Bluetooth: Architecture, Protocols and Scheduling ... - Springer Link

Cluster Computing 5, 117–131, 2002  2002 Kluwer Academic Publishers. Manufactured in The Netherlands.

Bluetooth: Architecture, Protocols and Scheduling Algorithms RAFFAELE BRUNO, MARCO CONTI and ENRICO GREGORI Consiglio Nazionale delle Ricerche, Istituto CNUCE, Via G. Moruzzi, 1, Ghezzano 56010 Pisa, Italy

Abstract. The paper focuses on one of the emerging technologies for constructing a Mobile ad hoc network: Bluetooth. Bluetooth can be exploited on small scales, to build ad hoc wireless Personal Area Networks (WPAN), i.e., networks that connect devices placed inside a circle with radius of 10 m. The Bluetooth technology is just starting to appear on the market and its architecture and protocols are not widely known. For this reason, the first part of the paper contains a tutorial-oriented description of the Bluetooth architecture and protocols. In addition, in the paper we extensively discuss the performance of this technology by investigating, through simulation, the main performance figures: the protocol efficiency (channel utilization), the response time, and the system power (i.e., the ratio between throughput and delay). Specifically, the performance analysis focuses on the algorithms for scheduling the transmissions in a Bluetooth network. The Bluetooth Specification indicates a Round Robin scheduler as possible solution, that is each slaves is polled in a consecutive order. For this reason, we first study in depth the performance figures of a Bluetooth network with a Round Robin scheduler. Our results point out the inefficient Bluetooth behavior under asymmetric traffic conditions. To solve this problem we propose and analyze an innovative scheduling algorithm specifically tailored to the Bluetooth characteristic. This algorithm, named efficient double-cycle (EDC), dynamically adapts the polling frequency to the traffic conditions. A performance study indicates that our EDC scheduler always outperforms the Round Robin scheduler. Keywords: wireless PANs, ad hoc networks, MAC protocol, scheduling, performance evaluation

1. Introduction A mobile ad hoc network is a system of wireless mobile nodes dynamically self-organizing in arbitrary and temporary network topologies. People and vehicles can thus be internetworked in areas without a pre-existing communication infrastructure, or when the use of such infrastructure requires wireless extension [1]. Bluetooth technology [2] is a de-facto standard for Wireless Personal Area Networks (WPAN). A WPAN is an ad hoc network constituted by connected devices placed inside a circle with radius of 10 m. The Bluetooth specification is released by the Bluetooth Special Interest Group (SIG). In addition, the IEEE 802.15 Working Group for Wireless Personal Area Networks has started a project to publish and approve a standard derived from the Bluetooth Specification. The Bluetooth technology is just starting to appear on the market and its architecture and protocols are almost unknown. Currently, to understand the details of this technology a reader must spend a lot of time analyzing Bluetooth SIG documents or reading some of the recently appeared books [2–4]. For this reason in this paper we also include a tutorial-oriented description of the Bluetooth architecture and protocols. This description is directly obtained by synthesizing the standard documents pointing out the aspects that are relevant to understand the Bluetooth behavior and to study its performance. The technologies for WPANs offer a wide space for innovative solutions and applications that could bring to a radical change in everyday life. In particular, the Bluetooth technology is much more than a wireless connection for a nomadic access to the Internet or for cable replacement, but it wants to be an enabling technology for the global mobility of devices and services. We can foresee that a Bluetooth module

will be embedded not only in devices as cellular phones, mobile computers, GPS navigator, PDAs and so on, but in every digital device where computing and wireless connectivity features could be used for providing innovative services. Obvious applications arise from the possibility of forming ad hoc networks with our workspace electronic devices. Let us imagine a PDA that automatically synchronizes with our desktop computer and transfers e-mails, files and schedule information. It is out the office that the most innovative applications can be thought, introducing services with innovative characteristics that will be totally different from traditional services. For example, let us imagine to have an electronic device that, when we are arriving at home, automatically unlocks the door. When we are getting in, it can also turn on the house lights and adjust the heat or air-conditioning to our pre-set preferences. The Bluetooth applications so far presented, before they can be deployed, need a considerable effort from the research community to resolve several technical issues. One important issue is the concrete coexistence of Bluetooth technology with other wireless ad hoc technologies designed for using the same radio spectrum, like IEEE 802.11, [10]. Furthermore, most of the application scenarios considered require a high density of Bluetooth networks in relatively small areas. Therefore, other researches have studied the mutual interference due to the frequency reuse of overlapping networks [11–13]. A field that is critical is the management of the limited available bandwidth as Bluetooth networks deliver much lower bandwidth than wired networks, i.e., 1 Mbps vs. 10– 100 Mbps [6]. In this paper we primarily concentrates on this last issue. Since Bluetooth relies on a common transmission medium, the transmissions of the network stations must be coordinated by the Medium Access Control (MAC) protocol.

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This coordination is achieved by means of control information that is carried explicitly by control messages traveling along the medium. Specifically, the Bluetooth MAC behaves as a polling system [8]. Hence, the MAC protocol scheduling algorithm by determining the order in which stations are polled, is the main element to determine the network efficiency. The Bluetooth Specification indicates a Round Robin scheduler as a possible solution, i.e., each slave is polled in a consecutive order. However our performance studies, with a Round Robin scheduler, indicate an inefficient Bluetooth behavior when the traffic in the network is asymmetric. The original contribution of this paper is the proposal and evaluation of an efficient scheduling algorithm named Efficient Double-Cycle (EDC). Our algorithm preserves the fairness typical of a Round Robin scheme but reduces the polling overheads. This is achieved by tuning the polling order to the network traffic conditions to limit the channel-bandwidth wastage caused by the polling of empty stations. A performance study indicates that our EDC scheduler outperforms the Round Robin scheduler. Specifically, we show that our algorithm (when compared with Round Robin) increases the network efficiency and at the same time reduces the user response time. The design of a scheduler suitable for the Bluetooth MAC layer was also considered by Johansson et al. that propose a Fair Exhaustive Polling (FEP) [14]. Similarly to our algorithm, FEP tries to avoid the polling of inactive stations. Specifically, they define the meaning of active and inactive state for the slaves, and introduce polling sub cycles where only the active slaves are polled in a Round Robin fashion. Our algorithm further extends this idea (i) by separating the scheduling of the uplink and downlink transmissions, and (ii) by adopting a truncated binary exponential-backoff algorithm to determine the amount of time an inactive slave is removed from the polling cycle. The paper is organized as follows. Section 2 introduces the Bluetooth technology. In particular we concentrates on physical and data link layers. Then, section 3 presents a complete performance analysis for a Bluetooth piconet adopting a Round Robin polling scheme for the channel access. In section 4 we propose and evaluate our scheduling algorithm. Concluding remarks are summarized in section 5.

