A Robust Zero-Calibration RF-based Localization System for Realistic Environments

Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with the used mobile device, changes of the transmit power of smart access points (APs), and dynamic changes in the environment; requiring re-calibration of the area of interest; which reduces the technology ease of use. In this paper, we present IncVoronoi: a novel system that can provide zero-calibration accurate RF-based indoor localization that works in realistic environments. The basic idea is that the relative relation between the received signal strength from two APs at a certain location reflects the relative distance from this location to the respective APs. Building on this, IncVoronoi incrementally reduces the user ambiguity region based on refining the Voronoi tessellation of the area of interest. IncVoronoi also includes a number of modules to efficiently run in realtime as well as to handle practical deployment issues including the noisy wireless environment, obstacles in the environment, heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on different Android phones using the iBeacons technology in a university campus. Evaluation of IncVoronoi with a side-by-side comparison with traditional fingerprinting techniques shows that it can achieve a consistent median accuracy of 2.8m under different scenarios with a low beacon density of one beacon every 44m2. Compared to fingerprinting techniques, whose accuracy degrades by at least 156%, this accuracy comes with no training overhead and is robust to the different user devices, different transmit powers, and over temporal changes in the environment. This highlights the promise of IncVoronoi as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201

Crowd-based cognitive perception of the physical world: Towards the internet of senses

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

Indoor location based services challenges, requirements and usability of current solutions

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge

Evaluating Sensor Data in the Context of Mobile Crowdsensing

With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach