  The sensory traffic in Machine-to-Machine (M2M) communications has fairly
heterogeneous service delay requirements. Therefore, we study the
delay-performance of a heterogeneous M2M uplink from the sensors to a M2M
application server (AS) via M2M aggregators (MA). We classify the heterogeneous
M2M traffic aggregated at AS into multiple Periodic Update (PU) and Event
Driven (ED) classes. The PU arrivals are periodic and need to be processed by a
prespecified firm service deadline whereas the ED arrivals are random with firm
or soft real-time or non real-time service requirements. We use step and
sigmoidal functions to represent the service utility for PU and ED packets
respectively. We propose a delay efficient multiclass packet scheduling
heuristic that aims to maximize a proportionally fair system utility metric.
Specifically, the proposed scheduler prioritizes service to ED data while
ensuring that the PU packets meet their service deadline. It also minimizes
successive PU failures for critical applications by penalizing their
occurrences. Furthermore, the failed PU packets are immediately cleared from
the system so as to reduce network congestion. Using extensive simulations, we
show that the proposed scheduler outperforms popular packet schedulers and the
performance gap increases with heterogeneity in latency requirements and with
greater penalty for PU failures in critical applications.
