Sparse Activity Discovery in Energy Constrained Multi-Cluster IoT Networks Using Group Testing

Current IoT networks are characterized by an ultra-high density of devices with different energy budget constraints, typically having sparse and sporadic activity patterns. Access points require an efficient strategy to identify the active devices for a timely allocation of resources to enable massive machine-type communication. Recently, group testing based approaches have been studied to handle sparse activity detection in massive random access problems. In this paper, a non-adaptive group testing strategy is proposed which can take into account the energy constraints on different sensor clusters. A theoretical extension of the existing randomized group testing strategies to the case of multiple clusters is presented and the necessary constraints that the optimal sampling parameters should satisfy in order to improve the efficiency of group tests is established. The cases of fixed activity pattern where there is a fixed set of active sensors and random activity pattern where each sensor can be independently active with certain probability are examined. The theoretical results are verified and validated by Monte-Carlo simulations. In massive wireless sensor networks comprising of devices with different energy efficiencies, our proposed low-power-use mode of access can potentially extend the lifetime of battery powered sensors with finite energy budget.