Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Internet of things (IoT) is the latest trend in our living spaces allowing machine to machine (M2M) communications at the extensive scale. To enable massive M2M communication and portable devices to run on limited power supplies for the extended duration of time, low-cost energy efficient wireless technologies are needed. Among the many competing technologies including Wi-Fi, Bluetooth has shown the potential to be one of the strong candidates to act as the connectivity solution for the IoT especially after the introduction of Bluetooth Low Energy (BLE). Nowadays BLE is one of the biggest players in the market of short-range wireless technologies. By 2020, nearly 30 billion BLE devices in the form of mobile phones, tablets, sports utilities, sensors, security systems and health monitors are expected to be shipped. This proliferation of low-cost devices may for the first time actualize the vision of IoT.
This thesis studies various mesh topology formation techniques that can be used to aid the development of large-scale networks in capillary networks focusing on BLE. In particular, the thesis focuses on how mesh networks can be established over BLE communications especially exploiting the heterogeneous characteristics of the devices. A novel algorithm is proposed called Topology Formation considering Role Suitability (TFRS) to maximize the network lifetime. The algorithm uses a newly introduced metric called role suitability metric (RSM) to assign the best role among master, relay and slave to a device. The RSM metric bases its decision on various device characteristics including, but not limited to, energy, mobility, and computational capability. We use the system-level simulation to evaluate the performance of the proposed algorithm against a reference under homogeneous deployment scenario consisting of heterogeneous devices.
Results show that the network lifetime can be improved significantly when the topology is formed considering the device characteristics for both master role selection and relay selection. TFRS can achieve moderate improvements ranging from 20% to 40% varying on the deployment characteristics over the reference case.
2015. , 104 p.