Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses

Abstract

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true.

Keywords: Cognitive Internet; Crowd Mapping; Crowd-Sensing; Internet of Things; Localization; Millimeter-waves; Personal Radar.

Figure 1

CPI functional scheme.

Figure 2

Human brain working memory model [34].

Figure 3

The CPI architecture.

Figure 4

Hidden Markov process applied to crowd mapping.

Figure 5

Original (a) and estimated magnetic fields (b,c).

Figure 6

Example of next generation smartphone-centric service: the personal radar (left); example of mapping result in a real indoor environment using a personal radar (right).