BRUNO, CONTI AND GREGORI

Bluetooth devices adopt the 48-bit addressing scheme defined by the IEEE 802 standard. From a logical standpoint, Bluetooth belongs to the contention-free token-based multiaccess networks. In a Bluetooth network, one station has the role of master and all other Bluetooth stations are slaves. The master decides which slave is the one to have the access to the channel. More precisely, a slave is authorized to deliver a single packet to the master only if it has received a polling message from the master. The units that share the same channel (i.e., are synchronized to the same master) form a piconet, the fundamental building block of a Bluetooth network. A piconet has a gross bit rate of 1 Mbps that represents the channel capacity before considering the overhead introduced by the adopted protocols and polling scheme. A piconet contains a master station and up to seven active (i.e., participating in data exchange) slaves contemporarily. Independent piconets with overlapping coverage areas form a scatternet when at least a unit participates to data exchange in more than one piconet. It is worth pointing out that a unit can be active in more than one piconet, but can be master into only one. A slave may communicate with different piconets only in a time-multiplexing mode. This means that, for any time instant, it can only transmit on a single piconet because it has to change its synchronization parameters before listening to different channels. 2.1. The Bluetooth architecture The main aim of the Bluetooth Specification is to guarantee the interoperability between different applications by supporting any kind of service and providing the mean to implement them (service discovery, connection oriented, connection less links). This is achievable only if a common Bluetooth data link and physical layer is used. As we can see in figure 1, the complete protocol stack contains a Bluetooth core of Bluetooth-specific protocols (dark green protocols in the figure) and non-Bluetooth-specific protocols. The Bluetooth core protocols comprise exclusively Bluetooth-specific protocols. In the next subsections we describe in details the Bluetooth Baseband and Radio layers. The radio layer provides the physical links among Bluetooth devices, while the Baseband layer provides a transport service of packets on the physical link. This service is exploited by the other Bluetooth data link layer protocol, i.e., the L2CAP, to permit

2. Bluetooth technology architecture and protocols The Bluetooth system is operating in the 2.4 GHz Industrial, Scientific, and Medicine (ISM) band. It is based upon a lowcost, short-range radio link, integrated into a microchip, enabling protected ad hoc connections for wireless communication of voice and data in stationary and mobile environments. The Bluetooth Special Interest Group (SIG) is working to impose the Bluetooth technology as the de-facto wireless standard for personal area wireless communication. The architecture and protocols presented in this paper refer to the Bluetooth 1.0B Specification released in December of 1999 [2].

Figure 1. Bluetooth protocol stack.

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to higher-level protocols and applications to transmit and receive their messages. The main features supported by L2CAP are: (i) protocol multiplexing: the L2CAP uses a protocol type field to distinguish between upper layer protocols; (ii) segmentation and reassembly: this feature uses two bits in the payload header, and is required because the Baseband packet size is smaller than the usual size of packets used by higher layer protocols; (iii) group management, i.e., the capability to map transparently groups of addresses, i.e., groups of devices, onto piconets without the need of knowing the Baseband routines; (iv) quality of service assurance: the L2CAP connection establishment permits the exchange of quality of service parameters, and the L2CAP would monitor the resource to assure the respect of agreed QoS. The LMP protocol is responsible for the set-up and management of physical links. The management of physical links consists of several activities: (i) putting a slave in a particular operating state (i.e., sniff, hold or park mode), (ii) monitoring the status of the physical channel and assuring a prefixed Quality of Service (e.g., LMP settles transmission power, the maximum poll interval, etc.). LMP also implements security capabilities at link level. Finally, RFCOMM is a serial line emulation protocol, i.e., a cable replacement protocol. It emulates RS-232 control and data signals over Bluetooth Baseband, providing transport capabilities for upper level services that use serial line as transport mechanism. The other protocols presented in the figure are applicationoriented protocol enabling applications to run over Bluetooth devices. In addition to this protocol layers, the Specification also defines a Host Controller Interface that provides a command interface to the baseband controller, link manager, and access to hardware status and control registers. 2.1.1. The Bluetooth physical layer A Bluetooth unit consists of a radio unit operating in the 2.4 GHz band. In this band are defined 79 different Radio Frequency (RF) channels that are spaced of 1 MHz. The physical layer utilizes as technique of transmission a frequency hopping spread spectrum (FHSS) where the hopping sequence is a pseudo-random sequence of 79-hop length, and it is unique for each ad hoc network we can establish. Therefore, the establishment of a physical channel is associated to the definition of a channel frequency hopping sequence which has a very long period length and which does not show repetitive patterns over short time interval. It’s possible to do it by exploiting the actual value of master clock and its unique Bluetooth device address, a 48-bit address compliant to the IEEE 802 standard. The FHSS system has been chosen to reduce the interference of nearby system operating in the same range of frequency (for example, IEEE 802.11 WLANs) and make

Figure 2. Multi-slot packet transmissions.

the link robust [8,9]. The nominal rate of hopping between to consecutive RF is 1600 hop/s. A Time Division Duplex (TDD) scheme of transmission is adopted. The channel is divided into time slots, each 625 µs in length, and each slot corresponds to a different RF hop frequency. The time slots are numbered according to the Bluetooth clock of the master. The master can transmit in even numbered time slots. Odd numbered time slots are reserved for slaves’ transmissions. The frame structure is shown in figure 2. The transmission of a packet nominally covers a single slot, but it may last up to five consecutive time slots. For multi-slots packets the RF hop frequency to be used for the entire packet is the RF hop frequency assigned to the time slot in which the transmission has begun. The changing of RF used after transmitting or receiving a packet reduces the interference from signals coming from other radio modules. The Bluetooth antenna has a nominal power that permit a range for radio link from 10 cm to 10 m. This range can be extended up to 100 m increasing the transmit power. 2.1.2. The Bluetooth Baseband layer The Baseband layer is responsible for: (i) the set-up of the physical connections between master and slaves; (ii) the sending and receiving of different packets upon the physical channel (channel access); (iii) the synchronization of devices belonging to a piconet on master clock, and (iv) the management of the different power saving state which the device can stay in. In the following we will describe in depth points (i) and (ii). Connections’ type. There are two types of physical links that can be established between Bluetooth devices: a Synchronous Connection-Oriented (SCO) link, and an Asynchronous Connectionless (ACL) link. The first type of physical link is a point-to-point, symmetric connection between the master and a specific slave. It is used to deliver delaysensitive traffic, mainly voice. In fact the SCO link rate is 64 kbps and it is settled by reserving a couple of consecutive slots for master-to-slave transmission and immediate slave-tomaster response. The SCO link can be considered as a circuitswitched connection between the master and the slave. The second kind of physical link, the ACL link, is a connection between the master and all slaves participating to the piconet, and it can be considered as a packet-switched connection between the Bluetooth devices that supports point-to-multipoint transmissions from the master to the slaves. The ACL channel guarantees the reliable delivery of data: a fast Automatic

