Context aware systems are able to adapt their behavior according to the environment in which the user is. They can be integrated into an Internet of Things (IoT) infrastructure, allowing a better perception of the user’s physical environment by collecting context data from sensors embedded in devices known as smart objects. An IoT extension called the Internet of Mobile Things (IoMT) suggests new scenarios in which smart objects and IoT gateways can move autonomously or be moved easily. In a comprehensive view, Quality of Context (QoC) is a term that can express quality requirements of context aware applications. These requirements can be those related to the quality of information provided by the sensors (e.g., accuracy, resolution, age, validity time) or those referring to the quality of the data distribution service (e.g, reliability, delay, delivery time). Some functionalities of context aware applications and/or decision-making processes of these applications and their users depend on the level of quality of context available, which tend to vary over time for various reasons. Reviewing the literature, it is possible to verify that the quality of context support provided by IoT-oriented middleware systems still has limitations in relation to at least four relevant aspects: (i) quality of context provisioning; (ii) quality of context monitoring; (iii) support for heterogeneous device and technology management; (iv) support for reliable data delivery in mobility scenarios. This paper presents two main contributions: (i) a state-of-the-art survey specifically aimed at analyzing the middleware with quality of context support and; (ii) a new middleware with comprehensive quality of context support for Internet of Things Applications. The proposed middleware was evaluated and the results are presented and discussed in this article, which also shows a case study involving the development of a mobile remote patient monitoring application that was developed using the proposed middleware. This case study highlights how middleware components were used to meet the quality of context requirements of the application. In addition, the proposed middleware was compared to other solutions in the literature.

Ambient Assisted Living (AAL) main goal is the development of health monitoring systems for patients with chronic diseases and elderly people through the use of body, home, and environmental sensors that increase their degree of independence and mobility. A comprehensive software infrastructure for AAL systems should be able to cover scenarios involving several patient mobility levels, locations, and physical and cognitive abilities. Cloud computing can provide to AAL systems the ability to extend the limited processing power of mobile devices, but its main role is to integrate all stakeholders through the storage and processing of health data and the orchestration of healthcare business logic. On the other hand, the Internet of Things (IoT) provides the ability to connect sensors and actuators, integrating and making them available through the Internet. This paper presents the Mobile-Hub/Scalable Data Distribution Layer, a middleware for AAL based on cloud computing and IoT. We discuss how this middleware can handle the requirements of the main health monitoring scenarios and present results that demonstrate the ability to opportunistically discover and connect with sensors in a timely manner and the scalability necessary for handling the connection and data processing of many connected patients. KEYWORDSambient assisted living, cloud computing, health monitoring systems, internet of things (IoT) 1 range of other applications for AAL systems, such as rescue and emer-gency response systems, fall detection, video surveillance systems, etc.Nowadays, AAL systems are regarded as a trend in a context of increasing awareness of how the Internet can be used to personal healthcare. Ambient Assisted Living systems are composed of several technologies: sensors and actuators, portable/wearable devices, heterogeneous wireless networks, medical applications executing on mobile devices (handhelds), personal computers, or in a cloud computing infrastructure. Among the variety of low-level sensors that can be applied in AAL systems, there are the wearable medical sensors, able to collect data from physiological signals (e.g., Electrocardiogram [ECG], Electromyogram, heart rate, and oxygen consumption) or data reflecting the body movement (e.g., accelerometer). Personal mobile devices, such as smartphones, are also usually equipped with motion and location sensors (e.g., accelerometer and GPS). Environmental sensors can also be used, as they collect information that helps determine if environmental conditions (e.g., temperature, light, humidity, and carbon dioxide levels) favor or not the patient's health. In addition to gathering data Concurrency Computat: Pract Exper. 2017;29:e4043. wileyonlinelibrary.com/journal/cpe

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Abstract. Providing a distributed cooperative environment is a challenging task, which requires a middleware infrastructure that provides, among others, management of distributed shared data, synchronization, consistency, recovery, security and privacy support. In this paper, we present the ECADeG project which proposes a layered architecture for developing distributed cooperative environments running on top of a desktop grid middleware that can encompass multiple organizations. We also present a particular cooperative environment for supporting scientific research focused at the health domain which uses the services supplied by the ECADeG architecture in order to allow researchers to share access to multiple institutions databases, visualize and analyze data by means of data mining techniques, edit research documents cooperatively, exchange information through forums and chats, etc.. Such a rich cooperative environment helps thus the establishment of partnerships between health care professionals and their institutions.

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