Computational self-awareness as design approach for visual sensor nodes

Abstract

Visual sensor networks (VSNs) represent distributed embedded systems with tight constraints on sensing, processing, memory, communications and power consumption. VSNs are expected to scale up in the number of nodes, be required to offer more complex functionality, a higher degree of flexibility and increased autonomy. The engineering of such VSNs capable of (self-)adapting on the application and platform levels poses a formidable challenge. In this paper, we introduce a novel design approach for visual sensor nodes which is founded on computational self-awareness. Computational self-awareness maintains knowledge about the system's state and environment with models and then uses this knowledge to reason about and adapt behaviours. We discuss the concept of computational self-awareness and present our novel design approach that is centred on a reference architecture for individual VSN nodes, but can be naturally extended to networks. We present the VSN node implementation with its platform architecture and resource adaptivity and report on preliminary implementation results of a Zynq-based VSN node prototype.