Energy-conserving access protocols for identification networks

IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 7, NO. 1, FEBRUARY 1999 51

Energy-Conserving Access Protocolsfor Identification Networks

Imrich Chlamtac,Fellow, IEEE,Chiara Petrioli,Member, IEEE,and Jason Redi,Member, IEEE

Abstract—A myriad of applications are emerging, in whichenergy conservation is a critical system parameter for communi-cations. Radio frequency identification device (RFID) networks,smart cards, and even mobile computing devices, in general, needto conserve energy. In RFID systems, nodes are small battery-operated inexpensive devices with radio receiving/transmittingand processing capabilities, integrated into the size of an ID cardor smaller. These identification devices are designed for extremelylow-cost large-scale applications, such that the replacement ofbatteries is not feasible. This imposes a critical energy constrainton the communications (access) protocols used in these systems,so that the total time a node needs to be active for transmittingor receiving information should be minimized. Among existingprotocols, classical random access protocols are not energy con-serving, while deterministic protocols lead to unacceptable delays.This paper deals with designing communications protocols withenergy constraint, in which the number of time slots in which tagsneed to be in the active state is minimized, while the access delaymeets the applications constraints. We propose three classes ofprotocols which combine the fairness of random access protocolswith low energy requirements.

I. INTRODUCTION

RADIO frequency identification devices (RFID’s) and in-frared identification devices (IRID’s), such as warehouse

identification tags and intelligent ID cards, are examples ofa new world of applications which use small inexpensivedevices for which battery conservation is a critical systemparameter. For reasons of conciseness, we shall refer to all ofthese devices simply as “tags” and to the connecting networksas people/item IDentification NETworks (IDNET’s — alsoreferred to in literature as RFID systems).

A typical IDNET is composed of a number of intercon-nected base stations communicating over a shared wirelesschannel to a large number of small low-cost wireless nodes ortags. These tags usually contain some sort of microprocessorpower source in the form of a battery, capacitor, or solar cell,as well as a radio frequency receiver, possibly a transmitter,and some support logic.

Manuscript received September 18, 1996; revised December 11, 1998;approved by IEEE/ACM TRANSACTIONS ONNETWORKING Editor D. Raychaud-huri.

I. Chlamtac is with the Erik Jonsson School of Engineering and ComputerScience, University of Texas at Dallas, Dallas, TX 75080 USA (e-mail:[email protected]).

C. Petrioli was with the Dipartimento di Scienze del Informazione, Univer-sity of Rome, Italy. He is now with the Politecnico di Milano, Italy (e-mail:[email protected]).

J. Redi was with the Electrical and Computer Engineering Department,Boston University, MA 02215 USA. He is now with BBN Technologies,Cambridge, MA 02138 USA (e-mail: [email protected]).

Publisher Item Identifier S 1063-6692(99)02270-0.

The range of potential uses for RFID tags is extremely large.It is estimated that the RFID tag market will expand 20-foldto over $5 billion within the next five years [4].

A few examples of current uses for RFID tags include thefollowing.

1) Tags attached to the ears or worn around the necksof livestock. These tags allow for location tracking ofthe animals, as well as specialized feeding, milking, ormedication schedules. The animals can be granted ordenied access to specific areas based on the tags theycarry.

2) Smart tags used in warehouses to track inventory. Theyallow companies to virtually immediately know thelocation of any item in the warehouse, as well as trackboxes as they enter or leave the building.

3) Numerous tag companies targeting the retail market.Prices displayed by electronic shelf labels can be au-tomatically adjusted based on current market prices orthe amount of excess inventory.

There are four fundamental characteristics of RFID wirelessnodes which make RFID networks distinct from other wirelesssystems:

Scale: There will be potentially a very large number ofwireless nodes per base station, e.g., in a warehouseapplication, there may be thousands of RFID nodesper base station.

Cost: Nodes must be remarkably inexpensive. Due to thenumber of nodes and the low cost of some of the“tagged” items, nodes cannot cost more than a fewdollars, sometimes even less than a dollar.

Size: Nodes must be very small. The size of a packof cards will be the maximum size for manyapplications.

Traffic: Communication is based on typically short, simplemessages.

From these characteristics, a number of important observa-tions follow which further define the unique constraints of anRFID network. Most importantly, due to the large number ofnodes, it is economically impossible to replace or recharge thebatteries in the tags. This can be attributed to either the costof the design or the sheer number of tags. Considering thatbattery life is not expected to increase more than 30% in thenear future [9], the battery’s energy is therefore a limited andscarce resource.

