A Service-Oriented Architecture for Body Area NanoNetworks with Neuron-based Molecular Communication

Molecular communication provides communication and networking capabilities for nanomachines such as biosensors and bio-actuators to form and enable Body Area NanoNetworks (BANNs). This paper considers neuron-based molecular communication, which utilizes natural neurons as a primary component to build BANNs, and proposes an end-to-end software architecture to manage and control neuron-based BANNs through a series of software services. Those services aid to realize end user applications in healthcare, such as biomedical and rehabilitation applications. In the proposed architecture, a neuron-based BANN consists of a This work is supported in part by the FiDiPro programme of Academy of Finland “Nanocommunication Networks” 2012– 2016. Junichi Suzuki Department of Computer Science University of Massachusetts, Boston, U.S.A. Tel.: +1-617-287-6462 Fax: +1-617-287-6433 E-mail: [email protected] Present address: 100 Morrissey Blvd., Boston, MA 02125, USA Sasitharan Balasubramaniam Department of Electronics and Communication Engineering Tampere University of Technology, Finland E-mail: [email protected] Sophie Pautot Center for Regenerative Therapies Dresden, Dresden, Germany E-mail: [email protected] Victor Didier Perez Meza Center for Regenerative Therapies Dresden, Dresden, Germany E-mail: [email protected] Yevgeni Koucheryavy Department of Electronics and Communication Engineering Tampere University of Technology, Finland E-mail: [email protected] set of nanomachines and a network of neurons that are artificially formed into a particular topology. This paper investigates two mechanisms in the proposed architecture: (1) an artificial assembly method to form neurons into specific three-dimensional topology patterns and (2) a communication protocol for neuronal signaling based on Time Division Multiple Access (TDMA), called Neuronal TDMA. The assembly method uses silica beads as growth surface and bead-bead contacts as geometrical constraints on neuronal connectivity. A web lab experiment verifies this method with neuronal hippocampal cells. Neuronal TDMA leverages an evolutionary multiobjective optimization algorithm (EMOA) to optimize the signaling schedules for nanomachines. Simulation results demonstrate that the Neuronal TDMA efficiently obtains quality solutions.