Design and evaluation of mission-oriented sensing platform with military analogy

Abstract

Purpose – The purpose of this paper is to perform large-scale environmental sensing with a lot of Internet of Things (IoT) devices, as typically seen in a Smart City, efficiently and for multiple applications. In this paper, we propose a novel sensing method, called mission-oriented sensing, which accepts multiple and dynamic sensing purposes on a single infrastructure. Design/methodology/approach – The proposed method achieves the purpose by dealing sensing configuration (application’s purpose) as a mission. It realizes sharing single infrastructure by accepting multiple missions in parallel, and it accepts missions’ update anytime. In addition, the sensing platform based on military analogy can command and control a lot of IoT devices in good order, and this realizes mission-oriented sensing above. Findings – Introducing mission-oriented sensing, multiple purpose large-scale sensing can be conducted efficiently. The experimental evaluation with a prototype platform shows the practical feasibility. In addition, the result shows that it is effective to update sensing configuration dynamically. Research limitations/implications – The proposed method focuses aggregating environmental sensor value from a lot of devices, and, thus, it can treat streamdata, such as video or audio or control a specific device directly. Originality/value – In proposed method, a single-sensing infrastructure can be used by multiple applications, and it admits heterogeneous devices in a single infrastructure. In addition, the proposed method has less technical restriction and developers can implement actual platform with technologies for context.

DOI: 10.1108/IJPCC-01-2017-0007

Cite this paper

@article{Inomoto2017DesignAE, title={Design and evaluation of mission-oriented sensing platform with military analogy}, author={Hikaru Inomoto and Sachio Saiki and Masahide Nakamura and Shinsuke Matsumoto}, journal={Int. J. Pervasive Computing and Communications}, year={2017}, volume={13}, pages={76-91} }