Radio frequency communication can be highly demandingof energy. A consequence of this is a limited uplink capability.Uplink transmission can typically use twice as much energy as

1063–6692/99$10.00 1999 IEEE

52 IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 7, NO. 1, FEBRUARY 1999

reception [3]. It is possible to use spread spectrum modulationsuch that the base station indirectly provides the energyfor limited uplink communication, but this requires that anyuplink traffic be base station initiated. Furthermore, limitedunlicensed bandwidth and simplicity of the tag means that alltags must share the same broadcast band.

For all of the above considerations, these types of systemsrequire new access protocols which are designed around theseunique constraints and provide a combination of two importantfactors: low delay and low energy requirements.

The allowable delay is an application-dependent constraint.For example, tracking the movement of tags across the cellswithin a system requires updates to be performed within a shortbounded amount of time. The system is already constrained bythe speed of the shared channel and has to manage a potentiallylarge number of tags. Therefore, it is very important for accessprotocols to not add significantly to the transmission delay.

Low energy consumption is the second requirement thatthe access protocol must satisfy. Since it is not possible toreplace batteries, it is necessary to design RFID nodes thatrequire a minimum of energy to operate. The tags need to bein the awakestate for communication. In order to conservebattery life, the tag can enter asleepstate, where the CPUis in a low power mode and radio reception is disabled. Theratio of energy consumed between the sleep and awake states(i.e., when the CPU operates at full energy) is typically on theorder of 100 or more.

Current access protocols, such as those described in [2],have not been designed to meet the dual requirements oflow energy consumption and low delay, and are consequentlynot appropriate for tag systems. Random access protocols,such as Aloha, applied to the tag network system translateto base stations sending packets at random times and tagsawaking at random times. The probability of a tag being awakein the same slot in which the base station is transmittingto it is very low. Therefore, due to repeated transmissionattempts, the energy required and the packet delay will be quitehigh. Deterministic protocols, such as classical time-divisionmultiple access (TDMA), assign each tag an individual slotin which it may receive transmissions. Although this has theadvantage of a low energy requirement since each tag onlyneeds to be awake slots, where is the number of tagsin the system, in a situation with a large number of tags andvery low transmission speeds, this access protocol will take aprohibitively long time to deliver each packet.

To meet the combined energy/delay objective, it is easy tosee that a fundamental tradeoff exists between tags wakingup too frequently, leading to a high energy consumptionand implying a short lifetime and sparse wake-up schedule,which although it conserves energy, leads to unacceptably highaccess delays and, therefore, inadequate mobility and otherapplication support.

This paper deals with the design ofcommunications proto-cols with energy constraint,in which the fraction of timeslotsin which tags need to be in the active (awake) state is mini-mized, and the access delay meets the applications constraints.In attempting to combine the fairness from random accessprotocols with low energy requirements from classical TDMA

while maintaining acceptable access delays, we present threedifferent protocol approaches to this problem.

We first describe the tag network model, followed by adetailed description of the protocols. In the ensuing analysisof energy consumption and access delay, we derive the systembehavior for both uniformly and nonuniformly distributedtraffic destinations. We follow with a detailed evaluation ofthe RFID protocols and conclude with a summary.

II. NETWORK MODEL AND PROTOCOLSDESCRIPTION

We consider a single-cell system where a base station com-municates with tags through a radio channel of bandwidth

. The communication is packet-oriented. We assume thetime to be slotted and the base station’s transmissions to besynchronized to the beginnings of slots. The packet lengthis constant, and exactly one packet can be transmitted duringeach slot. In this analytic model, we do not explicitly treattransmission errors.

We define anaccess protocolas consisting of two compo-nents: atransmission scheduling strategyat the base station,which in each slot selects a packet for transmission from thearrival queue, and awake-up scheduleat each tag, whichdetermines the slots in which the tag is awake. In general,the transmission scheduling strategy can take into accountdifferent parameters such as the number of packets in the queueand the packets’ ages, as well as the wake-up schedules oftheir destinations. In the protocols discussed here, the “oldestpacket” criterion is generally adopted to help meet the appli-cation delay requirements. We next present and compare thefollowing three classes of protocols for constructing efficientwake-up schedules: grouped-tag TDMA protocols, directoryprotocols, and pseudorandom protocols.

