Spectrum-aware Adaptive Communication for the Internet of Things

The Internet of Things (IoT) is widely expected to be the next stage of development for the Internet and brings together material artefacts and their information representations. One of its enabling infrastructures is low-power wireless networks which due to their restricted capacities are especially sensitive to ambient noise from co-located wireless devices utilising the same band. This thesis investigates mechanisms for enhancing lower-power wireless network communications in the presence of ambient noise. To this end, we extend the channel hopping technique of IEEE 802.15.4.e, proposing the Adaptive Time-slotted Channel Hopping (ATSCH) framework. A core ingredient of A-TSCH is the use of blacklisting to eliminate noisy channels from the channel hopping sequence. This approach employs knowledge derived from periodically probing the channel noise floor. Blacklists are generated through a series of cooperative processes among multiple nodes to account for spatial diversity of spectral condition. Cross-layer optimisations between A-TSCH and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) are also investigated. A new routing metric Spectral Link Cost (SLC) is introduced to reflect A-TSCH channel information thus incorporating spectral awareness to routing decisions. We evaluate the performance of A-TSCH and SLC through simulations and realistic deployment using motes. We find that the proposed framework and metric support improved communication performance when compared against existing techniques.