Hop count based optimization of Bluetooth scatternets
Ad hoc networks
In the past five years Bluetooth scatternets were one of the most promising wireless networking technologies for ad hoc networking. In such networks, mobility together with the fact that wireless network nodes may change their communication peers in time, generate permanently changing traffic flows. Thus, forming an optimal scatternet for a given traffic pattern may be not enough, rather a scatternet that best supports traffic flows as they vary in time is required. In this paper we study the
... timization of scatternets through the reduction of communication path lengths. After demonstrating analytically that there is a strong relationship between the communication path length on one hand and throughput and power consumption on the other hand, we propose a novel heuristic algorithm suite capable of dynamically adapting the network topology to the existing traffic connections between the scatternet nodes. The periodic adaptation of the scatternet topology to the traffic connections enables the routing algorithms to identify shorter paths between communicating network nodes, thus allowing for more efficient communications. We evaluate our approach through simulations, in the presence of dynamic traffic flows and mobility. I. Introduction Bluetooth is a short-range wireless network technology that supports ad hoc networking. In the Bluetooth technology a maximum of 8 nodes, out of a total of 256 devices, can actively communicate in a star-shaped cluster, called piconet. Within a piconet, the cluster head is called master while the other nodes are called slaves. Piconets interconnected through so-called bridge nodes form a scatternet. Bridges are nodes participating in more than one piconet on a time sharing basis. We call slave&bridges those nodes that have slave role in all of the piconets they participate in, while nodes having both, slave and master roles in different piconets are master&bridges. The latest Bluetooth Specification (v.1.2 ) introduces the concept of scatternet formation, but it does not define it in detail. Several scatternet formation algorithms however have been proposed in the literature. The work in  suggests the usage of the low power modes specified by the Bluetooth technology to enable up to 256 nodes to be part of the same piconet. Since only 8 nodes can actively communicate in a piconet, this approach implies that the master would continuously have to put and call back the nodes into/from low power modes, thus leading to a significant waste of radio resources. In  ,  ,  , as well as in  , algorithms for creating tree-shaped scatternets are proposed. Although tree network structures greatly simplify traffic routing, they also have important drawbacks: network partitions may arise as nodes move, source-destination paths may be quite long, the root node may become a bottleneck. These facts suggest that tree-like topologies can operate efficiently under specific scenarios but cannot be considered for general-purpose scatternets. Mesh-shaped scatternets do not have the limitations of the tree-shaped networks, but they require a more complex routing scheme. However, simulation results show that in general scenarios meshshaped scatternets perform better than their tree-shaped counterparts  . In the mesh-shaped arena, some early protocol proposals ,  required the nodes to be all in radio range, which simplifies node discovery and piconet formation. Later some solutions  , , ,  were defined to avoid the above shortcomings and operate well under general scenarios, providing high throughput and a balanced number of nodes per piconet. Despite these good characteristics, we can still identify some weaknesses. In particular, in  more than 7 slaves can be included in a piconet. The problem is solved in  and , however the protocol in  requires the nodes to know their geographic position. Despite the wide range of solutions proposed for optimal toplogy formation, the performance of scatternets over time is still challenged by the dynamic behavior of the nodes, since the varying communication needs of the users, node mobility, and channel conditions reshape the network topology in an unpredictable way. As an example, think of scatternets operating in interfering industrial environments with machinery that autonomously or semi-autonomously accomplishes its tasks. Components of such an automated environment include static as well as mobile robots, sensors of various type and human supervisors. All these components need to be networked for exchanging the data necessary for accomplishing their tasks. Raw data used for the tasks, progress reports and control data are all examples for information that need to be exchanged among the components. Also, each node may have multiple communication peers sustaining random data traffic sessions with them, sequentially and/or in parallel. To achieve high performance, a scatternet topology should be continuously maintained so that the current traffic flows can be supported in an optimal way. One of the factors that has a major impact on scatternet performance is the length of the communications paths. Intuitively, if a packet has to pass through many hops from its source to the destination, it occupies the communication bandwidth on more links and make the nodes consume more energy than in the case of shorter paths. To demonstrate the correctness of this intuition, in this paper first we provide an analytical model for estimating the throughput and power consumption on the communication paths of a scatternet, based on . Then we devise an algorithm suite that enables the reduction of the path length (or hop count) on all traffic connections in the scatternet. These algorithms take advantage of a local search strategy to find a network topology that can support the current traffic connections with a lower number of hops between the communicating nodes. Finally, the original contribution of this paper lies in the evaluation of the impact of the hop reductions on the throughput and power consumption through simulations, in the presence of node mobility and varying traffic connections. The remainder of this paper is organized as follows. In Section II we present our scatternet model and use it for providing a formal definition of our scatternet optimization problem. In Section III we calculate analytically the usable bandwidth on the links of the scatternet and use the results in Section IV to determine an analytical relationship between the hop count on the one hand, and throughput and power consumption on the other hand. In Section V we present and evaluate our algorithm suite that we use to reduce the hop count on communication paths, while in Section VI we evaluate the impact of these algorithms on the scatternet performance in the presence of mobility and changing traffic flows between the nodes.