Distributed Self-Healing Bluetooth Scatternet Formation

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1 Distributed Self-Healing Bluetooth Scatternet Formation K. Persson and D. Manivannan Laboratory for Advanced Networking Computer Science Department University of Kentucky Lexington, KY Abstract A Bluetooth scatternet is a scalable network topology that is formed by inter-connecting piconets. Piconets are the basic networking unit for Bluetooth devices, but can only accommodate up to eight devices; one master and seven slaves. In order to form scatternets, piconets can be inter-connected using bridge nodes that interleave their participation in multiple piconets. In this paper we present a new fault-tolerant approach to scatternet formation that is self-healing and works in a multi-hop environment. Our approach allows incremental node arrival and automatically heals partitions in the scatternet by reincorporating disconnected nodes. We also restrict the assignment of bridges to pure slaves, which reduces wasted time-slots when a master/slave bridge is away from the piconet. Keywords: Bluetooth, Scatternet Formation, Piconet, Personal Area Networking I. INTRODUCTION The Bluetooth specification [1] and the recent IEEE [2] standard, define the properties of a scatternet but do not provide a protocol for scatternet formation. A scatternet is formed by inter-connecting piconets in a way that does not violate the exisiting constraints imposed on the participating piconets. Due to the unique structure of a scatternet, traditional ad-hoc topology formation protocols cannot be directly applied. A piconet is an ad-hoc cluster with exactly one master and up to seven slaves (directly connected to the master.) Inter-connecting these piconets can be done by allotting a subset of nodes in the piconet to participate in other piconets as well. These, so called bridge nodes, divide their time between participating piconets, by switching frequency hopping sequences, and can thereby relay packets between them. This is done by scheduling disjoint time slots, during which the bridge node participates in a particular piconet. Inter Piconet Scheduling (IPS) itself is not the primary focus of this paper, so the reader is referred to [3],[4],[5],[6] for more details. In general, a node can only become the master in one piconet but is allowed to participate in other piconets as a slave. In our approach, the scatternet is formed by allowing nodes to probabilistically choose between becoming a master or a slave. Naturally, since each piconet can only contain a single master and as many as seven slaves, we want to select more slaves than masters. However, it is also important that newly arriving nodes or previously disconnected nodes are quickly either incorporated into an existing piconet, or is allowed to form its own piconet if necessary. Our approach handles this by using a local threshold value at each node. For newly arriving nodes and disconnected ones, the threshold is initially set low so that many more slaves are chosen than masters. Once a node becomes a slave, it stops attempting to connect other nodes (unless it is specifically directed to do so by the master, e.g to form a bridge inter-connection.) For piconet masters, this threshold is linearly increased proportional to the number of connected slaves. The master probabilistically chooses between trying to incorporate an additional slave, or assign an existing slave as a new bridge designate. This effectively makes the master of a larger piconet more likely to attempt to incorporate additional slaves over selecting local bridge designates. We assume that only pure slaves (slaves that are only connected to a single master) are considered as potential bridge nodes. We make this assumption since a master/slave bridge node wastes time slots when it is away from the piconet (in which it is a master) and participates as a slave in a different piconet. Kalia et al. also suggest, in [7], that the use of master/slave bridges negatively affect piconet performance since all inter-piconet communication must be put on hold while the master is participating in another piconet. Misic et al. present similar results in [8] and conclude that inter-piconet delay is significantly increased when master/slave bridges are used. We therefore only consider pure slaves as bridge designates in this paper. This further ensures a flat scatternet topology that is free from the bottlenecks seen in tree-based algorithms (eg. [9], [10]). Our approach allows nodes to incrementally arrive and join the existing scatternet. It is also self-healing, meaning that nodes that have been disconnected are easily re-incorporated into the scatternet. Since there is no single coordinator or phase-division in our approach, it also functions in a multihop environment where nodes are not necessarily within transmission range of every other node. Details are presented in Section IV. The rest of the paper is organized as follows. Section II briefly overviews Bluetooth technology and related concepts. We summarize previous approaches for device discovery and

