Energy-Conserving Access Protocols for ransmitting Data in Unicast and Broadcast Mode I. Papadimitriou and M. Paterakis Laboratories of elecommunication and Information and Computer etworks Department of Electronics and Computer Engineering echnical University of Crete 73 00 Chania, GREECE E-mail: ipapad@egnatia.ee.auth.gr, pateraki@telecom.tuc.gr ABSRAC In the recent years many applications have emerged, where the demand for communicating with a very large number of small-size and low-cost nodes becomes a necessity. Such applications involve for example, Radio Frequency IDentification (RFI and smart card networks, or even mobile computing devices, in general. A critical energy constraint is imposed on the communication (access) protocols used in these systems, so that the total time a node needs to be active for transmitting or receiving information should be minimized. Another area with great interest is that of I. IRODUCIO Radio Frequency Identification (RFI and smart card networks are the most characteristic examples of Automatic Identification and Data Capture (AIDC) applications, where the energy saving is a critical system parameter. he object of any RFID system is to carry data in suitable transponders, generally known as tags, and to retrieve data, by machine-readable means, at a suitable time and place to satisfy particular application needs. Data within a tag may provide identification for an item in manufacture, goods in transit, a location, the identity of a vehicle, an animal or individual. A typical IDentification Ework (IDE) is composed of a number of interconnected base stations, communicating over a shared wireless channel to a large number of small low-cost wireless nodes or tags. hese tags usually contain some sort of microprocessor power 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. Unlike previous work on energy-conserving protocols for unicast data transfer only [-, we consider that most of the packets are addressed to all tags, so the base station broadcasts each of these packets. In [, it is pointed out that the classical multiple access protocols, such as those described in [3, do not satisfy the dual requirement of low energy and acceptable delays. herefore, a new protocol, named pseudo-random, is proposed and it is shown that it performs well for various broadcast-based information systems which, compared to traditional unicast data transfer, can be much more efficient for disseminating information to a large number of nodes in applications where there is a high degree of commonality among node interests. Unlike previous work on energy-conserving protocols with unicast data transfer, we propose and evaluate broadcast-based communication protocols with energy constraint, in which the number of time slots during which nodes need to be in the active state is minimized, while the access delays are kept low. loads and its behavior is slightly affected by the use of heterogeneous destination distributions. his protocol combines the fairness of random access protocols with the low energy requirements of classical DMA. In [, apart from the pseudo-random protocol, two other protocols, the Grouped-ag DMA and the Directory protocol, are proposed. All three protocols perform well for various loads, and any performance differences among them depend on the traffic load and the destination distributions. For typical RFID applications, the pseudo-random protocol is proposed as being capable of providing a simple and effective access method, which includes energy constraint. In this paper we adopt many characteristics of the pseudo-random protocol and focus on the design and implementation of communication scheduling algorithms, so as to reduce the average delay of the packets that the base station has to transmit to the tags. he allowable delay is an application-dependent constraint. Using RFID tags designed to require a minimum of energy to operate is the solution to the constraint for low energy consumption. In order to conserve battery life, the tag can enter a sleep state, where its CPU is in a low power mode and radio reception is disabled. he communication with the base station is achieved when the tag enters the awake state, in which the CPU operates at full energy. We examine two scenarios: the first is the case of broadcast packet transmissions to the tags, where each packet is addressed to all tags and the second is the case
