The integration of passive RFID with WSNs is a rising phenomenon to improve the sensing capabilities free of battery lifetime constraints. Passive RFID based sensor nodes are part of the wireless passive sensor networks (WPSNs) that mainly deal with the col-lection or storage of data, and transmission of that data back to the interrogator [7]. The interrogator primarily collects and processes the data sent by the nodes.

The existing status of the development of key tech-nologies such as RFID, Sensor, Smart embedded technol-ogy, Nanotechnology of IoT implementations around the world are in application, exploratory, experimental and research stages [8]. Currently there is no unanimous sys-tem built for the IoT with respect to privacy, security and standardizations. Hence, it might take a while to stan-dardize the integration of the evolving smart RFID tech-nology into the IoT framework. This article emphasizes

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Figure 1. a basic model for Internet of things.

Internet of Things (IoT) is a rising technological phenomenon that enables the connections of the physical and the computing worlds forming the next

link in the chain of ubiquitous communication.

50 IEEE cIrcuIts and systEms magazInE sEcond QuartEr 2014

on the need to integrate passive smart RFID technology with IoT to enable new applications. This article focuses on the evolution of energy efficient smart RFID based ter-minal nodes in such passive communication systems.

II. Need for Smart Passive RFID NodesConventional battery assisted WSN are generally made up of a set of autonomous multifunctional sensor nodes distributed throughout a specific environment for monitoring real world data. These sensor nodes are used to collect environmental data and transfer this data to the user through the network. Besides collect-ing raw data, a node may also need to perform compu-tations on the recorded data, eliminating the need to transfer raw data to a central server for each measure-ment [9], [10].

Consider a scenario with many raw sensor data read-ings that must be sampled simultaneously so as not to skew the measurements in time and correspondingly reducing the possible control bandwidth. The number of sensors required may be very large for some applica-tions, e.g. environmental monitoring. This situation is illustrated in Fig. 2 for a set of n sensor nodes. In Fig. 2, f represents the time taken for data transmission from each sensor to the central server and T represents the time taken for the preprocessing or conditioning the

data at the individual sensors done in parallel. In many cases, the raw sensor data must be preprocessed or conditioned before being used in system calculations in order to reduce the transmitted data. The raw data readings are compared to a threshold value in order to determine whether this data needs to be transmit-ted or not. If the raw sensor data reading is above the threshold value, it is transmitted to the central server instantaneously; else an aggregate value is transmitted that includes the current reading along with the other data readings below the threshold [9]. The transmission time nf^ h especially in such a scenario is significantly reduced as opposed to preprocessing done at the cen-tral control where each and every sensor reading needs to be transmitted.

This decrease in the amount of transmitted data reduces the frequent radio transmissions and is critical in increasing the power efficiency of the node [9]. There are many scenarios, in which the sensor data at each node is preprocessed or conditioned before the central server can further use it e.g. biomedical, physiological monitoring, environmental monitoring, etc, [9], [10]. The main design constraint in such applications is the finite power budget for each battery-assisted sensor node, as they require continuous and detailed monitoring over a long period of time.

Power management is a much serious design con-straint in smart passive nodes as compared to battery-assisted nodes. Such passive nodes operate on the received incoming power and are based on the energy harvesting concepts and techniques used in passive RFID tags [11], [12]. The wireless passive sensing for enhanced computation is an emerging research area and there is little documentation on all the power effi-cient scenarios applicable to passive sensor devices. In [11], [13]–[16] efficient antenna designs, low-power transceivers were introduced for passive nodes. But it is not only important to have energy efficient front-end and power unit designs; there is also a need to have low power core designs that allow greater ranges for pas-sive nodes.

Passive RFID tags are known to have limited func-tionality, commonly applicable to simple tracking and identification. In recent years, there has been an evo-lution in the sensing and communication capabilities

Sensor 1

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Figure 2. timing chart for a sensor network.

