The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an $\epsilon$-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method.

Wi-Fi fingerprint-based localization has attracted significant research interest recently. Previous works in this area mainly focus on locating an individual user, whereas the additional assistance from peer-to-peer interactions has not been fully exploited. In this paper, we propose a cooperative localization method which not only utilizes the initial results by the fingerprint-based algorithm but also takes into account the physical constraint of pairwise distances to refine the localization estimates for multiple users simultaneously. The experimental results demonstrate that our algorithm is robust against the ranging error and the outdated fingerprint database. With the proposed peer selection scheme, it considerably improves localization accuracy. We further extend our framework to single-user motion tracking and localization based only on access-point-connectivity data.