where most of the packets are broadcast and the rest are unicast packets. Each of the latter packets is addressed to only one destination (selected using a uniform distribution). We first describe the network model and the pseudo-random protocol. Section III presents a detailed description of the algorithms proposed for both cases. Section IV presents the results of our experimental study together with performance comparisons of the examined algorithms. he paper is concluded in section V. II. EWORK MODEL AD PROOCOL DESCRIPIO We consider a single-cell wireless communication system where a base station communicates with tags through a radio channel of bandwidth B. he communication is packet-oriented. We assume that the time is slotted and that the base station s transmissions are synchronized to the beginning of the slots. he packet length, c, is constant and exactly one packet can be transmitted during each slot. In our system model, we do not explicitly treat transmission errors. We view the access protocol as consisting of two components: the transmission scheduling strategy at the base station, which in each slot selects a packet for transmission from the arrival queue, and the wake-up schedule at each tag, which determines the slots in which the particular tag is awake. For the latter, we adopt the wake-up schedule of the pseudo-random protocol [-, where all tags run the same pseudo-random number generator and determine their state (awake or asleep) at each slot based on a probability p and the stored state of the random number generator. In order to avoid a complete overlap of the wake-up schedules, the pseudorandom generator of each tag is initialized with a unique seed, which is known at the base station. herefore, by using the same pseudo-random number generator, it is possible for the base station to determine the schedules of the tags it wants to transmit to. Our main contribution is the design and implementation of certain transmission scheduling algorithms, aiming to reduce the average packet delay and the variability of the packet delays. III. DESCRIPIO OF RASMISSIO SCHEDULIG ALGORIHMS A. Broadcasting Case In this case each packet is addressed to all tags, so the base station broadcasts each packet. We consider the transmission of a broadcast packet complete when all tags have received the packet. hen the packet is deleted from the base station s queue. A.FCFS (First Come First Served) his is the worst performing algorithm for our model. he base station stores all arriving packets in a buffer and examines them according to their age, using the oldest packet criterion. his results to many wasted slots, as for example if the packet currently at the head of the queue must be delivered to the last two destinations (assume it has already been transmitted to the remaining - tags), but these last two destinations are not awake during the current slot. So the latter is wasted. We examine this algorithm because its performance provides a lower bound to the performance of any workconserving algorithm that can be employed by the base station. Furthermore, we can provide an analytical performance evaluation for this algorithm, which is not presented here, as it is similar to the one presented in section B. for the second examined case. A.FCFS-ES(First Come First Served-o Empty Slots) his algorithm improves FCFS in that it does not leave any slot go unused as long as there is work to be done. he base station examines the packets according to their age, using the oldest packet criterion, however, when the destinations left for the currently examined packet are not awake during the current slot, the base station checks the next packet in its queue. At the beginning of the next time slot the examination of packets starts again from the oldest packet in queue. A3.MDF m (Most Destinations First) m he base station checks some of the oldest packets from its waiting queue (e.g. the 3 oldest) and transmits the one that can reach the most destinations. he number of packets checked is determined by the algorithmic parameter m. his algorithm is a modification of the MRF (Most Requests First) algorithm [5. A4.