Passive node capabilities need to be explored with respect to affordable power consumption, possibly to suit target applications,

in order to enable more than just identification.

sEcond QuartEr 2014 IEEE cIrcuIts and systEms magazInE 51

of passive RFID nodes [17]. This led to the emergence of smart passive RFID enabled sensor devices that are known to use microcontrollers for sensor data process-ing [17]. In this context, passive node capabilities need to be explored with respect to affordable power con-sumption, possibly to suit target applications, in order to enable more than just identification.

Smart passive nodes provide the basis for developing new applications to enable RFID beyond basic identifi-cation and simple tracking applications. These wireless passive nodes target the application domains such as security, human implants, unmanned medical nursing, mobile robotics etc. [17]–[20]. Privacy and security con-cerns with RFID tags, typically used in financial trans-actions and tracking are well known. One of the ways to mitigate the security and privacy concerns is to have computation flexibility that enables implementation of reasonably strong cryptographic algorithms at the pas-sive nodes [17]. A significant medical application in the area of human implants is to embed a passive device (node) with reasonable signal processing capabilities in a transplanted portion of the human tissue for passively monitoring the patient’s condition. This is an important application as real-time signal processing and condition-ing on discrete time-sampled medical signal data helps accurately measure the required signal activity leading to an effective treatment for the patient’s condition. The major challenge in using such a smart passive node is power management [20].

An IoT framework, employing a smart WPSN as their terminal node network requires an investigation of energy efficient processing solutions.

III. Overview of Power Consumptions in Passive Sensor Nodes

In this section, an overview of the computation capa-bilities of passive RFID based nodes along with their corresponding power consumptions is presented. Fig. 3 presents the power consumption values of designs rang-ing from a typical RFID passive tag to a smart passive RFID node.

A. Basic Passive NodesA passive RFID tag mainly comprises of a microchip, and an antenna, which can be attached to an object as the identifier of the object. The main function of the tag’s baseband processor is to transmit a unique code or data to identify objects upon being queried by an RFID inter-rogator. The data exchange between an interrogator and a tag is through RF signals. The power values reported in Fig. 3 for Man et al. [21] and Yang et al. [22] correspond to a baseband processor design of the passive RFID tag. Such RFID designs are typically not programmable,

as they have conventional fixed function IC’s as their processors.

In Cho et al. [23] and Yin et al. [24], the sensor inte-grated passive RFID tag has a fixed ID assigned to each sensor in order to support the field deployment of the sensors. The associated baseband processor reports sensed data in addition to the RFID tag functionality. In [21]–[24], the baseband processors do not support any arbitrary computation and thus providing a restricted computational flexibility to the node.

B. Smart Passive NodesIn the context of having preprocessing done at the sen-sor side, existing smart passive nodes (Joshua et al. [25], Alanson et al. [26] and Sai et al. [27]) have higher power consumptions in comparison to the basic RFID tag design types as illustrated in Fig. 3. INTEL Research in collabo-ration with the University of Washington at Seattle has developed a battery-free WISP (Wireless Identification and Sensing Platform) sensing and computation platform that uses a complete programmable microcontroller [25], [26]. WISP design is the first of the enhanced passive RFID platforms to have powered a 16-bit microcontroller using the incoming RF energy from the RFID interrogator. Such enhanced passive RFID sensing platforms use program-mable microcontrollers for processing the sensor node data [25], [26]. The power consumption values for [25] and [26] represented in Fig. 3 are associated with micro-controllers used in WISP designs. In [25] and [26], the microcontrollers are operated at 6MHz and 3MHz fre-quencies respectively. One of the significant challenges faced with such WISP designs is managing the large power

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Figure 3. Power comparisons of passive nodes.

52 IEEE cIrcuIts and systEms magazInE sEcond QuartEr 2014

consumptions resulting due to the use of microcontrollers as part of the passive RFID designs.