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Repeat Request (ARQ) scheme is adopted to assure data integrity. Channel access. As stated before, the channel access is managed according to a polling scheme. The master decides which slave is the only one to have the access to the channel by sending to him a packet. The master packet may contain data or can simply be a polling packet. When the slave receives a packet from the master it is authorized to transmit in the next time slot. For SCO links the master periodically polls the corresponding slave. Polling is asynchronous for ACL links. Figure 3 presents a possible pattern of transmissions in a piconet with a master and two slaves. Slave1 has both a SCO (dark-gray packets) and an ACL (light-gray packets) link with the master, while Slave2 has an ACL link only (medium-gray packets). In this example the SCO link is periodically polled by the master every six slots, while ACL links are polled asynchronously. Furthermore, the size of the packets on an ACL link is constrained by the presence of SCO links. For example, in the figure the master sends a multi-slot packet to Slave2, which, in turn, can reply with a single-slot packet only, because the successive slots are reserved for the SCO link. The general format of Bluetooth packets is shown in figure 4. As we can observe three main parts characterize the packet. The Access Code (AC) is used for synchronization

Figure 3. Packet transmissions on SCO and ACL links.

and identification. In particular, after a physical link is established, it is identified through a Channel Access Code (CAC) that is derived from the master clock and its Bluetooth device address. The CAC permits the devices to identify packets coming from a piconet which they don’t belong to. The header integrity is doubly protected. Firstly, a HEC is introduced, and then the total header is encoded with a rate 1/3 FEC. The AM_ADDR, that is the address, is 3-bit long, and hence it is possible to have only seven active slaves (the all zero sequence is a broadcast address). The ARQN and SEQN bits are used for the ARQ scheme and are meaningful only for ACL packets. The different kinds of packets, i.e., control packets, ACL packets and SCO packets, are distinguished through the TYPE bits. The SCO packets, denoted by HVy, are never retransmitted and the payload isn’t protected by a CRC. The subscript y indicates how many SCO connections may be concurrently active. In addition, to these three pure SCO packets, a DV packet is defined which can carry asynchronous data but is still recognized on SCO links. The ACL packets are of two different groups, one denoted DMx and the other one denoted DHx. The former has a payload encoded with a rate 2/3 FEC and the latter hasn’t got a FEC encoding. The subscript x stands for the number of slots that are necessary to transmit the packet. All ACL packets have a CRC field for checking the payload integrity. ACL packets have also a payload-header and a payload-CRC field. The payload header is one or two bytes long, depending on packet type, and it specifies: (i) the logical channel (i.e., if the packet is a LMP message, or the start of an L2CAP message, or the continuation fragment of an L2CAP message), (ii) the user payload length, and (iii) the flow control on the logical channels. Tables 1 and 2 summarize SCO and ACL packet characteristics, respectively. In addition, the tables report, assuming a piconet with two only devices, the maximum Table 1 SCO packets.

Figure 4. Bluetooth packet structure (fields’ length in bits).

Type

User payload (bytes)

FEC

CRC

Symmetric max rate (kbps)

HV1 HV2 HV3 DVa

10 20 30 10 + (0–9)D

1/3 2/3 no 2/3 D

no no no yes D

64.0 64.0 64.0 64.0 + 57.6 D

a Items followed by “D” relate to data field only.

Table 2 ACL packets. Type

User payload (bytes)

FEC

CRC

Symmetric max rate (kbps)

DM1 DM3 DM5 DH1 DH3 DH5

0–17 0–121 0–224 0–27 0–183 0–339

2/3 2/3 2/3 no no no

yes yes yes yes yes yes

108.8 258.1 286.7 172.8 390.4 433.9

Asymmetric max rate (kbps) Forward Reverse 108.8 387.2 477.8 172.8 585.6 723.2

108.8 54.4 36.3 172.8 86.4 185.6

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channel throughput for symmetric and asymmetric communications. 2.1.3. Baseband routines for the ad hoc networking The Bluetooth technology has been devised for providing a flexible wireless connectivity among digital devices. Before starting a data transmission a Bluetooth unit needs to discover if any other Bluetooth unit is in its operating space. To do this, a Bluetooth unit enters the inquiry state. In this state, it continuously sends an inquiry message, i.e., a packet with only the access code. A station responds to the inquiry by exploiting a random access protocol. After an inquiry, a Bluetooth unit has discovered the Bluetooth addresses of the other Bluetooth units located in its radio coverage area, and has collected an estimation of their clocks. If it wants to activate a new connection it has to distribute its own Bluetooth device address and clock, and this is the aim of paging routines. The unit that starts paging is automatically elected the master of the new connection, and the paged unit is the slave. When a connection is established, the active slaves have to maintain the synchronization. This is carried out by active slaves simply listening to the channel at every master-toslave slot because only the CAC is necessary and every kind of packets is usable for this purpose. Further details about the ad hoc networking features for the Bluetooth technology, can be found in [2].

3. Performance evaluation of Bluetooth The maximum asymmetric throughput of 723.2 kbps reported in table 2 is only a reference value. The real throughput depends on many factors, e.g., flow-control mechanisms adopted by upper layers (preliminary results can be found in [15,16]), characteristics of ingoing traffic, segmentation overhead introduced by each layer, and so on. The main target of this work is not to analyze the interaction of the Bluetooth technology with transport mechanisms of upper layer protocols, but to evaluate the Bluetooth Baseband layer performances in a WPAN environment where several point-to-point connections are established. Hereafter, the performance evaluation of Bluetooth has been carried out by simulation. Results presented in this section have been obtained by applying the independent replication technique with a 90% confidence level. In the following sections we will describe the network and traffic models, and the performance indices. 3.1. The piconet model The simulated network topology is constituted by a single piconet with a master and six slaves. Each slave has an IP packet generator. When an IP packet is created (according to a specific arrival process) by the slave, it is encapsulated into an L2CAP packet that adds the 4-byte L2CAP header, and it is sent to the local transmission queue. This local queue has a

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finite size BS and the queued packets are served according to a FCFS policy. Large L2CAP packets must be segmented into smaller Baseband packets before their transmission. A new L2CAP packet cannot be served till all fragments (generated during the segmentation) of the previous L2CAP packet have been successfully transmitted. The segmentation procedure is accomplished, just before the transmission, in such a way to generate the minimum number of Baseband packets. In particular let be xL2CAP the number of bytes to be sent, then a new DHx packet will be created with a user payload UP, according to the following procedure: xL2CAP 27 ⇒ DH1

with UP = xL2CAP,

27 < xL2CAP 183 ⇒ DH3 xL2CAP > 183 ⇒ DH5

with UP = xL2CAP ,

(1)

with UP = min{xL2CAP, 339}.

