All of the objects in the real world are envisioned to be connected and/or represented, through an infrastructure layer, in the virtual world of the Internet, becoming Things with status information. Services are then using the available data from this Internet-of-Things (IoT) for various social and economical benefits which explain its extreme broad usage in very heterogeneous fields. Domain administrations of diverse areas of application developed and deployed their own IoT systems and services following disparate standards and architecture approaches that created a fragmentation of things, infrastructures and services in vertical IoT silos. Coordination and cooperation among IoT systems are the keys to build “smarter” IoT services boosting the benefits magnitude. This article analyses the technical trends of the future IoT world based on the current limitations of the IoT systems and the capability requirements. We propose a hyper-connected IoT framework in which “things” are connected to multiple interdependent services and describe how this framework enables the development of future applications. Moreover, we discuss the major limitations in today’s IoT and highlight the required capabilities in the future. We illustrate this global vision with the help of two concrete instances of the hyper-connected IoT in smart cities and autonomous driving scenarios. Finally, we analyse the trends in the number of connected “things” and point out open issues and future challenges. The proposed hyper-connected IoT framework is meant to scale the benefits of IoT from local to global.

Human mobility information is widely needed by many sectors in smart cities. This work designs lightweight algorithms to perform stop detection, passenger flow tracking, and passenger estimation in an automated train. The on-board learning and detection algorithms are running on an edge node that is integrated with Wi-Fi sniffing technology, a GPS sensor, and an IMU sensor. The stop detection algorithm determines if the automated train is static at which train station based on GPS and IMU data. When the train is moving, the correlations between statistical properties extracted from Wi-Fi probes and the actual number of passengers change. The passenger flow tracking algorithm analyzes the number of incoming and outgoing passengers in the train. The passenger estimation algorithm approximates the number of passengers inside the train based on a multidimensional regression model created by statistical properties extracted from different device brands. The prototype is deployed in an automated train to conduct real-world experiments. The experimental results indicate that the passenger flow tracking algorithm reduces the average errors of 62.5% and 70% compared against two clustering algorithms. Compared with two counting-based approaches, the passenger estimation algorithm results in lower errors with a mean of 3.15 and a variance of 9.29.