We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.Comment: 15 pages, 4 figures, 3 tables, Accepted for publication in Computer Communications, special issue on the Internet of Things: Research challenges and solution

Abstract. The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

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Adoption of cloud infrastructure promises enterprises numerous benefits, such as faster time-to-market and improved scalability enabled by on-demand provisioning of pooled and shared computing resources. In particular, hybrid clouds, by combining the private in-house capacity with the on-demand capacity of public clouds, promise to achieve both increased utilization rate of the in-house infrastructure and limited use of the more expensive public cloud, thereby lowering the total costs for a cloud user organization. In this paper, an analytical model of hybrid cloud costs is introduced, wherein the costs of computing and data communication are taken into account. Using this model, a cost-efficient division of the computing capacity between the private and the public portion of a hybrid cloud can be identified. By analyzing the model, it can be shown that, given fixed prices for private and public capacity, a hybrid cloud incurs the minimum costs. Furthermore, it is shown that, as the volume of data transferred to/from the public cloud increases, a greater portion of the capacity should be allocated to the private cloud. Finally, the paper illustrates analytically that, when the unit price of capacity declines with the volume of acquired capacity, a hybrid cloud may become more expensive than a private or a public cloud.

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