Within the master N local transmission queues are implemented, where N is the number of active slaves. Each master local queue has a finite size BM and the queued packets are served according to a FCFS policy. When an L2CAP packet is completely received by the master, the master accomplishes the reassembly procedure, then it extracts the encapsulated IP packet and forms a new L2CAP packet for the slave that is the destination of that IP traffic. The master puts the new L2CAP packet in the local queue reserved to the slave that is the destination of that packet. In the transmission phase the master behaves in the same way of slaves. The master and the slaves transmit the ACL packets according to the Bluetooth transmission scheme described in the previous sections. The slave to be polled by the master is selected by the scheduling algorithm. The Bluetooth Specification indicates as possible solution the Round Robin polling algorithm: slaves are polled in a cyclic order. In our simulator we have implemented both the Round Robin polling algorithm and the Efficient Double-Cycle (EDC) algorithm that is explained in section 4. 3.2. Traffic characterization A realistic characterization of the traffic in a PAN is quite difficult due to the lack of an exact knowledge of the future applications based upon the Bluetooth technology. What we have modeled is the distribution of the traffic among the slaves in a piconet, rather than the correlation between the arrival times of consecutive packets. The ability to effectively react to various traffic distributions among slaves is one of the most important features of a scheduling algorithm in an highly dynamic scenario like a Bluetooth piconet. We have modeled the intra-piconet communications, i.e., no traffic comes (goes) from (to) the outside of the piconet. Each slave is a source of IP packets and the interarrival times between consecutive packet generations are exponentially distributed, hence the IP packet arrival process is Poissonian. The packet length is uniformly distributed in the range from 500 to 1500 bytes. Let us introduce some notation adopted hereafter. For each slave Si , i = 1, . . . , N, we denote with γUPi the average of-

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fered load of Si towards the master, and with γDW i the average offered load of the master towards Si . We will refer to the aggregated offered load from all slaves towards the master with OLUP , and we will refer to the aggregated offered load from the master towards all slaves with OLDW . Since we have considered only a closed piconet, i.e., all the traffic is generated by the slaves, we have OLUP = OLDW . If we indicate with x(i) the fraction of OLUP that is generated by Si , then we have: γUPi = x(i) · OLUP ,

N

x(i) = 1,

i = 1, . . . , N.

i=1

Furthermore, if we indicate with pij the fraction of ongoing traffic from Si that is addressed to Sj , and by noting that Si cannot send traffic towards itself, we have: γDW i =

N

pj i γUPj ,

j =1

pii = 0,

N

pij = 1,

j =1

i = 1, . . . , N. Now we can easily define a vectorial notation by introducing the following quantities: – vectors of uplink loads and downlink loads as: T γUP = [γUP1 . . . γUPN ],

T γDW = [γDW1 . . . γDWN ];

– vector of uplink loads distribution and addressing matrix as: x T = x(1) . . . x(2) , P = {pij }. By exploiting this compact notation, the distribution of ongoing traffic from the slaves and ingoing traffic towards the slaves is exhaustively described by the following expressions: γUP = OLUP x,

γDW = PT γUP .

Each different couple of addressing matrix and vector of uplink loads distribution will correspond to different profiles for the uplink and downlink loads. In the section related to the performance analysis we have considered two different scenarios that can be recognized as representative of completely different applications. The first scenario has been called Symmetric Case because all slaves produces the same amount of traffic, and distribute their ongoing traffic uniformly among all possible destinations. The second scenario has been called Asymmetric Case because a single station produced the 90% of OLUP . As in the symmetric case, the slave-generated traffic is uniformly spread among all possible destinations. We believe that the symmetric case models a typical cooperative application were each participant has the same role (e.g., during multicasting video-streaming), and the asymmetric case models an application that operates in a typical server-client fashion (e.g., during information retrieval). Table 3 summarizes the vector components of uplink loads distribution and the addressing matrix elements that correspond to the previous described scenarios.

Table 3 Description of traffic scenarios. Symmetric case

Asymmetric case Uplink loads distribution

x T = [1/N, . . . , 1/N ]

x T = [0.9, 0.1/(N − 1), . . . , 0.1/(N − 1)] Addressing matrix

pij = 1/(N − 1)

pij = 1/(N − 1)

3.3. Performance indices One of the performance indices we use to evaluate the efficiency of the MAC protocol is the channel utilization, which is defined as the fraction of channel bandwidth used by successfully transmitted messages. This fraction is known as the utilization of the channel, and the maximum value it can attain is known as the capacity of the MAC protocol [7,8]. It is worth noting that by multiplying the capacity and the channel speed we obtain the network maximum throughput. For this reason in the following we refer to the throughput figures to measure the protocol efficiency. The capacity is used to evaluate the efficiency of MAC protocol algorithms, however from the user standpoint other performance figures are needed to measure the Quality of Service (QoS). The most commonly used performance measure is the delay, which can be defined in several forms [5,8] depending on the time instants considered during its measurement (access delay, queuing delay, propagation delay, etc.). In computer networks the response time, defined as the time between the generation of a message at the sending station and its reception at the destination station, is the best figure to measure the QoS perceived by the users. However, it depends on the amount of buffering inside the network and it is not always meaningful to evaluate a shared-medium technology. For example, during congested periods, the buffers fill up and the response time does not depend on the network technology but it is just a function of the buffer length. For this reason, in this paper, we also estimate the MAC delay. The MAC delay of a station in a LAN is the time between the instant at which a packet comes to the head of the station transmission queue and the end of the packet transmission [8]. The two main indices we have identified, throughput and response time, cannot be both maximized at the same time. We can try to increase as much as possible the throughput in order to maximize the bandwidth utilization, but it would be also reasonable to decrease the response time in order to improve the promptness of the transmission service perceived by the users. These two different targets are competing each other: an increase in the system utilization deteriorates the response time. A single combined measure of these two performance indexes is the power that is defined as the ratio between the throughput and the response time of the system [9]. The power can be considered a nice criterion for the evaluation of scheduling algorithms because its maximum value is a trade-off between promptness and efficiency. In the following we will formalize the power concept for a Bluetooth piconet.

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Let us now introduce some notations. We define as MACUPi and MACDW i the average MAC delay experienced by the IP packets that traverse the link from Si to the master, and from the master to Si , respectively. In particular the power PUPi is associated to the link from Si to the master, and the power PDW i is associated to the link from the master to Si . PUPi and PDW i have the following definitions: PUPi =

γUPi , MACUPi

PDW i =

γDW i . MACDW i

(2)

Equations (2) define the power index for a link analyzed in isolation. The definition of a power index that expresses the behavior of the overall piconet requires the introduction of the average aggregate throughput γ and of an average delay D of the system. In fact we can consider the following definition for the system power: PSystem =

γ D

(3)

,

where γ is equal to the sum of and γDW i , with i = 1, . . . , N. The choice of D leaves some degree of freedom as different definitions for D may highlight different aspects of the system. The simplest choice for D is to consider the average system response time τ . It is worth noting that τ corresponds to the average end-to-end delay experienced by IP packets. In this case, equation (3) can be written as: N [γUPi + γDW i ] P1 = i=1 . τ P1 provides a criteria to analyze the system performance from the user standpoint as the delay added to its data-packet stream is the most important figure for the single user. Moreover, we want to evaluate the effectiveness of scheduling algorithms that operate at the Baseband layer. So we introduce a second definition for the power. In this second case D is the weighted sum of MACUPi and MACDW i , where the weights are the fraction of total bandwidth used by each link, that is: D=

