Situation-Aware Quality of Service Enhancement for Heterogeneous Ultra-Dense Wireless IoT Networks
Abstract
By engaging a massive number of heterogeneous devices, future Internet of Things (IoT) systems are expected to support diverse applications ranging from eHealthcare to industrial control. In highly-dense deployment scenarios such as Industrial IoT (IIoT) systems, meeting the stringent Quality of Service (QoS) requirements such as low-latency and high reliability becomes challenging due to the uncertainty and dynamics within the IoT networks. To enhance the overall QoS performance, this thesis aims to address the technical challenges of IoT networks. Firstly, to enhance the network reliability, a cloud-assisted priority-based channel access and data aggregation scheme is proposed to minimize the network latency. Besides, the joint impact of packet scheduling and aggregation is considered by using the preemptive M/G/1 queuing model. Subsequently, the sector-based device grouping scheme is proposed for fast and efficient channel access in IEEE 802.11ah based IoT networks. In the proposed framework, the Access Point (AP) forms the sectors and divides into different groups according to the number of stations and their corresponding locations. In addition, the sector-based grouping allows the substantial improvement on packet collision rate and the throughput by utilizing the spatially orthogonal access mechanism.
Similarly, provisioning of accurate synchronization and low latency communication has become critical for IoT networks to support distributed sensing and control. Due to the contention-based channel access, achieving accurate synchronization could be extremely challenging. An efficient clock synchronization scheme is proposed to enhance the synchronization precision of the event critical applications. The proposed scheme assigns time slots with high preference to the timestamp packets of critical nodes and also guarantees the channel access in event-based situations. Furthermore, the proposed scheme provides the deterministic packet scheduling, reduces the channel access delay, and enhances the synchronization precision.
Moreover, in mobile IoT networks such as Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. To solve this problem, a location-based k-means clustering algorithm is proposed by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network. The principle of the proposed mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. To further improve the network performance, the CH facilitated optimal collaborative computing scheme is proposed by considering both the computing capabilities and the communication link status. Moreover, the graph-based wireless link scheduling algorithm is present to find the shortest distance to transfer the information among UAVs to deal with a link scheduling problem. Simulation results show that the proposed method significantly reduces the network overheads and improves packet delivery ratio and network latency as compared to the conventional schemes.