Our research, at the University of Pittsburgh’s RFID Center of Excellence, involved exploring a target micro-controller’s reduced instruction set architecture (ISA) for low power applications [27]. The interrogator and a set of passive nodes in combination are viewed as a wireless SIMD distributed system with the remote execution unit (REU) as the digital processing core of the passive node [28]. In Sai et al., low power programmable REU based on a subset of the 8051 ISA was introduced. The power con-sumption value for [27], shown in Fig. 3, corresponds to a REU design operating at a clock frequency of 80 MHz. The ongoing research efforts include the development of asynchronous REU design targeting high instruction exe-cution speeds and low power consumption.

Depending upon the application or design require-ments, the processing unit can be any one of the three: basic baseband processor (Cho et al. and Yin et al.), microcontroller (Joshua et al. and Alanson et al.) or REU (Sai et al.). In Fig. 3, the power consumption val-ues of the processing units used in [21]–[27] are appli-cable for a supply voltage of 1.8, 1.1, 1.5, 0.8, 3, 8 and 1.1 Volts, respectively. The survey data presented in Fig. 3 cannot be directly compared due to the use of dif-ferent parameters such as supply voltage, technology, implementation and architecture in each of the above passive design implementations. But this survey gives a general overview of power consumption values for vari-ous existing advancements in processing units for RFID based passive nodes.

IV. key Challenges to Realize low Power Smart Passive Nodes

The existing wireless passive nodes have laid the founda-tion to empower IoT, especially while providing the basis for a low power and programmable passive processing unit for distributed computing. But there is scope for develop-ment of passive node architectures, efficient energy har-vesting techniques that are needed specifically for passive smart sensing platforms to be used in IoT networks.

Typical wireless passive sensor node hardware blocks are shown in Fig. 4. The antenna is connected to an impedance matching circuit that feeds the received RF signal from the interrogator to the RF power harvester circuit. A typical power harvester circuit is a RF-to-DC converter-capacitor network that acts as the power unit for a WPSN as opposed to a battery, which is the power unit in a WSN. This rectified DC power is stored in the capacitor to be used whenever needed to operate the node. Stream of serial data is extracted from the RF car-rier based Amplitude shift keyed data by the demodu-lator that is part of the RF transceiver. Based on the computational requirements of the node, the process-ing unit manages this serial data to receive the downlink data from the interrogator. The uplink data is fed to the modulator and by modifying the antenna impedance the signal is backscattered from the node.

One of the key challenges in such passive nodes is that it requires sufficient energy to be powered at long distances. The restrictions on the amount of power trans-ferred and the associated path loss adds responsibility to the node for maximizing the energy received to acti-

vate it. There has been significant progress in having low power RF transreceiver and its correspond-ing components for passive node systems [11], [13]–[16]. The main goal for such transreceiver design especially the RF rectifier, the qui-escent current consumption is kept minimum so that the mini-mum operating voltage can be rec-tified with lowest possible input power [17]. In case of the modula-tion and demodulation, for exam-ple, [17], [29] illustrate the EPC

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Figure 4. Wireless passive sensor node architecture.

The combination of smart passive RFID with IoT illustrates the potential of extending the application space to passive biomedical sensing,

environmental monitoring, defense, supply chain logistics, transportation, health care, etc.

sEcond QuartEr 2014 IEEE cIrcuIts and systEms magazInE 53

Gen 2 standard that uses ASK modulation on a 902-928 MHz frequency range of the carrier wave. In the context of complex computations, there is still need for harvesting circuits that provide sufficient power to the node to able to operate on a continuous basis [30], [31].

Due to energy constraints, especially node architectures using microcontrollers as the processing unit as in the case of the WISP design needs the power management unit [17], [25], [26]. One of the methods used in these WISP designs for this purpose is the use of large storage capacitors (in µF). In case, if the power requirements of a single incoming command are not met then the WISP is allowed to sleep for many interrogator transmissions in order to accumu-late charge on the capacitor [25]. This may not be a very effective way of having efficient communication between the interrogator and the passive node. There are ongoing research efforts to be able to provide an energy efficient way to deliver power to the passive node [30], [31].