N 1 [γUPi MACUPi + γDW i MACDW i ]. γ

it should avoid treating unfairly slaves with small amounts of traffic with respect to heavily-loaded slaves. 3.4. Numerical results The performance evaluation of Round Robin polling algorithm has been carried out by simulations in which we assume an ideal channel with no transmission errors. This assumption has been done in order to analyze the protocol in ideal conditions and to highlight the constraints due to the adopted transmission scheme. Within each simulation we have utilized the DH type for ACL packets, and the buffer sizes are BS = 15000 bytes and BM = 15000 bytes. The use of buffers with a finite size is necessary to perform correct simulations of steady state even in overload conditions. By exploiting (1), given an IP packet of length k, it is possible to determine the set (k) of ACL packets that are generated by its segmentation. We indicate with Nx(k) the number of (k) (k) packets of type DHx , and with τIP the time (expressed as a number of time slots) the channel is used to transmit an (k) IP packet. Hence, τIP has the following expression: (k) τIP = x · Nx(k) . x∈{1,3,5}

Finally, to determine the number of time slots to transmit an IP packet, τIP , we need to select the IP packet-length distribution. For example, in figure 5 we have plotted the Pr{τIP = m}, i.e., the probability mass function of τIP , for an IP packet-length uniformly distributed in the range from 500to 1500 bytes. The mean value of τIP , say τ¯IP , is equal to m m Pr{τIP = m}. In the considered case, we have τ¯IP = 15.998 time slots. The maximum values of aggregate throughput, γMAX , and MAC (i.e., average aggregate MAC delay) are obviously obtained in the asymptotic condition, i.e., when there is always a queued packet in the master and in the slaves local queues. In this case no NULL packets are sent and the channel is completely loaded.

(4)

i=1

It is worth noting that (4) is equivalent to the aggregate MAC delay, that is the mean value of MACUPi and MACDW i , hereafter called MAC. By exploiting (4), we can write equation (3) as: P2 = N

γ2

i=1 [PUPi (MACUPi )

2

+ PDW i (MACDW i )2 ]

.

P2 highlights that it is not desirable to increase the system power by increasing the power of a single link and deteriorating excessively the MAC delay of the others. However, it is worth noting that polling a slave with an empty transmission queue does not decrease its MAC delay but cause a waste of bandwidth. Hence, a policy more efficient than the Round Robin polling algorithm would be one that avoids polling slaves with empty transmission queues. Furthermore,

Figure 5. τIP probability mass function.

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In asymptotic conditions the channel is continuously used by the stations to transmit the IP-packet fragments. On average, a station uses the channel for a τ¯IP · ts time (ts being the slot duration) to transmit a complete IP packet. Hence, the maximum aggregate throughput is equal to: γMAX =

E[IP packet length] = 800.1 kbps. τ¯IP · ts

The maximum value of MAC is defined by the following lemma. Lemma 1. In a Bluetooth piconet with N active slaves and a Round Robin scheduling algorithm, by denoting with τ¯IP · ts the average time the channel is used for an IP packet transmission time, the maximum value of MAC is MACMAX = 2 × N × τ¯IP · ts .

Figure 6. Throughput performance of a single piconet.

Proof. The maximum value of MAC is obtained when all slaves and the master queues always contain at least a packet ready for transmission. In this case, given a tagged queue, say i, and by denoting with MACi,m the MAC delay of the mth IP packet transmitted from queue i, and with MACi the average MAC delay of queue i, it follows that MAC = MACi = lim

h→∞

MACi,1 + MACi,2 + · · · + MACi,h . h (L.1)

Now we note that (i,m) MACi,m = C1(i,m) + C2(i,m) + · · · + C , m

(L.2)

where m is the number of fragments required to transmit the mth IP packet, and Cj(i,m) is the polling cycle during which is transmitted the j th fragment of the mth IP packet. Hence, (i,1) MACi = lim C1(i,1) + C2(i,1) + · · · + C + C1(i,2) 1 h→∞

(i,2)

+ C2

(i,2)

(i,h)

+ · · · + C2 + · · · + C1 (i,h) h . (L.3) + C2(i,h) + · · · + C h Let us now divide the numerator and the denominator of (L.3) by n, where n is the number of polling cycles required to transmit the h IP packets. Hence, (L.3) becomes MACi = E[C] · E[],

(L.4)

where E[C] is the average length of a polling cycle and E[] is the average number of fragments in which an IP packet is segmented, or, in other words, the average number of polling cycles required to transmit an IP packet.

2N Fi = 2N · E[Fi ], (L.5) E[C] = E i=1

where Fi is the length of a fragment transmitted from queue i and 2N is the number of queues in our piconet with N active slaves. As the IP packet-length distribution is the same for all

slaves E[Fi ] = E[F ] where E[F ] is the average length of a fragment. Hence, MACi = 2N · E[F ] · E[].

(L.6)

Finally, by applying the same type of reasoning we used to derive (L.4) from (L.1) we can show that E[F ]·E[] = τ¯IP ·ts and hence the lemma is proved. By applying, the Lemma 1 in our case study with N = 6 active slaves we have: MACMAX = 2 × N × τ¯IP × ts = 119, 9625 ms. It is worth noting that these analytical results derived for a completely loaded piconet (i.e., a piconet where there is always at least a packet waiting to be transmitted in all the slaves’ and master’s queues) are confirmed by the following simulative analysis. In figure 6 we plot the aggregate throughput that is achievable in the symmetric and asymmetric case. It is known that the Round Robin polling algorithm is the best policy when the system is symmetric and completely loaded, and the plotted curves confirm that. However, it is also clear that the Round Robin polling algorithm is very inefficient under asymmetric condition because the master continues to poll slaves that have no traffic to send, and this behavior implies a bandwidth wastage. In the asymmetric scenario the Slave1 local queue saturates, i.e., there are packet losses due to buffer overflow, when the offered load is equal to 400 kbps. By increasing the OL beyond 400 kbps, the throughput performance increases very slowly. In figures 7(a) and (b) we plot the MAC measured in the symmetric and asymmetric case, respectively. Moreover in figure 7(b) we plot also the MACUP for Slave1 (heavily loaded) and Slave2 (lightly loaded). We can notice that in the symmetric case, when the OL is equal to the maximum throughput, the MAC reaches its maximum value. It could appear erroneous that the MAC decreases when the piconet is heavily loaded, but this is a side effect of the finite buffer size.

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(a)

(a)

(b)

(b)

Figure 7. MAC delay performance of a piconet (a) symmetric case and (b) asymmetric case.

Figure 8. End-to-end delay performance in a piconet (a) symmetric case and (b) asymmetric case.

