Adaptive Medium Access Control for Internet-of-Things Enabled Mobile Ad Hoc Networks

An Internet-of-Things (IoT) enabled mobile ad hoc network (MANET) is a self organized distributed wireless network, in which nodes can randomly move making the network traffic load vary with time. A medium access control (MAC) protocol, as a most important mechanism of radio resource management, is required in MANETs to coordinate nodes’ access to the wireless channel in a distributed way to satisfy their quality of service (QoS) requirements. However, the distinctive characteristics of IoT-enabled MANETs, i.e., distributed network operation, varying network traffic load, heterogeneous QoS demands, and increased interference level with a large number of nodes and extended communication distances, pose technical challenges on MAC. An efficient MAC solution should achieve consistently maximal QoS performance by adapting to the network traffic load variations, and be scalable to an increasing number of nodes in a multi-hop communication environment. In this thesis, we develop comprehensive adaptive MAC solutions for an IoT-enabled MANET with the consideration of different network characteristics. First, an adaptive MAC solution is proposed for a fully-connected network, supporting homogeneous best-effort data traffic. Based on the detection of current network traffic load condition, nodes can make a switching decision between IEEE 802.11 distributed coordination function (DCF) and dynamic time division multiple access (D-TDMA), when the network traffic load reaches a threshold, referred to as MAC switching point. The adaptive MAC solution determines the MAC switching point in an analytically tractable way to achieve consistently high network performance by adapting to the varying network traffic load. Second, when heterogeneous services are supported in the network, we propose an adaptive hybrid MAC scheme, in which a hybrid superframe structure is designed to accommodate the channel access from delay-sensitive voice traffic using time division multiple access (TDMA) and from best-effort data traffic using truncated carrier sense multiple access with collision avoidance (T-CSMA/CA). According to instantaneous voice and data traffic load conditions, the MAC exploits voice traffic multiplexing to increase the voice capacity by adaptively allocating TDMA time slots to active voice nodes, and maximizes the aggregate data throughput by adjusting the optimal contention window size for each data node. Lastly, we develop a scalable token-based adaptive MAC scheme for a two-hop MANET with an increasing number of nodes. In the network, nodes are partitioned into different one-hop node groups, and a TDMA-based superframe structure is proposed to allocate different TDMA time durations to different node groups to overcome the hidden terminal problem. A probabilistic token passing scheme is adopted for packet transmissions within different node groups, forming different token rings. An average end-to-end delay optimization framework is established to derive the set of optimal MAC parameters for a varying network load condition. With the optimal MAC design, the proposed adaptive MAC scheme achieves consistently minimal average end-to-end delay in an IoT-based two-hop environment with a high network traffic load. This research on adaptive MAC provides some insights in MAC design for performance improvement in different IoT-based network environments with different QoS requirements

Efficient Multi-Hop Communications for Software-Defined Wireless Networks

PhD thesisSoftware-Defined Networking (SDN) recently emerged to overcome the difficulty of network control by decoupling the control plane from the data plane. In terms of the wireless medium and mobile devices, although new challenges are introduced into SDN research, SDN promises to address many inherited problems in wireless communication networks. However, centralised SDN control brings concerns of scalability, reliability, and robustness especially for wireless networks. Considering these concerns, the use of physically distributed SDN controllers has been recognized as an effective solution. Nevertheless, it remains a challenge in regard to how the physically distributed controllers effectively communicate to form a logically centralised network control plane. Dissemination is a type of one-to-many communication service which plays an important role in control information exchange. This research focuses on the strategic packet forwarding for more efficient multi-hop communications in software-defined wireless networks. The research aim is to improve the delivery efficiency by exploiting the delay budget and node mobility. To achieve this objective, existing multi-hop forwarding methods and dissemination schemes in wireless networks are investigated and analysed. In the literature, information from the navigation system of mobile nodes has been utilised to identify candidate relay nodes. However, further studies are required to utilise partially predictable mobility based on more generalised navigational information such as the movement direction. In this research, the feasible exploitation of directional movement in path-unconstrained mobility is investigated for efficient multi-hop communications. Simulation results show that the proposed scheme outperforms the state-of-the-art because directional correlation of node movement is considered to dynamically exploit the delay budget for better selection of the relay node(s).Chinese Scholarship Council (CSC

