Adaptive packet transmission techniques for energy-efficient and quality-of-service optimized heterogeneous IoT networks

The Internet-of-Things (IoT) is an intelligent network infrastructure wherein a large number of uniquely identifiable things or objects are interconnected to perform complex tasks in cooperative manners. Recently, IoT applications based on a heterogeneous wireless sensor network (WSN) architecture have been found in many domains, such as smart home, smart health-care, smart transportation, smart city and smart grid. In IoT networks, the demand for energy efficiency (EE) and quality-of-service (QoS) is on the rise. However, maintaining higher EE is difficult when high QoS requirements are required. It is therefore pivotal to simultaneously address EE and QoS issues for the IoT networks. In this thesis, the issues of EE and QoS for IoT applications are studied by developing an adaptive packet transmission algorithm at the MAC sub-layer of an IEEE 802.15.4-based network. This algorithm can efficiently adapt to the varying traffic load generated by IoT applications and the queue status of sensor nodes, and adjust the IEEE 802.15.4 super-frame parameters of sensor nodes accordingly. In the presence of energy harvesting (EH) techniques including solar-based, moving vehicles-based and radio frequency (RF)-based, I develop new energy-harvesting and QoS-aware algorithms. The proposed algorithms minimize the network contention level, which in turn improve the EE and the QoS values for IoT outdoor applications. These algorithms allow the sensor nodes to harvest sufficient energy to power them up and achieve self-sustainable operability. Cloud computing and fifth-generation (5G) mobile networks have been attracting much attention from the research and industrial communities. In this work, I address an integration of the IoT, cloud computing and 5G technology to extend the coverage and tackle bottlenecks due to the high demand for data transmission in the network.