(DxW) m (Destinations times Wait) m his algorithm is a modification of the RxW algorithm [6, it combines the benefit offered by MDF with the fairness of FCFS. DxW broadcasts a packet either because it can be transmitted to many tags or because it has been waiting for long in the queue. he base station again checks the m oldest packets, computes for each of them the product D*W (where D is the number of tags the packet can be sent and W the age of the packet, in slots) and transmits the packet with the largest value of D*W. A5.P-MDF m and P-(DxW) m (Preemptive MDF m and Preemptive (DxW) m ) hese algorithms correspond to modified versions of the MDF m and (DxW) m algorithms, respectively. he only difference is that, while the base station examines the m oldest packets, if a packet is found that can be transmitted during the current slot to all tags that have not received it yet, then this packet is selected, no matter if the value of D or D*W for another packet maybe larger. he improvement introduced is due to the fact that no additional delay is imposed on a packet for which all tags that have not received it yet are awake during the current slot, so this packet can be transmitted and removed from the base station queue. hese two algorithms take advantage of the properties of the Shortest Job First algorithm [7 and of the preemptive broadcasting [8, that is, interrupting a job
(e.g., a long one) in favor of another (e.g., a shorter one), to be resumed later from where it was interrupted. B. Mixed Unicasting and Broadcasting Case In this case it is assumed that most of the packets are addressed to all tags (broadcasted by the base station) and the rest are unicast packets. At this point we make an assumption, which we believe is close to reality. We assume that it is necessary for the unicast packets to have priority against broadcast packets. A real situation could be the following: a tag enters a controlled access area. hen, there should be an instantaneous point-to-point communication with the base station, so that the latter identifies the agid. his communication must have higher priority against the broadcasting of packets to tags, already identified and placed somewhere in the area. B.FCFS (First Come First Served) his is the same algorithm, as described earlier. he base station stores all arriving packets (both unicast and broadcast) in a common buffer and examines them according to their age, using the oldest packet criterion. Again, this algorithm is examined because it provides a performance lower bound. Furthermore, we can provide the following analysis for it. Analysis of FCFS algorithm We first make the following definitions: b Random variable denoting the number of slots needed for a broadcast packet to be transmitted to all tags (i.e., the broadcast packet service time at the base station queue) ) Average packet service time u ) Average unicast packet service time b ) Average broadcast packet service time Average total packet delay X Fraction of unicast packets We assume that the packets arrive at the base station according to a Poisson process with arrival rate λ (expressed in packets per slot). In [4 it has been shown that the packet arrival stream at the base station from Poisson to self-similar traffic does not have a substantial effect on the energy vs. delay characteristics of the pseudo-random and other energy-conserving protocols. he probability that b is equal to is P ( ) p () equal or smaller than P ( ) [ ( p) () and retroactively, we have that P ( b k ) [ ( k p ) (3) he average broadcast packet service time, b ), is given by ) k * P( k ) P( k ) k k [ P( b < k ) k he average number of slots needed for a unicast packet to be transmitted to its destination, u ), is /p and for the average packet service time ), we have )X* u ) + (-X)* b ) (5) k { [ ( p) k } (4) In order for our system to be stable [3, the mean arrival rate of packets must be smaller than the maximum packet service rate, that is λ < λ max (6) ) k X *(/ p) + ( X)* { [ ( p) } hus far, we have derived an expression, which gives the maximum arrival rate of packets that can be supported, given the system parameters p, and X. λ max converges to a positive value, since for large values of k, the terms added to the series k { [ ( p ) } converge to zero. k ext, observing that our system is an M/G/ queue, we use the Pollaczek and Khinchin formula [3 to derive the following expression for the average total packet delay λ * ) ) + *[ λ * ) (7) where ) is given from (5) and ) is the second moment of the packet service time, given by ) X * (8) u ) + ( X )* ) b ) is the second moment of service time for broadcast packets only and is given by ) k As for u ), it is given by u k ) k * P( k) k k *{[ ( p) k k * P( u k [ ( p) k) k k *[ P( k) P( k ) (9) (0) B.FCFS-ES(First Come First Served-o Empty Slots) his algorithm improves FCFS for the mixed case of unicast/broadcast packets. he base station again examines the packets according to their age, using the oldest packet criterion, but when the tag