Remote experimentation is an effective e-learning paradigm for supporting hands-on education using laboratory equipment at distance. The current trend is to enable remote experimentation in mobile and ubiquitous learning. In such a context, the remote experimentation software should enable effective telemonitoring and teleoperation, no matter the kind of device used to access the equipment. It should also be sufficiently lenient so as to handle the rapidly evolving wireless and mobile communication environment. While the current Internet bandwidth allows remote experimentation to work flawlessly on fixed connections such as LANs, mobile users suffer from both the versatile nature of wireless communications and the limitation of the mobile devices. These conditions impose that the remote experimentation software should integrate adaptation features. For effective ubiquitous remote experimentation, it should ideally be guaranteed that the information representing the state of the remote equipment is rendered (to the end user) at the same pace at which it has been acquired, yet possibly at the cost of a somewhat minimal time delay between the acquisition and rendering phases. In this respect, an end-to-end adaptation scheme is proposed that explicitly handles the inherent variability of the connection and the versatility of the mobile devices considered in ubiquitous remote experimentation. Instead of relying on a stochastic approach, the proposed adaptation scheme relies on a deterministic mass-balance equivalence model. The effectiveness of the proposed adaptation scheme is demonstrated in critical conditions corresponding to remote experimentation carried out using a PDA over a Bluetooth link.