In fact, when the piconet is heavy loaded the local queues are saturated and the probability to discard long packets is higher than the probability to discard short packets. This means that the average length of queued packets decreases, hence even the MAC decreases (in the symmetric case, with an OL that is two times γMAX , we have measured an average length of queued packets equal to 850 bytes). In the asymmetric case the MAC curve shows a flat slope for OL grater than 400 kbps because the throughput increases very slowly. Moreover, the MACUP1 is lower than the MACUP2 . This is due to the fact that, during the transmission of an IP packet generated in a lightly loaded slave, it is high the probability to have a contemporary transmission of an IP packet generated in the heavily loaded slave. On the other hand, during the transmission of an IP packet generated in heavily loaded slave, the probability to have a contemporary transmission on an IP packet generated in a lightly loaded slave is very small. The average aggregate MAC, MAC, is lower than MACUP1 and MACUP2 . This happens because MAC is computed taking into consideration also the transmissions on the downlink and MAC delays in the downlink are lower than the MAC delays in the uplink. In figures 8(a) and (b) we plot the average end-to-end delay measured in the symmetric and asymmetric case, respec-

tively. Moreover, in figure 8(b) we plot also the end-to-end delay for IP packets generated by Slave1 (heavily loaded) and Slave2 (lightly loaded). We can notice that the end-to-end delay increases together with the offered load, due to the increase in the number of queued packets. The end-to-end delay for Slave1 is significantly higher than the one for Slave2 because in the Slave2 local queue there are few packets than in the Slave1 local queue. To summarize this set of numerical results in figures 9(a) and (b) we plot P1 and P2 , respectively. The P1 curves present a maximum that corresponds to the optimal operating point from the P1 -index standpoint. We can observe that in the asymmetric case the optimal condition is reached for a lower offered load than the symmetric case. Moreover, the P1 curve in the asymmetric case is above the P1 curve in symmetric case only for heavily loaded piconet. The P2 curves show a different behavior. In particular, we can observe that in the symmetric case a maximum for the power still exists, and it is reached for an OL equal to 400 kbps, whereas in the asymmetric case the P2 curve is a growing function. We believe that this behavior is due to the inefficiency of Round Robin algorithm for the asymmetric scenario that implies piconet is very far from optimum operating point with respect to the P2 -index standpoint. This

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(a)

An additional problem to have a fair and efficient scheduling in Bluetooth is due to the coupling between the transmission in uplink and downlink (i.e., a master to slave transmission implies also a polling of the slave and hence a – possibly NULL – slave to master transmission). Therefore, it is not possible to remove a slave from the polling cycle without blocking at the same time the master transmissions towards this slave. To introduce a (partial) decoupling in the scheduling of the transmissions in uplink and downlink we introduce the idea of a double polling cycle: an uplink polling cycle, hereafter called CycleUP , and a downlink polling cycle, hereafter called CycleDW . During both of these cycles the master selects a subset of slaves that are eligible for the polling. For the sake of brevity we will refer to the subset of eligible slaves selected during a CycleUP as E(Up), and to the subset of eligible slaves selected during a CycleDW as E(Dw). E(Dw) is calculated considering only the state of master local queues, i.e., the ongoing traffic loads, whereas E(Up) is calculated considering only the estimated slaves’ activity, i.e., the ingoing traffic loads. The distinction between polling rules for the downlink, and polling rules for the uplink permits to preserve a separate fairness. 4.1. EDC specification

(b) Figure 9. Power performance of a piconet (a) P1 vs. offered load and (b) P2 vs. offered load.

hypothesis will be better justified when a more efficient algorithm will be presented in the next section. Finally, we can observe the P2 curve in the asymmetric case is always above the P2 curve in the symmetric case, due to the low MAC delay experienced in the former case. 4. The Efficient Double-Cycle (EDC) algorithm The Efficient Double-Cycle (EDC) algorithm is based upon two main ideas: first of all it is necessary to avoid NULL transmissions towards and from the slaves; furthermore the fairness typical of a Round Robin scheme should be preserved. These targets can be accomplished if the selection of the slave to be polled takes into consideration the master knowledge of local queues occupancy. It is worth noting that the master has a deterministic knowledge of the state of its local queues, i.e., the queues containing the ongoing traffic towards the slaves. On the other direction (i.e., traffic incoming from the slaves), we only assume that the master has a probabilistic knowledge based on the feedback the master gets when polling the slaves. Therefore, the master can only estimate the probability that a slave will send a NULL packet by exploiting the knowledge of the slave behavior in the previous polling cycles.

Firstly, let us introduce the variables used by the EDC algorithm. The outcome of our polling algorithm is the next slave to be polled, i.e., its AM_ADDR (in the EDC specification it is referred as next). Because there is a double cycle, we have a nextUp calculated during a CycleUP , and a nextDw calculated during the CycleDW . A polling interval ci , and a polling window wi are associated to each Si . These variables are used to estimate the slave activity, and to implement a truncated binary exponential backoff mechanism to control the slaves polling frequency during a CycleUP , as explained afterwards. The variable Cycle_End indicates if the previous cycle is completed, i.e., if all the slaves which belong to E(Up) or E(Dw) have been polled. The variable Curr_Cycle stores the type of cycle the master is executing. One of the most important task that is accomplished by the master at the beginning of each cycle is the updating of E(Up) and E(Dw) according to its most up-to-date knowledge about traffic loads. E(Up) is constituted by all slaves Si that have ci = 0 at the beginning of a CycleUP . Because the ci are decreased before the beginning of each CycleUP , if Si has a ci = n, then n · CycleUP must elapse before Si is polled during a CycleUP . E(Dw) is constituted by all slaves Si to which the master has traffic to send at the beginning of a CycleDW , i.e., slaves corresponding to the not-empty master local queues. In the rest of this section we provide a pseudo-code description of the EDC algorithm, and we comment it. At the beginning of each new master transmission, according to the piconet timing, the master executes the following actions:

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1. begin 2. if (Cycle_End==FALSE) then 3. if (Curr_Cyle==Up) then goto Up_Cycle; 4. else goto Dw_Cycle; 5. if (Cycle_End==TRUE) then 6. if (Curr_Cyle==Up) then 7. begin 8. Cycle_End = FALSE; 9. Curr_Cycle = Dw; 10. ; 11. goto Dw_Cycle; 12. end 13. else 14. begin 15. for (i = 1; i N; i++) do ci = max{0, (ci - 1)}; 16. Cycle_End = FALSE; 17. Curr_Cycle = Up; 18. ; 19. goto Up_Cycle; 20. end If the cycle is not finished EDC determines if the next transmission belongs to a CycleUP or a CycleDW [lines 2–4]. If the previous cycle is finished and it was a CycleUP , then EDC updates E(Dw) and a new CycleDW begins [lines 6–11]. Otherwise, if the previous cycle is finished and it was a CycleDW , then EDC decreases the polling interval variables [line 15], it updates E(Up) and a new CycleUP begins [lines 16–19]; 21. Dw_Cycle: 22. if (E(Dw)=={0}) then 23. begin 24. for (i = 1; i N; i++) do ci = max{0, (ci - 1)}; 25. Curr_Cycle = Up; 26. ; 27. goto Up_Cycle; 28. end 29. Search_Dw: 30. if (SnextDw ∈ E(Dw)) then 31. begin 32. E(Dw) = E(Dw) - SnextDw ; 33. next = nextDw; 34. nextDw = nextDw+1; 35. if (E(Dw)=={0}) then 36. begin 37. nextDw = 1; 38. Cycle_End = TRUE; 39. end 40. exit 41. end 42. else nextDW = nextDw+1; 43. goto Search_Dw; 44. exit

