Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach

Abstract

In IEEE 802.15.4 standard for low-power lowrange wireless communications, only one channel is employed for transmission which can result in increased energy consumption, high network delay and poor packet delivery ratio (PDR). In the subsequent IEEE 802.15.4-2015 standard, a Time-slotted Channel Hopping (TSCH) mechanism has been developed which allows for a periodic yet fixed frequency hopping pattern over 16 different channels.Unfortunately, however, most of these channels are susceptible to high-power coexisting Wi-Fi signal interference and to possibly some other ISM-band transmissions. This interference manifests itself in the form of the presence/absence of other devices with either or both static and dynamic channel selection policies. In order to isolate channels with undesirable conditions, blacklisting mechanisms are defined to adapt the channel hopping process. However, the existing solutions which form blacklists unrealistically assume that the statistical model of the external interference remains fixed, and do not vary over time. In this paper, we realistically assume that the impact of external interferes on 802.15.4 may generally follow a non-stationary pattern, and accordingly formulate the adaptive channel hopping problem as a Dynamic Multi-Armed Bernoulli Bandit (Dynamic MABB) process from the machine learning theory. We then propose an online learning algorithm with track-ability properties for computing an adaptive hopping policy. Simulations confirm that when the statistics of the external interference has a switching regime, the proposed solution outperforms the previous schemes in terms of both energy efficiency as well as two important KPIs for TSCH-based networks, i.e., PDR and latency.

Ultralow Power and Green TSCH-Based WSNs With Proactive Reduction of Idle Listening

“…Generally speaking, all the approaches aimed at increasing communication quality (latency and reliability) by decreasing the average number of retransmissions have a positive impact on power consumption as well. This is the case of black and white listing techniques, which are meant to dynamically select the best channel on which transmissions are performed: in [22] bad channels are blacklisted based on the packet delivery ratio, [23] introduces the concept of probabilistic blacklisting, where the probability that a channel is not skipped depends on its perceived communication quality, in [24] cells are scheduled based on network metrics including the packet delivery ratio and end-to-end latency, and finally in [25] blacklisting is managed as a dynamic multi-armed Bernoulli bandit process to adapt selected channels to time-varying interference.…”

Enhanced Energy-Saving Mechanisms in TSCH Networks for the IIoT: The PRIL Approach

“…Transmission channels can be selected on the fly by means of black and white listing techniques. A better quality of the communication support at the medium access control (MAC) layer results in improvements to energy consumption, reliability, and latency at the same time [25], [26].…”

Multi-Armed Bandit-Based Channel Hopping: Implementation on Embedded Devices

“…Previous work employing MAB algorithms in IEEE 802. 15.4 networks focused on simulations and neglected practical implementation issues [11,15,21]. Implementing MAB algorithms on typical IEEE 802.15.4 nodes is, however, challenging.…”