Multi-Drone-Cell 3D Trajectory Planning and Resource Allocation for Drone-Assisted Radio Access Networks

Equipped with communication modules, drones can perform as drone-cells (DCs) that provide on-demand communication services to users in various scenarios, such as traffic monitoring, Internet of things (IoT) data collections, and temporal communication provisioning. As the aerial relay nodes between terrestrial users and base stations (BSs), DCs are leveraged to extend wireless connections for uncovered users of radio access networks (RAN), which forms the drone-assisted RAN (DA-RAN). In DA-RAN, the communication coverage, quality-of-service (QoS) performance and deployment flexibility can be improved due to the line-of-sight DC-to-ground (D2G) wireless links and the dynamic deployment capabilities of DCs. Considering the special mobility pattern, channel model, energy consumption, and other features of DCs, it is essential yet challenging to design the flying trajectories and resource allocation schemes for DA-RAN. In specific, given the emerging D2G communication models and dynamic deployment capability of DCs, new DC deployment strategies are required by DA-RAN. Moreover, to exploit the fully controlled mobility of DCs and promote the user fairness, the flying trajectories of DCs and the D2G communications must be jointly optimized. Further, to serve the high-mobility users (e.g. vehicular users) whose mobility patterns are hard to be modeled, both the trajectory planning and resource allocation schemes for DA-RAN should be re-designed to adapt to the variations of terrestrial traffic. To address the above challenges, in this thesis, we propose a DA-RAN architecture in which multiple DCs are leveraged to relay data between BSs and terrestrial users. Based on the theoretical analyses of the D2G communication, DC energy consumption, and DC mobility features, the deployment, trajectory planning and communication resource allocation of multiple DCs are jointly investigated for both quasi-static and high-mobility users. We first analyze the communication coverage, drone-to-BS (D2B) backhaul link quality, and optimal flying height of the DC according to the state-of-the-art drone-to-user (D2U) and D2B channel models. We then formulate the multi-DC three-dimensional (3D) deployment problem with the objective of maximizing the ratio of effectively covered users while guaranteeing D2B link qualities. To solve the problem, a per-drone iterated particle swarm optimization (DI-PSO) algorithm is proposed, which prevents the large particle searching space and the high violating probability of constraints existing in the pure PSO based algorithm. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO based algorithm. Secondly, to improve overall network performance and the fairness among edge and central users, we design 3D trajectories for multiple DCs in DA-RAN. The multi-DC 3D trajectory planning and scheduling is formulated as a mixed integer non-linear programming (MINLP) problem with the objective of maximizing the average D2U throughput. To address the non-convexity and NP-hardness of the MINLP problem due to the 3D trajectory, we first decouple the MINLP problem into multiple integer linear programming and quasi-convex sub-problems in which user association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DC 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation scheme and a search-based start slot scheduling scheme are also designed to improve network performance and control mutual interference between DCs, respectively. Compared with the static DBS deployment, the proposed trajectory planning scheme can achieve much lower average value and standard deviation of D2U pathloss, which indicate the improvements of network throughput and user fairness. Thirdly, considering the highly dynamic and uncertain environment composed by high-mobility users, we propose a hierarchical deep reinforcement learning (DRL) based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of environment, we decouple the multi-DC TPRA problem into two hierarchical sub-problems, i.e., the higher-level global trajectory planning sub-problem and the lower-level local TPRA sub-problem. First, the global trajectory planning sub-problem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the sub-problem, we propose a multi-agent DRL based global trajectory planning (MARL-GTP) algorithm in which the non-stationary state space caused by multi-DC environment is addressed by the multi-agent fingerprint technique. Second, based on the global trajectory planning results, the local TPRA (LTPRA) sub-problem is investigated independently for each DC to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient based LTPRA (DDPG-LTPRA) algorithm is then proposed to solve the LTPRA sub-problem. With the two algorithms addressing both sub-problems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the non-learning-based TPRA scheme. In summary, we have investigated the multi-DC 3D deployment, trajectory planning and communication resource allocation in DA-RAN considering different user mobility patterns in this thesis. The proposed schemes and theoretical results should provide useful guidelines for future research in DC trajectory planning, resource allocation, as well as the real deployment of DCs in complex environments with diversified users