Improving Link Reliability of IEEE 802.15.4g SUN with Re-Transmission Shaping

Abstract

Packet re-transmissions are a common technique to improve link reliability in low-power wireless networks. However, since packet re-transmissions increase the end-device energy consumption and the network load, a maximum number of re-transmissions per packet is typically set, also considering the duty-cycle limitations imposed by radio-frequency regulations. Moreover, the number of re-transmissions per packet is typically set to a constant value, meaning that all packet re-transmissions are treated the same regardless of actual channel conditions (i.e., multi-path propagation or internal/external interference effects). Taking that into account, in this paper we propose and evaluate the concept of re-transmission shaping, a mechanism that manages packet re-transmissions to maximize link reliability, while minimizing energy consumption and meeting radio-frequency regulation constraints. The proposed re-transmission shaping mechanism operates by keeping track of unused packet re-transmissions and allocating additional retransmission when the instantaneous link quality decreases due to channel impairments. To evaluate the re-transmission shaping mechanism we use trace-based simulations using a IEEE 802.15.4g SUN data-set and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packets). The obtained results show that re-transmission shaping is a useful mechanism to improve link reliability of low-power wireless communications, as it can increase PDR from 77.9% to 99.2% while sustaining a RNP of 2.35 re-transmissions per packet, when compared to using a single re-transmission per packet.

Towards Reliable IEEE 802.15.4g SUN with Re-transmission Shaping and Adaptive Modulation Selection

“…Moreover, we also demonstrate that combining both mechanisms is feasible and beneficial to improve link reliability, reaching a PDR=99% while maintaining a RNP=1.70. Please notice that the work presented in this paper extends the research presented in [23], [10] and [19], where we explored the feasibility of using RTS and AMS independently. Specifically, the dataset presented in [23] is the driver of our evaluation.…”

“…Moreover, compared to [10], we introduce the use of reinforcement learning to determine the best suitable IEEE 802. 15.4g SUN modulation in AMS, and, compared to [19], we combine RTS with AMS to further improve PDR while reducing the RNP.…”