2 scatternet formation in Section III, before presenting the algorithm in Section IV. Section IV-A discusses the problems involved with device discovery and scatternet formation. We first outline the basic idea in Section IV-B. In Section IV- C our scatternet formation algorithm is described, and then explained in detail in Sections IV-C.1 and IV-C.2. In Section V we present our performance evaluation. The paper is concluded in Section VI. II. BACKGROUND A Wireless Personal Area Network (WPAN) is defined as a limited range ad-hoc network that inter-connects devices within close proximity [11]. Devices enabled with Bluetooth radio interfaces are connected into WPANs called piconets. A piconet is a cluster that contains a maximum of seven active slaves and a single master. Piconets are also capable of keeping up to 255 parked, or inactive slaves, but we only consider the active devices. In general, a cluster consists of a number of connected nodes where one node is designated as the cluster head [12]. All other nodes in the cluster are directly connected to the cluster head. In Bluetooth piconets, the master is the cluster head and controls all communication by addressing each slave individually in a round-robin fashion. Slaves are expected to respond to a master poll in the directly following time slot(s) whether it has data to send or not. Bluetooth utilizes a frequency hopping spread spectrum (FHSS) modulation technique across 79 channels in the ISM (Industrial Scientific Medical) 2.4Ghz range [1],[13]. This reduces interference and allows multiple piconets within close proximity to operate independently on different frequency hopping sequences. The master is responsible for establishing the frequency hopping sequence (FHS), which the slaves must follow in order to stay synchronized to the piconet. The reader is referred to [1],[13],[14] for more details. III. RELATED WORK Previous Bluetooth scatternet formation solutions can be classified into single-hop and multi-hop solutions [15]. Singlehop solutions such as [16],[17],[18] require that all devices are within proximity of each other, and also elect a leader that assigns roles to devices and forms the topology. Multi-hop solutions do not require that all devices are within proximity of each other. In [9] and [10], the scatternets are formed as a rooted tree, while in [19] and [20] mesh and star topologies are formed respectively. In [21], the authors take a different approach and form scatternets on-demand for the duration of a route. In the remainder of this section the above mentioned approaches are overviewed. In [17], Salonidis et al. present their Bluetooth Topology Construction Protocol (BTCP). Devices discover each other and form initial piconet links through a symmetric link formation protocol, in which they alternate between INQUIRY and INQUIRY SCAN until pairs of devices meet in complementary states. In the second stage of the protocol a leader is elected, whom thereafter establishes the topology and assigns bridge roles. BTCP is one of the first proposed scatternet formation approaches, but it suffers from several drawbacks. BTCP can only accommodate devices that are all within proximity of each other, since it is based on a leader election process. Further, it uses inefficient master/slave bridges and also suffers from scalability problems (it restricts the number of participating devices to 36). Another drawback that is shared by many other proposals, is the multi-phase design of the protocol. This severely limits the performance of the protocol, since there has to be strict synchronization between the phases for correct operation. Law et al. present another single-hop solution in [16]. Their approach is similar to [17], but has the advantage that it is not divided into several phases. However, it shares many of the drawbacks of BTCP and also suffers from migration overhead as piconets are merged. In [22], a different approach is proposed by Lin et al. Their BlueRing protocol forms a scatternet by interconnecting piconets into a ring. The approach greatly simplifies routing; however, it is also based on leader election and suffers from expensive ring maintenance operations. Zaruba et al. present the Bluetrees protocol in [9]. In their approach, a rooted spanning tree is grown from a single arbitrary root node. Child nodes are connected as slaves and made masters of their own piconets. Thereafter, the new masters recursively connect neighbors until a complete spanning tree is formed. An optimized distributed solution allows several Bluetrees to be formed and merged into a spanning tree scatternet. The main drawback of the Bluetrees approach is that the hierarchical topology makes higher level nodes bottlenecks and partition points in the scatternet. The distributed version creates a more robust scatternet; however, both approaches use master/slave bridges and depend on one or more designated root nodes. In [10], Tan et. al present another distributed tree based approach. The algorithm uses a randomized symmetric state machine for device discovery, in which nodes alternated between inquiry and inquiry scan states. The tree is formed by merging several rooted spanning trees into a forest. The algorithm is optimized for communication, so that busy nodes do not spend all their time forming trees. It displays some promising tree-based routing properties and provides faulttolerance by self-healing partitioned subtrees. Nevertheless, the bridge nodes are inefficient master/slave bridges and the tree topology introduces bottlenecks in the scatternet. Wang et. al[19] extend the Bluetrees approach into Bluenets. Bluenet is a distributed mesh approach that attempts to efficiently form a topology while minimizing the interpiconet connections. Bluenets suffers from the drawback of master/slave bridges as well as the division of the algorithm into multiple phases. Basagni et. al present another algorithm in [20] where BlueStar piconets are connected into a BlueConstellation scatternet. BlueStars are formed using symmetric link formation, similar to BTCP, and by nodes relinquishing master roles to higher weight nodes in the neighborhood. These BlueStars are then interconnected into a scatternet by letting higher weight

