Context-aware collection, decision, and distribution (C2D2) engine for multi-dimensional adaptation in vehicular networks
Wireless vehicular networks have highly complex and dynamic channel state leading to a challenging environment to maintain connectivity and/or achieve high levels of throughput while also satisfying latency requirements of diverse vehicular applications. Adaptation over a large parameter space such as multiple frequency bands, novel modulation and coding schemes, and routing protocols is important in achieving good performance in this challenging setting. Vehicles now include a plethora of sensors which can be used to establish a clearer notion of the environmental context. However, while it is well understood that wireless performance greatly depends on this contextual information, protocols that leverage this information to improve wireless performance have yet to be fully developed. In this work, we will lay a foundation for developing context-aware intelligence to interface with existing adaptation protocols at multiple layers of the network stack. The core of this system consists of a context-aware collection, decision, and distribution (C2D2) engine. We give a brief overview of the architecture, design, and operation of the C2D2 engine.