Network navigation with scheduling

Abstract

Network navigation is a promising paradigm for enabling location-awareness in dynamic wireless networks. A wireless navigation network consists of agents (mobile with unknown locations) and anchors (possibly mobile with known locations). An agent can estimate its locations based on inter- and intra-node measurements, as well as prior knowledge. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements at a time. The procedure of selecting node pairs at different time instants for inter-node measurements, referred to as network scheduling, affects the time evolution of agents' localization errors. The key to achieve high navigation accuracy and efficient channel usage is to maximize the benefit from agents' inter-node measurements. Therefore, it is critical to design scheduling algorithms that decide for each agent with whom and when to perform inter-node measurements. This thesis introduces situation-aware scheduling that exploits network states to adaptively schedule agents' inter-node measurements. In particular, an analytical framework is developed to determine the effects of scheduling strategies and network settings on the localization error evolution. Furthermore, efficient and distributed situation-aware scheduling algorithms tailored for wireless navigation networks are designed, leading to high navigation accuracy and efficient channel usage. The first part of the thesis develops an analytical framework to determine the localization error evolution as a function of scheduling algorithms and network settings. In particular, both sufficient and necessary conditions for the boundedness of the error evolution are provided. Furthermore, opportunistic and random situation-aware scheduling strategies are proposed, and bounds on the corresponding time-averaged network localization errors are derived. These strategies are proved to be optimal in terms of the error scaling with the number of agents. Finally, the navigation accuracy is shown to be improved by sharing the wireless resources among multiple measurement pairs instead of allocating all the resources to a single pair at a time. The second part of the thesis designs efficient slotted and unslotted situation-aware scheduling algorithms tailored for wireless navigation networks based on the analytical results from the first part. The algorithm parameters, such as access probabilities and access rates, are optimized based on bounds for the time-averaged network localization error (NLE). The proposed algorithms lead to significant performance improvement compared with scheduling algorithms from wireless communication networks. The third part of the thesis develops a framework for the design of random-access-based distributed and asynchronous scheduling algorithms for wireless navigation networks, in which the channel access probabilities are optimized based on the evolution of agents' localization errors. The proposed algorithm achieves higher navigation accuracy and more efficient channel usage than the commonly used carrier sensing multiple access (CSMA) algorithm from wireless communication networks, at the cost of minimal communication overhead and computational complexity. The performance improvement is shown via numerical and experimental results. The contributions of this thesis provide a framework for the analysis and design of scheduling algorithms for wireless navigation networks, leading to high-accuracy, efficient, and flexible network navigation.