Using innovative architectures for processing units, such as the REU, is another avenue of providing energy efficient solution [27], [28]. The data and program instructions are stored on a powered interrogator provid-ing wireless supervisory control for the REU at the node. The interrogator and the passive node (REU) combina-tion can be viewed as a complete processor or as multiple processing units forming the basis for a wireless distrib-uted SIMD processor. The design implementation used in [27] is synchronous in nature. Use of effective asynchro-nous implementation of this synchronous design further reduces the dynamic power consumption [18], [32].

However, a widespread adaptation of such SIMD archi-tectures in a standard format requires further develop-ment of existing communication protocols, effective CAD tools, new compilation techniques that focus on optimizing the execution of distributed programs [28].

A combination of effective energy harvesting cir-cuits, passive communication system standards along with innovative architectural solutions will be neces-sary for a low power passive node to be successfully adopted for IoT and passive RFID applications.

V. ConclusionLow power computation intensive designs play an impor-tant role in embedding intelligence into the passive nodes in order to realize IoT. The evolutionary perspective of

recent advancements in the computational aspect of wireless passive nodes applicable to an IoT framework was presented in this article. The combination of smart passive RFID with IoT illustrates the potential of extend-ing the application space to passive biomedical sensing, environmental monitoring, defense, supply chain logis-tics, transportation, health care, etc. Innovative research and sustained development of both the smart passive RFID technology and the IoT are necessary for the favor-able advancements in ubiquitous networking.

AcknowledgmentThe authors wish to acknowledge that this research was part of the work done at the RFID Center of Excel-lence in the Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.

Vyasa Sai is currently working in the graphics hardware division at Intel Corporation, Folsom, CA, USA. He received his Ph.D. from the Depart-ment of Electrical and Computer Engineering at the University of Pittsburgh, Pittsburgh, PA, USA in

2013. He has also received a M.S. degree in Electrical and Computer Engineering at North Dakota State Uni-versity, ND, USA and a B.Tech. degree in Electronics and Communications Engineering at Jawaharlal Nehru Technological University, India. His research interests include Low Power Design, Hardware Security, VLSI, RFID Design and Wireless Passive Device Architecture. He has authored a book, coauthored a book chapter, patent applications and several refereed journal pub-lications in the fields of RFID, Low Power Electronics, PRBG design for lightweight security. He has interned at INTEL Corporation, Hillsboro, OR, USA in 2007. He is an IEEE member since 2001. He is a member of the Editorial Board for the International Journal of Radio Frequency Identification Technology and Applications. He is also a reviewer for Springer Circuits, Systems & Signal Processing, IEEE/ASME International Conference on Advanced Intelligent Mechatronics 2014, IEEE Inter-national Symposium on Circuits & Systems 2012, Inter-national Journal of Computers and Applications and Journal Communications.

Effective energy harvesting circuits, passive communication system standards along with innovative architectural solutions will be necessary for a low power passive node to be successfully adopted

for passive RFID based IoT applications.

54 IEEE cIrcuIts and systEms magazInE sEcond QuartEr 2014

Marlin H. Mickle is currently Profes-sor Emeritus of Electrical and Com-puter Engineering. He was formerly the Bell of PA/ Bell Atlantic Professor, Nickolas A. DeCecco Professor. He was previously Professor of Computer Engi-neering, Telecommunications, and

Industrial Engineering at the University of Pittsburgh. He was the Director of the RFID Center of Excellence. He received the B.S.E.E., M.S.E.E., and the Ph.D. University of Pittsburgh in 1961, 1963, and 1967. Marlin received the Carnegie Science Center Award for Excellence in Corpo-rate Innovation—2005; he has 35+ patents, received the Pitt Innovator Award 2005, 2006, 2007, 2008, 2009, 2010 and 2011; 1988 Recipient of the Systems Research and Cybernetics Award of the IIASSRC; the Robert O. Agbede Faculty Award for Diversity, 2005–06; Distinguished Alumnus, Department of Elec. & Comp. Engr.—2008, a member of the AIDC100, the Ted Williams Award from AIM, and is a Life Fellow of the IEEE.

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