Reliable Data Collection and Dissemination in Machine-to-Machine (M2M) Networks

Machine to machine (M2M) communication, which is defined as the communica- tion between machines or devices, has been widely discussed in the past few years. With M2M technologies, machines can monitor the environment, collect data and send information through wired or wireless networks to the cloud. The information can then be translated into useful knowledge for better action taking and decision making. Many applications such as home automation, remote health care, intelligent trans- portation System (ITS) and Building Management rely on M2M networks. In general, these M2M networks are large-scale, multi-hop networks that contain hundreds or even thousands of machines. How to disseminate data through such a huge network in a reliable and time-efficient manner is a real challenge. In this thesis, we focus on the so-called one-to-many data dissemination and many-to-one data collection. First, by ensuring that all the neighbors of a device successfully receiving the broadcasted frame, it is able to provide 100% reliable data dissemination. Second, rather than se- lecting the shortest route, a device selects a reliable end-to-end route so that reliable data collection can be achieved. In order to evaluate the performance of our solution in real environments, a test- bed composed of 106 IEEE 802.15.4 nodes is developed in BL building, National Taiwan University. The experiment shows that our solution guarantees 100% data dissemination and above 97% data collection.