to which a unicast packet is addressed to, or the destinations left for a broadcast packet are not awake during the current slot, we don t let the slot go unused and the base station checks the next packet in its queue. At the beginning of the next time slot the examination of packets starts again from the oldest packet in queue. B3.L-(FCFS-ES) (wo Lists - First Come First Served-o Empty Slots) he base station stores unicast and broadcast packets in two different buffers (lists), with the list of unicast packets having higher priority, so as to meet the requirement for minimizing unicast packet delays. he examination of packets starts from the unicast list, where the FCFS-ES algorithm is applied and only if no packet is found for which its destination is awake, then the broadcast list is checked and the same algorithm is applied. B4.L-[P-(DxW) m (wo Lists - Preemptive (DxW) m ) he only difference of this algorithm from the previous one, is that the P-(DxW) m and not the FCFS-ES algorithm is applied to the list of broadcast packets. he k } k k *[( p) k * p
reason we chose the particular algorithm will be explained in section IV. IV. EXPERIMEAL RESULS For all simulations we considered a system of 500 tags, so as to compare the performance of the scheduling algorithms. he value of probability p was kept small (below 0.5), to meet the energy constraints. he arrival rates, λ, started at 0.0 packets/slot till the value λ max that can be supported by the system, depending on the algorithm applied. We simulated the reception of 30.000 packets for each algorithm. We note that the experimental results presented for the FCFS algorithm in both cases are almost identical with the corresponding analytical results. Figs. -3 concern the first case, of Broadcasting only. Figs. and present the average total packet delay (in slots) as a function of the load λ (in packets/slot), for 500 tags and p0.3. From the analysis of the FCFS algorithm in this case we find the value of λ max for the particular values of p and, to be λ max 0.0564. Clearly, for all values of λ in Fig. the FCFS-ES algorithm gives the smallest average total packet delay. his is due to the fact that the values of λ in this graph are quite small (smaller than λ max ) and there is rarely a large number of packets in base station s queue, so as to take advantage of the improvements offered by the other algorithms. he latter can be observed in Fig., where P-(DxW) outperforms all other algorithms. he fact that each algorithm gives unacceptable high delays after a certain value of λ gives an indication about the throughput of each of them, that is the maximum arrival rate of packets that can be supported, depending on the algorithm applied.fig. 3 presents the quotient of standard deviation to average total packet delay, defined as σd E{[ D } () as a function of λ, for 500 tags and p0.3. Again, it is seen that FCFS-ES algorithm gives the smallest σ D / for small λ s, till λ is about 0.05, followed by P-(DxW) for larger values of λ. At this point, we would like to summarize our observations about the parameter m, which determines the maximum number of packets examined by the base station, to select the one to transmit. One could claim that m should be set equal to, that is the base station should check all packets in queue. We found though, that for each value of λ there is an optimum value of m, which gives the minimum average total packet delay, compared with larger values of m. his is due to the fact that, when the base station has to select among many packets the one with the most destinations awake for example, it will postpone the transmission of an older packet, in favor of a packet, which has arrived later. So, the careful selection of m for all algorithms that use this parameter, offers an extra benefit. Due to lack of space we do not present graphs for different values of p and. It was found however, as Avg. otal Delay per Packet (slots) 60 50 40 30 0 0.0 0.05 0.0 0.05 0.03 0.035 0.04 FCFS FCFS-ES MDF Fig.. Broadcasting, 500, p0.3, λ [0.0, 0.04 Avg. otal Delay per Packet (slots) 500 400 300 00 00 0 0.045 0.05 0.055 0.06 0.065 0.07 0.075 0.08 0.085 FCFS FCFS-ES MDF Fig.. Broadcasting, 500, p0.3, λ [0.045, 0.085 Standard Deviation / Avg. otal Delay per Packet 0.4 0. 