3 masters instruct gateway nodes to form bridge connections to neighboring masters. The approach results in a fairly good scatternet topology that unfortunately also suffers from the phase division problem. Liu et. al propose a radically different approach in [21]. In their scheme scatternet formation is seen as a precursor to on-demand route formation. Scatternets are formed backwards along the reverse route when the destination returns a reply, and after the on-demand routing protocol initiates a route request. Their approach has the advantage that it does not require a network-wide scatternet to be maintained. However, it requires that the time slots of all piconets along a route are aligned in order to obtain good performance. The following three key points summarize the main disadvantages of previous approaches, although each item is not necessarily a drawback of every described solution. Reliance on a leader/coordinator to initiate and control the formation is not scalable and requires that all nodes are within transmission range of each other. Approaches that are divided into phases do not allow incremental node arrival and require complex coordination. The use of master/slave bridges negatively impacts performance, as well as incurring bottlenecks and severe partitioning problems for hierarchical solutions. It is also important to take into consideration the manner in which devices are distributed to piconets, and how many piconets each bridge node must switch between. Our multihop algorithm runs in a single phase and uses only pure slaves as bridge candidates. Existing master nodes are prioritized over disjoint nodes, in order to connect slaves. This property balances the number of nodes in each piconet and prevents formation of many small piconets. The self-healing property of the algorithm allows it to heal partitions and incorporate disconnected and late arriving nodes into the existing scatternet. A. Device Discovery IV. SCATTERNET FORMATION In order to form a scatternet, neighboring devices must first discover each other. This is done by engaging in inquiry procedures. A disjoint device discovers other nodes by forming a brief peering in the complementary INQUIRY and INQUIRY SCAN states. When a device enters the INQUIRY state, it starts transmitting INQUIRY packets across a predetermined set of inquiry frequencies. Peers that simultaneously reside in the complementary INQUIRY SCAN state can thereafter respond to the inquiry. Consequently, an inquiring node must briefly connect to each one of the inquiry scanning nodes in order to discover and subsequently incorporate them into a piconet. In traditional piconets, as defined in the Bluetooth specification [1], device discovery is accomplished by explicitly determining the role of each device; either master or slave. However, in a scatternet topology this should be done in an ad-hoc manner without the need for specific role assignments. In both [10] and [17], the authors propose self-configuring schemes based on symmetric device discovery. Devices alternate between INQUIRY and INQUIRY SCAN states until pairs of devices meet in complementary states and form a connection. Although these methods produce a scatternet, piconet master and slaves roles are assigned randomly. These approaches often produce unevenly sized piconets. In a piconet, slaves are clustered around the master and share the bandwidth capacity of the piconet. It is therefore important to evenly distribute nodes to piconets in order to maximize throughput. Uneven node distributions result in some bandwidth saturated piconets while other piconets have a lot of available capacity. Our probabilistic approach is designed to form a flat topology that does not suffer from added delays due to master/slave bridges. Another important feature is that it functions in a multi-hop environment, meaning that it does not require all devices to be within radio transmission range of each other. B. Basic Idea We initially assume that all devices are disjoint. However, the algorithm is capable of accommodating existing piconets and late arriving devices without modification. Before a scatternet can be created, individual piconets must first be formed and populated. This is accomplished by extending the current device discovery mechanism. The first step is to determine the master/slave roles of participating devices. Our probabilistic approach assigns a small number of devices as masters and the rest as slaves. As more slaves join the piconet, a