An Energy-Efficient Convolution-Based Partitioned Collaborative Perception Algorithm for Large-Scale IoT Services

The perception layer of Internet of Things (IoT) not only needs to perceive service requests rapidly, but also considers reducing energy consumption intelligently. This issue becomes crucial in the scenario of large-scale IoT services. The existing research usually focus on one single aspect only, either energy consumption or perception rate. Inspired from the human visual direction-sensitive system and convolutional neural network, we propose an energy-efficient convolution-based partitioned collaborative perception algorithm (CPCPA) for large-scale IoT services. The perception range of each node is divided into multiple regions. First, by introducing the direction sensitive mechanism, the preferred search orientation can be determined quickly and become directional. Then, a selection operator of the partitioned region is designed to keep the search region updating and prevent CPCPA from getting stuck in local optimums. Meanwhile, a convolutional method is used to filter out unhelpful nodes to adapt the self-adaptive wake-up probability, which precisely controls the state switch of the nodes to reduce energy consumption. Finally, simulation results verify that CPCPA enables IoT to discover large-scale random service requests. The results also indicate that the proposed algorithm achieves better energy maintenance and maintains a higher perception rate than the state-of-the-art algorithms including directional sensitivity-based perception algorithm, sensor node activation method using bat algorithm, coverage aware scheduling for optimal placement, intelligent self-organizing scheme. An overall average perception rate improvement of 2.46% is achieved by CPCPA than the compared algorithms.