Bluetooth Enabled Ad-hoc Networks: Performance Evaluation of a Self-healing Scatternet Formation Protocol

Bluetooth wireless technology is a robust short-range communications system designed for low power (10 meter range) and low cost. It operates in the 2.4 GHz Industrial Scientific Medical (ISM) band and it employs two techniques for minimizing interference: a frequency hopping scheme which nominally splits the 2.400 - 2.485 GHz band in 79 frequency channels and a time division duplex (TDD) scheme which is used to switch to a new frequency channel on 625 μs boundaries. During normal operation a Bluetooth device will be active on a different frequency channel every 625 μs, thus minimizing the chances of continuous interference impacting the performance of the system. The smallest unit of a Bluetooth network is called a piconet, and can have a maximum of eight nodes. Bluetooth devices must assume one of two roles within a piconet, master or slave, where the master governs quality of service and the frequency hopping schedule within the piconet and the slave follows the master’s schedule. A piconet must have a single master and up to 7 active slaves. By allowing devices to have roles in multiple piconets through time multiplexing, i.e. slave/slave or master/slave, the Bluetooth technology allows for interconnecting multiple piconets into larger networks called scatternets. The Bluetooth technology is explored in the context of enabling ad-hoc networks. The Bluetooth specification provides flexibility in the scatternet formation protocol, outlining only the mechanisms necessary for future protocol implementations. A new protocol for scatternet formation and maintenance - mscat - is presented and its performance is evaluated using a Bluetooth simulator. The free variables manipulated in this study include device activity and the probabilities of devices performing discovery procedures. The relationship between the role a device has in the scatternet and it’s probability of performing discovery was examined and related to the scatternet topology formed. The results show that mscat creates dense network topologies for networks of 30, 50 and 70 nodes. The mscat protocol results in approximately a 33% increase in slaves/piconet and a reduction of approximately 12.5% of average roles/node. For 50 node scenarios the set of parameters which creates the best determined outcome is unconnected node inquiry probability (UP) = 10%, master node inquiry probability (MP) = 80% and slave inquiry probability (SP) = 40%. The mscat protocol extends the Bluetooth specification for formation and maintenance of scatternets in an ad-hoc network.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.