Linear Programming Models for the Design of Energy-Efficient IoT Networks With Transmission Constraints

Networks consisting of sensors of multiple transmission ranges are preferred for next-generation wireless sensor networks because of their functional advantages. However, such multiple transmission ranges enforce restrictions in signal transmissions. In this work, we conduct the signal flow analysis in such networks and address the energy-efficient signal aggregation techniques that rely on linear programming techniques. We introduce a mathematical program that captures the widely different characteristics associated with the networks with sensors of multiple transmission ranges and converts it into an integer linear program which is hard to solve. To solve the scalability issues, we devise a linear program relaxation method to arrive at a solution that reduces the total signal transmissions in the network. We accommodate efficient signal compaction techniques based on compressive sensing. The simulation results demonstrate the impressive performance of the proposed method in reducing transmissions and conserving energy.