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In the lines 21–44, EDC prepares a transmission for a CycleDW . If E(Dw) is an empty set [line 22], then EDC decreases the polling interval variables, it updates E(Up) and a new CycleUP begins [lines 24–27]. This behavior corresponds to have a CycleDW with null duration. All the Si that belongs to E(Dw) are polled in a cyclic order (i.e., we adopt a Round Robin policy for the E(Dw) set). nextDw is incremented [line 42] until EDC finds a slave eligible, and when this happens nextDw is incremented again [line 34]. After a slave has been polled it is extracted from E(Dw) [line 32]. Therefore a CycleDW is completed only when E(Dw) becomes empty [lines 35–39]. 45. Up_Cycle: 46. if (E(Up)=={0}) then 47. if (E(Dw)=={0}) then goto Empty_Cycle; 48. else 49. begin 50. ; 51. Curr_Cyle = Dw; 52. goto Dw_Cycle; 53. end 54. Search_Up: 55. if (SnextUp ∈ E(Up)) then 56. begin 57. E(Up) = E(Up) - SnextUp ; 58. next = nextUp; 59. nextUp = nextUp+1; 60. if (E(Up)=={0}) then 61. begin 62. nextUp = 1; 63. Cycle_End = TRUE; 64. end 65. exit 66. end 67. else nextUp = netxUp+1; 68. goto Search_Up; 69. exit In the lines 45–69, EDC prepares a transmission for a CycleUP . If E(Up) and E(Dw) are empty sets [lines 46–47] EDC has no information to discriminate a slave from the others, then EDC jumps to the Empty_Cycle, where a strict Round Robin polling rule is applied. If only E(Up) is an empty set then E(Dw) is updated and a new CycleDW begins [lines 50–52]. This behavior corresponds to have a CycleUP with null duration. All the Si that belong to E(Up) are polled in a cyclic order (i.e., we adopt a Round Robin policy for the E(Up) set). In fact, nextUp is incremented [line 67] until EDC finds a slave eligible, and when this happens nextUp is incremented again [line 59]. After a slave has been polled it is extracted from E(Up) [line 57]. Therefore, a CycleUP is completed only when E(Up) becomes empty [lines 61–64].

128


70. Empty_Cycle: 71. next = (next + 1)MOD N ; 72. Cycle_End = TRUE; 73 exit The master polls the next slave [line 71]. Cycle_End is settled TRUE [line 72] in order to execute an update of E(Dw) and E(Up) at the beginning of next cycle. At the end of each Sk ’s packet reception, the master updates its polling interval ck and its polling window wk with the following procedure: 74.

if (==NULL) then wk = min{wMAX , (2 × wk )}; 75. else wk = 1; 76. ck = wk ; 77. end If Sk sends a packet with a null payload, then its polling window wk is doubled till a maximum value wMAX is reached; otherwise wk is settled to be 1. The polling interval ck takes the wk value.

(a)

4.2. Numerical comparison between Round Robin and EDC In this section we report the numerical results obtained with EDC in the same set of simulations performed in section 3.4. By exploiting these numerical results we will point out the performance enhancement achieved by EDC, with respect to the performance indices introduced in section 3.3. During all simulations we have assumed wMAX = 32. In figures 10(a) and (b) we plot the aggregate throughput achieved by the Round Robin algorithm and EDC in the symmetric and asymmetric case, respectively. We can observe that in the symmetric case EDC behaves exactly as the Round Robin algorithm from a throughput standpoint. Instead in the asymmetric case EDC is much better than the Round Robin algorithm because it reduces the number of NULL messages received by the master from the lightly loaded slaves. In figures 11(a) and (b) we plot the MAC achieved by the Round Robin algorithm and EDC in the symmetric and asymmetric case, respectively. In the symmetric case, EDC behaves exactly as the Round Robin algorithm only if the piconet is heavy loaded, because in this case with probability one we always have a packet in the master and slaves’ local

(b)

Figure 10. Aggregate throughput comparison (a) symmetric case and (b) asymmetric case.

(a)

(b)

Figure 11. Aggregate MAC delay comparison (a) symmetric case and (b) asymmetric case.

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queues. When the piconet is lightly loaded EDC overcomes the Round Robin algorithm due to the dynamic adaptation of the slaves’ polling-frequency to the slaves activity. In the asymmetric case, the EDC efficiency is evident. We can observe a halving of the aggregate MAC delay measured in the piconet. The dynamic behavior of a polling system is quite complex, and to better understand it in figures 12(a) and (b) we plot the MAC delay measured in the uplink and downlink for the Slave1 (heavily loaded) and Slave2 (lightly loaded), respectively. It is worth noting that the station MAC delay can be viewed as the speed at which the master serves the slave’s local transmission queue. From figure 12(a) we observe that EDC is able to adapt to the different rates of the slaves’ packet generators. On average, Slave1 produces a traffic load nine times greater than Slave2, hence EDC makes a significant difference between the polling frequency allotted to slaves in the uplink direction. As the ongoing traffic from the master is distributed quite uniformly among all slaves, EDC makes no significant

(a)

differences between the polling frequency of the slaves in the downlink direction, how we can observe in figure 12(b). It is worth noting that MACDW i is halved for all slaves. The increase in the MAC delay experienced by the traffic generated in the lightly loaded slaves does not imply a decrease in the transmission service perceived by the users. This is clearly shown by the performance figures based on the endto-end delay. In particular in figures 13(a) and (b) we plot the average end-to-end delay achieved by the Round Robin algorithm and EDC in the symmetric and asymmetric case, respectively. We have adopted a logarithmic scale for the yaxis to highlight the improvement achieved with EDC both in the lightly loaded piconet (i.e., with small end-to-end delay) and in the heavily loaded piconet (i.e., with high end-to-end delay). These results point out that the improvement in the aggregate throughput is not achieved by means of worsening the delay performances perceived by users, but by using more efficiently the channel bandwidth. We can observe that, in the symmetric case, the end-to-end delay achieved with EDC is lower than the one achieved with the Round Robin algo-

(b)

Figure 12. Slaves’ MAC delay comparison (a) uplink and (b) downlink.

(a)

(b)

Figure 13. End-to-end delay comparison (a) symmetric case and (b) asymmetric case.

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(a)

(b)

Figure 14. Power performance comparison (a) P1 vs. offered load and (b) P2 vs. offered load.

rithm even when the piconet is heavily loaded. We can explain this phenomenon by considering the probability to have an empty local queue in the master. In particular, when OL is equal to 120% of γMAX , by applying the Round Robin and the EDC algorithms, the master local queue reserved for transmissions towards Slave1 is empty with probability 0.06394 and 0.10017, respectively. We summarize this wide set of results by considering the power indices. In figures 14(a) and (b) we plot P1 and P2 , respectively. We observe that, by adopting EDC, when OL is lower then γMAX , the P1 curves for the symmetric and asymmetric scenarios are overlapping. This means that the transmission service offered by the MAC layer it is the same, no matter of the traffic distribution. Furthermore, the maximum power achievable with EDC is significantly higher than the one achievable with the Round Robin algorithm in both scenarios, due to the better resource utilization granted by EDC. Similar considerations arise from the analysis of P2 curves. In particular, it is worth highlighting that with EDC also in the asymmetric case it is possible to find a maximum for the P2 function.