A. Grouped-Tag TDMA Protocols

Classical TDMA can be adapted for use in IDNET’s in thefollowing way. We divide tags into disjoint groups,with the cardinality of each group differing by at most onetag, and assign (reserve) each slot of the TDMA cycle to aunique group. Compared with the one-tag-per-slot TDMA, agrouped approach increases the average energy consumptionper slot by the cardinality of the groups, but decreases theaverage delay, as there is a greater probability that a tag will beawake soon after a packet for it has arrived at the base station.Since intuitively, for a given load, there is an optimal groupsize, it is possible to optimize energy and delay parametersin this protocol. However, the grouped-tag TDMA protocolhas a number of potential drawbacks. Since tags’ schedulesare cyclic and independent of packets in the base station’squeue (a tag does not know when the base station has apacket destined for it), tags continue to wake up cyclically.Therefore, significant energy waste may occur. Secondly, itis easy to see that if the packets’ destination distribution isheavily clustered, the performance of the protocol can degradeseverely and the reorganization of the groups, even if possible,can be excessively time consuming.

Many paging protocols use a variation of the grouped-tag TDMA protocol for energy conservation. For example,

CHLAMTAC et al.: ENERGY-CONSERVING ACCESS PROTOCOLS FOR IDENTIFICATION NETWORKS 53

Europe’s most prevalent paging protocol, POCSAG, dividesthe pagers into eight groups based on their fixed ID number [8].The small, static number of pager groups provides a limitedpaging delay at the cost of a fixed, minimal energy savings,even in situations of very low traffic, where waking up lessfrequently would be advantageous.

B. Directory Protocols

The use of a directory for the transmission of wireless datahas been introduced by Imielinski, Vishnatwan, and Badrinath[5], who considered this principle for applications whichdo not have strict delay constraints. Their work, therefore,concentrates on the intelligent and efficient organization ofthe directory. The IEEE 802.11 wireless LAN draft standardalso suggests the use of a directory for energy conservationpurposes [1]. This system utilizes variable-sized packets on ashared nonslotted CSMA/CA channel, such that the transmis-sion of the directories may be deferred for extended periodsof time when one of the mobile nodes is transmitting on thechannel. In contrast to these works, our application requiresoptimal selection of the transmission set size and carefulscheduling of the idle periods leading to a different protocol.

In our directory protocol, the base station waits for agroup of packets in the queue to accumulate before startingthe transmission. The transmission consists of broadcasting adirectory which lists the destinations of thepackets which areto be transmitted, followed by the transmission of the packetsthemselves. Tags listen to the directory to find out if and whenpackets will be sent to them. They can then schedule theirwake-up slots to coincide with the broadcast of their packets.When there is no group being currently transmitted, the tagswake up periodically every slots, in order to give the basestation an opportunity to start the transmission of a new group.

The choice of the parameter depends on the load andmust take into account the tradeoff between the increase inthe delay due to a larger and the energy savings frommore infrequent broadcasting of directories. Additionally, theparameter should depend upon and the load. A tag systemwith small value of and a low load will have the tags wakingup frequently until enough packets have accumulated at thebase station, while a large value ofwill incur an increase indelay before the start of group’s transmission.

The directory approach solves the potential unfairness en-countered by the grouped-tag TDMA when the destinationdistribution is heavily clustered. However, for the directoryprotocol, when group size is large, the size of the directorybecomes prohibitively large, and therefore the amount ofenergy used by the tags just to read the directory becomesa major factor.

Several improvements can be considered for reducing theenergy consumption during the directory transmission. By or-dering the directory according to destination ID’s and sendingthe boundaries of the “transmission interval” (i.e., the upperand lower value of the destination ID’s of the packets),it is possible for tags to go to sleep after discovering whentheir packet will be transmitted, or as soon as they determinethat there will be no packet addressed to them in this group.

The latter event occurs if either the tag does not fall within thetransmission interval or the directory indicates the transmissionof a packet to a higher order ID.

C. Random Access Protocol

In the random access protocol, the transmission schedulingstrategy adopted by the base station is to randomly select oneof the packets in the queue. The packet is successfully receivedduring the current slot if, and only if, the destination of thepacket (randomly chosen by the base station) is awake. Tagsare awake with probability . As mentioned previously, theprobability of a tag being awake when a packet is sent to itis extremely low unless is large, indicating a high energyconsumption. Therefore, this protocol is considered only forthe sake of a baseline comparison.

