ABSTRACT
This chapter aims to continue the discussion on real-time resource allocation for digital microfluidic biochips. The focus of this work is to provide dynamic adaptation to protocol decisions under spatio-temporal constraints. The target design is modeled in terms of real-time multiprocessor scheduling and a heuristic algorithm is utilized to solve this NP-hard problem. As a case study, the presented framework performs fluidic task assignment, scheduling, and dynamic decision-making for quantitative epigenetics. Simulation results show that the proposed algorithm is computationally efficient and it generates effective solutions for multiple sample pathways, while taking into consideration the temporal constraint imposed by droplet evaporation. Experimental results using an embedded micro-controller as a testbed are also presented.