local probabilistic threshold value at each master is linearly increased. As the threshold is increased, the likelihood of the master device scanning for additional slaves increases. On the other hand, disjoint devices with a low threshold are more likely to join one of the existing piconets than to form their own new piconet. The algorithm thereby favors compactness and prevents excessive formation of small piconets. As the piconet reaches full capacity, the master stops attempting to connect more slaves. It instead randomly picks a pure slave, if one exists, as a new bridge designate. This is also done by existing piconet masters, below full piconet capacity, that do not attempt to incorporate more slaves (which means that the probabilistic selection fell below the threshold value). A bridge designate is defined as a connected slave of an existing piconet that responds to inquiries (from another master), in order to form a bridge connection between the two piconets. The slotted time-division duplex (TDD) allocation scheme restricts slave devices from sending packets other than directly after being addressed by the master. Therefore, bridge designate slaves have unoccupied slots during which they can scan for inquiries. In this manner, the bridge designate will resume inquiry scanning and respond to inquiries from neighboring masters. If a new bridge connection is formed, the bridge node informs both piconet masters of the identity of the other device. Since inter-piconet switching is expensive, we restrict bridge nodes to only participate in exactly two piconets. Each

4 master will thereafter update its Bridge Table as to indicate the AM ADDR of the bridge and the BD ADDR of the connected piconet s master. This table also serves to detect and prevent redundant bridge connections, as well as for future scatternet routing. C. Algorithm The algorithm is based on periodic executions of the initialization procedure BT Init (illustrated in Fig. 1) by every participating device. In this procedure, only masters and disjoint devices are able to choose whether to initiate a device discovery, as a master or slave, or scan for bridges. This restriction is enforced by checking if the device has an AM ADDR. Every slave device is assigned a 3-bit AM ADDR by its master, so this check effectively excludes all connected slaves. First the threshold value p thres is set. We initially want a low master/slave ratio to maximize the number of slaves in each piconet and minimize the number of small piconets. Our preliminary analysis indicate that 0.10 is a good initial value for p thres, since it initially on average generates 10% masters and 90% slave nodes. The threshold value is then linearly increased as the slave count S count increases. The threshold is increased until full piconet capacity is reached, upon which the threshold value is reset to 0. A master of a full piconet (7 slaves) is assigned a 0 threshold value so that it will always attempt to form bridge connections over performing inquiry for slaves that it cannot accommodate. The random variable p determines which sub procedure the device will execute. The BT Master inquiry procedure (Fig. 2) is executed by inquiring masters and described further in Section IV-C.1. Devices that fall below the probabilistic threshold execute one of the inquiry scanning device procedures, depending on the device s status. A disjoint device enters the INQUIRY SCAN state and scans for inquiries. 1 A piconet master executes the BT Bridge (Fig. 3) procedure and assigns a pure slave as a bridge designate to form a new bridge connection. This is described further in Section IV-C.2. 1) Piconet Formation: The BT Master procedure in Fig. 2 is the main procedure of the piconet formation. Devices that fail to enter BT Master (and lack their own slaves) will enter the INQUIRY SCAN state, indicated by BT Slave, and attempt to connect to a master. The BT Master procedure operates in two rounds, the inquiry round followed by the paging round. The inquiry round lasts for the duration of the inquiryto interval, or until a limit on the number of responses has been reached. Assuming that no SCO links are present, the inquiryto is set to the scan interval of both the inquiry scan trains. Each inquiry scan train, over 16 frequencies, is covered in 2.56s [1]. Therefore, we set the inquiryto to 5.12s. For each response, the inquiry scanning peer sends a FHS response packet. The packet contains the Frequency Hopping Sequence (FHS) (based on the slave s BD ADDR) that the 1 We indicate this by the BT Slave procedure, which is simply the inquiry scan operation from the Bluetooth specification [1]. BT Init() { If!AM ADDR /* Only active slaves have AM ADDR */ p thres 0.10 } p thres p thres + p rand(0,1) If p < p thres BT Master() Else If Slave count = 0 BT Slave() Else BT Bridge() Fig. 1. { pthres if S count = 7 S count / 8 if 0 < S count < 7 0 else Pseudo code for initialization procedure master should use for paging the device, as well as clock synchronization and device address information. When the master receives an FHS response packet, it enqueues it and waits for additional responses for the remainder of the timeout interval. The paging round follows the inquiry round if any responses were received. It lasts for the duration of the pageto interval or until full piconet capacity is reached. The pageto is conservatively set to twice the maximum page scan window, or 5.12s, similar to inquiryto. The response at the front of the queue is then dequeued and the device is paged. For each successful connection, the Slave count is also incremented. If a bridge connection is successfully formed, the bridge node sends each master a link manager PDU lmp scat rep containing the BD ADDR of the inter-connected piconet master. This way the master device can update its neighborhood information, in particular what slaves are bridge nodes and to which piconet. This is described in more detail in Section IV-C.2. 