5. Conclusions The paper, after providing a tutorial description of the Bluetooth technology has studied the performance evaluation of the Baseband transmission procedures. In particular we have analyzed the efficiency of a Round Robin scheme for the Medium Access Control protocol. This analysis has clearly shown the ineffectiveness of this scheme to face the requirements of a WPAN highly dynamic scenario. Therefore, we have proposed a new scheduling algorithm, named EDC that exploits master knowledge of local queues’ occupancy to avoid NULL packet transmission. EDC employs two polling cycles with different polling rules in such a way to guarantee a separate and fair treatment of uplink and downlink connections. At the same time, when compared to Round Robin, EDC increases for aggregate network throughput and decreases the (MAC and End-to-End) delays.

Results obtained are very promising but several research issues need to be further investigated. Specifically we are currently working in two main directions: optimal scheduler, TCP over Bluetooth. In this paper we have shown that EDC is a promising direction for improving the efficiency of a Bluetooth network. However, EDC has been designed by adopting only a heuristic criterion, i.e., reducing the number of polling attempts that do not produce useful transmissions. What we are now looking for is a criterion that formally identifies the optimal performance that can be achieved for a given traffic characterization. More precisely we are working on analytical models to identify, given a traffic characterization, the polling order that maximizes the power index. With this criterion we will try to construct a scheduler that mimics the behavior of the optimal one. The performance study presented in this paper has been carried out without taking into consideration the impact of the Internet protocols on the traffic characteristics. Specifically we have assumed that the packet size corresponds to a typical IP packet but we have not taken into consideration the impact of TCP control messages (ACK), and the impact of TCP flow and congestion control mechanisms on the spacing between IP packets. This choice was done to study in a general case the properties of the scheduling algorithms. It is left for further studies the performance of TCP when Bluetooth is the access technology to Internet.

References [1] M. Scott Corson, J.P. Macker and G.H. Cirincione, Internet-based mobile ad hoc networking, IEEE Internet Computing (July–August 1999) 63–70. [2] Specification of the Bluetooth System, Version 1.0B, December 1999. [3] B.A. Miller and C. Bisdikian, Bluetooth Revealed (Prentice Hall, 2000). [4] J. Muller, Bluetooth Demystified (McGraw-Hill, 2000). [5] J.L. Hammond and P.J.P. O’Reilly, Performance Analysis of Local Computer Networks (Addison-Wesley Publishing Company, 1988). [6] W. Stallings, Local & Metropolitan Area Networks (Prentice Hall, 1996).

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[7] J.F. Kurose, M. Schwartz and Y. Yemini, Multiple access protocols and time constraint communications, ACM Computing Surveys 16 (1984) 43–70. [8] M. Conti, E. Gregori and L. Lenzini, Metropolitan Area Networks, Springer Verlag Limited Series on Telecommunication Networks and Computer Systems (Springer, 1997). [9] L. Kleinrock, Power and deterministic rules of thumb for probabilistic problems in computer communications, IEEE Transactions on Information Theory (1979). [10] J.C. Haartsen and S. Zurbes, Bluetooth voice and data performance in 802.11 DS WLAN environment, Technical Report, Ericsson, May 1999. [11] S. Galli, K.D. Wong, B.J. Koshy and M. Barton, Bluetooth technology: Link performance and networking issues, in: Proc. European Wireless 2000, Dresden, Germany, September 2000. [12] S. Zurbes, Consideration on link and system throughput of Bluetooth networks, in: Proc. PIMRC 2000 (London, 2000) pp. 1315–1319. [13] N. Johansson, U. Korner and P. Johansson, Wireless ad-hoc networking with Bluetooth, in: Proc. Proceedings of Personal Wireless Communications, Copenhagen, March 1999. [14] N. Johansson, U. Korner and P. Johansson, Performance evaluation of scheduling algorithm for Bluetooth, in: Proc. IFIP Broadband Communications, Hong Kong, November 1999. [15] N. Johansson, M. Kihl and U. Korner, TCP/IP over Bluetooth wireless ad-hoc network, in: Proc. IFIP Networking 2000, Paris (1999) pp. 799– 810. [16] M. Gerla, P. Johansson, R. Kapoor and F. Vatalaro, Bluetooth: “Last Meter” technology for Nomadic wireless internetting, in: Proc. 12th Tyrhennian International Workshop on Digital Communications.

Raffaele Bruno has received his Laurea degree in telecommunications engineering from the University of Pisa, Italy, in 1999. He is currently working toward his Ph.D. degree at the CNUCE Department in the National Council of Research, Pisa, Italy. His research interests are in the areas of wireless and mobile networks with emphasis on efficient wireless MAC protocols, scheduling algorithms for Internet traffic integration and QoS. E-mail: [email protected]

Marco Conti received the Laurea degree in computer science from the University of Pisa, Italy, in 1987. In 1987 he joined the Networks and Distributed Systems department of CNUCE, an institute of the Italian National Research Council (CNR) where is currently a senior researcher. He has written over 80 research papers in the areas of design, modeling and performance evaluation of computernetwork architectures and protocols. His current research interests include Internet architecture and protocols, wireless networks, mobile computing, multimedia systems, and QoS in packet switching networks. He is co-author of the book “Metropolitan Area Networks” (Springer, London 1997). He was the coordinator of two minitracks (“Mobile adhoc networking” and “QoS in Web Services”) at the HICSS-34 conference. He is serving as a Co-Guest Editor for IEEE Transactions on Computers (special issue on “Quality of Service issues in Internet Web Services”) ACM Mobile Networks & Applications Journal (MONET) (special issue on “Mobile Ad-hoc Networks”) and the Cluster Computing Journal (special issue on “Mobile Ad-hoc Networking”). He is a member of the IEEE and IFIP WG 6.3 and WG 6.8. E-mail: [email protected]

Enrico Gregori received the Laurea in electronic engineering from the University of Pisa in 1980. He joined CNUCE, an institute of the Italian National Research Council (CNR) in 1981. He is currently a CNR research director. In 1986 he held a visiting position in the IBM research center in Zurich working on network software engineering and on heterogeneous networking. He has contributed to several national and international projects on computer networking. He has authored a large number of papers in the area of computer networks and has published in international journals and conference proceedings. His current research interests include: wireless access to Internet, wireless LANs, quality of service in packet-switching networks, energy saving protocols, evolution of TCP/IP protocols. He is on the editorial board of the Cluster Computing Journal. He is a member of the IEEE. E-mail: [email protected]