AmI Context-based Cross-Layer Optimization of MAC Performance in WSNs

In layered protocol architectures, direct interactions between network protocols in non-adjacent layers are prohibited to maintain the modularity of protocol designs. However, recent research has shown that allowing information to be directly exchanged between layers (a.k.a cross-layer interaction) can optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing schemes on cross-layer optimization mainly involve information exchange between physical, medium access control (MAC), and routing layers, with only a handful involving application layer. In this paper, we examined the issue of cross-layer optimization in WSNs deployed for ambient intelligence (AmI) applications. In AmI, WSNs perform human-centric sensing where low-level sensor data on users and their surroundings are collated and processed to infer higher-level user context information for context-adaptive AmI applications. For the first time, this paper proposes a cross-layer optimization scheme based on AmI context at the application layer for WSNs. As part of this cross-layer scheme, an ontology-based context modelling and reasoning mechanism is also proposed. We applied the proposed cross-layer scheme and context mechanisms to adapt the backoff behaviour of a contention-based WSN MAC protocol to AmI contexts. Results show that the AmI context-aware MAC protocol with cross-layer interaction can yield appreciable performance improvement in terms of throughput, frame delay, and energy efficiency.