2) Piconet Interconnection: Piconet masters that do not perform inquiries execute the BT Bridge procedure (illustrated in Fig. 3.) This procedure is the core of the actual interconnection of piconets. Each master uses a lookup table, called Bridge Table (Table 4), that contains the AM ADDR of the bridge node and the BD ADDR of the connected piconet s master. This table is used to prevent redundant bridges between piconets from forming, and to prevent a bridge node from participating in more than two piconets. As illustrated in Fig. 3, the master first performs a lookup from the Bridge Table and then randomly selects a slave that does not already have an entry in the table (called a pure slave) as a bridge designate. If every slave is already in the Bridge Table, then there are no pure slaves and the master will simply exit the procedure. We propose the addition of two new link manager PDUs for scatternet formation. The lmp scat inq scan PDU is sent from a master executing the BT Bridge procedure to a bridge designate. It forces the

5 BT Master() { Num Inq 0 Q empty() While!inquiryTO and Num Inq < MAX INQUIRIES INQUIRY() If INQ RESP FHS Q.enqueue(INQ RESP FHS) Num Inq++ /* Inquiry round completed */ While!pageTO and Num Inq > 0 and Slave count < 7 PageDev Q.dequeue() PAGE(PageDev) If CONNECTED(PageDev) BD ADDR pcnt lmp scat rep If BD ADDR pcnt /* Connected a bridge node */ If!Piconet Lookup(Bridge Table,BD ADDR pcnt) Add(Bridge Table,AM ADDR,BD ADDR pcnt) Slave count++ Else DISCONNECT(PageDev) /* Bridge already exists */ Else Slave count++ /* Pure slave connected */ Num Inq /* Page round completed */ } AM ADDR Piconet 001 <BD ADDR piconeta > 010 <BD ADDR piconetb > 011 <BD ADDR piconetc > 100 <BD ADDR piconetd > 101 <BD ADDR piconete > 110 <BD ADDR piconetf > 111 <BD ADDR piconetg > Fig. 4. Bridge Table and corresponding master s BD ADDR are added to the Bridge Table. The BT Bridge procedure is periodically executed whenever the piconet is either at full capacity or when the master does not enter the BT Master inquiry procedure. This ensures that the best possible scatternet connectivity is maintained, since an unexpectedly disconnected bridge node would put the piconet below capacity and the master would again, with high probability, enter the BT Master inquiry procedure. S1E M1 S1D S1A S1C SBR S2A S2C M2 Fig. 2. Pseudo code for master procedure BT Bridge() } If Length(Bridge Table) < Slave count /* Randomly pick a pure slave */ Bridge Designate Rand Pure Slave If Bridge Designate lmp scat inq scan(bridge Designate) While!pageTO If BD ADDR pcnt lmp scat rep Add(Bridge Table,Bridge Designate,BD ADDR pcnt) } Fig. 3. Pseudo code for bridge scan procedure slave to enter the inquiry scan state in between polling slots. It does not require explicit acknowledgment; however, if a bridge connection is formed within the pageto interval a lmp scat rep PDU must be returned to both piconet masters. The lmp scat rep PDU is sent by the new bridge slave to each master and contains the BD ADDR of the other connected piconet s master. Thereby, each piconet master can identify the neighboring piconets by their master. If the master finds a bridge candidate, it sends a link manager PDU lmp scat inq scan to the new bridge designate in the next master to slave polling slot. If an lmp scat rep PDU is returned within the timeout period, the slave s AM ADDR Fig. 5. Bridge node connection An example of a bridge connection is illustrated in Fig. 5. M1 is the master of piconet 1 and M2 is the master of piconet 2. Suppose initially SBR is a slave node of piconet 2. Piconet 1 is larger than piconet 2, so we assume that M1 enters BT Master again to connect more slaves with high probability. Meanwhile, let s assume that M2 enters the BT Bridge procedure and selects SBR as its bridge designate. M2 thereafter sends a link manager command to SBR in the next polling slot. Upon receiving the lmp scat inq scan PDU, SBR enters the INQUIRY SCAN state. As previously stated, M1 is performing an inquiry so SBR will respond to M1 upon receipt of the inquiry. If a connection is formed, the bridge node will return a lmp scat rep PDU, containing the BD ADDR of the other piconet, to each of the two connected masters. Both masters then update their Bridge Table accordingly. V. PERFORMANCE EVALUATION Our performance evaluation was conducted using ns-2[23] and the Blueware extension module[24]. We simulated our algorithm for initially disjoint devices in a limited area of 10 by 10 meters. For the purpose of Personal Area Networks, we believe that these simulation parameters reflect real-world conditions.

