In this study, we propose a method for localizing an unknown moving emitter by measuring a sequence of the frequency-of-arrival using a single moving observation platform. Furthermore, we introduce the position and velocity errors of the moving observation platform into the theoretical localization error equation to analyze the effect of these errors on the localization accuracy without Monte-Carlo simulations. The proposed theoretical error equation can propagate toward the time direction; therefore, the theoretical localization error can be evaluated at an arbitral time. We demonstrate that the localization error value obtained by the proposed equation and the RMSE evaluated by the Monte-Carlo simulation sufficiently coincide with one another.

In IoT era, the growth of data variety is driven by cross-domain data fusion. In this paper, we advocate that “local production for local consumption (LPLC) paradigm” can be an innovative approach in cross-domain data fusion, and propose a new framework, geolocation-centric information platform (GCIP) that can produce and deliver diverse spatio-temporal content (STC). In the GCIP, (1) infrastructure-based geographic hierarchy edge network and (2) adhoc-based STC retention system are interplayed to provide both of geolocation-awareness and resiliency. Then, we discussed the concepts and the technical challenges of the GCIP. Finally, we implemented a proof-of-concepts of GCIP and demonstrated its efficacy through practical experiments on campus IPv6 network and simulation experiments.

Availability of network access "anytime and anywhere" will impose new requirements to presence services - server load sharing and privacy protection. In such cases, presence services would have to deal with sensor device information with maximum consideration of user's privacy. In this paper, we propose FieldCast: peer-to-peer system architecture for presence information exchange in ubiquitous computing environment. According to our proposal, presence information is exchanged directly among user's own computing resources. We illustrate our result of evaluation that proves the feasibility of our proposal.