With the rapid growth of the Internet of Things (IoT), an increasing number of wireless appliances is crowding the same unlicensed Industrial, Scientifi c, and Medical (ISM) frequency bands, causing severe cross-technology interference. The latter leads to loss of packets, increased delays, and to a reduced performance, especially for wireless IoT devices operating at low power. It is hence crucial for a low-power wireless IoT device to get an understanding of the RF spectrum usage in its surrounding and dynamically adapt its protocol con figuration accordingly, so to maximize the dependability of its communications.
Such an understanding includes, for example, which channels are highly congested, as well as which (or how many) devices are operating on a given frequency and their traffic pattern. We are interested in finding an efficient and accurate way to obtain such an understanding of the RF spectrum usage. As collecting information using energy detection (i.e., by sampling the received signal strength at high frequency) is highly energy expensive for battery-powered IoT devices, we are also looking for possible schemes that o ff-load this task to more powerful and unconstrained devices. For example, wall-powered Wi-Fi devices (e.g., Raspberry Pi) can get an understanding of the RF spectrum usage and communicate this info to the surrounding low-power IoT devices using Cross-Technology Communication (CTC), a scheme that allows a direct communication between devices using incompatible wireless technologies.
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Within this context, the student can explore several directions and perform different tasks:ˆ