D. Pseudorandom Protocols

The pseudorandom protocols are a new class of protocols[6], [7] based on deterministic (pseudorandom) scheduleswhich can preserve the power of randomization for fairness,while providing the advantages of determinism, i.e., the basestation’s ability to predict tags’ state in each slot. In this classof protocols, all tags run the same pseudorandom numbergenerator and determine their state (awake or asleep) at eachslot based on a probabilityand the stored state of the randomnumber generator. In order to avoid a complete overlap of thewake-up schedules, the pseudorandom generator of each tagis initialized with a unique seed, which is known at the basestation. Therefore, by using the same pseudorandom numbergenerator, it is possible for the base station to determine theschedules of the tags it wants to transmit to. The base stationcan initiate changes in the value of as a function of theload, the number of tags, etc. Moreover, different tags canoperate with different ’s based upon tags’ individual expectedtraffic rates, making the protocol appropriate for handlingheterogeneous traffic patterns.

III. A NALYSIS

To compare the performance of the various protocols pro-posed, we consider the behavior of a single-cell tag system.Since the time needed to successfully receive a packet isexactly one slot, given that the tag is awake, then the slotduration is , with denoting the packet transmissiontime and the maximum propagation delay. Thepresented evaluation utilizes the following definitions:

average waiting time in slots experienced by a packet inthe system from arrival at the base station to successfulreception at the tag;average percentage of slots in which a tag is awake;average number of packets in the system.

The energy measure proposed does not take into accountthe contribution during slots where a tag is asleep. The energyused while in this state is consumed over the lifetime of thetag, whether the tag is being used or not. Therefore, only thepercentage of awake slots is necessary for comparing differentenergy-conserving access protocols.


We assume that the packets arrive at the base stationaccording to a Poisson process with interarrival rate. Eachpacket is addressed to a single destination selected from eithera uniform or a Gaussian distribution. The latter is introduced inorder to model the heterogeneous nature of the traffic, whichis common to many tag applications. We use the followingnotations:

mean arrival rate expressed in packets per slot

mean service rate expressed in packets per slot;utilization factor

A. Analysis of the Grouped-Tag TDMA

The service time for packets whose destinations are indifferent groups is independent. Therefore, for each group,we can associate a different queueing system whose Poissontraffic stream has rate . Every time the queue is empty, theserver goes “on vacation” for one TDMA cycle (slots). Otherwise the service time of a packet is constant andequals one. Thus, the average waiting time in the queue for apacket addressed to groupis equivalent to that of anqueueing system with vacations [2]

(1)

(2)

Let denote the average number of slots, as a function of theparameter , during which a packet is waiting in the system.Then

(3)

(4)

It remains to calculate the . For the sake of presentation, wecompute these values under the assumption is integer.However, it is easy to see that the approach can be easilyextended to the noninteger case. The group interarrival rateparameters depend on the destination distribution utilized.Under a uniform destination distribution, the percentage ofthe global traffic dedicated to each group is proportional tothe number of tags in that group. Thus

(5)

We next consider the case of when the packets’ destinationsare chosen according to a Gaussian distribution with a densityfunction , average micrometers, and variance. ,where denotes the probability of choosing a packet in theth group. is the area of the region bounded above byand

the interval associated to theth group, normalized by the areaof the usablepart of the Gaussian (i.e., the one correspondingto the interval ). So

(6)

where

(7)

and

(8)

To compute the average energy consumption, we observe thateach tag is awake exactly once in a grouped-tag TDMA cycle.Thus

(9)

B. Analysis of the Directory Protocol

We analyze an improved version of the directory protocol,where the directory is ordered by increasingid of packets’destinations and the first two records of the directory containthe extreme values of the transmission interval. Let

be the number of different records which fit intoa slot. Then, the service time of a group is , whereindicates the time needed for transmitting the directory andis given by . When a cycle ends and no completedgroups are in the queue, the tags go to sleep for an idle intervalof slots. Thus, a server can start processing a new group if,and only if, there is at least one completed group in the queueand either an idle or a service cycle ended in the previousslot. We compute the average waiting timeof a packet inthe system as

(10)

(11)

where

average waiting time of a packet in the queue beforeits group is completed and isaverage waiting time of a group in the system;average time between the successful reception of apacket and the completion of its group’s transmission

.