0.0 0.0 0.03 0.04 0.05 0.06 0.07 0.08 FCFS FCFS-ES MDF Fig. 3. Broadcasting, 500, p0.3, λ [0.0, 0.085 expected, that larger values of p incur lower average packet delays and σ D / values and increase the throughput of the algorithms. Exactly the opposite was observed when we increased the number of tags,. Figs. 4-7 concern the second case of mixed Unicasting and Broadcasting. Fig. 4 presents the average total delay for unicast packets only, D u ), (in slots) as a function of λ (in packets/slot), for 500, p0.5 and X0.3. From the analysis of the FCFS algorithm in this case, the value of λ max for the particular values of p, and X, is λ max 04. It can be seen that L-(FCFS-ES) and L-(P-DxW) perform very similarly, outperforming the other two algorithms, because of the higher priority they give to the unicast packets. In Fig. 5 the average total delay for broadcast packets, D b ), is presented for the same values of p, and X. For values of λ greater than λ max the improvement achieved with the algorithm L-(P- DxW) is obvious. his is explained by the results of Fig., where the algorithm P-DxW performed the best for λ s greater than λ max, in the case of broadcast packets
only. he fact that the scheduling algorithms perform almost the same for values of λ smaller than λ max is due to the small number of unicast packets and the negligible average service time of a unicast packet compared to that of a broadcast one. herefore, the algorithms, which give priority to unicast packets, do not add significantly to average total delay for broadcast packets. In Fig. 6 the quotient of standard deviation to the average total delay for unicast packets, defined as σ () Du Du) E{[ Du Du) } Du) is presented. he fact that the algorithms L-(FCFS- ES) and L-(P-DxW) give low and almost constant delay (Fig. 4) explains the almost constant σ Du /D u ) they also offer. In Fig. 7 we have the corresponding graph for broadcast packets. All algorithms, except FCFS, perform very similarly till λ0.06, and for larger λ s the L-(P-DxW) outperforms all. We recall the conclusions obtained from Fig. 3, where the algorithm P-DxW performed the best for large λ s, in case of broadcast packets only. We again make the same observations for the parameter m of the algorithm L-[P-(DxW) m. V. COCLUSIOS In this paper, we addressed the problem of energy conserving wireless access protocols, together with the demand for broadcasting information to a large number of nodes. We adopted many characteristics of the pseudo-random protocol, which minimizes the energy required, and focused on the design and implementation of algorithms to meet the application delay constraints. We examined two scenarios, one for broadcast only packet transmissions to the tags and the other for the mixed case of unicast/broadcast packet transmissions. he performance evaluation of the algorithms was obtained via simulations. REFERECES [ Imrich Chlamtac, Chiara Petrioli and Jason Redi, An Energy-Conserving Access Protocol for Wireless Communication, he IEEE Intl. Conf. on Comm., ICC'97, Montreal, Quebec, Canada, June 997. [ Imrich Chlamtac, Chiara Petrioli and Jason Redi, Energy-Conserving Access Protocols for Identification etworks, IEEE/ACM ransactions On etworking, Vol.7, o, Feb. 999. [3 Bertsekas, D. and Gallager, R., Data etworks, Prentice Hall, 99. [4 Jason Redi and Dimiter Avresky, Performance of Energy-Conserving Access Protocols Under Self-Similar raffic, he IEEE Wireless Commun. and etworking Conf., WCC 99, ew Orleans, LA, Sep.999 [5 H.D. Dykeman, M. Ammar and J.W. Wong, Scheduling Algorithms for Videotex Systems Under Broadcast Delivery, IEEE Intl. Conf. on Comm., ICC 86, oronto, Canada 986. Avg. otal Delay per Unicast Packet (slots) (logarithmic scale) Fig. 4. Unicasting/Broadcasting, 500, p0.5, X0.3 Avg. otal Delay per Broadcast Packet (slots) (logarithmic scale) 000 00 0 0.0 0.03 0.05 0.07 0.09 0. 0.3 0.5 FCFS FCFS-ES L-(FCFS-ES) L-(P-DxW) Fig. 5. Unicasting/Broadcasting, 500, p0.5, X0.3 Standard Deviation / Avg. otal Delay per Unicast Packet. 0.9 0.7 0.5 0.0 0.03 0.05 0.07 0.09 0. 0.3 0.5 FCFS FCFS-ES L-(FCFS-ES) L-(P-DxW) Fig. 6. Unicasting/Broadcasting, 500, p0.5, X0.3 Standard Deviation / Avg. otal Delay per Broadcast Packet 000 00 0 0.9 0.7 0.5 0.4 0.3 0. 0.0 0.03 0.05 0.07 0.09 0. 0.3 0.5 FCFS FCFS-ES L-(FCFS-ES) L-(P-DxW) 0.0 0.03 0.05 0.07 0.09 0. 0.3 0.5 FCFS FCFS-ES L-(FCFS-ES) L-(P-DxW) Fig. 7. Unicasting/Broadcasting, 500, p0.5, X0.3 [6 Demet Aksoy and Michael Franklin, Scheduling for Large-Scale On-Demand Data Broadcasting, IEEE IFOCOM Conf., 998. [7 E. Gelenbe and I. Mitrani, Analysis and Synthesis of Computer Systems, Academic Press, 980. [8 Swarup Acharya and S. Muthukrishnan, Scheduling On-Demand Broadcasts: ew Metrics and Algorithms, Proc. of ACM/IEEE MobiCom, Dallas, Oct. 998.