  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.
