Coordinating negotiations in data-intensive collaborative working environments using an agent-based model-driven platform

This paper tackles the interoperability problems of enterprise information systems by presenting a distributive model-driven platform for parallel coordination of multiple negotiations in data-intensive collaborative working environments. The proposed model was validated and verified by an industrial application scenario within the European research project H2020 C2NET (Cloud Collaborative Manufacturing Networks). This real scenario developed data-intensive collaborative and cloud-enabled tools that allow the optimisation of the supply network of manufacturing SMEs, proposing a negotiation solution based on a model-driven interoperable decentralised architecture.info:eu-repo/semantics/acceptedVersio

On the genericity properties in networked estimation: Topology design and sensor placement

In this paper, we consider networked estimation of linear, discrete-time dynamical systems monitored by a network of agents. In order to minimize the power requirement at the (possibly, battery-operated) agents, we require that the agents can exchange information with their neighbors only \emph{once per dynamical system time-step}; in contrast to consensus-based estimation where the agents exchange information until they reach a consensus. It can be verified that with this restriction on information exchange, measurement fusion alone results in an unbounded estimation error at every such agent that does not have an observable set of measurements in its neighborhood. To over come this challenge, state-estimate fusion has been proposed to recover the system observability. However, we show that adding state-estimate fusion may not recover observability when the system matrix is structured-rank ($S$-rank) deficient. In this context, we characterize the state-estimate fusion and measurement fusion under both full $S$-rank and $S$-rank deficient system matrices.Comment: submitted for IEEE journal publicatio