To compute we construct the following analytic model.We observe the system at the regeneration points embedded atthe beginning of each slot and model the system as a discretetime, infinite Markov chain .Each state denotes the numberof completed groupsin the system, the number of nongrouped packets in thequeue, and the index of the current slot in either the idle

or the servicecycle. Note that within either a service or idle cycle, we onlyhave to increase the position of the slot and keep track of thearrivals, while during the last slot of a cycle we also have todecide the nature (busy/idle) of the next interval and possiblyindicate the exit of a group from the system. We obtain thefollowing transition probabilities.


Case:

otherwise(12)

Case:

otherwise(13)

Case:

otherwise

(14)

is the probability of messages originated by a Poissonprocess during a slot and is given by

(15)

The steady state probability of being in thestate is obtained by solving the following system of equations:

(16)

Let us denote as the probability of having completedgroups in the system

(17)

The average number of completed groups in the systemcan then be given by

(18)

Finally, applying Little’s theorem, we can compute the averagewaiting time of a group in the system

(19)

The energy consumption can be computed as

(20)

where

percentage of slots belonging to idle cycles and isgiven by

(21)

percentage of busy slots,average energy consumption per tag per slot duringan idle period and is equal to ;average energy consumption per tag per slot during aservice cycle

(22)

and indicates the average energy consumptionduring the directory transmission.

Let us define as the probability that theth tag isawake during the th slot of the directory. expresses theprobability that theth tag is in the transmission interval and atleast packets have been chosen in ,then

(23)

When using a uniform destination distribution

(24)

We now assume that the packet destination is chosen accordingto a Gaussian distribution of averagem and variance . Theprobability of choosing the th tag as the destination isgiven by

(25)

Then the probability associated to a sequenceis

(26)

Let us call the set of distinct sequences havingelements in and including in the transmission

interval, then

(27)

It remains to compute . Let be the thsequence we get when we enumerate all the possible ways tochoose elements out of a set of cardinality.

Case:

(28)

(29)


otherwise(30)

Case: :

(31)

where

otherwise(32)

C. Analysis of the Random Access Protocol

We evaluate this protocol’s performance by introducing adiscrete time infinite Markov chain embedded at the beginningof a slot . The states represent the numberof packets in the system.

Thus

(33)

otherwise(34)

Let denote the steady-state probabilities given by thesolution of the following linear equations:

(35)

expresses the probability that there arepackets in thesystem. Then, can be computed as follows:

(36)

and using Little’s result

(37)

Finally, from the definition of energy consumption given inthe previous section, it follows that .

D. Approximate Analysis of the Pseudorandom Protocol

We analyze the protocol under the assumption that all tagsuse the same awake probability parameter. To obtain an exactdescription of the system behavior through a Markov chain, thestates should include the number of packets addressed to eachtag, since the probability of successfully transmitting dependson the number of unique packet destinations in the queue. Itis therefore impossible to analyze systems of a realistic size.We solve this problem by making use of Stern’s independenceassumption [10], stating that at the beginning of a time slot,each message draws a new destination from a given uniformdistribution. The Markov chain describing the system is thenrepresented by the total number of packets at the beginning of

a slot. The transition probability matrix is given similar tothe random access case, by

(38)

otherwise

(39)

is the probability that packets are addressed todifferent destinations and is given by

(40)

We note that the difference between the pseudorandom se-quences and the random access protocol is reflected in thedifferent definition of successful reception. It is now sufficientthat at least one destination is awake during the current slot,instead of only the destination of the packet chosen by thebase station.

The steady-state probabilities are the solutions of thefollowing set of equations:

(41)

We can now compute the average numberof packets in thesystem

(42)

and using Little’s result

(43)

Finally, as in the previous section, the average energy con-sumption is obviously .

IV. PERFORMANCE

For validation purposes, we simulated the reception of15 000 packets for each of the protocols in a system of1000 tags. Interarrival rate parameters were equal to 0.05, 0.2,and 0.5. We considered the cases of uniform and heterogeneousdestination distributions. The latter was simulated in two cases,the first being a Gaussian with mean and variance 10 ,and the second a Gaussian with mean and variance .To validate the approximate analytical models described inthe previous section, we compared computationally derivedresults of the analysis with the results of the simulations.The infinite Markov chain of the pseudorandom protocol wasapproximated by truncating the transition probability matrixafter the first 100 states while up to 800 states were consideredfor the directory protocol with the limitation


Fig. 1. Classical access protocols.

