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We describe our experience from the implementation of the T-MAC protocol for wireless sensor networks in the open-source Castalia simulator. Notwithstanding the popularity of the protocol in the research literature in recent years, we find several practical issues that are not addressed in the original protocol description, which lead to a degree of freedom in the protocol design and implementation and have an impact on its resulting performance. These issues include the ability of the underlying physical layer and hardware to efficiently detect the activation events in the protocol, and necessary changes to the collision resolution and clock synchronization procedures in the presence of varying sleep patterns. Our results highlight the need for rigorous detail in protocol descriptions in the research literature and provide important insights into some of the common pitfalls.

We consider a typical body area network (BAN) setting in which sensor nodes send data to a common hub regularly on a TDMA basis, as defined by the emerging IEEE 802.15.6 BAN standard. To reduce transmission losses caused by the highly dynamic nature of the wireless channel around the human body, we explore variable TDMA scheduling techniques that allow the order of transmissions within each TDMA round to be decided on the fly, rather than being fixed in advance. Using a simple Markov model of the wireless links, we devise a number of scheduling algorithms that can be performed by the hub, which aim to maximize the expected number of successful transmissions in a TDMA round, and thereby significantly reduce transmission losses as compared with a static TDMA schedule. Importantly, these algorithms do not require a priori knowledge of the statistical properties of the wireless channels, and the reliability improvement is achieved entirely via shuffling the order of transmissions among devices, and does not involve any additional energy consumption (e.g., retransmissions). We evaluate these algorithms directly on an experimental set of traces obtained from devices strapped to human subjects performing regular daily activities, and confirm that the benefits of the proposed variable scheduling algorithms extend to this practical setup as well.

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