6 An important property of our approach is the probabilistic selection between masters and slaves, which improves piconet density and balances the size of piconets in the scatternet. We therefore investigated the master to slave ratio in participating piconets for our protocol. Fig. 6 shows an almost linear relation for the average master to slave ratio. This directly corresponds to the linear increase of the p thres threshold value in the algorithm. We can conclude from this that our algorithm is effective in balancing the number of member piconets. In order to have a benchmark to compare our algorithm against, we also simulated the Tree Scatternet Formation (TSF) protocol[10],[25]. TSF, described in Section III, is one of the most promising tree hierarchy scatternet formation approaches. Our completely distributed approach attempts to remedy some of the disadvantage of TSF, inherent to tree hierarchy solutions, while still allowing incremental arrival and the healing of scatternet partitions. In TSF, rooted subtrees are merged into a connected scatternet. Only the roots of the subtrees are allowed to merge other subtrees, or join disjoint nodes as children. This requires additional coordination between sub-tree root nodes. To provide a fair comparison between our approach and TSF, we simulated and analyzed the scatternet formation delay of the two algorithms. Scatternet formation delay can be defined as the time from when the formation is initiated until every node is incorporated into the scatternet. In order to present an unbiased comparison, we assumed in our simulation that once a node was connected it did not lose connectivity. We also required that every piconet had formed at least one bridge connection to another piconet before the simulation was complete. Fig. 7 compares the scatternet formation delay between our algorithm and TSF. Our algorithm had consistently lower scatternet formation delay when compared to TSF. We believe that this stems from the fact that our algorithm is distributed in nature, while TSF involves the merging of several rooted trees. As the number of participating nodes increase, TSF suffers from additional overhead due to the coordination between the Delay (s) Fig. 7. Scatternet Formation Delay TSF BTDSP Nodes sub-tree root nodes. It should also be noted that neither algorithm can completely prevent scatternet partitioning, although both approaches attempt to heal partitions. While our algorithm is a multihop solution, TSF must be considered a single-hop solution since only sub-tree roots are able to form inter-connections. This behavior causes heavy partitioning problems in multihop scenarios. In contrast, our approach allows every pure slave to become a bridge node and form an inter-connection. This enables the majority of nodes in each piconet to form a partition-healing bridge connection to another master, since the algorithm keeps the number of masters and bridge nodes relatively small compared to pure slaves. Although this does not completely prevent partitioning, our algorithm is able to heal most partitions in multi-hop scatternets. As part of our future work, we are investigating how to improve partition healing in order to ensure connectivity in multi-hop scenarios. VI. CONCLUSION Ave # Masters Fig. 6. Average Master to Slave Ratio Slaves In this paper we have proposed a new algorithm for scatternet formation that remedies some of the drawbacks of previous approaches; including the use of inefficient master/slave bridges, dividing the algorithm into multiple phases, and inefficient grouping of nodes in piconets. Our algorithm forms a scatternet in a single phase and accommodates late arriving nodes without modification. Its self-healing property allows disconnected nodes to quickly be re-incorporated and healing of scatternet partitions. The algorithm minimizes the bridge switching delay, by only allowing bridge nodes to participate in two piconets, as well as balances the number of slaves in participating piconets. Only pure slave bridge designates are used, which results in a flat scatternet topology, free of bottleneck links associated with tree-based algorithms. It is also completely distributed and works in a multi-hop environment, meaning that it does not require that all devices are within transmission range of each other.

