The Collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as Smart Cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such IoT scenarios need in order to be pervasive. In our paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, Mobile Crowdsensing (MCS) management for environmental data, information representation and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.

Machine-to-Machine (M2M) communication technologies enable autonomous networking among devices without human intervention. Such autonomous control is of paramount importance for several deployments of the Internet of Things (IoT), including smart manufacturing applications, healthcare systems and home automation just to name a few. As a result, several M2M technologies are nowadays available on the market as either proprietary solutions or the effort of standardization initiatives, each targeted for a specific class of IoT applications and characterized by unique features in terms of achievable performance, frequency in use and supported network topologies. In this paper, we aim to organize the existing M2M approaches and technologies into a consistent framework that provides an in-depth vision of the main trends, future directions and open issues. We provide three main contributions in this survey. First, we identify the main use cases and requirements of M2M scenarios and we introduce a multi-layer taxonomy for M2M solutions, taking into account both deployment types and PHY/MAC characteristics. Second, in light of such characteristics, we provide an in-depth review of the existing M2M wireless technologies, considering both proprietary and open/standardized solutions for proximity-based, short-range and large-scale networks. Finally, we perform a critical comparison of the surveyed solutions over different M2M use cases and requirements, and we identify the research directions and open issues that still have to be addressed.

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smartphones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.

Abstract-Nowadays, the increasing popularity of smartphone devices equipped with multiple sensors (e.g. accelerometer, gyroscope, etc) have opened several possibilities to the deployment of novel and exciting context-aware mobile applications. In this paper, we exploit one of this possibility, by investigating how to detect the user motion type through sensors data collected by a smartphone. Our study combines experimental and analytical contributions, and it is structured in three parts. First, we collected experimental data that demonstrate the existence of specific sensors data patterns associated to each motion type, and we propose methods for data analysis and features extraction. Second, we compare the performance of different supervised algorithms for motion type classification, and we demonstrate that jointly utilizing the multiple sensor inputs of a smartphone (i.e. the accelerometer and the gyroscope) can significantly improve the accuracy of the classifiers. At the same time, we analyze the impact of sampling parameters (e.g. the sampling rate) on the system performance, and the corresponding tradeoff between classification accuracy and energy consumption of the device. Third, we integrate the motion type recognition algorithm into an Android application, that allows to associate a specific smartphone configuration to each detected motion type, and to provide this information at system-level to other contextaware Android applications. Experimental results demonstrate the ability of our application in detecting the user's motion type with high accuracy, and in mitigating the classification errors caused by random data fluctuations.

In this paper, we study the problem of how to detect the current transportation mode of the user from the smartphone sensors data, because this issue is considered crucial for the deployment of a multitude of mobility‐aware systems, ranging from trace collectors to health monitoring and urban sensing systems. Although some feasibility studies have been performed in the literature, most of the proposed systems rely on the utilization of the GPS and on computational expensive algorithms that do not take into account the limited resources of mobile phones. On the opposite, this paper focuses on the design and implementation of a feasible and efficient detection system that takes into account both the issues of accuracy of classification and of energy consumption. To this purpose, we propose the utilization of embedded sensor data (accelerometer/gyroscope) with a novel meta‐classifier based on a cascading technique, and we show that our combined approach can provide similar performance than a GPS‐based classifier, but introducing also the possibility to control the computational load based on requested confidence. We describe the implementation of the proposed system into an Android framework that can be leveraged by third‐part mobile applications to access context‐aware information in a transparent way. Copyright © 2016 John Wiley & Sons, Ltd.