Fig. 2. Grouped TDMA protocol, uniform destination distribution, and op-timal group size (varying interarrival rates with a sampling of analysis resultsdepicted in discrete points for� = 0:05).

and . Our simulator counted in each slotthenumber of awake tags and the delay incurred to each re-ceived packet. These quantities were then used to compute theaverage access delay packets and the aver-age energy consumption ( simulation length,where the length of the simulation was the amount of time togenerate and send 15 000 packets with the appropriate Poissonarrival distribution). The average delay for the grouped-tagTDMA sequences computed with the analysis was consistentlyaround half a slot greater than that obtained through thesimulations, since using slotted arrivals “shifts” the arrivalsof packets from within a certain slot to the end of it. Inall other cases, the comparison of the simulations with theanalytic results showed an excellent approximation with errorunder 2%. A sampling of the analytical results, for each ofthe protocols for the case of , is displayed as discretepoints in Figs. 2–4.

The simulation results are shown in Figs. 1–6. Fig. 1 depictsthe energy versus delay behavior of the random access andclassical TDMA protocols. Notice that it is necessary to havea much coarser scale in order to show the results for these twoprotocols. The random selection criteria adopted by the base

Fig. 3. Directory protocol, uniform destination distribution, and optimalK

(varying interarrival rates with a sampling of analysis results depicted indiscrete points for� = 0:05).

Fig. 4. Probabilistic sequences protocol and uniform destination distribution(varying interarrival rates with a sampling of analysis results depicted indiscrete points for� = 0:05).

Fig. 5. Energy conserving protocols and uniform destination distribution.

station in the random access protocol makes a synchronizationof the two schedules unlikely and leads to poor performanceof the protocol, which is stable only for high values of


Fig. 6. Energy conserving protocols and Gaussian destination distribution(� = N=2; �2 = 10N).

Fig. 7. Energy conserving protocols and Gaussian destination distribution(� = N=2; �2 = N).

and therefore a high energy consumption (is a necessarycondition). The classical TDMA shows on the other hand avery good energy consumption (0.001 for ) butan extremely long delay ( slots) since the cycle timeincreases as the number of tags.

Figs. 2–4 compare the performance of the three proto-cols proposed using a uniform destination distribution. Fig. 2shows the tradeoff between energy and delay for optimizedvalues of group size using the grouped-tag TDMA protocol.The choice of a larger size decreases the average delay, butincreases the energy consumption per tag per slot. We alsonote that the performance is better for low loads becausethere are fewer packets available for transmission for the sameslot in a cycle. The performance of the directory protocoldepends on the choice of the parametersand (Fig. 3).By waiting for a larger number of packets, we reduce theenergy required for reading the directory but heavily increasethe average delay experienced by a packet due to having towait for its group to be completed before it can be transmitted.The length of this waiting time decreases asincreases.Given a value of , a higher results in a lower energyconsumption but an increase in delay before the start of

next group’s transmission. Fig. 4 shows the energy versusdelay graph for the pseudorandom protocol given a uniformdestination distribution. The performance achieved is slightlyworse than that of the grouped-tag TDMA, although thisdifference decreases asincreases. The simulation results foreach protocol under a traffic load of is reproduced inFig. 5 for ease of direct comparison.

Figs. 6 and 7 compare protocol performance for the case ofheterogeneous traffic. The directory protocol is hardly affectedby the destination distribution. The only slight difference is dueto the reduced number of tags which must be awake duringthe directory slots. Therefore, the results for this protocolare not displayed in the graphs. Instead, we show how theperformance of the grouped-tag TDMA degrades rapidly underheterogeneous traffic. Packets belonging to a group with ahigh probability of traffic are severely delayed due to thehigh volume of localized destinations. Since the number ofpackets in the queue addressed to the same group increases aseither the load increases or the Gaussian distribution becomesnarrower, the performance of the protocol decreases in boththese cases. The case of , with a destination distributionof Gaussian , is completely unstableand therefore not presented in Fig. 7. It can be seen that theheterogeneous traffic affects the behavior of the pseudorandomprotocol, since a higher concentration of packets decreases theprobability of successful transmission. However, this effectcan only be seen for extremely high loads (or very narrowGaussian destination distributions) and is much weaker thanthe one noticed for the grouped-tag TDMA protocol. Fora wide Gaussian destination distribution ((Gaussian

)) the pseudorandom protocol outperformsthe grouped-tag TDMA starting from , while fora narrower Gaussian (Gaussian ) thepseudorandom protocol is much more effective.