7 ACKNOWLEDGEMENT This research was supported in part by the National Science Foundation, CAREER Award # CCR REFERENCES [1] Bluetooth Specification Version 1.1, Bluetooth Special Interest Group, February [2] IEEE Working Group for WPANs, IEEE, [3] A. Racz, G. Miklos, F. Kubinszky, and A. Valko, A pseudo random coordinated scheduling algorithm for bluetooth scatternets, in Proceedings of the ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Long Beach, California, USA, 2001, pp [4] Gy. Miklos, A. Racz, A. Valko, and P. Johansson, Performance aspects of bluetooth scatternet formation, in Proceedings of the First Annual Workshop on Mobile Ad Hoc Networking and Computing, MobiHoc, 2000, pp [5] M. Kazantzidis, Local optimal bluetooth scatternet formation, UCLA Computer Science WAM Lab, Tech. Rep. TR , [6] N. Johansson, F. Alriksson, and U. Jonsson, Jump mode - a dynamic window-based scheduling framework for bluetooth scatternets, in Proceedings of the ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Long Beach, California, USA, October [7] M. Kalia, S. Garg, and R. Shorey, Scatternet structure and inter-piconet communication in the bluetooth system, in IEEE National Conference on Communications New Dehli, India, 2000, [8] J. Misic and V. B. Misic, Bridges of bluetooth county: Topologies, scheduling and performance, in IEEE Journal of Selected Areas in Communications, ser. Wireless Series, Special issue on Wireless LANs and Home Networks, vol. 21, February 2003, pp [9] G. V. Zaruba, S. Basagni, and I. Chlamtac, Bluetrees - scatternet formation to enable bluetooth-based ad hoc networks, in IEEE International Conference on Communications (ICC2001), 2001, pp [10] G. Tan, A. Miu, J. Guttag, and H. Balakrishnan, An efficient scatternet formation algorithm for dynamic environments, in IASTED International Conference on Communications and Computer Networks, Boston, MA, November [11] P. Johansson, M. Kazantzidis and M. Gerla, Bluetooth: An enabler for personal area networking, IEEE Network, vol. 15, no. 5, pp , September/October [12] A. Aggarwal, M. Kapoor, L. Ramachandran, and A. Sarkar, Clustering algorithms for wireless ad hoc networks, in Proceedings of the 4th International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Boston, Massachusetts, USA, August 2000, pp [13] J. Bray and C. F. Sturman, Bluetooth: connect without cables. Prentice Hall, [14] B. A. Miller and B. Chatschik, Bluetooth Revealed. Prentice Hall, [15] K. Persson, D. Manivannan, and M. Singhal, Bluetooth scatternet formation: Criteria, models, and classification, in IEEE Consumer Communications and Networking Conference 2004 (CCNC 04), Las Vegas, NV, USA., January [16] C. Law, A. K. Mehta, and K. Siu, Performance of a new bluetooth scatternet formation protocol, in Proceedings of the ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc2001), Long Beach, California, USA, October [17] T. Salonidis, P. Bhagwat, L. Tassiulas, and R. LaMaire, Distributed topology construction of bluetooth personal area networks, in IEEE INFOCOM Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies., vol. 3, April 2001, pp [18] F. Chun-Choong and C. Kee-Chaing, Bluerings - bluetooth scatternets with ring structures, in IASTED International Conference on Wireless and Optical Communication (WOC 2002), Banff, Canada, July [19] Z. Wang, R. J. Thomas, and Z. Haas, Bluenet - a new scatternet formation scheme, in 35th Hawaii International Conference on System Science (HICSS-35), Big Island, Hawaii, January [20] S. Basagni and C.Petrioli, Multiphop scatternet formation for bluetooth networks, in Vehicular Technology Conference, ser. IEEE 55th, vol. 1, May 2002, pp [21] Y. Liu, M. J. Lee, T. N. Saadawi, A bluetooth scatternet-route structure for multihop ad hoc networks, IEEE Journal on Selected Areas in Communications, vol. 21, no. 2, pp , February [22] T. Y. Lin, Y. Tseng, K. Chang, and C. Tu, Formation, routing, and maintenance protocols for the bluering scatternet of bluetooths, in Proceedings of the 36th Hawaii International Conference of System Sciences, Big Island, Hawaii, January [23] ns2 - Network Simulator, [24] Blueware: Bluetooth Simulator for ns, MIT Laboratory for Computer Science : Networks and Mobile Systems, [25] G. Tan, A. Miu, J. Guttag, and H. Balakrishnan, Forming scatternets from bluetooth personal area networks, Massachusetts Institute of Techonology, Tech. Rep. MIT-LCS-TR-826, October 2001.

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