An Ubiquitous Multi-agent Mobile Platform for Distributed Crowd Sensing and Social Mining

Abstract

Smart mobile devices are fundamental date sources for crowd activity tracing. Large-scale mobile networks and the Internet-of-Things (IoT) expand and become part of perva-sive and ubiquitous computing offering distributed and trans-parent services. With the IoT, Crowd Sensing is extended by Things Sensing, creating heterogeneous smart environments. A unified and common data processing and communication methodology is required so that the IoT, mobile networks, and Cloud-based environments seamlessly integrate, which can be fulfilled by self-organizing mobile agents, discussed in this work. Currently, portability, resource constraints, security, and scalability of Agent Processing Platforms (APP) are essen-tial issues for the deployment of Multi-agent Systems (MAS) in highly heterogeneous networks. Beside the operational aspects of MAS, an organizational structure is required for the deployment of MAS in crowd sensing and social mining applications. The Planetary Nervous system (Nervousnet) consists of virtual sensors building the core functionality for such applica-tions running on smart phones with a Cloud-like architecture. The virtual sensors enable a holistic composition and modelling approach. Self-organizing and adaptive mobile agents are well known as the core cells of holistic and modular systems. In this work, both concepts are combined. JavaScript agents are introduced as virtual sensors in the Nervousnet environment, evaluated with a simulation of a distributed sensor fusion use-case in a mobile network based on real-world data from Nerv-ousnet, showing the suitability of the hybrid approach, benefit-ing from local and event-based sensor processing performed by the MAS.