V. CONCLUSION

In this paper, we addressed the problem of wireless accessprotocols which include an energy constraint. We showedthat classical multiple access protocols such as TDMA andAloha are either not energy-conserving or lead to unacceptabledelays. Therefore, we have proposed three energy-conservingprotocols: grouped-tag TDMA, directory, and pseudorandom.Careful selection of protocol parameters addresses the goal ofminimizing the energy required for reception of packets, whilemeeting the application delay constraints. Both analytical andsimulation results were presented and described.

All three protocols perform very well for various loads.In particular, the grouped-tag TDMA protocol achieves thebest performance for either a low traffic load or a uniformdestination distribution. When the destination distribution be-comes more realistically clustered, other protocols such as thedirectory or pseudorandom protocols need to be adopted.

The pseudorandom protocol consistently outperforms thedirectory protocol in both energy and delay for all loadsand most destination distributions. However, for cases wherethe destination distribution becomes heavily clustered, it isapparent that the directory protocol is the best solution, unless


dynamic handling of the protocol parameters is introduced.For typical RFID applications, the pseudorandom protocolappears to be capable of providing a simple and effectiveaccess method which includes energy constraint.

REFERENCES

[1] IEEE, “Wireless LAN media access control (MAC) and physical layer(PHY) specification,”IEEE 802.11 Draft Version 4.0, May 1996.

[2] D. Bertsekas and R. Gallager,Data Networks. Englewood Cliffs, NJ:Prentice-Hall, 1992.

[3] E. P. Harris, S. W. Depp, E. Pence, S. Kirkpatrick, M. Sri-Jayantha, andR. R. Troutman, “Technology directions for portable computers,”Proc.IEEE, vol. 83, pp. 636–658, Apr. 1995.

[4] D. Dunn, “RFID engine chugs onto scene,”Electron. Buyer’s News,Feb. 1996.

[5] T. Imielinski, S. Vishnatwan, and B. R. Badrinath, “Data on air,”IEEETrans. Knowledge Data Eng., vol. 8, July 1996.

[6] I. Chlamtac, C. Petrioli, and J. Redi, “An energy-conserving accessprotocol for wireless communication,” inProc. IEEE Int. Conf. Commun.(ICC’97), Montreal, Canada, June 1997, pp. 1059–1062.

[7] I. Chlamtac, C. Petrioli, and J. Redi, “Extensions to the pseudo-randomclass of energy-conserving access protocols,” inProc. 2nd IEEE Int.Workshop Wireless Factory Communication Systems, Barcelona, Spain,Oct. 1997, pp. 11–16.

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Imrich Chlamtac (M’86–SM’86–F’93), for photograph and biography, seethis issue, p. 9.

Chiara Petrioli (S’96–M’99) received the Laureadegree with honors in computer science in 1993, andthe Ph.D. degree in computer engineering in 1998,both from the University of Rome “La Sapienza,”Italy.

She is currently a research scientist at the Po-litecnico di Milano, Italy, where she does researchon QoS over IP and wireless networks. Previously,she was with the Italian Space Agency (ASI) andAlenia Spazio. In 1996 and 1997, she worked asVisiting Research Associate at Boston University

and the University of Texas at Dallas, doing research in the area of energyconserving protocols.

Dr. Petrioli was a Fulbright scholar and has been reviewer of many journaland conference papers.

Jason Redi(S’89–M’98) received the B.S. degreein computer engineering from Lehigh University,Bethlehem, PA, in 1992, and the M.S. and Ph.D.degrees in computer engineering from BostonUniversity, MA, in 1994 and 1998, respectively.

He is currently with BBN Technologies in themobile networking systems group. His current workconsists of ad hoc networks, energy-conservingrouting, adaptive media-access protocols, andnetwork security. Prior to BBN, he was a PrincipalInvestigator in wireless network protocols for

Boston Communications Networks, and a Consultant in the area of MAC-level protocols for Motorola ISG. He is the author of nearly 20 papers andpatents in the area of mobile computing and communications.

Dr. Redi is a Founding Editor ofACM SIGMOBILE Mobile Computingand Communications Review (MC2R), Technical Editor of theInterJournal ofComplex Systems, and serves on the organizing committee of MobiCom’99.He is a member of numerous technical program committees, and a memberof ACM, Tau Beta Pi, Sigma Xi, and the Order of the Engineer.

Documents

Energy-conserving access protocols for identification networks