Energy-balanced task allocation for collaborative processing in wireless sensor networks

We propose an energy-balanced allocation of a real-time application onto a single-hop cluster of homogeneous sensor nodes connected with multiple wireless channels. An epoch-based application consisting of a set of communicating tasks is considered. Each sensor node is equipped with discrete dynamic voltage scaling (DVS). The time and energy costs of both computation and communication activities are considered. We propose both an Integer Linear Programming (ILP) formulation and a polynomial time 3-phase heuristic. Our simulation results show that for small scale problems ( with less than or equal to 10 tasks), up to 5x lifetime improvement is achieved by the ILP-based approach, compared with the baseline where no DVS is used. Also, the 3-phase heuristic achieves up to 63% of the system lifetime obtained by the ILP-based approach. For large scale problems ( with 60 - 100 tasks), up to 3.5x lifetime improvement can be achieved by the 3-phase heuristic. We also incorporate techniques for exploring the energy-latency tradeoffs of communication activities ( such as modulation scaling), which leads to 10x lifetime improvement in our simulations. Simulations were further conducted for two real world problems - LU factorization and Fast Fourier Transformation (FFT). Compared with the baseline where neither DVS nor modulation scaling is used, we observed up to 8x lifetime improvement for the LU factorization algorithm and up to 9x improvement for FFT.