With the emergence of big data, designing an efficient distributed algorithm is significantly important. While most existing distributed algorithms consider distributed processing only for commodity computers, this paper introduces a Computationally Efficient, Dynamic distributed Algorithm (CEDA) for big data processing on a framework that comprises data processing both at the data collection end and data processing at the central server end. The proposed CEDA algorithm works both in low powered nodes and high speed commodity computers. Additionally, it performs sequential and parallel processing based on the amount of data received at the central server. Simulation results demonstrate that the CEDA algorithm achieves processing efficiency in terms of data processing time as compared to traditional distributed algorithms, which do not consider processing data at sensors.